Applied Science, Faculty of
Electrical and Computer Engineering, Department of
DSpace
UBCV
Deeba, Farah
Doctor of Philosophy - PhD
University of British Columbia
Recent developments in the understanding of the role of the placenta as the ‘master regulator’ of the intra-uterine environment underline the importance of placental evaluation in pregnancy screening. The placenta plays a major role in the pathogenesis of many pregnancy complications such as preeclampsia (PE) and intrauterine growth restriction (IUGR). These complications are leading causes of maternal and perinatal mortality and morbidity, affecting as many as 34% of all pregnancies. Antenatal monitoring of placentas using non-invasive, real time imaging techniques could potentially identify sensitive biomarkers of placental health and thereby offer opportunity for early intervention enabling improved perinatal outcomes. The overarching objective of this thesis is to develop an effective and objective pregnancy screening system. To attain this objective, the first part of the thesis focused on the theoretical development of several user- and system-independent Quantitative Ultrasound (QUS) algorithms. Different approaches were adopted for improved QUS estimation by addressing the fundamental precision-resolution trade-off and effect of tissue heterogeneity. Particularly, we proposed a prior-based regularization technique, the prior being derived from ultrasound physics, and a deep learning based approach, predictUS. In the second part of the thesis, a multimodal placental imaging study, including the ultrasound, MRI, and histopathology data from 47 placentas ex vivo, was designed and conducted in collaboration with a multi-disciplinary team. The dataset from this study was utilized for the validation of the proposed QUS algorithms. Specifically, we analyzed the efficacy of different QUS parameters, including attenuation coefficient, backscatter coefficient, scatterer diameter, elasticity, and viscosity, to detect placenta-mediated diseases and clinical outcomes.
A Multimodal Approach for Placenta Characterization:Towards an Objective and Effective Pregnancy ScreeningSystembyFarah DeebaB.Sc. Engg., Bangladesh University of Engineering & Technology, 2013M.Sc., University of Saskatchewan, 2016A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFDoctor of PhilosophyinTHE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES(Electrical and Computer Engineering)The University of British Columbia(Vancouver)July 2022© Farah Deeba, 2022The following individuals certify that they have read, and recommend to the Facultyof Graduate and Postdoctoral Studies for acceptance, the dissertation entitled:A Multimodal Approach for Placenta Characterization: Towards anObjective and Effective Pregnancy Screening Systemsubmitted by Farah Deeba in partial fulfillment of the requirements for the degreeof Doctor of Philosophy in Electrical and Computer Engineering.Examining Committee:Robert Rohling, Professor, Electrical and Computer Engineering, UBCSupervisorTim Salcudean, Professor, Electrical and Computer Engineering, UBCSupervisory Committee MemberAlexander Beristain, Associate Professor, Obstetrics and Gynaecology, UBCUniversity ExaminerRaymond Ng, Professor, Computer Science, UBCUniversity ExaminerMostafa Fatemi, Professor, Physiology and Biomedical Engineering, Mayo ClinicExternal ExaminerAdditional Supervisory Committee Members:Matthew Yedlin, Associate Professor, Electrical and Computer Engineering, UBCSupervisory Committee MemberiiAbstractRecent developments in the understanding of the role of the placenta as the ‘masterregulator’ of the intra-uterine environment underline the importance of placentalevaluation in pregnancy screening. The placenta plays a major role in the pathogen-esis of many pregnancy complications such as preeclampsia (PE) and intrauterinegrowth restriction (IUGR). These complications are leading causes of maternaland perinatal mortality and morbidity, affecting as many as 34% of all pregnancies.Antenatal monitoring of placentas using non-invasive, real time imaging techniquescould potentially identify sensitive biomarkers of placental health and thereby offeropportunity for early intervention enabling improved perinatal outcomes.The overarching objective of this thesis is to develop an effective and objectivepregnancy screening system. To attain this objective, the first part of the thesisfocused on the theoretical development of several user- and system-independentQuantitative Ultrasound (QUS) algorithms. Different approaches were adoptedfor improved QUS estimation by addressing the fundamental precision-resolutiontrade-off and effect of tissue heterogeneity. Particularly, we proposed a prior-basedregularization technique, the prior being derived from ultrasound physics, and adeep learning based approach, predictUS.In the second part of the thesis, a multimodal placental imaging study, includingthe ultrasound, MRI, and histopathology data from 47 placentas ex vivo, wasdesigned and conducted in collaboration with a multi-disciplinary team. The datasetfrom this study was utilized for the validation of the proposed QUS algorithms.Specifically, we analyzed the efficacy of different QUS parameters, includingattenuation coefficient, backscatter coefficient, scatterer diameter, elasticity, andviscosity, to detect placenta-mediated diseases and clinical outcomes.iiiLay SummaryThe current ultrasound-based pregnancy screening methods are subjective, as ultra-sound imaging is heavily dependent on the system settings and user interpretation.Also, these methods overlook the placental evaluation and lack efficacy in the earlydetection of pregnancy complications. This thesis aims at introducing non-invasivequantitative ultrasound (QUS) based tools for placental imaging that would con-tribute toward an objective and effective pregnancy screening system. Particularly,we develop accurate and robust algorithms to measure QUS parameters that willprovide objective measures of placental health. The translation of the proposed al-gorithm into clinical practice would enable health workers, irrespective of expertiselevel, to identify pregnancy complications and make appropriate referrals for timelyintervention.ivPrefaceThis thesis is based on multiple manuscripts (published or submitted for publication)and conference proceedings, resulting from collaborative experiments involvingresearchers from multiple disciplines. Ethical approvals for clinical studies onhuman placenta ex vivo (H15-00974 and H17-00331), human liver in vivo (H14-01964) and MRI protocols included in this thesis have been provided by the UBCChildren’s and Women’s Research Ethics Board, UBC Clinical Research EthicsBoard, UBC Clinical Research Ethics Board, and BC Children’s Hospital (BCCH)MRI Research Facility’s Protocol Review Committee. A detailed list of publicationsassociated with different chapters of this thesis is listed below.The study from Chapter 2 has appeared in the following publication:J1. F. Deeba, M. Ma, M. Pesteie, J. Terry, D. Pugash, J. Hutcheon, C. Mayer,S. Salcudean, R. Rohling, “Attenuation Coefficient Estimation of NormalPlacentas,” Ultrasound in Medicine and Biology, vol. 45(5), 1081–1093,2019.The research was the result of a collaborative effort involving multiple researchers.Dr. Jeffrey Abeysekera, Dr. Mehran Pesteie and Manyou Ma designed the experi-mental protocol and collected the experimental data. The author was responsiblefor developing, implementing, and evaluating the proposed algorithm and writingthe manuscript. Ashton Ellis and Tamsin Tarling obtained consent from the pa-tients. Dr. Denise Pugash and Dr. Chantal Mayer provided radiology support at BCWomen’s Hospital. Dr. Jennifer Hutcheon provided support for statistical analysis.Dr. Tim Salcudean provided support for the elastography system. Dr. Jeff Terry co-ordinated placenta handling at BC Women’s Hospital and provided information onvplacenta anatomy and pathology. Dr. Robert Rohling developed the main researchideas behind the project, helped design the experimental protocol, and provideduseful suggestions.The study from Chapter 3 has appeared in the following publications:C1. F. Deeba, R. Rohling, “PredictUS: A Method to Extend the Resolution-Precision Trade-off in Quantitative Ultrasound Image Reconstruction,” Inter-national Workshop on Machine Learning for Medical Image Reconstruction(MLMIR), 255–264, Springer, Cham, 2019.The author was responsible for the problem formulation, development and evaluationof the proposed algorithm and composition of the manuscripts with the guidancefrom Dr. Robert Rohling.The study from Chapter 4 has been presented and published in the conferenceproceedings in:C2. F. Deeba, C. Schneider, S. Mohammed, M. Honarvar, E. Tam, S. Salcudean, R.Rohling. “SWTV-ACE: Spatially Weighted Regularization based AttenuationCoefficient Estimation Method for Hepatic Steatosis Detection,” InternationalConference on Medical Image Computing and Computer-Assisted Interven-tion (MICCAI), 610–618. Springer, Cham, 2019.C3. F. Deeba, R. Hu, J. Terry, D. Pugash, J. Hutcheon, C. Mayer, S. Salcudean,R. Rohling, “A Spatially Weighted Regularization Method for AttenuationCoefficient Estimation,” IEEE International Ultrasonics Symposium (IUS),2023–2026, IEEE, 2019.C4. F. Deeba, R. Rohling, “Repeatability and Reproducibility of QuantitativeUltrasound Parameter Estimation Using Spatially Weighted Total Variation(SWTV) Regularization,” 2020 IEEE International Ultrasonics Symposium(IUS), 1–4, IEEE, 2020.The study from Chapter 5 has appeared in the following publications:J2. F. Deeba, C. Schneider, S. Mohammed, M. Honarvar, J. Lobo, E. Tam, S.Salcudean, and R. Rohling, “A Multiparametric Volumetric QuantitativeviUltrasound Imaging Technique for Soft Tissue Characterization,” MedicalImage Analysis, vol 74, 102245, 2021.The study from Chapter 6 has appeared in the following publication:J3. F. Deeba, J. Terry, D. Pugash, J. Hutcheon, C. Mayer, S. Salcudean, R.Rohling, “Project SWAVE 2.0: An overview of the study design for multi-modal placental image acquisition and alignment,” MethodsX, vol 9, 101738,2022.The study from Chapter 7 has been submitted for publication as:J4. F. Deeba, J. Terry, D. Pugash, J. Hutcheon, C. Mayer, S. Salcudean, R.Rohling, “SWAVE Imaging of Placental Elasticity and Viscosity Part II(SWAVE 2.0): Potential Biomarkers for Placenta mediated Disease Detection,”submitted.The study from Chapter 8 has been presented and appeared in the conferenceproceedings in:C5. F. Deeba, C. Schneider, J. Terry, D. Pugash, J. Hutcheon, C. Mayer, R.Rohling, “A Quantitative Ultrasound Approach for Detecting Placenta Medi-ated Diseases,” 2021 IEEE International Ultrasonics Symposium (IUS), 1–3,IEEE, 2021.C6. F. Deeba, J. Terry, D. Pugash, J. Hutcheon, C. Mayer, R. Rohling,“UltrasonicAttenuation Coefficient Estimate of Placenta Is Correlated to MRI Proton-Density-Fat Fraction: A Preliminary Ex-Vivo Study,” 2021 IEEE Interna-tional Ultrasonics Symposium (IUS), 1–4, IEEE, 2021.The contribution of the author was developing the method, implementing andevaluating the method, and writing the manuscripts. The publications C2 and J2 arebased on the in vivo liver study. Dr. Caitlin Schneider contributed in the phantompreparation, patient recruitment, and in vivo liver ultrasound data acquisition. JulioLobo contributed to ultrasound data acquisition and software development for theultrasound data acquisition. Shahed Khan Mohammed contributed to the codeviidevelopment and results for MRI validation. Dr. Mohammad Honarvar conductedthe MRI data acquisition.The publications C3, C5, C6, J3, and J4 were based on SWAVE 2.0, the ex vivomultimodal placenta study. In these publications, the contribution of the author wasin developing the method, implementing and evaluating the method, and writingthe manuscript. Ricky Hu cooperated in placenta sample handling, ultrasoundand elastography data acquisition. Victoria Lessoway, the sonographer for theSWAVE 2.0 study, helped develop the ultrasound scanning protocol, data acquisitionand annotation. Ashton Ellis obtained consent from the patients. Dr. DenisePugash and Dr. Chantal Mayer provided radiology support at BC Women’s Hospital.Dr. Jennifer Hutcheon provided support for statistical analysis. Dr. Jeff Terrycoordinated placenta handling at BC Women’s Hospital and provided informationon placenta anatomy and pathology. Dr. Septimiu Salcudean provided support,advice and guidance for elastography system development and QUS algorithmdevelopment. Dr. Robert Rohling developed the main research ideas behind theproject, helped design the experimental protocol, and provided useful suggestions.viiiTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ixList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xivList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xviGlossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxviAcknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxix1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Pregnancy Screening: Challenges and Scopes . . . . . . . . . . . 11.1.1 Inaccessibility . . . . . . . . . . . . . . . . . . . . . . . 11.1.2 Inefficiency . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Quantitative Ultrasound for Tissue Characterization . . . . . . . . 41.2.1 Spectral QUS Parameters: Definition . . . . . . . . . . . 61.2.2 Spectral QUS Parameters: Applications . . . . . . . . . . 91.2.3 Spectral QUS Parameters: Challenges . . . . . . . . . . . 131.2.4 Elastographic QUS Parameters . . . . . . . . . . . . . . . 131.3 The Human Placenta . . . . . . . . . . . . . . . . . . . . . . . . 15ix1.3.1 Placental Pathology . . . . . . . . . . . . . . . . . . . . . 161.3.2 Placental Imaging . . . . . . . . . . . . . . . . . . . . . . 181.4 Proposed Solution: Towards an Accessible and Effective PregnancySystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231.5 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . 272 Attenuation Coefficient Estimation using a Region-of-Interest Selec-tion (AEROI) Approach . . . . . . . . . . . . . . . . . . . . . . . . . 322.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 322.2 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342.2.1 Reference Phantom Method . . . . . . . . . . . . . . . . 342.2.2 AEROI Selection Parameters . . . . . . . . . . . . . . . . 362.3 Material and Method . . . . . . . . . . . . . . . . . . . . . . . . 392.3.1 Study Population . . . . . . . . . . . . . . . . . . . . . . 392.3.2 RF Data Processing and Analysis . . . . . . . . . . . . . 402.3.3 AEROI Selection Method . . . . . . . . . . . . . . . . . 422.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . 442.4.1 Time-Gated Window Dimension . . . . . . . . . . . . . 442.4.2 Validation of AEROI Selection Method on Tissue-MimickingPhantom . . . . . . . . . . . . . . . . . . . . . . . . . . 452.4.3 Attenuation Coefficient Estimates for ex vivo Placenta Data 482.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 512.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543 Quantitative Ultrasound Imaging using PredictUS . . . . . . . . . . 553.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 553.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 563.2.1 ACE Computation . . . . . . . . . . . . . . . . . . . . . 573.2.2 Network Architecture . . . . . . . . . . . . . . . . . . . . 583.3 Experiments and Results . . . . . . . . . . . . . . . . . . . . . . 593.3.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 593.3.2 Training and Testing . . . . . . . . . . . . . . . . . . . . 593.3.3 RF Data Processing and Analysis . . . . . . . . . . . . . 59x3.3.4 Performance Metrics . . . . . . . . . . . . . . . . . . . . 603.3.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 613.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 644 Attenuation Coefficient Estimation using A Spatially Weighted Reg-ularization (SWTV) Method . . . . . . . . . . . . . . . . . . . . . . 654.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 654.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 674.2.1 Spatially Weighted Regularization based ACE (SWTV-ACE) 674.2.2 Derivation of Spatially Weighted Matrix . . . . . . . . . . 694.2.3 Repeatabiltiy and Reproducibility . . . . . . . . . . . . . 714.3 Experiments and Results . . . . . . . . . . . . . . . . . . . . . . 724.3.1 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . 724.3.2 Tissue Mimicking Phantoms: Validation . . . . . . . . . . 754.3.3 Ex vivo Placenta . . . . . . . . . . . . . . . . . . . . . . 774.3.4 In Vivo Liver: Steatosis Detection . . . . . . . . . . . . . 774.3.5 Repeatability and Reproducibility Result . . . . . . . . . 784.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 805 Multiparametric Volumetric Quantitative Ultrasound Imaging us-ing 3D QUS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 815.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 815.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 825.2.1 3D QUS: Proposed Volumetric Quantitative UltrasoundParameter Estimation Algorithm . . . . . . . . . . . . . . 825.2.2 Data Sets . . . . . . . . . . . . . . . . . . . . . . . . . . 915.2.3 Data Acquisition and Processing . . . . . . . . . . . . . . 935.2.4 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . 955.3 Experiments and Results . . . . . . . . . . . . . . . . . . . . . . 965.3.1 Phantom Validation . . . . . . . . . . . . . . . . . . . . . 965.3.2 Effect of Compounding using three-dimensional regulariza-tion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1015.3.3 Effect of Spatial Weighting in Total Variation Regularization103xi5.3.4 In vivo Liver: Steatosis Detection Performance . . . . . . 1035.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1075.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1126 An Overview of SWAVE 2.0 . . . . . . . . . . . . . . . . . . . . . . . 1136.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1136.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1146.2.1 Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . 1146.2.2 Placenta Preparation . . . . . . . . . . . . . . . . . . . . 1186.2.3 Region-of-Interest Identification and Annotation/ Marking 1186.2.4 Magnetic Resonance Imaging . . . . . . . . . . . . . . . 1196.2.5 Quantitative Ultrasound Imaging . . . . . . . . . . . . . . 1206.2.6 Histopathology . . . . . . . . . . . . . . . . . . . . . . . 1226.2.7 Multimodal Registration . . . . . . . . . . . . . . . . . . 1226.2.8 Statistical Analysis . . . . . . . . . . . . . . . . . . . . . 1266.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1266.3.1 Registration Performance Evaluation . . . . . . . . . . . 1266.3.2 Correlative Multi-modality Imaging . . . . . . . . . . . . 1276.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1287 SWAVE 2.0 Imaging of Placental Elasticity and Viscosity . . . . . . 1317.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1317.2 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . 1337.2.1 Study Population, Scanning System Overview, Data Acqui-sition and Data Processing . . . . . . . . . . . . . . . . . 1337.2.2 Classifier Training and Evaluation . . . . . . . . . . . . . 1367.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1377.3.1 Elasticity of Placenta ex vivo . . . . . . . . . . . . . . . . 1387.3.2 Viscoelastic Characterization . . . . . . . . . . . . . . . . 1397.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1447.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1508 Applications of Placental QUS . . . . . . . . . . . . . . . . . . . . . 1528.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152xii8.2 QUS for Placenta-Mediated Disease Detection . . . . . . . . . . . 1528.3 Ultrasonic ACE of Placenta: A measure of Placental Fat . . . . . 1558.3.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1568.3.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 1588.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1599 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . 1609.1 Thesis Contributions . . . . . . . . . . . . . . . . . . . . . . . . 1619.1.1 Attenuation Coefficient Estimation using a Region-of-InterestSelection Approach . . . . . . . . . . . . . . . . . . . . . 1619.1.2 Quantitative Ultrasound Imaging using PredictUS . . . . 1629.1.3 Attenuation Coefficient Estimation using A Spatially WeightedRegularization (SWTV) Method . . . . . . . . . . . . . . 1639.1.4 Multiparametric Volumetric Quantitative Ultrasound Imag-ing using 3D QUS . . . . . . . . . . . . . . . . . . . . . 1649.1.5 An overview of SWAVE 2.0 . . . . . . . . . . . . . . . . 1649.1.6 SWAVE 2.0 Imaging of Placental Elasticity and Viscosity 1659.1.7 Applications of Placental QUS . . . . . . . . . . . . . . . 1659.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168A Spectral QUS Parameters: Theoretical Background . . . . . . . . . 198xiiiList of TablesTable 2.1 Description of sub-categories in ex vivo placenta dataset. . . . . 40Table 2.2 AEROI selection parameter values and ACE for tissue-mimickingphantom. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44Table 2.3 AEROI selection parameter values and ACE results for ex vivoplacenta. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53Table 4.1 Description of the categories of the data acquired for repeatabil-ity and reproducibility test. . . . . . . . . . . . . . . . . . . . 71Table 4.2 Performance metrics of ACE measures obtained using referencephantom method (RPM), unweighted total variation (TV) andspatially weighted total variation (SWTV) methods for CIRStissue mimicking phantoms. . . . . . . . . . . . . . . . . . . . 74Table 4.3 ACE result in terms of mean (dB/cm/MHz), standard deviation(dB/cm/MHz), and coefficient of variation (%). . . . . . . . . . 79Table 4.4 logBSC result in terms of mean (dB) and standard deviation (dB) 79Table 5.1 Ground truth values for the phantoms . . . . . . . . . . . . . . 91Table 5.2 Estimated mean and standard deviation of absolute percentageerror for the reconstructed ACE and IBC maps for the experi-mental phantoms. . . . . . . . . . . . . . . . . . . . . . . . . 98Table 5.3 Estimated mean and standard deviation of absolute percentageerror for the reconstructed ESD map for phantom 3. . . . . . . 100Table 5.4 Steatosis classification results for different QUS feature combi-nation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105xivTable 6.1 Example of commercial imaging modalities for which proposedalignment protocol is applicable. . . . . . . . . . . . . . . . . 115Table 6.2 Example of histopathology algorithms for which proposed align-ment protocol is applicable. . . . . . . . . . . . . . . . . . . . 116Table 7.1 Clinical characteristics of pregnancies for placentas studied. . . 138Table 7.2 Thresholds, AUROC, sensitivity and specificity for receiver op-erating characteristic analyses of elasticity for control and PE/I-UGR Placentas. . . . . . . . . . . . . . . . . . . . . . . . . . 142Table 7.3 Comparison between the elasticity (E) obtained in this study andthose obtained in previous studies. . . . . . . . . . . . . . . . 149xvList of FiguresFigure 1.1 Application of QUS in Steatosis detection. Top: Differentstages of NAFLD. Steatosis is the first stage, which is reversible,but can progress into irreversible fibrosis and cirrhosis. Bottom:QUS estimates can differentiate between tissue from steototicand normal liver. . . . . . . . . . . . . . . . . . . . . . . . . 11Figure 1.2 Thesis Overview . . . . . . . . . . . . . . . . . . . . . . . . 24Figure 1.3 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . 28Figure 2.1 ACE map of a selected Attenuation Estimation Region-Of-Interest (AEROI) as an overlay on the B-mode image of anex-vivo placenta. . . . . . . . . . . . . . . . . . . . . . . . . 34Figure 2.2 Effect of AEROI selection parameters on attenuation coefficientestimation error. Left: B-mode images with manually selectedROIs representing three different cases for 0.7 dB/cm/MHzattenuation layer (case I and case II) and 0.5 dB/cm/MHz atten-uation layer (case III) of a TM phantom. Right: Variation inAEROI selection parameters SNRe and corresponding error inACE along the depth of ROIs. . . . . . . . . . . . . . . . . . 43xviFigure 2.3 Effect of time-gated window dimension on attenuation coef-ficient estimation variance and accuracy. (a) Attenuation co-efficient estimate obtained using different window length, (b)Attenuation coefficient estimate obtained with different numberof RF scanlines taken from each of the 10 parallel planes. Thedashed (’- -’) error bars and the dash-dotted (’-.’) error barsrepresent the standard deviations of estimated values for 0.7dB/cm/MHz attenuation layer and 0.5 dB/cm/MHz attenuationlayer, respectively. . . . . . . . . . . . . . . . . . . . . . . . 46Figure 2.4 Effect of threshold T on attenuation coefficient estimation.Color plots showing (a) correlation between ACE error andthreshold values; (b) correlation between standard deviation ofACE and threshold values; (c) number of attenuation estimationregion selected as a fraction of total number of AERs. . . . . 47Figure 2.5 Effect of AEROI selection method on attenuation coefficientestimation error for a typical ex vivo placenta sample. (a) B-mode image with ROI defining the maximum rectangular areacontaining placental tissue, (b)box and whisker representationshowing the attenuation coefficient estimates before and af-ter applying AEROI selection method (c) ∆SNRe map for theselected ROI, (d) CoVFWHM map for the selected ROI, (e) at-tenuation coefficient estimates in the ROI, and (f) attenuationcoefficient estimates in the AEROI after applying the AEROIselection method. . . . . . . . . . . . . . . . . . . . . . . . 49Figure 2.6 Attenuation coefficient estimate for ex vivo placentas before andafter applying AEROI selection method: (a) box and whiskerrepresentation of mean ACE; (b) box and whisker representa-tion of intra-sample standard deviation of ACE. . . . . . . . . 50Figure 2.7 Box and whisker representation of ACEAEROI for three cate-gories of ex vivo placenta data. . . . . . . . . . . . . . . . . 50Figure 2.8 Box and whisker representation of ACEAEROI for region-of-interest near the fetal surface and near the maternal surface. . 51xviiFigure 3.1 Overview of the proposed PredictUS method. Blue solid line in-dicates the original input window, whereas the blue dash-dottedline indicates the predicted signal in the “WaveNet Network”block and the power spectrum obtained from the optimum (i.e.PredictUS) window in the “QUS Parameter Estimation” block. 56Figure 3.2 (a) An example of RF line segment prediction using the pro-posed sequence-to-sequence CNN. (b) Comparison of powerspectra estimation using original large window (10λ ), origi-nal small window (7.5λ ) and PredictUS large window (10λ )predicted from 7.5λ original window. . . . . . . . . . . . . . 60Figure 3.3 RF data prediction performance: (a) for n-step ahead predic-tion with varying n, and (b) for varying ACE in term of MeanAbsolute Scaled Error (MASE). . . . . . . . . . . . . . . . . 61Figure 3.4 PredictUS performance for case I (top), case II (middle), andcase III (bottom). (a) Example RF line segments and theirdivisions; (b) ACE error for diffirent ACE values; (c) Standarddeviation of ACE for different ACE values. . . . . . . . . . . 62Figure 4.1 Feasibility test of the proposed SWTV-ACE method on a tissue-mimicking phantom. (a) Ultrasound image of the phantom withbackscatter variation; (b) Spatially weighted matrix formationas a function of the envelope SNR deviation map; (c) & (d)ACE and BSC results with and without SWTV. . . . . . . . . 69Figure 4.2 ACE results for simulated phantoms: (a) phantom 1 (uniformACE and BSC); (b) phantom 2 (variable ACE and uniformBSC); and (c) phantom 3 (uniform ACE and variable BSC). . 70Figure 4.3 In-vivo human liver ACE from six patients and their corre-lation with proton density fat fraction. The error bars showthe standard deviation whereas the square represents the meancalculated over a region-of-interest. . . . . . . . . . . . . . . 72xviiiFigure 4.4 (a) An example of ACE computation for in-vivo human liverusing three different methods. (b) Effect of window size on es-timation variance of ACE for a homogeneous region-of-interest(top) and inhomogeneous region-of-interest (bottom). . . . . 73Figure 4.5 ACE (dB/cm/MHz) results for CIRS tissue-mimicking phan-toms. Top, middle and bottom rows correspond to phantom 1,phantom 2 and phantom 3, respectively. The first two columnsshow the B-mode images and ground truth ACE overlaid onthe B-mode images. The last three columns show ACE mapsobtained from the RPM, unweighted TV and SWTV method,respectively. The dashed lines outline the inclusions insidephantom 2 and phantom 3. . . . . . . . . . . . . . . . . . . . 74Figure 4.6 ∆SNRe map (left) and ACE maps obtained from the RPM, TVand SWTV methods for a placenta ex vivo. . . . . . . . . . . 75Figure 4.7 Boxplots of ACE (top) and logBSC (bottom) for phantom layersZ1 and Z2, measured using reference phantom method andSWTV method. The black dashed horizontal lines are groundtruth QUS values of the respective phantom layer reported byCIRS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76Figure 5.1 Efficacy of the adaptive regularization parameter ∆SNRenv toidentify BSC variation within the ROI. (a) B-mode image withdashed white line outlining the three inclusions. Four color-coded ROIs are selected at the boundaries of the inclusions andthe background. Inclusion 1 - red, inclusion 2 - green, inclusion3 - blue, and background - gray. (b) The adaptive regularizationparameter ∆SNRenv map. (c) Rayleigh distribution fit associatedto the selected ROIs. (d) IBC and ∆SNRenv along the curveshown in (b). The dashed lines correspond to the center pointof the selected ROIs. . . . . . . . . . . . . . . . . . . . . . . 87xixFigure 5.2 Diagram of the custom-made CIRS phantoms.(a) Phantom 1with two vertical layers; and (b) phantom 2 with three sphericalinclusions (variable ACE and similar backscatter (IBC) andscatterer diameter (ESD) property) and (c) phantom 3 withthree spherical inclusions (similar ACE and variable backscatter(IBC) and scatterer diameter (ESD) property) . . . . . . . . . 90Figure 5.3 (a) ∆SNRenv map, ground truth ACE map and reconstructedACE map using different methods for inclusion 1 in phantom2 (variable ACE and approximately uniform backscatter andscatterer size property) and (b) the profile of the reconstructedACE along the axially central line through the inclusion. . . . 92Figure 5.4 (a) ∆SNRenv map, ground truth IBC map and reconstructed IBCmaps using different methods for phantom 3 (similar ACE andvariable backscatter and scatterer size property). Inclusion 1, 2,and 3 have been annotated in all the maps. (b) The profile of thereconstructed IBC along the axial central line through inclusion2. (c) Reconstructed ACE maps using different methods forphantom 3 and (d) the profile of the reconstructed ACE alongthe axial central line through inclusion 2. . . . . . . . . . . . 97Figure 5.5 (a) ∆SNRenv map and reconstructed ESD maps using differentmethods for phantom 3 (approximately uniform ACE and vari-able backscatter and scatterer size property) with inclusion 1, 2,and 3. (b) The profile of the reconstructed ESD along the axialcentral line through inclusion 2. . . . . . . . . . . . . . . . . 99Figure 5.6 Mean and standard deviation of ACE in phantom 2 (left), IBC(middle) and ESD (right) in phantom 3 using different methods. 99Figure 5.7 CNR for ACE, IBC and ESD measures obtained from differentmethods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100xxFigure 5.8 Standard deviation of absolute percentage error in IBC andESD measure obtained using three-dimensional TV regulariza-tion and two-dimensional TV regularization vs. the number ofplanes used in the computation. The dashed lines represent thetheoretical (1/√N) curves obtained by averaging N completelyuncorrelated planes. . . . . . . . . . . . . . . . . . . . . . . 102Figure 5.9 Correlation coefficient between two 2-D radio-frequency (RF)signals separated by ∆N planes for a phantom and an in vivoliver example. . . . . . . . . . . . . . . . . . . . . . . . . . 102Figure 5.10 Effect of spatial weighting. (a) Cost at ground truth QUS param-eter values for regularization parameter values (upper surface:2D QUS; lower surface: 3D QUS). (b) Cost at ground truth val-ues for different regularization parameter values, λ2; λ1 = 2−4and λ3 = λ2. Shaded regions: range of ∆SNRenv, Mid line:mean ∆SNRenv, upper and lower lines: standard deviation. (c)Error in QUS estimation for different regularization parametervalues. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104Figure 5.11 In vivo human liver QUS measures from twenty-one patientsand their correlation with proton density fat fraction. The ver-tical and horizontal error bars show the standard deviation inQUS measure and PDFF measure, respectively, whereas thesquare represents the mean calculated over a region-of-interest. 105Figure 5.12 Scatter plot of QUS measures representing normal and steatosiscases. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106Figure 6.1 Overview of project SWAVE 2.0. . . . . . . . . . . . . . . . 117Figure 6.2 Multi-modality images obtained from SWAVE 2.0. . . . . . . 119Figure 6.3 The co-ordinate systems involved in the multimodality imagingsystem: world coordinate system W , ultrasound coordinatesystem U , pathology coordinate system P, MRI coordinatesystem M and the transformations between them. . . . . . . . 121xxiFigure 6.4 Possible sources of registration error during data acquisition.Left: ultrasound probe alignment process during QUS imagingusing SWAVE and right: full-thickness placenta sampling, bothalong the line indicating the plane-of-interest. . . . . . . . . 123Figure 6.5 Co-registration between digital photograph taken at the PRIMEcentre and the coronal slice of the MRI localiser sequence. (a)Point clouds before and after registration. Co-ordinates of thecod liver oil pills as well as a portion of the placenta bound-ary were used to generate the point cloud in the moving andreference images. (b) The digital photograph was used as themoving image. The line connecting the pills indicates the plane-of-interest. Two other lines perpendicular to this line markthe two regions-of-interest to be imaged in US and pathology.The dot indicates the location with suspected lesion. (c)Thecoronal slice corresponding to the fetal surface acquired during3D localiser sequence was used as the reference image. (d)Registration result where ’pink’ denotes the moving point cloudand ’green’ denotes the fixed point cloud. Magnified viewsshow the overlapping of the oil pills and the fetal surface bound-aries between the registered images. The distance between thecentroids of the pills in the registered digital photograph (130.6mm) closely matches to the measure indicated by the ruler (132mm). (e) The corresponding regions-of-interest in axial MRIhave been localized using the registration result. . . . . . . . 124Figure 6.6 Fiducial registration error (FRE) (left) and target registrationerror (TRE) (right) of the MRI/digital photograph registrationalgorithm of 41 placenta samples. The lines indicate the meanFRE (left) and mean TRE (right). . . . . . . . . . . . . . . . 127xxiiFigure 6.7 Correlative imaging of a retroplacental hematoma for an exam-ple placenta. (a) Registered MRI, ultrasound, and histopathol-ogy images containing retroplacental hematoma at the maternalside of the disc (indicated by the arrows). (b) Profiles corre-sponding to multiparametric QUS and MRI measures alongthe white line showing in (a). The shaded area highlights thehematoma region. . . . . . . . . . . . . . . . . . . . . . . . 128Figure 7.1 SWAVE imaging of ex vivo placental elasticity for representa-tive examples from the control and the PE/IUGR group. (a)Elasticity results obtained using 2D LFE and 3D LFE are shownas overlays. The ROIs are selected from the registered histolog-ical sections. (b) The phasor planes corresponding to the slicesshown on the B-mode images have been shown. . . . . . . . . 140Figure 7.2 (a) Elasticity results obtained using 2D LFE vs. elasticity resultsobtained using 3D LFE. 2D LFE overestimates the stiffness formost placenta examples from both control and PE/ IUGR class(indicated by the points on the right side of the identity line).The histograms of the differences between E measures obtainedusing 2D LFE and 3D LFE for both classes also shows thisoverestimation. (b) Violin plots of placentas from the control,IUGR and PE group obtained using 2D LFE (left) and 3D LFE(right). All violin and boxplots indicate the 1st, 2nd and 3rdquartiles; the whiskers indicate the minimum and maximumvalues. ∗p < 0.05;∗∗ p < 0.01;∗∗∗p < 0.001 . . . . . . . . 141xxiiiFigure 7.3 Viscoelastic characterization of the placentas from control,IUGR and PE groups. (a) Elasticity measures as a functionof frequency (n = 41). The line and the shaded regions indicatethe mean elasticity and the standard deviation at correspond-ing frequency. (b) Violin plots and boxplots of shear modu-lus (top) and viscosity (bottom) of placentas from the control(n= 17), IUGR (n= 13) and PE (n= 10) group. All violin andboxplots indicate the 1st, 2nd and 3rd quartiles; the whiskersindicate the minimum and maximum values. ∗p < 0.05;∗∗ p <0.01;∗∗∗p < 0.001. (c) Shear wave speed dispersion and thefitted Voigt model obtained by averaging the actual shear wavespeed and estimated shear wave speed from the Voigt model forthe placentas in control, IUGR and PE group. . . . . . . . . . 143Figure 7.4 Receiver operating characteristic curves for elasticity estimatesbetween control and PE/IUGR placentas using single frequencyand 3D LFE technique (left), elasticity estimates using multi-frequency (middle), and for Voigt parameters (right). Thedashed line indicate an AUROC of 0.5. . . . . . . . . . . . . 145Figure 7.5 Receiver operating characteristic (ROC) curves and correspond-ing AUC values with 95% confidence intervals for elasticityestimates between cases with and without NICU admission. Allelasticity measures were obtained using 3D LFE. The dashedline indicate an AUROC of 0.5. . . . . . . . . . . . . . . . . 145Figure 8.1 ACE, IBC and ESD maps overlaid on ultrasound B-mode imagefor an example placenta. . . . . . . . . . . . . . . . . . . . . 153Figure 8.2 Scatter plot of QUS measures representing control, IUGR andPE cases. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153Figure 8.3 Cross validation matrix for Fine Gaussian Support Vector Ma-chine (SVM) classifier. . . . . . . . . . . . . . . . . . . . . 154Figure 8.4 Receiver operating characteristic curve. . . . . . . . . . . . . 154Figure 8.5 Set of 4 phantoms with the actual fat percentage annotated (top)and the MRI proton-density-fat-fraction (PDFF) map (bottom). 157xxivFigure 8.6 Ultrasonic ACE versus MRI PDFF for phantom (left) and ACEversus PDFF for ex vivo placental tissue. . . . . . . . . . . . . 157Figure 8.7 Left: Multimodal registration for an example placenta case:MRI PDFF image (top), ultrasound overlaid on MRI (middle),and pathology images overlaid on MRI (bottom). Middle: Acemap overlaid on the MRI. The mean ACE value for two regions-of-interest have been shown. Right: PDFF map showing meanPDFF for the corresponding regions-of-interest. . . . . . . . . 158xxvGlossary2D Two Dimensional3D Three DimensionalACE Attenuation Coefficient EstimateAER Attenuation Estimation RegionAEROI Attenuation Estimation Region-Of-InterestAR AutoregressiveARFI Acoustic Radiation Force ImpulseASL Arterial Spin LabelingAUROC Area Under the Receiver-Operating-Characteristics curveBOLD Blood Oxygen Level-Dependent ImagingBSC Backscatter CoefficientCNN Convolutional Neural NetworkCNR Contrast-to-Noise RatioCOV Coefficient of VariationDDE Dual-Dual-EchoDTI Diffusion Tensor ImagingDWI Diffusion Weighted ImagingxxviEAC Effective Acoustic ConcentrationECL Echogenic Cystic LesionsESD Effective Scatterer DiameterFFT Fast Fourier TransformFPS Frame-Per-SecondFRE Fiducial Registration ErrorFWHM Full-Width at Half-MaximumIBC Integrated Backscatter CoefficientIUGR Intrauterine Growth RestrictionMASD Minimum Average Squared DeviationMASE Mean Absolute Scaled ErrorMRI-PDFF Magnetic Resonance Imaging Proton Density Fat FractionNAFLD Non-Alcoholic Fatty Liver DiseaseNICU Neonatal Intensive Care UnitOE-MRI Oxygen Enhanced MRIPRIME Perinatal Research and Imaging EvaluationPSD Power Spectral DensityPSWE Point Shear Wave ElastograhyQDA Quadratic Discriminant AnalysisQMRI Quantitative Magnetic Resonance ImagingQUS Quantitative UltrasoundRF Radio FrequencyRMS Root Mean SquareROC Receiver Operating CharacteristicsxxviiROI Region-of-InterestRPM Reference Phantom MethodSGA Small for Gestational AgeSNR Signal-to-Noise RatioSWAVE Shear Wave Absolute Vibro-ElastographySWEI Shear Wave Elasticity ImagingSWTV Spatially Weighted Total VariationTE Transient ElastographyTRE Target Registration ErrorUA Umbilical ArteryUBC The University of British ColumbiaUS BI-RADS Ultrasound Breast Imaging-Reporting and Data SystemVUE Villitis of Unknown AetiologyWHO World Health OrganizationxxviiiAcknowledgmentsI cherish the first conversation with my supervisor, Prof. Robert Rohling, whenhe mentioned the placenta project as a potential research topic in my prospectivePhD lab, Robotics and Control Laboratory (RCL). This defining moment, I believe,has helped me to identify a purpose that is truly meaningful to me. I sincerelythank Professor Rohling for choosing me for this dream project. I want to expressmy sincerest gratitude to you, for guiding me with your knowledge and expertise,for instilling in me the spirit and passion of an independent researcher, and mostimportantly, for the invaluable trust, patience, and support through some of the mostdifficult periods of my life.I would also sincerely thank Dr. Tim Salcudean, with whom I was fortunate towork on several projects. Your advice and guidance were instrumental in directingthe trajectory of liver QUS research. I dearly appreciate your generous supportin every aspect of my research career, including writing grants and applying forfellowships.I am grateful to my supervisory committee member, Dr. Matthew Yedlin, forproviding valuable comments and advice. I am thankful to our clinical collaborators:Dr. Jefferson Terry, Dr. Chantal Mayer, Dr. Denise Pugash, and Dr. JenniferHutcheon. It was a true pleasure to work with Dr. Terry, with our shared curiosityin placenta pathology and imaging. I also thank Victoria Lessoway, an immenselyresourceful sonographer and an amazing storyteller. Your passion for research andnew technology is contagious. I consider myself lucky to be surrounded by thetalented labmates at RCL, who are enthusiastic, funny, and immensely helpful.I cannot help but thank my ‘village’, the excellent support system I have got tocontinue my research while taking care of a toddler: Marisol Gaitan, Dalia Sumy,xxixAmanda Mendes and Rossa Baborelli. You have kept me floating on the mostchallenging days.Last but not least, my love and gratitude go to my son, who has added a newsense of appreciation for life, my husband for giving me the wings to fly, and myparents for always being there for me.xxxChapter 1Introduction1.1 Pregnancy Screening: Challenges and Scopes1.1.1 InaccessibilityEach year, an estimated 295 000 maternal deaths occur [252]. Despite the decliningtrend in the estimated maternal mortality rate, spurred by the global initiativeof setting ‘improving maternal health’ as one of the Millennium DevelopmentGoals, the disparity in maternal mortality among and within countries persists to bealarmingly high [60]. Around 99% of preventable maternal deaths take place in low-and lower-middle-income countries, making a woman from one of these countries120 times more susceptible to death due to pregnancy and childbirth compared to awoman in a high-income country [252]. The inequities in death, often cited as the“largest discrepancy of all public-health statistics” [142], reflect the inequalities inaccess to pregnancy health care. Further, poor maternal outcomes as well as theunderlying health inequities, significantly raise the risk of stillbirth and neonataland perinatal deaths. Most of the stillbirths and neonatal deaths that occur annuallyare clustered around Sub-Saharan Africa and the Southern and Central Asia region[252], overlapping with the regions corresponding to high maternal death. Also,studies on the consequence of a mother’s death on children showed that the infantswhose mothers died are more vulnerable to dying within the first month of theirlives, which is 46 times higher than the infants of surviving mothers [153, 160].1Identifying the immediate and long-term global burden of maternal death, involvinginter-generational impact on health, education and economy of the family and thecommunity, research efforts to design accessible pregnancy screening would beamong the first steps towards solving the ‘remediable injustice’ of maternal death.Access to effective and appropriate pregnancy screening enables adopting time-sensitive, life-saving interventions during pregnancy. Particularly, obstetric ultra-sound constitutes an essential component of pregnancy care, with a crucial role inthe identification and management of complicated pregnancies [4]. World HealthOrganization (WHO) recommends only one ultrasound examination before 24 weeksof gestation, considering the trade-off in resource allocation [251]. Growing ev-idence, however, advocates for wider access to ultrasound screening to realizeits true potential. For example, universal first-trimester and third-trimester ultra-sound evaluation significantly improved the detection of growth-restricted fetuscompared with selective ultrasound [12, 223]. Until recently, the high cost of in-stallation and maintenance of ultrasound machines has been among the barriersto the widespread implementation of the ultrasound scan. With the emergence oflow-cost, light-weight, point-of-care ultrasound in recent years, there are greateropportunities for increased ultrasound access in resource-scarce settings [92]. How-ever, traditional ultrasound is highly system- and operator-dependent. Therefore,the benefit of ultrasound in terms of screening for complications and appropriatereferral would be limited by the lack of training and expertise in sonography, aswell as sub-optimal ultrasound image quality compared to the high-end alternatives[138, 241]. To address the perennial challenge of inaccessibility to antenatal care,this thesis proposes multiple techniques for accurate, precise and high-resolutionestimation of Quantitative Ultrasound (QUS) parameters. The objective would be toincorporate QUS in the existing pregnancy screening system, which would provideobjective, quantitative measures irrespective of the system used and independent ofuser interpretation.1.1.2 InefficiencyPre-eclampsia, intrauterine growth restriction and pre-term labor are responsiblefor most cases of maternal and perinatal mortality and morbidity. These pregnancy2related disorders fall within the spectrum of “great obstetric syndromes”, a termcoined by Roberto Romero to underpin their shared characteristic and underlyingpathways [200] and possible reasoning behind the lack of success in their predictionand prevention, despite advances in perinatal care and extensive research efforts.One of the general features of these pregnancy complications is that the un-derlying pathophysiology involves a change in placental structure and function[77]. Current pregnancy screening is not yet at the stage to identify the specificpathological mechanisms, rather would identify the clinical manifestation. However,clinical symptoms are often presented too late to alter pregnancy outcomes. Forexample, the abnormal change in sFlt-1 and PlGF can be detected 5–8 weeks beforethe clinical onset of preeclampsia [237]. Another pitfall of clinical symptom basedscreening is evident in the current definition of intrauterine growth restriction asthe estimated fetal weight < 10th percentile, which fails to distinguish fetuses thatare pathological growth-restricted from those that are constitutively small. Studiesshow that approximately 50-70% of the Small for Gestational Age (SGA) neonateshave normal pathophysiology, whereas a fetus suffering from restricted growth dueto placental damage and dysfunction might not be SGA, according to the clinicaldefinition [12, 177]. Research shows that preventive therapy in early pregnancy byadministration of aspirin and low-molecular-weight heparin in high-risk women[48] and women with an indication of abnormal placentation [26] has role in the pre-vention of the onset pre-eclampsia and intrauterine growth restriction, emphasizingthe importance of early diagnosis.Secondly, the etiopathogenesis of the pregnancy complications, a topic ofextensive research, is currently recognized to be multifactorial. Therefore, anysingle test will not be adequate to identify all or most of the cases [200]. Forexample, Umbilical Artery (UA) Doppler waveforms were found to be normal intwo-thirds of late-onset Intrauterine Growth Restriction (IUGR) cases [32], whereasincreased pulsatility in the UA waveforms (UA PI>95th percentile) were highlypredictive of early-onset IUGR, which could predict acute fetal deterioration up toseven days before the clinical manifestation [12]. There have been much interestsin developing multiparameter screening models, comprising maternal risk factors,maternal serum biomarkers, maternal blood pressure and Doppler for predictingplacental dysfunction before the clinical onset. The findings from the studies3adopting multiparametric approaches remained conflicting: promising results havebeen found to identify early-onset IUGR [12, 45, 46], while other studies reportedno benefit of early screening to identify preeclampsia and other adverse perinataloutcomes [9, 43]. These findings, while underlining the lack of efficacy of thecurrent markers, show the strength of a multiparametric approach as a potentialscreening tool.Finally, the placenta plays a key role in the development of the pregnancycomplications, with specific placental pathology and findings. However, the roleof traditional ultrasound in identifying the underlying pathologic condition fromthe texture or echogenecity based placental evaluation is limited [65, 215]. Forexample, a poor correlation was found between ultrasound texture measures at19-23 weeks of gestation and the presence of specific lesions, characteristic of poorperinatal outcomes, found at delivery [229]. Adding to the absence of diagnostictool to be able to monitor the placenta, most studies are conducted on the placentasdelivered at term, which attribute to a knowledge gap between the snapshot in timeexamined after delivery and underlying pathophysiology [73]. It is critical to be ableto monitor the placenta across the pregnancy to understand the placental functionand development [114].In light of the above discussion, an effective pregnancy screening tool wouldbe: (i) multiparametric, (ii) noninvasive and real time, and (iii) able to identify thechange in the underlying pathophysiology. Therefore, this thesis proposes a mul-timodality imaging approach, Shear Wave Absolute Vibro-Elastography (SWAVE)2.0, including quantitative ultrasound and Quantitative Magnetic Resonance Imag-ing (QMRI), with an ability of non-invasive intra-uterine monitoring of the placenta.The objective would be to identify multiparametric biomarkers, that would besurrogate of the placental function (i.e., underlying pathophysiology), from the mul-timodal imaging analysis of the placentas from healthy and complicated pregnancies,using histopathology as the gold standard.1.2 Quantitative Ultrasound for Tissue CharacterizationIn this thesis, we use the following definition for the term “tissue characterization”:“the identification of one or more physical parameters of a small volume of tissue4that are sufficiently well correlated with the type or condition of the tissue thatthe measurement of these physical parameters may alone be used as an effectiveindex of the type or condition of that volume of tissue [37].” Generally, tissuecharacterization approaches aim at differentiating between normality and abnor-mality (e.g., healthy liver vs. fatty liver), and monitoring changes over time (e.g.,assessment of therapeutic response through imaging cell death) associated with aspecific structure or function in the human body. The thesis particularly includestissue characterization techniques based on quantitative ultrasound imaging, builton the assumption that the acoustic properties of soft tissue, captured through theinteraction of the ultrasound (US) with the tissue, are significantly different in tissuewith different pathological conditions.Conventional ultrasound B-mode images are formed from backscattered RadioFrequency (RF) echo spectrum, which are created as a result of interaction withmacro- and micro- scale tissue structures. The B-mode image processing includingenvelope detection and log compression, highlights the large-scale (> ultrasoundwavelength) features such as the organ boundaries. However, these processing stepsdiscard the frequency-dependent smaller scale (< ultrasound wavelength) structuralinformation. The speckle pattern and the brightness of the B-mode image couldbe related to the complex organization of smaller scale tissue structures and theiraverage scattering strength. For example, ‘bright liver’ echo pattern in ultrasound B-mode is widely recognized as a sign of hepatic steatosis, i.e., fat accumulation withinhepatic cells [185]. However, the B-mode image brightness is also a function of theinstrument parameters, making it impossible to benchmark and compare imagesobtained from different scanners or from the same scanners with different settings.Therefore, conventional US imaging, which is qualitative, system-dependent, andopen to user interpretation, while suitable for displaying macroscopic structures, isnot ideal for tissue characterization.However, the rf echo spectrum backscattered from soft tissue contains informa-tion about structural and elastic properties of the interacting medium. Particularly,the frequency dependence of the rf spectrum depends on the size, shape, and elasticproperties of the scattering materials, while its magnitude depends on the size andnumber density of scatterers [99]. The amplitude and frequency dependence ofrf spectrum is further affected by the attenuation of the interacting medium. QUS,5a research field pioneered by the work of Lizzi et al. [41] and Insana et al. [99],involves the system- and user-independent measurement of these structural andacoustic properties. Three key research streams within QUS include: (1) developing,improving and validating theoretical frameworks to remove system dependence,(2) applying QUS methods on biological tissue with different pathological statesor clinical diagnosis in an effort to identify particular QUS measures correlatedwith the presence of specific conditions, and (3) identifying physical changes intissue microstructure leading to the change in the corresponding QUS measures.Two prominent branches of QUS are: spectral based parameters and elastography(measuring the tissue response to applied perturbation). The thesis will discuss threespectral based QUS parameters, namely Attenuation Coefficient Estimate (ACE),Backscatter Coefficient (BSC), and Effective Scatterer Diameter (ESD), and twoelastographic QUS parameters, namely elasticity and viscosity. In the followingsections, we will discuss the theoretical development of the QUS parameter estima-tion methods, and their applications for tissue characterization and identification ofunderlying changes leading to the change in QUS parameters.1.2.1 Spectral QUS Parameters: DefinitionAttenuation Coefficient Estimate (ACE)An ultrasound pulse is attenuated as an exponential decay function of the prop-agation depth. The ultrasound ACE is a measure of the ultrasound amplitudedissipation (expressed in decibel per centimeter of propagation per megahertz) dueto a combined effect of scattering and absorption. The dissipation associated withthe scattering is small compared to absorption (less than 10% for typical biologicaltissue) [72]. Several methods, using frequency domain, time-frequency domainand time domain approaches, have been developed to measure the ACE, which areprimarily based on estimating the change in spectral contents, that is, amplitudedecay (spectral difference methods) or center frequency downshift (spectral shiftmethods) in the backscattered power spectrum along the depth.Spectral shift methods assume a Gaussian spectral shape of the ultrasoundpulse and estimate the downshift of its center frequency along the propagation6depth providing a measure of the ACE slope [18, 72, 109, 165, 186]. Among thetime domain approaches, the center frequency is estimated from the density of zerocrossings, under the narrow-band approximation [72, 165]. In the frequency domain,the central frequency can be measured by computing the first moment of the powerspectrum [187], by fitting a Gaussian function to the power spectrum and findingthe center frequency [18], or by using spectral cross-correlation to compute thespectral downshift of the entire spectrum [109]. Fink et al. [71] computed the downshift of the spectral centroid along the depth by using a short time Fourier transformand calculating the n−th moment of the power spectrum. The limitations of thespectral shift based methods include the effects of diffraction, which is particularlychallenging to be compensated for clinical array transducers [18].On the contrary, spectral difference and spectral log difference methods measurethe decay of the ultrasound spectral contents with respect to depth to estimatethe attenuation coefficient as a function of frequency. These methods can beincorporated in a reference phantom framework to compensate for diffractionand other system-dependent effects by utilizing a well-characterized referencephantom [116, 255, 256]. Contrary to the frequency-shift method, these methodscan analyze attenuation as a function of frequency over the transducer bandwidth.These methods, however, cannot compensate for the backscatter variation withinthe region-of-interest. The hybrid method was introduced to fuse the spectraldifference and spectral shift method, which was found to minimize the disadvantagesof both the methods [110]. A study that compared spectral difference, spectrallog difference, and hybrid methods showed that the spectral difference methodis the most accurate and precise method in a homogeneous region-of-interest;however, the performance drastically degrades for a region with backscatter variation.The spectral log difference and hybrid method could provide accurate attenuationresults for backscatter variation due to varied scatterer number density, while all themethods fail when scatterer size varies [189, 256].Backscatter Coefficient (BSC)The BSC is defined as the time-averaged scattered intensity in the backward directionper unit solid angle per unit volume normalized by time-averaged incident intensity7(cm−1 Sr−1). The theoretical formulation of the backscatter coefficient of a random,heterogeneous medium, according to the model developed by Insana et al. [99], hasbeen detailed in Appendix A.Effective Scatterer Diameter (ESD)The effective scatterer diameter (ESD) is a quantitative ultrasound measure thatgives an estimate of average particle size in random media. The ESD can bedirectly related to the correlation coefficient of inhomogeneities in the medium.The correlation coefficient, Rγ(∆r), a measure of similarity between the impedanceat positions separated by ∆r, would tend to zero for a distance ∆r D, as thecorrelation distances γ1−〈γ1〉 and γ2−〈γ2〉 at those positions would be statisticallyindependent. Therefore, scatterer diameter can be measured from the zeros of thecorrelation function [34] or from the acoustic intensity form factor (FF), which is theFourier transform of the autocorrelation function[99]. In this thesis, we will focuson the methods where the ESD is measured by comparing the measured data to theappropriate form factor that describes the geometrical properties and organizationof scattering particles in frequency space.Reference Phantom Method (RPM)The speckle pattern generated during ultrasound imaging is a function of both thesystem-dependent factors and the tissue properties. To extract tissue-dependentinformation of the sub-resolution scatterers, researchers have adopted differentapproaches to account for the system effects. To estimate the spectral bases parame-ters, the average power spectrum is computed from the gated rf echo signals. Theaverage power spectrum obtained from the considered Region-of-Interest (ROI) isthen calibrated and compensated for attenuation and diffraction effects. Previousresearch efforts were directed to design accurate and appropriate calibration tech-niques for different types and geometries of ultrasound transducers, ranging fromsingle-element transducer to clinical array transducer.For single-element sources, the substitution method using a planar rigid reflectorhas been commonly adopted [121, 133]. However, the planar reflection calibrationtechniques, valid only in the focal plane, suffer from degraded accuracy in the8region-of-interest far from the focal plane. While different research groups haveattempted to design appropriate diffraction correction terms, particularly for dis-tances away from the focus, large differences were found in the BSC estimates andtheir derivatives depending on the focus, aperture and scatterer size [121]. Yao et al.proposed a reference phantom method that calibrates the sample data with the dataacquired from a well-characterized reference material with known acoustic proper-ties [255], with an assumption of equal sound of speed in the reference phantom andthe sample under study. The method can be used for any transducer geometry andsettings, including single-element transducers and array transducers. We outline thetheoretical derivation of QUS parameters using the reference phantom method inAppendix A, that provides the foundation for the methods proposed in this thesis.1.2.2 Spectral QUS Parameters: ApplicationsThe basis of tissue characterization using QUS parameters is built on the assumptionsthat disease processes alter the tissue structure and thereby cause observable changesin acoustic scattering properties. Initial approaches for tissue characterizationapplied QUS techniques on normal and diseased tissues with an aim to identifychanges in the QUS parameters that can be correlated to the presence of a specificdisease. The findings further lead to fundamental studies for understanding thestructural changes in the tissue and the pathophysiological processes responsible forit [134].Among the QUS parameters, ultrasound attenuation has numerous successfulclinical applications, including cancer detection in prostate and lymph nodes [67],bone health assessment [120], breast lesion characterization [164, 166], and my-ocardial tissue characterization [136, 208]. An early study on ultrasonic attenuationin myocardium found an association between infarction and quantitative change inattenuation [154]. Other studies found the role of ultrasonic attenuation in detectingthe risk of embolism in patients with myocardial infarction, who went throughpercutaneous coronary intervention [62, 123]. These findings led to fundamentalstudies to understand the underlying mechanism, where the presence of micro-calcification and cholesterol crystals were found to be responsible for increasedattenuation [91]. Attenuation was also found to be valuable in detecting osteopenia,9with a significantly reduced value of broadband ultrasound attenuation for patientswith osteoporotic vertebral fractures [198]. Ultrasound attenuation is also impor-tant to characterize breast masses, as it quantifies the ‘posterior acoustic features’within a mass, a descriptor in Ultrasound Breast Imaging-Reporting and Data Sys-tem (US BI-RADS) lexicon. A study reported a 20% increase in the breast massescorresponding to a diagnosis of carcinoma compared to those in fibroadenomas[164]. Backscatter coefficient (a surrogate of ‘echo pattern’, another descriptor ofUS BI-RADS lexicon) and its derivatives (effective scatterer diameter) were alsoused for characterization of breast masses [164, 166].Among the earliest applications of BSC, ultrasonic backscatter fluctuations ofmyocardium over a cardiac cycle was used to detect cardiac abnormalities [139]and risk of developing diabetic cardiomyopathy [79]. Another new advancementof QUS is its application in detecting the morphological change due to cell death,advancing the scope of QUS from the tissue characterization. Structural changesin the cell nuclei (nuclear condensation and fragmentation) due to cell death leadsto increase in tissue echogenicity and subsequent increase in the backscatteredsignal. QUS parameters derived from the backscatter coefficient, such as IntegratedBackscatter Coefficient (IBC) and ESD, therefore, can detect cell death, whichserves as a surrogate marker of cancer treatment outcome [205, 240]. Multiplestudies conducted on in vitro cell samples, preclinical mouse samples and humanbreast tumors treated with different cancer treatments (including radiation andchemotherapy) and imaged with high-frequency ultrasound transducers [113, 234]and low frequency clinical transducers [59, 206, 207] showed significant increasein integrated backscatter and scatterer diameter [210] associated with tumor celldeath due to treatment response. Backscatter coefficient also found applicationsfor characterization of cancellous bones, with its ability to measure bone mineraldensity, a strong indicator of bone content. The backscatter coefficient is alsodependent on the trabecular thickness, therefore can predict the risk of fracture[244]. Midband fit, an equivalent of the integrated backscatter and effective scattererdiameter showed values in prostrate cancer detection, offering effective targetedbiopsy and focal treatment solutions [66, 199].Liver steatosis quantification using spectral-based QUS parameters has under-gone a recent surge in an effort to address the alarming increase in non-alcoholic10Figure 1.1: Application of QUS in Steatosis detection. Top: Different stagesof NAFLD. Steatosis is the first stage, which is reversible, but canprogress into irreversible fibrosis and cirrhosis. Bottom: QUS estimatescan differentiate between tissue from steototic and normal liver.fatty liver disease prevalence (25% globally) [135]. Simple hepatic steatosis (exces-sive fat accumulation in liver cells) is the early stage of Non-Alcoholic Fatty LiverDisease (NAFLD), which can potentially progress into advanced fibrosis, cirrhosisand malignancy (Figure 1.1). Liver biopsy is not feasible for routine screeningdue to its potential risk and prohibitive cost. Thus, there is a significant interest indeveloping reliable, inexpensive and noninvasive biomarkers to detect and monitorthe progression of NAFLD. Previous works demonstrated that the cellular balloon-ing due to fat infiltration in the fatty liver affects the ultrasonic scattering process,resulting in an increase in ACE and Backscatter coefficient [15, 192]. Based on thisprinciple, ACE and BSC can be utilized as promising tools to detect and quantify11hepatic steatosis. Several clinical studies on QUS showed promising results for non-invasive quantification of fat content in liver, with efficacy comparable to MagneticResonance Imaging Proton Density Fat Fraction (MRI-PDFF) and biopsy, the currentgold standard for steatosis grading [76, 130, 184]. More recently, investigations arebeing undertaken to determine the ultrasound scattering mechanism in relation withthe relative change in the spatial position of hepatocyte nuclei due to fat accumu-lation with an aim to design more accurate scattering models and therefore betterdiagnostics [253, 254].In the field of obstetrics, the utility of QUS parameters is being explored topredict pregnancy outcome and pregnancy-related complications. Attenuation,backscatter power loss and effective scatterer diameter have been found to be im-portant parameters for the characterization of cervical remodelling and cervicalripening, with implications for predicting labor induction success and preventingpreterm labor [69, 86, 87, 148]. A recent study found strong correlation betweencervical smooth muscle activity at the internal Os in vitro and effective scatterer di-ameter [211]. In the early 1980s, researchers started exploring the use of ACE in theplacenta alongside fetal liver and fetal lung, with the aim of assessing fetal maturityand diagnosing intrauterine growth restriction. Benson et al. [16] hypothesized thatattenuation and scattering properties of placental tissue may change with maturationas a result of an increase in collagen content and fibrin deposition. They reported arelative increase in the slope of the frequency-dependent attenuation coefficient withthe maturation of placental tissue (n = 16). Another ex vivo study found a positivecorrelation of gestation age with the placental attenuation coefficient (n = 12) [29].A more recent study performed serial ultrasound assessment on 54 patients, whichfound an increase in attenuation with advancing gestation [219]. Compared to otherorgans, the research area focusing on the application of quantitative ultrasound forplacental tissue characterization is sparse. Larger cohort studies including placentasfrom normal and complicated pregnancies are needed to realize the potential ofQUS for non-invasive placenta evaluation.121.2.3 Spectral QUS Parameters: ChallengesCurrent challenges towards the clinical translation of QUS imaging is high variabil-ity in QUS measures, which often obscures the separation between measures fromdifferent tissue groups. QUS measures are subject to the fundamental trade-off be-tween precision and resolution [132, 173]. Tissue heterogeneity further deterioratesthe trade-off. The achievable accuracy and precision for the maximum availableregion-of-interest might not be sufficient to distinguish different tissue or differentpathological states [148]. This is even more challenging for thin tissue (e.g., dermis)or heterogeneous tissue (e.g., placenta). Recently, several approaches have beenadopted to improve the resolution-precision trade-off [40, 196, 235]. However,there remains an unmet need to develop methods to improve resolution-precisiontrade-off in the presence of tissue heterogeneity.1.2.4 Elastographic QUS ParametersThe idea of ‘elastography’, i.e. assessing the mechanical properties of inaccessibleorgans using “remote palpation” was described well in one of the earliest worksby Kit Hill in the book “Physical Principles of Medical Ultrasonics” [95]. Sinceits inception, the technology has greatly evolved in the last three decades andhas validated and extended the long-established value of manual palpation for theassessment of tissue stiffness in many clinical applications. Besides the commondiagnostic and treatment monitoring applications such as breast mass evaluation,focal and diffuse liver disease detection, prostate gland evaluation and thermaltherapy monitoring [212], new areas of applications are steadily emerging. In thefield of obstetrics, the utility of elasticity imaging is being explored with a focuson cervix and placenta to predict labor onset and to assess the risk of pregnancycomplications, respectively [68].After the first exploratory study on placental elasticity evaluation in 2012 [129],several studies have been conducted with an objective to establish baseline elasticityfor normal placenta tissue [2, 28, 147] and to understand the changes in placentalstiffness due to pregnancy complications such as preeclampsia (PE) [7, 38, 75, 104,108, 224], intrauterine growth restriction (IUGR) [61, 63, 88, 178, 226], pretermbirth [8] and gestational diabetes mellitus (GDM) [20, 119, 258]. These pregnancy13complications affect the placenta and manifest different types of pathologies depend-ing on the severity and chronicity of the placental injury and placental maladaptation.The changes in placenta pathology will affect its stiffness, which can be quantifiedusing elastographic methods. Previous studies showed the efficacy of quantitativeelasticity measure as a global parameter to differentiate between placentas fromnormal pregnancies and pregnancies affected by PE, IUGR and gestational diabetesmellitus [38, 61, 178, 226, 258], as well as a local parameter to identify fibroticlesions, inflammation and infarction from healthy placental tissue [178, 226].Strain elastography and Acoustic Radiation Force Impulse (ARFI) based elas-tography techniques (Shear Wave Elasticity Imaging (SWEI) and ARFI imaging)are the predominant approaches currently being considered for measuring placentalstiffness in clinical practice. Elastography techniques generally measure an estimateof Young’s Modulus (E), the physical parameter corresponding to the tissue stiff-ness, by applying an external force to the studied tissue and tracking the resultingmotion. Strain elastography [182] applies a constant stress (σ ) (quasi-static) witharbitrary value and measures the strain (ε) map, where strain gives an indirect mea-sure of Young’s Modulus via Hooke’s law (σ = Eε). Strain elastography, despitebeing simple to implement, has limited clinical uptake due to operator dependencefor the control of stress applied and absence of quantification, which hinders thecomparison of stiffness between different tissue types and pathological states acrossdifferent studies. Additionally, the requirement of a stress being applied by theoperator excludes the pregnancies with posterior placentation (due to the increasedgap between the transducer and the placenta caused by the positioning of the fetusin between them), which is more prevalent compared to the accessible placentationsites (e.g. anterior and lateral) [141]. Contrary to the quasi-static strain elastog-raphy technique, the dynamic approaches apply a time-varying force (transientor steady-state). The ARFI based techniques apply a focused high power beam(pushing beam) to displace tissue at the focal region of the pushing beam. Thepushing beam generates shear waves with a speed (cs) directly related to the elas-ticity (E) via E = 3ρc2s , under the assumption that the propagation medium withdensity ρ is linear, isotropic, incompressible, and elastic. ARFI imaging computesa relative measure of tissue stiffness by measuring the on-axis displacement andrecovery [170], while shear wave elasticity imaging (SWEI) measures the shear14wave speed by tracking tissue motion at several lateral positions along the shearwave propagation path [209]. ARFI based techniques are available in several com-mercial diagnostic ultrasound scanners and have demonstrated promising resultsfor measuring placental elasticity. The bio-effects of the radiation on the fetal andplacental tissue are yet to be determined [100, 218]. Alternatively, the elastographytechniques that use low frequency mechanical vibrations externally to generateshear waves, such as Transient Elastography (TE) [28] and shear wave absolutevibro-elastography (SWAVE) [2] offer alternatives to measure the placental stiffnesswithout acoustic radiation force impulses.1.3 The Human PlacentaThe placenta is named after the Greek word plako`enta (”flat cake”) for its round,flat appearance. This transient organ forms the critical interface between the fetusand the mother. Structurally, the mammalian placenta is defined as an ‘apposition orfusion of the fetal membranes to the uterine mucosa’ [159], identifying the fetal andmaternal components of the placenta. However, the distinction of maternal and fetalcomponents, and maternal-placental interfaces become blurred in the hemochorialplacentas, such as the human placenta, where maternal uterine epithelium andvasculature are eroded by fetal trophoblast cells, permitting their direct exposureto maternal blood [190, 221]. Functionally, the placenta, contrary to the historicnotion of being a ‘passive conduit’ for nutrients, is now viewed as a highly-complex,multi-faceted organ, performing the actions of all the major organ systems for thedeveloping fetus, during the course of pregnancy. The placenta also functions as amajor neuroendocrine organ, producing hormones and several important factors tomodulate maternal physiology and metabolism, and to nourish and protect the fetus,and thereby playing the role of master regulator in the intrauterine environment[114].Placental abnormalities are widely recognized to have adverse effects on theneonatal outcome and life-long health of the offspring as well as the mother. Placen-tal insufficiency plays a central role in the development of preeclampsia, intrauterinegrowth restriction and pre-term labor [157], which are among the ‘great obstetricsyndromes’ responsible for most cases of maternal and perinatal mortality and mor-15bidity. Preeclampsia is characterized by new-onset of hypertension (systolic bloodpressure of > 140 mm Hg and diastolic pressure > 90 mmHg) with proteinuria(Spot urine protein/creatinine >30 mg/mmol or at least 300 mg in a 24-h portion, orat least 1 g/L on dipstick testing) [230]. It is a leading cause of maternal mortality(15% to 20% in developed country) [217]. Preterm birth is defined as birth occurringat less than 37 weeks of gestation. Intrauterine growth refers to the condition wherethe fetus is unable to maintain expected growth, with estimated fetal weight or actualbirth weight below the 10th percentile for gestational age [176]. Preterm birth andIUGR attribute to the incidence of low birthweight, which accounts for 60-80% oftotal neonatal deaths [122]. The etiopathogenesis of these pregnancy-specific com-plications, a topic of extensive research, is currently accepted to be multi-factorial.Early onset type of PE (≤32 weeks), the more severe phenotype which is oftenaccompanied with fetal growth restriction, is dominated by placental factors. De-fects in the placentation process between week 8 and 18 cause an incomplete spiralartery remodeling and therefore an intermittent and high-velocity uteroplacentalhypoperfusion [225]. The resulting oxidative and hydrodynamic stress trigger therelease of placenta-derived factors and therefore maternal inflammatory response,inducing novo onset of hypertension and other signs of PE. Also, the insufficientuteroplacental perfusion can contribute to the associated growth restriction of thefetus. On the contrary, late-onset type of PE (>32 weeks) and gestational dia-betes are predominantly ‘maternal’, where predisposing cardiovascular or metabolicrisks for endothelial dysfunction exacerbates the systemic inflammatory responseto the physiological burden of pregnancy [225]. Irrespective of the underlyingpathophysiological process, these pregnancy complications affect the placenta andmanifest different types of pathologies depending on the severity and chronicity ofthe placental injury and placental maladaptation.1.3.1 Placental PathologyThe placenta is a dynamic organ that develops over the course of pregnancy, un-dergoing macro- and microscopic changes over this period. Placental pathology,providing an autopsy of the pregnancy, has important implications for explanation ofpregnancy outcomes and management of subsequent pregnancies. The occurrences16and severity of placental features are correlated to the severity of preeclampsia[239, 247] and IUGR [152]. A particularly important step to incorporate the patho-logical findings into patient management by the clinicians was the group effortof pathologists, clinicians and scientists at the Amsterdam placental workshop in2014. The aim of the workshop was to standardize the nomenclature and definitionsof pathological findings, to enable meaningful clinical correlations, to indicatehow strong the evidence is for stated clinical correlations and, thereby, providemanagement guidance for clinicians.Gordijn et al. [82] argued that while symptoms and associated placental featuresmight vary depending on the interplay of multiple maternal and fetal variables,the pathological features may be grouped to uncover a pattern, characteristic ofearly-onset preeclampsia and IUGR. Early-onset preeclampsia are associated withdefective placentation and incomplete spiral remodelling, leading to maternal vas-cular malperfusion and ischemia. Reduced uteroplacental flow due to maternalvascular malperfusion is also a dominant cause of early-onset IUGR. The incom-plete spiral artery remodeling in early onset preeclampsia and IUGR is relatedto greater pulsatility of the maternal blood flow leading to significant increase inarterial lesions [167], including acute atherosis, thrombosis, and abruption [82]. Theplacental adaptation to reduced perfusion, i.e., placental insufficiency further mani-fests through accelerated villous maturation [157], which in turn leads to increaseddiffusion distance for exchange, disrupting optimal exchange in the placental-fetalunit present in IUGR. Inflammatory lesions, such as perivillous fibrin deposition andVillitis of Unknown Aetiology (VUE), are among the common placental findings forpreeclampsia and IUGR, suggestive of abnormal materno-fetal immune reaction[82]. Lesions such as thrombus occludes the spiral arteries; increased perivillous orintervillous fibrin/ fibrinoid deposition strangulate the placental villi; thromboticvasculopathy impairs the fetal circulation; and thereby further lead to placentalinfarction [152, 194]. Infarcts affect 40% of patients with mild preeclampsia while70% of those with severe preeclampsia, contrary to 20% of those with normalpregnancies [239].Placental infarcts and thromboses are most frequently found lesions in IUGRwith or without preeclampsia. While isolated small thrmoboses and infarcts are gen-erally not clinically significant, larger thromboses and infarcts are mostly reported in17pregnancies complicated by IUGR. Larger infarcts are associated with intervillousthromobses and extensive fibrin deposition. The thromboses that involve >50% ofthe chorionic plate account for 70% of IUGR cases with pregnancies >24 weeksof gestation [27]. Massive fibrin deposition with >50% of the placental mass arefound to be related with severe IUGR [27]. Late-onset preeclampsia, contrary tothe early-onset counterpart, is associated with increased placental size and possibleabsence of pathological features [167]. Similarly, late-onset IUGR represents amore heterogeneous group with less specific pathological features [152].1.3.2 Placental ImagingPathological examination conducted on the delivered full-term placentas, whileproviding critical information regarding pregnancy outcome, represents a snapshotof the end-stage disease process. Therefore, pathological studies alone are notsufficient to explain the underlying pathophysiology leading to a certain pregnancyoutcomes. Therefore, there is a need for novel approaches to monitor placentalhealth in utero throughout the pregnancy for real-time assessment of placentaldevelopment and function during this period. Current research efforts directedtowards developing non-invasive and in utero placenta imaging techniques offer thepromise of identifying early and sensitive biomarkers of placental health [127, 220].Particularly, ultrasound and MRI are two promising imaging modalities, with apotential to offer insight into placental anatomy and function.Placental Imaging: UltrasoundUltrasound has been the first-line diagnostic tool in clinical obstetrics for around lastfive decades. Determining the placental location (normal such as anterior, fundal,posterior or lateral uterine wall vs. abnormal such as near the cervical internalopening) is one of the earliest documented applications of obstetric ultrasound [30].Ultrasound also has definitive roles in detecting anomaly in placenta implantation,including placenta accreta spectrum (creta, increta, precreta), placenta previa, Vasaprevia [183] and placental abruption, with 77-93% sensitivity and 71-95% specificityfor detecting placenta accreta using appropriate sonographic criteria [42, 47]. Otherapplications of placental evaluation using ultrasound include assessing placental18thickness, volume and shape, with clinical implications for abnormal fetal growth,gestational diabetes, antepartum hemorrhage, miscarriage and preterm delivery [3].Sonographic appearance of the placental texture (historically classified intofour grades according to Grannum classification) has been found to be correlatedwith fetal maturity [84]. While grade 0 characterizes a placenta with homogeneousechogenicity and no indentations at the chorionic plate found till early 2nd trimester,grade 3 marks placentas with prominent, irregular calcifications and pronouncedindentations delineating the cotyledons, found in late term (38–39 weeks and later)pregnancies. Premature aging (highly calcified grade 3 before 37 gestational weeks)has been linked with higher risk of expedited labor induction [155] and adversepregnancy outcomes [35, 150]. The applicability of Grannum classification could befurther enhanced through the adoption of advanced computer-aided image analysistechniques [145].Ultrasound examination is also able to identify a number of placental parenchy-mal lesions, that are correlated with preeclampsia, intrauterine growth restrictionand adverse perinatal outcomes, particularly in more advanced stages of gestation.The sonographic findings of ’Echogenic Cystic Lesions (ECL)’, often describedas ‘rounded intraplacental haematomas’, are defined as a hypo-echoic cavity withirregular borders and an echogenic rim [195]. They have been found to be associatedwith placental infarcts, inter-villous thrombosis, and advanced villous maturation[194, 195]. Among these, placental infarcts are reflective of maternal vascularunderperfusion, which can be seen as intraplacental masses close to the basal platesonographically with a low sensitivity (about 10%) [27, 215]. Intervillous thrombi,on the other hand, mark a site of fetal bleeding into the intervillous space. Con-flicting reports can be found regarding the relationship between the presence ofECL and adverse pregnancy outcomes [112, 195, 197], while a few studies reportedpositive correlation between increased size of such lesions and adverse outcomes[112]. Maternal floor infarction, characterized by fibrin deposition, thrombosis andhematoma in the decidua beneath the placenta, appear as diffuse hyperechogeniclesions in ultrasound, that increases in size with advancing gestation [27].Another sonographic finding with important clinical implication is that ofchorangioma and related vascular tumors, characterized by well-circumscribedhypoechoic lesions located at the fetal surface of the placenta, with abnormal19Doppler flow. Chorangioma, though is a rare (1% prevalence) and benign finding inmost cases, larger ones are associated with negative clinical impact with a 30% to40% neonatal mortality rate [10]. Combining Doppler with the sonographic findingswas found to improve the prediction of suspected lesions and respective clinicaloutcomes [112, 238, 260]. The sonological identification of the placental lesionsare possible at the advanced stage of pregnancy for most cases, where no correla-tion was found between ultrasound measures at mid-pregnancy (19-23 weeks) andlesions present in the delivered placentas [229]. While placental evaluation usingultrasound would not be a sufficiently sensitive and time-efficient tool to identifyperinatal outcomes, analyzing the pathological correlates of sonographic findingswill still be an important component in the research of advancing the understandingof the pathophysiology of pregnancy complications.Placental Imaging: MRIMRI is a valuable adjunct in obstetric imaging, providing a larger field-of-view, andhigher soft tissue contrast. Similar to ultrasound, MRI can also be used for placentalgrading. Placental MRI demonstrates a decrease in signal intensity (the ratio ofplacental and amniotic fluid signal intensities) with increased gestational age, witha strong correlation to ultrasound grading [21]. MRI is also useful for identifyingplacental accreta spectrum, particularly in cases where ultrasound is inconclusive,with sensitivity and specificity of 75%-100% and 65-100%, respectively [47].A few studies on MRI findings with pathological correlates have been reported[131, 151]. One of the first studies used T2-, T1-, diffusion-weighted and echo-planar sequences on 45 placentas and identified pathologies, including infarction(sensitivity = 96%, specificity = 63%), subchorionic hematoma (sensitivity = 64%,specificity = 100%), retroplacental hematoma (sensitivity = 100%, specificity =98%), intervillous hemorrage (sensitivity = 75%, specificity = 97%) and perivillousfibrin deposition (sensitivity = 0%, specificity = 97%). [131]. Other studies reportedthat diffusion weighted MRI could identify placental dysfunction (defined by thepresence of infarction, hemorrhage or infection) with 99% accuracy, which was animprovement over 91% attainable using ultrasound [23]. Another study found thatthe increasing extent of vascular lesions seen in MRI was able to predict neonatal20mortality with an accuracy of 82% [151]. MRI offers the advantage of panoramicimages across different acquisition planes and is relatively operator independent.However, MRI is expensive and causes patient discomfort. Therefore, MRI, whileoffering clinically useful and complementary information on placental pathology, isrecommended as an adjunct imaging technique.Placenta Imaging: Quantitative Ultrasound and Quantitative MRISpectral based quantitative ultrasound applications for the placental tissue charac-terization has only been scantly explored [49, 51, 53, 54]. The research works onelastographic QUS parameters for placental characterization have been reviewed inthe previous section. Among the other quantitative measures of ultrasound images,texture-based analysis of placenta ultrasound B-mode images can be traced back tothe mid 1980s. Several authors attempted to correlate placental grades according toGrannum grading system or placental maturity stage using different textural featuressuch as coefficient of variation of gray level in placental tissue [191], gray-levelhistogram [140], gray-level difference matrix [33], fractal dimension and lacunarity[193], and observed varied level of success. Recently, novel feature descriptors, suchas pyramidal shift invariant feature transform, discriminative learning and hybriddescriptors from deep convolutional network and handcrafted features are beinginvestigated for automatic placental grading [124–126], and placental abnormalitydetection [96].While qualitative MRI, with anatomic/structural imaging capability, has a longhistory in diagnosis and management of placenta adhesion disorder, there is agrowing interest in the field of quantitative MRI, with ability to provide quantitativemeasures related to the tissue function. Particularly, MRI techniques sensitive to thevascular pattern and to oxygenation and perfusion properties are being investigated[13]. Diffusion Weighted Imaging (DWI) is among the qMRI techniques, whichprovides a parametric apparent diffusion coefficient (ADC) map, where the ADCvalue at any voxel represents the degree of water diffusion. IUGR complicatedpregnancies are associated with significantly lower ADC measure compared to thosein normal controls [74]. Diffusion Tensor Imaging (DTI) also estimates the restrictedwater diffusion in the tissue, which has showed significantly reduced measures in the21IUGR complicated pregnancies [101]. Arterial Spin Labeling (ASL) is an imagingsequence that could visualize large arterial vessels through the magnetic labellingof water component in the blood and thereby provides a direct measure of bloodperfusion. ASL also showed significant reduction in basal plate signals in IUGRpregnancy compared to that in normal pregnancy [57].T2 relaxometry is another qMRI parameter, that measures the hydrogen protonrelaxation, a characterizing property of tissue. T2 relaxation time in the placentadecreases with advancing pregnancy, as well as with the onset of IUGR, as a re-sult of increased fibrin deposition, fibrosis, necrosis and infarcts and reductionin oxygen saturation [13]. Oxygen Enhanced MRI (OE-MRI) and Blood OxygenLevel-Dependent Imaging (BOLD) are two MRI sequences, that have been used tomeasure oxygen saturation. BOLD MRI has been found to reflect tissue oxygensaturation, which could differentiate placentas in controlled hyperoxia and in IUGR[222]. Susceptibility-weighted imaging, on the other hand, is sensitive to com-pounds with paramagnetic or ferromagnetic properties, and therefore, can detectdeoxyhemoglobin, ferritin, and calcification in tissue.This sequence has been usedto enhance visualization of venuous structures [115]. Also, qMRI measures fromT2- and diffusion weighted images were found to differentiate placental pathology,including maternal vascular malperfusion, masssive perivillous fibrin depositionand chronic villitis of unknown etiology [22]. But these advanced techniques areonly available in certain MRI machines and may add significantly to the screeningtime.Correlative Multimodal ImagingCorrelative imaging that acquires, aligns and fuses complementary information us-ing multiple multiscale modalities from the same specimen is particularly importantto advance the understanding of placental structure, function and their relation-ship. Multiscale structural imaging including ultrasound and magnetic resonanceimaging (MRI) combines the high axial and lateral resolution achievable usingUS and the large field-of-view in MRI. Additionally, the quantitative counterpartsof these modalities, qMRI and QUS can further offer complimentary informationregarding the placental micro- and macro- structure, perfusion and function. A22major challenge in designing a multimodal imaging project would be the develop-ment of a structured workflow enabling sample preparation, data acquisition andalignment, data processing, and deposition. Particularly, attaining the alignmentamong different modalities for enabling correlative analysis could be challengingfor the placenta, as, unlike many other organs such as prostrate [80], it does notcontain sufficient anatomic landmarks visible in different modalities. Therefore,anatomic landmark based registration would not be feasible for most cases. Fordesigning an external-fiducial based approach, the fiducials are required to be visiblein one or more modalities, should not disrupt any imaging or histological processing,and should not damage the tissue such that it interferes the interpretation by theradiologist and the pathologist [242].Imaging Fat: Role of Ultrasound and MRILipid trafficking through the placenta supports the increased metabolic needs ofthe developing fetus. Lipid trapping into the placenta, a sign of reduced utilization,rather than the increased storage [137], might perturb lipid transport to the fetus.Research shows that human trophoblasts in the placenta deposit lipids in the formof lipid droplets when exposed to hypoxia [214]. Hypoxia, a key component ofplacental dysfunction, is associated with IUGR and PE. Based on the hypothesis thatincreased fat trapping is an indicator of placental dysfunction, in vivo imaging ofplacental fat deposition could improve the understanding regarding the underlyingmechanisms for placenta development and dysfunction. Emerging technologies suchas MRI-PDFF and ultrasound ACE have showed promises to measure parameters,which are directly correlated to the quantity of lipid storage in liver cells (Figure 1.1).Therefore, QUS and qMRI analysis in fatty liver could serve as a useful validationframework for the study of placental fat imaging [184].1.4 Proposed Solution: Towards an Accessible andEffective Pregnancy SystemTowards a vision to develop an objective and effective ultrasound-based pregnancyscreening system (Figure 1.2), this thesis aims at identifying non-invasive imagingfeatures that would serve as biomarkers of placental health. The big picture of the23Figure 1.2: Thesis Overviewthesis work is illustrated in Fig. 1.2, where the limitations and scopes have beenencompassed under the following two objectives:• Objective 1: As a low-cost, portable, and safe imaging modality, ultrasoundcan offer optimal point-of-care solutions for pregnancy screening. However,the subjective nature of conventional ultrasound poses a significant obstaclein a low-resource setting due to the lack of necessary expertise required forimage interpretation. The user interpretation is further affected by the imagequality, largely varying from the lower-end hand-held scanners to the hospital-grade systems. The system- and user-dependence, a fundamental limitationof traditional ultrasound, limits the benefit of accessible pregnancy screeningoffered by the point-of-care ultrasound. To address the limitations arising24due to the subjective nature of ultrasound imaging, the first objective of thethesis is to design user- and system-independent QUS-based algorithms (witha vision to be integrated within the pregnancy screening system), addressingthe existing limitations, namely the trade-off between resolution and QUSparameter estimation quality, and presence of heterogeneity.• Objective 2: The current pregnancy screening system does not include ade-quate evaluation of the placenta. Placental insufficiency leads to the devel-opment of severe pregnancy complications, including PE and IUGR, andpre-term labor. The findings for these placenta-mediated diseases includechanges in placental tissue architecture. With the paucity of non-invasivetools for assessing placental pathology, current clinical practice rather relieson symptom-based abnormality identification, which often fails to distinguishthe underlying pathophysiology between normal and abnormal pregnancies.Additionally, most placenta-mediated diseases are late clinical manifesta-tions of chronic processes precedented by long subclinical phases. Clinicalmanifestation-based screening, therefore, often leads to delayed and inac-curate detection of pregnancy complications. To address these issues, thesecond objective of the thesis is to investigate multimodal imaging of theplacenta, towards a vision to integrate placental evaluation with the pregnancyscreening system.To achieve the above-mentioned objectives, the following contributions havebeen made:1. Developing objective quantitative ultrasound (QUS) algorithms. In thesealgorithms, we have adopted different approaches to address the existinglimitations in QUS estimation. We have conducted comprehensive validationof the proposed algorithms using data from simulations, phantoms, placentaex vivo and liver in vivo. The in vivo liver study provided both the ultrasounddata and the MRI-PDFF data (considered as gold-standard for hepatic fatquantification), from healthy and steatotic patients, and thereby enabledrigorous validation of the proposed QUS algorithms. The proposed algorithmsare briefly described below:25• AEROI: A new Attenuation Estimation Region Of Interest (AEROI)selection method was proposed to minimize the variance in the QUSparameters (specifically in ACE) due to the presence of inhomogeneitiesin biological tissue. This method selects the AEROI based on the (i)envelope signal-to-noise ratio deviation and (ii) coefficient of variationof the transmit pulse bandwidth.• PredictUS: A novel Quantitative Ultrasound (QUS) parameter estima-tion technique was proposed to improve the trade-off between QUSestimation quality and resolution. Using a sequence-to-sequence con-volutional neural network based on WaveNet, the proposed methodimproves the estimation resolution by predicting the next data points ofa given window, thereby extending the tissue property available for alimited region to a larger region.• SWTV-ACE: A spatially weighted total variation regularization basedmethod was proposed for the estimation of QUS parameter, ACE,that would simultaneously address both limitations in QUS estima-tion, namely the estimation quality and resolution trade-off, and thepresence of tissue heterogeneity. We employed envelope signal-to-noise-ratio deviation, an indicator of tissue heterogeneity, to adapt the localregularization.• 3D QUS: 3D QUS imaging can be advantageous over the 1-D or 2-Dcounterpart, as it allows volumetric reconstruction, multimodal registra-tion and enhanced visualization and interpretation, as well as improvedestimation quality due to availability to more data. Therefore, a multi-parametric 3D weighted QUS (3D QUS) method was proposed basedon the previous spatially weighted regularization scheme. The methodinvolves the reconstruction of three QUS parameters: attenuation co-efficient estimate (ACE), integrated backscatter coefficient (IBC) andeffective scatterer diameter (ESD).2. Designing, conducting and analyzing the data from SWAVE 2.0, a mul-timodal placental imaging study.26• A multimodal imaging protocol was designed to acquire and align datafrom ex vivo placenta specimens derived from healthy pregnancies andpregnancies with preeclampsia and/ or intrauterine growth restriction.This unique project, which is first of its kind, involved extensive data ac-quisition, including novel multiparametric quantitative ultrasound (suchas elasticity, attenuation coefficient estimate, backscatter coefficient andeffective scatter diameter), multiparametric MRI, and histopathology. Aco-registration method was proposed using surface markers and externalfiducials to enable alignment and fusion of multimodal data.• SWAVE imaging of placental elasticity and viscosity were conductedand demonstrated as potential biomarkers of placenta-mediated diseases.• Quantitative ultrasound imaging of placenta was conducted. QUSshowed potential to distinguish between healthy and diseased placentas.Additionally, placental attenuation was found to be correlated to the fatcontent, assessed using MRI proton density fat fraction.1.5 Thesis OrganizationThe current chapter discusses the existing challenges and scopes in the currentpregnancy system, while providing a background in quantitative ultrasound, humanplacenta and placental imaging. The rest of the thesis are organized in two parts:Part I describes the theoretical development of objective QUS algorithms; Part IIincludes the description of the clinical study and clinical applications of QUS. Assummarized in Fig. 1.3, the rest of the thesis is organized as follows:Part I: Theoretical Development of QuantitativeUltrasound AlgorithmsChapter 2: Attenuation Coefficient Estimation using a Region ofInterest Selection (AEROI) ApproachThe presence of inhomogeneities in biological tissue results in a large variance in themeasurement of the ACE, restricting its clinical utility. In this chapter, we propose27Figure 1.3: Thesis Outlinea new Attenuation Estimation Region Of Interest (AEROI) selection method forcomputing the ACE based on the (i) envelope signal-to-noise ratio deviation and(ii) coefficient of variation of the transmit pulse bandwidth. The method was firstvalidated on a tissue-mimicking phantom, where both the accuracy and precision ofthe ACE measurement were improved. A study on 59 post-delivery clinically normalplacentas was then performed. The proposed AEROI selection method reduced theintra-subject standard deviation of ACE. The measured ACE of 59 placentas alsoestablishes a baseline for future studies on placental tissue characterization.Chapter 3: Quantitative Ultrasound Imaging using PredictUSA fundamental limitation in window-based QUS parameter estimation algorithmsis the trade-off between the resolution and precision. To address this limitation,we propose a QUS estimation algorithm, PredictUS, which would improve theresolution and precision trade-off using augmented ultrasound data. The ultrasounddata is generated using a sequence-to-sequence convolutional neural network basedon WaveNet. The method provides better estimates of local tissue properties withhigh resolution by virtually extending the property to a larger region. This proof-of-concept study based on attenuation coefficient estimate (ACE), attains a resolutionreduction up to 50% while maintaining comparable estimation precision.28Chapter 4: Attenuation Coefficient Estimation using A SpatiallyWeighted Regularization (SWTV) MethodIn this chapter, we propose a spatially weighted total variation regularization basedmethod for ACE measurement, to address the resolution-estimation quality trade-offirrespective of tissue heterogeneity. We propose a new approach to adapt the localregularization by employing envelope signal-to-noise-ratio deviation, an indicatorof tissue heterogeneity. We evaluate our approach with simulations and physicalphantoms. We demonstrate its utility for hepatic steatosis detection and placentaltissue characterization.Chapter 5: Multiparametric Volumetric Quantitative UltrasoundImaging using 3D QUSBased on the spatially weighted regularization scheme in the previous chapter, wepropose a multiparametric 3D weighted QUS (3D QUS) method, involving thereconstruction of three QUS parameters: attenuation coefficient estimate (ACE),integrated backscatter coefficient (IBC) and effective scatterer diameter (ESD).With the phantom studies, we demonstrate that our proposed method accuratelyreconstructs QUS parameters, resulting in high reconstruction contrast and thereforeimproved diagnostic utility. Additionally, the proposed method offers the abilityto analyze the spatial distribution of QUS parameters in 3D. The application of athree-dimensional total variation regularization method for the volumetric QUS re-construction involving multiple planes resulted in a high QUS estimation precision,with an improvement of standard deviation over the theoretical rate achievable bycompounding given planes. In the in vivo liver study, we demonstrate the advantageof adopting a multiparametric approach over the single parametric counterpart,where a simple quadratic discriminant classifier using feature combination of threeQUS parameters was able to attain a perfect classification performance to distin-guish between normal and fatty liver cases. We also evaluate the repeatability andreproducibility of the quantitative ultrasound (QUS) parameters obtained usingthe proposed method by using a well-characterized phantom, which was scannedmultiple times with different measurement conditions.29Part II: Clinical Applications in Project SWAVE 2.0Chapter 6: An overview of SWAVE 2.0In this chapter, we present the overview of SWAVE 2.0, a multimodal imagingstudy. We describe the protocol designed to acquire and align multimodality data,including ultrasound, MRI and histopathology, from ex vivo placenta specimensderived from both healthy and complicated pregnancies. Qualitative and quantitativevalidation of the alignment method were also performed. The standard workflowwould essentially lay the foundation of the subsequent data management, analysisand visualization. The proposed protocol could serve as a simple yet practicaltool for the placenta scientific community to replicate and extend ex vivo placentaimaging using a wide range of commercial and research imaging modalities. Theprotocol attains the goal of capturing US, MR and pathology images on placentas.The co-registration process with india ink markings and MRI-compatible fiducials isdeveloped in a way that does not disrupt imaging using any of the three modalities.The alignment protocol includes readily available tools, such as ink markings, oilpills, laser guides, containers, ruler and camera as well as an open source registrationalgorithm. Therefore, the protocol offers a promising tool for multimodal placentaimage alignment, a crucial step for correlative imaging of placenta.Chapter 7: SWAVE Imaging of Placental Elasticity and ViscosityCurrently available elastography techniques have demonstrated promising results todetect placenta abnormalities, however limitations include inadequate measurementdepth and safety concerns from high negative pressure pulses. This chapter proposesSWAVE 2.0 elastography that incorporates a multi-frequency acquisition systemand uses a three-dimensional local frequency estimation (LFE) method. We studied46 post-delivery placentas, including diseased and normal control placentas. Theelasticity measures for the IUGR and PE placentas were found to be significantlystiffer compared to the control placentas. A viscoelastic characterization of placentasusing Voigt’s rheological model revealed that the viscosity measures of IUGR andPE placentas were also significantly higher compared to those obtained from normalplacentas. This chapter shows the potential relevance of elasticity and viscosity30imaging using SWAVE 2.0 as a non-invasive technology for detection of placentalabnormalities and the prediction of pregnancy outcomes.Chapter 8: Applications of Placental QUSIn this chapter, we presented two applications of placental QUS for detecting locallesions as well as for detecting placental diseases. First, we applied a quantitativeultrasound approach to detect placenta-mediated diseases. We measured the quanti-tative ultrasound parameters: attenuation coefficient estimate, integrated backscattercoefficient and effective scatterer diameter from ex vivo placentas collected fromnormal pregnancies and pregnancies affected by intra-uterine growth restrictionand preeclampsia. Preliminary results show the potential of quantitative ultrasoundto distinguish between health and diseased placentas. Next, we explored a non-invasive imaging approach for quantifying placental fat. We demonstrated that thespatial variation in MRI PDFF map and ultrasonic ACE map were correlated for theplacentas ex vivo.Chapter 9: Conclusions and Future WorkThis chapter includes a short summary of the thesis followed by discussions andsuggestions for future work.31Chapter 2Attenuation CoefficientEstimation using aRegion-of-Interest Selection(AEROI) Approach2.1 IntroductionThe accurate and objective estimation of the attenuation coefficient has importantclinical implications. In the early 1980s, researchers started exploring the use ofACE in the placenta alongside fetal liver and fetal lung, with an aim of assessingfetal maturity and diagnosing intrauterine growth restriction [181]. Benson et al.hypothesized that attenuation and scattering properties of placental tissue maychange with maturation as a result of an increase in collagen content and fibrindeposition [17]. They reported a relative increase in the slope of the frequency-dependent attenuation coefficient with the maturation of placental tissue (n = 16).A more recent ex-vivo study found a positive correlation of gestation age with theplacental attenuation coefficient (n = 12) [29]. None of the studies to date aresufficiently large to form a statistical baseline for the normal placental attenuationcoefficient. A larger cohort study establishing baseline attenuation levels is needed32as a first step for reliable comparison studies between normal and diseased placentas.Spectral methods measure the ACE based on the change in spectral contents,i.e., amplitude decay or center frequency down shift in the backscattered powerspectrum along the depth [97, 118]. However, there are additional mechanismssuch as diffraction and backscatter variation that also affect the spectrum of thebackscattered radio frequency (RF) signal. Diffraction effects due to transducerbeam focal properties affect the attenuation coefficient computation in spectral shiftmethods. For focused transducer, the attenuation coefficient measured in spectralshift methods is underestimated in the pre-focal region and overestimated in thepost-focal region [19]. The spectral difference method, also referred to as thereference phantom method, compensates for diffraction and other system-dependenteffects by utilizing a well-characterized reference phantom [255]. However, thereference phantom method does not compensate for the backscatter variation withinthe region-of-interest (ROI), which may both be depth and frequency dependent.A change in frequency dependence of backscatter along the penetration depthwould result in variation in the bandwidth of the backscattered spectrum, whichis unrelated to the attenuation variation [181]. Therefore, the reference phantommethod will be erroneous when the tissue region-of-interest under considerationis not homogeneous. The selection of suitable ROIs is therefore needed to ensureaccurate measurement of ACE.The placenta undergoes a gradual change in the ultrasonic appearance duringits maturation process. An immature placenta is homogeneous, whereas a matureplacenta is irregular with prominent undulations (i.e., indentations), and scatteredechogenic areas [83]. The inhomogeneity of matured placentas poses a challengein selecting suitable attenuation estimation regions required for low-variance andaccurate computation of ACE. In addition, the attenuation estimation methods,which are typically validated on simulation data and well-characterized referencephantoms, may yield unreliable ACE measures in clinical applications due to severalfactors. Among these factors, envelope Signal-to-Noise Ratio (SNR) and soundspeed mismatch [179, 203], difference in scatterer size and distribution betweenthe reference phantom and the tissue under investigation [180, 203] and within thetissue region-of-interest [118], and violation of stationary and incoherent scatteringcondition in tissue [201] are a few of them. Therefore, large estimation variance33Figure 2.1: ACE map of a selected Attenuation Estimation Region-Of-Interest(AEROI) as an overlay on the B-mode image of an ex-vivo placenta.including infeasible negative attenuation coefficients may result [19, 203].In this chapter, we establish an ACE measurement method with lower varianceand higher accuracy suitable for ex vivo placentas. To this end, we define a newAttenuation Estimation Region-Of-Interest (AEROI) selection method to ensurehomogeneity and consistency in backscatter frequency dependence, two key as-sumptions for the reference phantom method. Additionally, we analyze the effect ofAEROI parameters and optimize the parameter values to achieve minimum estimatevariance. We analyze the variation in ACE obtained from tissues near the fetalsurface compared with the maternal surface. Finally, we report the ACE values mea-sured from the selected AEROIs using the reference phantom method for clinicallynormal placentas.2.2 Theory2.2.1 Reference Phantom MethodFirst, we give a brief review of the reference phantom method which has primarilybeen used for ACE computation in this work. The reference phantom method34accounts for system-dependent parameters, and requires smaller regions to computeACE compared to other methods [202]. In the reference phantom method [255],the radio-frequency (RF) data are acquired from both the tissue sample underexperiment and a well-characterized reference phantom using the same transducerand system settings. For a time-gated RF signal window centered at depth z, theratio of the power spectrum S from the sample to the reference phantom at frequencyf can be written as [118, 162]:RS( f ,z) =Ss( f ,z)Sr( f ,z)=Gs( f )Ds( f ,z)As( f ,z0)Bs( f ,z)e−4αs( f )(z−z0)Gr( f )Dr( f ,z)Ar( f ,z0)Br( f ,z)e−4αr( f )(z−z0). (2.1)Here, the s and r subscript denote sample and reference, respectively, G( f ) rep-resents the transducer transfer function and transmit pulse, D( f ,z) denotes thediffraction effects related to transducer geometry and beamforming, A( f ,z0) is thetotal attenuation effect from the transducer surface to the depth z0 which correspondsto the beginning of the ROI, B( f ,z) represents the backscatter coefficient of themedium within the ROI, α is the attenuation coefficient within the ROI and z isthe distance from the transducer surface to the center of a particular time-gated RFsignal window within the ROI.Using the same system settings, and assuming same sound speed in the tissuesample and reference phantom allows to cancel the system dependent terms, namelyG( f ) and D( f ,z). Additionally, the assumption of homogeneity within the ROI en-sures that backscatter coefficient, B( f ,z) = B( f ) remains constant along penetrationdepth inside the ROI. Therefore, equation 2.1, after taking the natural logarithm,reduces to:ln [RS( f ,z)] = 4(αr( f )−αs( f ))(z− z0)+ ln[As( f ,z0)Bs( f )Ar( f ,z0)Br( f )]. (2.2)The attenuation of the sample at each frequency component, αs( f ) (Np/cm) canbe calculated from the slope of the straight line that fits equation 2.2, i.e.,35αs( f ) = αr( f )− 14∂ [ln(RS( f ,z))]∂ (z). (2.3)We assume a linear frequency dependence of attenuation, αs (Np/cm) withinthe usable bandwidth of the ultrasound transducer, which is a good approximationfor several soft tissues including placental tissue [29]. Therefore, the attenuationcoefficient estimate (ACE), denoted by βs (Np/cm/MHz), can be determined byfinding the slope of the straight line that fits the following equation:αs( f ) = βs f . (2.4)The reference phantom method is based on several simplifying assumptions.A critical assumption for the reference phantom method is homogeneity withinthe ROI with the same scatterer density and same scatterer size (i.e. invariantfrequency dependence of backscatter). Violation of the homogeneity assumptionsleads to inaccurate estimate of the ACE [118]. Therefore, it is required to ensure theselection of a ROI where the assumption of the reference phantom methods are met.2.2.2 AEROI Selection ParametersThe placental region imaged in a scan includes multiple possible Attenuation Es-timation Region (AER), allowing the selection of AEROIs (attenuation estimationregion-of-interest) which satisfy conditions for accurate ACE measurement. AERis defined as the region from which a single ACE is obtained, whereas AEROI isthe aggregate of AERs selected based on a set of proposed parameters (an exampleis given in Figure 2.1). The proposed parameters, envelope signal-to -noise ratio(SNR) deviation (∆SNRe) and Coefficient of Variation (CoV) of Transmit PulseBandwidth (CoVFWHM) are intended to ensure the identification of a region wherecrucial assumptions of the reference phantom methods are satisfied.1. Envelope SNR Deviation:Envelope SNR (SNRe), defined as the ratio of the mean to the standarddeviation of the RF signal envelope, can serve as a metric of homogeneoustexture. A previous study showed that as the SNRe approaches 1.91, theSNR of the Rayleigh distribution, the ACE in the reference phantom method36is more precise [203]. This is because spectral methods are based on theunderlying assumption that the RF signal generation is a stationary scatteringprocess, arising from a large number of randomly distributed scatterers withidentical size [216]. This is called fully developed speckle and leads to theRayleigh distribution for the RF signal envelope. The Rayleigh distributionimplies homogeneity of the medium, which is an important assumption for thereference phantom method. Therefore, we choose envelope SNR deviation,∆SNRe as an AEROI selection parameter, which is defined as follows:∆SNRe =|SNRe−SNRopt|SNRopt×100%, (2.5)where SNRopt = 1.91, which is the average SNR of Rayleigh distribution.SNRe is the envelope SNR of the sample under investigation. The envelopeSNR is defined as follows:SNRe =〈E〉√〈(E−〈E〉)2〉 , (2.6)where 〈〉 denotes expectation, and E are the RF signal envelope values overthe AERs.2. CoV of Transmit Pulse Bandwidth: We assume that the backscatter coefficientB( f ) is depth-invariant, which allows us to derive the attenuation estimationequation (equation 2.3). B( f ) can be modeled as a power function of fre-quency and can be approximated by an exponential form of the lower orderterms in a Taylor series expansion. For an ultrasound transmit pulse with aGaussian-shaped envelope, center frequency fc and variance σ2,B( f ) = f n ∝ exp[−n · ( f2−4 fc f )2 f 2c], (2.7)where n is the characteristic scatter exponent which varies from 0 for aspecular reflection to 4 for pure Rayleigh scatterers [181]. If n changesabruptly within the AER, the term B( f ,z) remains depth dependent andequation 2.2 will not hold.37The violation of the assumption is reflected as a change in the Gaussiancharacteristics of the ultrasound pulse. The amount of centroid shift and thebandwidth are both modified by the frequency dependent backscatter process,whereas the bandwidth remains unaltered in the presence of attenuationprocess alone [117, 181]. The variation of the characteristic scatter exponent nmodifies the ultrasound pulse bandwidth according to the following equation:σˆ2 = σ2f 2cf 2c +nσ2. (2.8)We can quantify the variation of n with depth by computing the variation inultrasound pulse bandwidth. Given that pulse variance at depth z1 is σ21 andat depth z2 is σ22 , equation 2.8 would lead to the following relation:σ21 −σ22 ∝ n1−n2. (2.9)We propose to measure the variation in pulse bandwidth by computing theCoefficient of Variation (COV) of the pulse spectrum’s Full-Width at Half-Maximum (FWHM). CoV gives the measure of dispersion of bandwidth fromthe mean value along the propagation depth. The CoV of transmit pulsebandwidth is defined as follows:CoVFWHM =SD(FWHM)µ(FWHM)×100% (2.10)where SD is the standard deviation and µ is the mean of the FWHM valuesof ultrasound power spectra along the depth.The above-described AEROI selection parameters are utilized to find an op-timum AEROI, where RF data follow the assumption required for the referencephantom method.382.3 Material and MethodScanning System Overview and Data AcquisitionThe ultrasound RF data from the reference phantom and ex vivo placentas wereacquired with an Ultrasonix SonixTouch ultrasound machine (Analogic, Richmond,BC, Canada) using a 4DL14-5/38 4D linear transducer operated at 5 MHz centertransmit frequency. The time-gain compensation, power level, depth, and focuswere adjusted to ensure optimum image quality for placenta samples. Accordingly,the depth was set to 3 cm with a focus at 2 cm to capture the full thickness of thefull-term placentas. The system was equipped with the Porta research interface, thatenables developing a software module (SWAVE system), primarily designed as anelastography system [1, 2]. The system collects volumetric RF data by sweeping a2D frame over 10 elevation angles, with an angle increment of 0.4◦. Each 2D framein the 3D volume consists of 128 RF scanlines spaced at 0.295mm. The samplingrate was 40MHz resulting in 1568 samples in each scanline.Tissue Mimicking PhantomsBefore applying to the placenta data, the effectiveness of the AEROI selectionmethod was validated on a tissue mimicking phantom. The RF data acquired fromthe phantom were also used as the reference for the reference phantom method. Themulti-purpose multi-tissue ultrasound phantom (model 040GSE) manufactured byCIRS (Northfolk, VA, USA) was made of proprietary Zerdine® hydrogel polymer.The phantom has two separated attenuation regions with attenuation coefficient 0.5dB/cm/MHz and 0.7 dB/cm/MHz and speed of sound 1540 m/s as reported by themanufacturer. It contains nylon monofilaments as scattering targets with differentsize and therefore different frequency dependence compared to the background.The phantom also includes targets made of same material as the background withdifferent scatterer density.2.3.1 Study PopulationPlacentas (gestational age between 37-41 weeks) were collected after deliveryfrom pregnant women between ages of 19 and 47 years (n = 59). This study39Table 2.1: Description of sub-categories in ex vivo placenta dataset.Sub-Category Description Sample SizeCategory A No appreciable abnormalities 13Category BAbnormalities that are withina diagnostic threshold30Category CAbnormalities passing oneor more diagnostic thresholds16(H15-00974) was performed under written informed consent after approval by theUniversity of British Columbia Children’s and Women’s Research Ethics Board.After delivery, the placentas were immediately stored at 4◦C for an average of 4hours and warmed to 37◦C by submerging in a constant-temperature water bath(Cole-Parmer, Montreal, QC, Canada) to simulate the in vivo temperature beforeacquiring RF data [2]. In this study, women with uncomplicated term pregnancywere recruited based on the clinical presentation. After delivery, all placentasunderwent pathological examination performed by a placental pathologist. Afterexamination, the placentas in this study were sub-divided into three categories(Table 2.1) [106]. Category A included 13 placentas without any specific lesions,category B includes 30 placentas manifesting specific lesions that did not reach adiagnostic threshold, and category C is comprised of the remaining 16 placentas withabnormalities passing one or more diagnostic thresholds. It should be emphasizedthat regardless of the presence of the lesions, all the placentas were consideredto be clinically normal based on the criteria that the pregnancies had no clinicalcomplications. Therefore, the categories include subclinical pathological changesthat may be found in clinically normal placentas, recognizing that it is normal forall placentas to accrue some abnormalities over time. These abnormalities mayproduce areas of inhomogeneity.2.3.2 RF Data Processing and AnalysisThe attenuation coefficient estimation algorithm was implemented in MATLAB2016a (MathWorks Inc., Natick, MA,USA). For the tissue mimicking referencephantom, a ROI was defined axially from 6 mm to 30 mm to exclude the near-field data suffering from ringdown and other artifacts associated with transducer40to phantom surface [162]. For the placenta samples, the ROI was defined as themaximum rectangular area containing only placenta tissue.The RF data within the selected ROIs were divided into overlapping AERs. Theconsecutive AERs were spaced at one axial correlation length apart and one lateralcorrelation length apart in the axial and lateral direction, respectively. Correlationlength was defined as the range of spatial lags (number of samples axially, andscanlines laterally) over which the one-sided correlation coefficient remained above2% [201, 202]. The spacing allowed uncorrelated estimates of the parameters [201].Each attenuation estimation region was divided into a number of time-gatedwindows spaced at one axial correlation length apar [202]. Initially, the dimensionsfor the time-gated windows were selected to be N = 30 scanlines (8 uncorrelatedscannlines) laterally and ∆z =7.7 mm (400 samples or ∼ 24 pulse lengths) axially.We investigated the effect of time-gated window parameters on ACE variance anderror by varying N from 1 to 50 and ∆z from 20 mm to 130 mm. We kept thenumber of time-gated windows in each AER fixed at 5.The Welch method [248] was used to obtain the power spectrum from the RFscanlines within each time-gated window across the 10 acquired planes. Accordingto the Welch method, each RF scanline was sub-divided into overlapping sections,with length equal to 50% of the original RF scanline and with 50% overlap (50%overlap minimizes the PSD estimation variation [248]). Each segment was thenmultiplied with a Hamming window. The Power Spectral Density (PSD) wasobtained after averaging the periodograms obtained from the windowed segments.We considered the -10 dB bandwidth of the received power spectrum as the usablefrequency range. The PSD values obtained at the time-gated windows of each AERswere used as the regression points to compute the rate of amplitude decay along thepropagation depth. The slope estimates obtained from the regression analysis wereused to compute ACE. The AEROI selection parameters ∆SNRe and CoVFWHMwere computed at each time-gated window and averaged over the windows in anAER to get a single value for each parameter corresponding to each AER. TheAERs where ∆SNRe and CoVFWHM values satisfied the empirically determinedthresholding criteria were selected for subsequent processing, which were definedas the AEROI.The sample size in this experiment is relatively small with a distribution, that is41not strictly Gaussian. Therefore, the non-parametric Mann-Whitney U test and theKruskal-Wallis test were used to compare the statistical significance of the differencebetween mean ACE and standard deviation of ACE of placentas computed withand without AEROI selection criteria, among ACE of placentas from differentcategories, and among ACE of tissue near fetal and maternal surface of placentas.We consider a p-value< 0.05 as statistically significant.2.3.3 AEROI Selection MethodBased on the AEROI selection parameters, we define an AEROI selection methodin this section. First we examine the effect of AEROI selection parameters onattenuation estimation accuracy. We manually select two different regions in thetissue-mimicking phantom from the 0.7 dB/cm/MHz layer, and one from the 0.5dB/cm/MHz attenuation layer, representing different cases. Case I represents ahomogeneous region (0.7 dB/cm/MHz), case II includes nylon scattering targetsrepresenting inhomogeneous region with varying frequency dependence of backscat-ter (0.7 dB/cm/MHz), and case III represents an inhomogeneous region includingvariation in scatterer density (0.5 dB/cm/MHz).Figure 2.2 visually demonstrates the correlation between the AEROI selectionparameter values and ACE error. In the homogeneous region (case I), CoVFWHMand ∆SNRe both remain below 10%. The ACE error in the homogeneous regionalso remains below 0.14 dB/cm/MHz (20% of the actual value). Case II includestwo targets causing variation in the frequency dependence of backscatter. We canrecognize two peaks in the ACE error corresponding to two peaks in ∆SNRe devia-tion curve representing 37% and 30% deviation. There was also a moderate increasein CoVFWHM (17%). For case III corresponding to an inhomogeneous region withvarying scatterer density, the spike representing ACE error of 106% correspondsto an increase in CoVFWHM (by 26%) and ∆SNRe (17%). We also noticed thatthere was a spatial lag between the pattern in the ∆SNRe and ACE error. This canbe explained by the limited spatial resolution, and complex spatial dependence ofthe backscatter process. However, appropriate thresholding will enable selectingAERs from homogeneous regions. We empirically set a threshold for CoVFWHM,TCoV(FWHM) and a threshold for ∆SNRe, T∆SNRe and define the following criteria for425 10 15 20 25 30 35Width (mm)5 1015202530Depth (mm)10 12 14 16 18 20Depth (mm)00.20.40.60.81 ACE Error (dB/cm/MHz)010203040Variation in AEROI Selection Parameters(%) ACE ErrorCoVFWHM∆SNR ecase I5 10 15 20 25 30 35Width (mm)5 1015202530Depth (mm)10 15 20 25Depth (mm)00.20.40.60.811.21.4ACE Error (dB/cm/MHz)01020304050Variation in AEROI Selection Parameters (%)ACE ErrorCoVFWHM∆SNRecase II5 10 15 20 25 30 35Width (mm)5 1015202530Depth (mm)13 14 15 16 17 18 19 20Depth (mm)00.20.40.60.81 ACE Error (dB/cm/MHz)010203040Variation in AEROI Selection Parameters (%)ACE ErrorCoVFWHM∆SNRecase IIIFigure 2.2: Effect of AEROI selection parameters on attenuation coefficientestimation error. Left: B-mode images with manually selected ROIsrepresenting three different cases for 0.7 dB/cm/MHz attenuation layer(case I and case II) and 0.5 dB/cm/MHz attenuation layer (case III) of aTM phantom. Right: Variation in AEROI selection parameters SNRe andcorresponding error in ACE along the depth of ROIs.43Table 2.2: AEROI selection parameter values and ACE for tissue-mimickingphantom.Reported ∆SNRe CoVFWHM ACE ACEAEROI(dB/cm/MHz) (%) (%) (dB/cm/MHz) (dB/cm/MHz)Case I: 0.7 4.39 ± 2.98 5.16 ±2.54 0.70 ± 0.08 0.70 ± 0.10Case II: 0.7 23.61 ± 8.79 8.38 ± 5.01 0.91 ± 0.38 0.74 ±0.25Case III: 0.5 9.23 ± 4.37 11.14 ± 4.0 0.58 ±0.20 0.50 ±0.10selecting homogeneous AERs:EAR(∆SNRe,CoVFWHM) =“homogenous”, if ∆SNRe < T∆SNReand CoVFWHM < TCoV(FWHM);“inhomogenous”, otherwise.The threshold T =[T∆SNRe ,TCoV(FWHM)]for each ROI is empirically defined asfollows:T =[min(T∆SNRe),min(TCoV(FWHM))]such that NAER > Nmin, (2.11)where NAER is the number of selected AERs and Nmin is a constant. Nmin wasdefined as a fraction of total number of AERs for each subject. We empiricallyselect a Nmin value, which gives a reasonable estimate variance for all the subjects.2.4 Experimental Results2.4.1 Time-Gated Window DimensionWe experimented with different window sizes, varying number of scanlines N from1 to 50, and the window length ∆z from 19.13 mm (100 samples or≈ 6 pulse length)to 13.4 mm (700 samples or ≈ 41 pulse length) for tissue mimicking referencephantom. The result for both the 0.7 dB/cm/MHz layer and the 0.5 dB/cm/MHzlayer is shown in Figure 2.3. We found that the minimum window dimension to be44∆z = 5 mm (260 samples or ≈ 15 pulse length) and N = 25 lateral scanlines from10 parallel planes (6 uncorrelated scanlines × 10 = 60) for a stable and accurateattenuation coefficient estimation.2.4.2 Validation of AEROI Selection Method on Tissue-MimickingPhantomThe proposed AEROI selection method lower-bounds the threshold by the constraintof assuring a sufficient number of AERs. We require a sufficient number of AERsto reduce spatial variance noise. To fully appreciate the effect of AEROI selectionmethod on the accuracy and variance of ACE with respect to the number of selectedAERs, we plot mean ACE error, standard deviation of ACE, and fractional AERcount as functions of T∆SNRe and TCoV(FWHM). Figure 2.4 shows an example for TMphantom (0.7 dB/cm/MHz layer) where the attenuation error and standard deviationmonotonically increase with the threshold values. This means we can improve theACE accuracy and reduce the variance by more than 50% while retaining 60% ofthe total AERs. Though these statistics are highly dependent on the homogeneityof a particular sample, it justifies the importance of AEROI selection criteria forimproving ACE accuracy and precision. We report the average envelope SNR devia-tion (∆SNRe) and CoV of pulse full-width at half-maximum (CoVFWHM) values forthe TM phantom in Table 2.2. We also report the attenuation coefficient estimationvalues obtained before and after applying the AEROI selection criteria. For homoge-neous regions, all the AERs are homogeneous and therefore the mean and standarddeviation of ACE remain unaffected after applying AEROI selection criteria. Forcase II, applying AEROI selection criteria reduces the ACE error from 30% to 6%,and standard deviation has been reduced from 54% to 36% of the reported ACE.For case III, the ACE error is reduced from 15% to 0.8% of the reported ACE. Italso reduces the standard deviation from 40% to 19% of the reported attenuationcoefficient estimate. The effect of AEROI selection criteria is more prominent forcase II, which corresponds to scatterers with different size (i.e., varying frequencydependence of backscatter). The results from case II emphasizes that the variationin backscatter frequency dependence has a comparatively severe effect on ACE,which is in agreement with the previous literature [118].4520 40 60 80 100 120Window Size (mm)00.20.40.60.811.21.4ACE (dB/cm/MHz)Experimental (0.7 dB/cm/MHz Layer)Reported (0.7 dB/cm/MHz Layer)Experimental (0.5 dB/cm/MHz Layer)Reported (0.5 dB/cm/MHz Layer)(a)0 10 20 30 40 50 Number of Scanlines0.20.40.60.811.2ACE (dB/cm-MHz)Experimental (0.7 dB/cm/MHz)Reported (0.7 dB/cm/MHz)Experimental (0.5 dB/cm/MHz)Reported (0.5 dB/cm/MHz)(b)Figure 2.3: Effect of time-gated window dimension on attenuation coefficientestimation variance and accuracy. (a) Attenuation coefficient estimateobtained using different window length, (b) Attenuation coefficient esti-mate obtained with different number of RF scanlines taken from each ofthe 10 parallel planes. The dashed (’- -’) error bars and the dash-dotted(’-.’) error bars represent the standard deviations of estimated values for0.7 dB/cm/MHz attenuation layer and 0.5 dB/cm/MHz attenuation layer,respectively.465 10 15TCoV(FWHM)1520253035T ∆SNReMean ACE Error (dB/cm-MHz)0.010.020.030.04(a)5 10 15TCoV(FWHM)1520253035T ∆SNReStd. Dev. of ACE (dB/cm-MHz)0.10.150.20.250.30.35(b)5 10 15TCoV(FWHM)1520253035T ∆SNReFractional AER Count0.40.60.81(c)Figure 2.4: Effect of threshold T on attenuation coefficient estimation. Colorplots showing (a) correlation between ACE error and threshold values;(b) correlation between standard deviation of ACE and threshold values;(c) number of attenuation estimation region selected as a fraction of totalnumber of AERs.472.4.3 Attenuation Coefficient Estimates for ex vivo Placenta DataWe estimate the mean and standard deviation of the AEROI selection parametervalues for placenta samples. The results are listed in Table 2.3. The envelopeSNR values obtained were lower than SNRopt and SNRe for the phantom, whereasCoVFWHM values were found to be in the similar range as in the phantom.Figure 2.5 shows the effect of applying AER selection criteria on an exampleplacenta sample. The sample has appreciable inhomogeneity within the selectedROI. Evaluating ACE for all the AERs in the ROI results in a large estimatevariance. From the attenuation map, we find that the outlying estimates values arepredominantly caused by the inhomogeneities. The regions with outlying ACEvalues overlap with high AEROI selection parameter values in ∆SNRe map orCoVFWHM map. Therefore, the AEROI selection criteria successfully removes theoutlier values retaining 20 AERs out of total 228 AERs and results in an ACE withlower variance.We report the mean ACE and the intra-subject standard deviation of ACE valuesobtained for the placentas in each category and also for all placenta data in Table2.3 before and after applying the AEROI selection method. To select the thresholdT , the minimum number of AERs, Nmin was set as 10% of the total number ofAERs. The average of the mean ACE was 0.57 dB/cm/MHz and 0.55 dB/cm/MHzbefore and after applying AEROI selection, respectively. The mean ACE valuesbefore and after applying AEROI selection were not significantly different (p =0.19). The standard deviation of ACE within each subject was 0.25 dB/cm/MHzafter AEROI selection, which was significantly lower than that (0.48 dB/cm/MHz)obtained before AEROI selection (p= 3.3×10−6). The distribution of intra-subjectmean and standard deviation has been demonstrated in Figure 2.6. The inter-subjectstandard deviation for the three categories of placenta classification were 0.20, 0.42,and 0.35 dB/cm/MHz respectively and the overall inter-subject standard deviationwas 0.37 dB/cm/MHz. Figure 2.7 shows the box and whisker representation ofACEAEROI for three categories of placenta classification. The differences amongthe three categories do not reach statistical significance (p = 0.71) according to theKruskal-Wallis test. The result agrees to previous work based on the same datasetwhere no significant difference was found for shear wave speed among these three485 10 15 20 25 30 35Width (mm)51015202530Depth (mm)(a)Without AEROI With AEROI-2-1012ACE (dB/cm/MHz)(b)∆SNRe5 10 15 20 25 30 35Width (mm)5 1015202530Depth (mm)2030405060(c)CoVFWHM5 10 15 20 25 30 35Width (mm)5 1015202530Depth (mm)51015(d)ACE (dB/cm/MHz)5 10 15 20 25 30 35Width (mm)5 1015202530Depth (mm)00.511.52(e)ACE AEROI (dB/cm/MHz)5 10 15 20 25 30 35Width (mm)5 1015202530Depth (mm)-2-1012(f)Figure 2.5: Effect of AEROI selection method on attenuation coefficient es-timation error for a typical ex vivo placenta sample. (a) B-mode imagewith ROI defining the maximum rectangular area containing placentaltissue, (b)box and whisker representation showing the attenuation co-efficient estimates before and after applying AEROI selection method(c) ∆SNRe map for the selected ROI, (d) CoVFWHM map for the selectedROI, (e) attenuation coefficient estimates in the ROI, and (f) attenuationcoefficient estimates in the AEROI after applying the AEROI selectionmethod.49Without AEROI With AEROI00.511.5Mean ACE (dB/cm/MHz)Without AEROI With AEROI00.20.40.60.811.2Std. Dev. of ACE (dB/cm/MHz)Figure 2.6: Attenuation coefficient estimate for ex vivo placentas before andafter applying AEROI selection method: (a) box and whisker representa-tion of mean ACE; (b) box and whisker representation of intra-samplestandard deviation of ACE.Category A Category B Category C00.511.5ACE AEROI(dB/cm/MHz)Figure 2.7: Box and whisker representation of ACEAEROI for three categoriesof ex vivo placenta data.categories [2]. In other words, the presence of placental abnormalities in clinicallynormal patients did not significantly affect attenuation coefficient estimate.In addition, we compared the ACEAEROI values from regions near the maternalsurface and the fetal surface obtained before and after thresholding. The box andwhisker plot is provided in Figure 2.8. We found that the fetal side is associatedwith slightly higher attenuation coefficient compared to the maternal side (p =5.4×10−13).50Fetal Maternal00.511.522.53ACEAEROI(dB/cm/MHz) Figure 2.8: Box and whisker representation of ACEAEROI for region-of-interest near the fetal surface and near the maternal surface.2.5 DiscussionOnly a few studies that report the attenuation coefficient estimates for placenta areavailable. Given the absolute value of the ACE, the values measured in this studycan be directly compared with the existing studies. Using the proposed referencephantom method, we obtained an average ACE of (0.55 ± 0.25) dB/cm/MHz for exvivo placentas at 37-41 weeks (n = 59). Akaiwa [5] reported ACE of (0.4±0.11)dB/cm/MHz for in-vivo normal placentas at 34 weeks gestational age (n = 13).Caloone et al. [29] reported ACE of 0.76-0.85 dB/cm/MHz for ex vivo placenta at36-40 weeks (n = 4). Moreover, the study was designed to apply high-intensityfocused ultrasound to create placental lesions and therefore, the clinical conditionswere not among the selection criteria. Our study is the largest study to date to reportattenuation coefficient estimate of normal ex vivo placentas.We analyzed the effect of time-gated window dimension of ACE. Rosado-Mendez et al. has emphasized the importance of uncorrelated samples in reducingcoherent noise components in power spectral density estimate [202]. The minimumrequired window length is 15 pulse lengths, which is in the range of widely acceptedwindow length (7-15 pulse lengths) [132, 202]. However, the number of minimumindependent scanlines is found equal to 60 (6 uncorrelated scanlines from 10 parallelplanes), which is higher (N = 60) than the range of 10-20 independent scanlinessuggested in different studies [132, 202]. A possible reason for the bias could be51that the consecutive parallel acquisition of ultrasound planes are not completelyuncorrelated.In this study, we have discussed the importance of homogeneity criteria, afundamental assumption for the reference phantom method for accurate and preciseestimation of ACE. We have proposed a new attenuation estimation region (AEROI)selection method to ensure the selection of a region that satisfies the underlyingassumptions for the reference phantom method. We showed that change in scattererdensity and scatterer size influence the envelope SNR and the pulse bandwidth.Defining AEROI selection criteria based on thresholding the parameters, namelyenvelope SNR deviation and coefficient of variation of pulse FWHM allows ho-mogeneous regions to be optimally chosen. The proposed method improves theestimate variance significantly, reducing the intra-subject standard deviation from0.48 dB/cm/MHz to 0.25 dB/cm/MHz.Several works have acknowledged the difficulty in achieving ACE with lowvariance. Rubert et al. showed that biological variation is a minor contributingfactor [19], whereas a large deviation of envelope SNR from 1.91 is responsiblefor high estimate variance [203]. Besides, Kuc et al. [117] showed that ultrasoundpulse shape and bandwidth do not alter in the process of attenuation . To ourknowledge, our study is the first to explicitly demonstrate the correlation betweenenvelope SNR deviation and pulse bandwidth variation to the ACE error and estimatevariance. We found that the outlying ACE contributing to the increase variancepredominantly arises from the inhomogeneities in the sample. The outlying ACEalso coincides with high envelope SNR deviation and pulse bandwidth variation.Therefore, imposing the AEROI selection criteria based on envelope SNR and pulsebandwidth essentially excludes most of the inhomogeneous regions. However, weacknowledge that we may lose clinically important information while discardinginhomogeneous regions. As the spectral difference and spectral shift methods willfail to measure ACE in the case of rapid change in the scatterer characteristics,improved methods could be devised for estimating ACE in the inhomogeneousregions.We also reported the ACE obtained from tissues near the fetal and the maternalsurfaces. We found that the ACE values at fetal surfaces are slightly higher comparedwith the maternal surface, where the difference in median values is statistically52Table 2.3: AEROI selection parameter values and ACE results for ex vivoplacenta.Data ∆SNRe CoVFWHM ACE ACEAEROI(%) (%) (dB/cm/MHz) (dB/cm/MHz)Category A 42.44 ± 18.73 4.88 ±4.96 0.54± 0.41 0.43 ±0.29Category B 46.73 ± 17.60 6.37± 8.25 0.58 ± 0.49 0.60± 0.24Category C 43.48± 16.46 6.27 ± 7.13 0.60 ± 0.50 0.57 ± 0.27Total 44.84± 17.67 5.99 ±7.32 0.57 ± 0.48 0.55 ±0.25significant. This could be a consequence of the relatively larger (stem) villi presenton the fetal side. Finally, we analyzed the ACE values in relation to birth weight(R2 = 0.04), placental weight (R2 = 0.04), placental volume (R2 = 0.003), placentaldensity (R2 = 0.07), and birth weight to placental weight ratio (BPW) (R2 = 0.006).We did not find any correlation between ACE and any of these parameters.The RF data utilized for ACE computation in this study was primarily acquiredfor elastographic measurement of ex vivo placentas. This presents a unique advan-tage of SWAVE system, which enables the multi-parametric estimation yieldingthe elasticity and ACE maps with the same data. The simultaneous computationof ACE and elasticity with the same data reduces the scantime and obviates therequirement of registration. Previously, Fibroscan (Echosens, Paris, France), avibration-controlled transient elastography (VCTETM) device, provides similar ca-pability of simultaneous measurement of liver elasticity and controlled attenuationparameter (CAP) [213]. The main difference between the SWAVE system andFibroscan is that SWAVE provides a 2D elasticity map and a 2D ACE map as anoverlay on the B-mode ultrasound image, rather than a median estimate providing asingle value. As an example, an ACE map has been presented as an overlay on theB-mode image of an ex-vivo placenta in Figure 2.1.ACE could be a potential biomarker to assess placental health. The validity ofthe utility of ACE in differentiating normal and pathological placentas needs to bedetermined in the future studies.532.6 ConclusionThe placenta has received relatively little attention in the literature compared withother organs. This work presents the largest study to date to report the ACE valuesbased on the examination of 59 clinically normal placentas. This study establishes arobust and accurate ACE measurement method to measure ex vivo ACE of humanplacentas. An attenuation estimation region-of-interest (AEROI) selection methodwas proposed and validated on tissue-mimicking phantoms. The AEROI selectionmethod was able to significantly reduce the intra-sample ACE variance. We reportthe ACE in ex vivo placenta to be 0.55 dB/cm/MHz, which are in agreement with theprevious results. Future studies are required to compare the ACE between normaland pathological placentals.54Chapter 3Quantitative Ultrasound Imagingusing PredictUS3.1 IntroductionQUS extracts acoustic scattering and attenuating properties using algorithms basedon estimates of power spectra of ultrasound radiofrequency (RF) signal backscat-tered from the interrogated tissue. The power spectral estimation, typically obtainedfrom Fast Fourier Transform (FFT) based periodogram of windowed RF signal,imposes a fundamental trade-off on QUS between image resolution and estimationprecision. To expand the precision-resolution trade-off, different modifications ofperiodogram have been investigated. Welch method was found to yield the mostaccurate and precise spectral estimate with reasonable computational cost [132].Alternatively, Autoregressive (AR) techniques have been reported to exceed theperformance of FFT based periodogram, especially for smaller windows [245].However, AR techniques show degraded performance with increasing depth inhigher attenuating media due to violation of the stationarity assumption. Morerecently, deep neural networks such as WaveNet [236], have significantly improvedstate-of-the-art performance in fields of forecasting non-stationary and non-linearprocesses, such as speech and financial time-series [24, 236].In this chapter, we present PredictUS, a spectral based QUS technique basedon US RF signal prediction using a sequence-to-sequence Convolutional Neural55Figure 3.1: Overview of the proposed PredictUS method. Blue solid lineindicates the original input window, whereas the blue dash-dotted lineindicates the predicted signal in the “WaveNet Network” block and thepower spectrum obtained from the optimum (i.e. PredictUS) window inthe “QUS Parameter Estimation” block.Network (CNN) modelled with a WaveNet inspired architecture. Given a smallwindowed RF signal, this method predicts the next data points, resulting in a largerwindow. Therefore, the method yields better estimates of power spectra as well ascan characterize local tissue properties with high resolution by essentially extendingthe property to a larger region. We demonstrate the applicability of PredictUs forimproved measurement of attenuation coefficient estimate (ACE), a QUS parameter.3.2 MethodThe first signal processing step in spectral-based QUS techniques is the estimationof power spectra from a limited-length RF signal window. The proposed PredictUS56method adds a WaveNet inspired deep neural network before the power spectraestimation step (Figure 3.1). The network results in a larger window by sequentiallypredicting the next samples. The power spectra estimated from the larger RF signalwindows are fed for the subsequent QUS parameter estimation.3.2.1 ACE ComputationThe ultrasound ACE is a measure of ultrasound amplitude dissipation due to thecombined effect of scattering and absorption. ACE can be measured using thereference phantom method [255], a standard method to account for the systemdependent factors.According to this method (previously described in section 2.2.1), the RF dataare acquired from both the tissue sample, s and a reference phantom, r with knownproperties using the same transducer and system settings. For a RF signal windowcentered at depth z from the transducer surface, the natural logarithm of the ratioof the power spectrum S from the sample to the reference phantom at frequencyf ∈ ( f1, fk) can be written as [255]:lnSr( f ,z)Ss( f ,z)=−4(αs(z)−αr(z)) f z+ ln BsBr . (3.1)where α is the effective ACE for the total ultrasound propagation path z and B isthe backscatter coefficient (BSC). Substituting the following variables in equation3.1 as: ln Sr( f ,z)Ss( f ,z) = Y ( f ,z),αr−αs = α, lnBsBr= β , we get,Y ( f ,z) =−4α(z) f z+β . (3.2)The above equation can be written in a matrix form: y = Ax+N (0,σNI), whereA =4z f1 1......4z fk 1k×2,y =Y ( f1,z)...Y ( fk,z)k×1,x =[αβ]2×1.A least square fitting method [163] can be applied to solve for x = [α,β ] from the57noisy estimation y as follows:xˆ = argminx{‖(y−Ax)‖22}, (3.3)with the following constraints:αmin ≤ α ≤ αmax,βmin ≤ β ≤ βmax (3.4)Solving equation 3.2 gives us the effective ACE (α) for the total ultrasoundpropagation path. The local ACE at depth zi can be computed as: α local(zi) =α(zi)zi−α(zi−1)zi−1zi−zi−1 .3.2.2 Network ArchitectureWe employed a sequence-to-sequence CNN using the WaveNet model. First in-troduced by the researchers from DeepMind, Wavenet is an autoregressive model,where each predicted sample is conditioned on the previous ones [236]. One keyelement of WaveNet is stacked layers of 1-dimensional dilated causal convolutions.Causal convolutions are used to ensure that a prediction at time step t only dependson the previous time steps, whereas the use of a dilation rate increased as a factorof 2 results in an exponentially growing receptive field with depth. For the USRF signal prediction, a receptive field is required which is large enough to captureseveral wavelengths. For our application, one wavelength (λ ) is approximately 20samples for US transmission frequency of 5 MHz and sampling frequency of 50MHz. We use 14 dilated causal convolution layers with a dilation rate of factor 2with a reset (20,21, ...,26,20,21, ...,26) and 64 filters with width of 2. As in originalarchitecture, there are gated activation unit (combining a hyperbolic tangent and asigmoid activation branch), residual, and skip connections (Figure 3.1) in each ofthese layers to speed up the convergence and enable improved training for deepermodels. Finally, the WaveNet output is passed through a ReLU activation followedby a linear projection.583.3 Experiments and Results3.3.1 DataWe used the k-Wave toolbox [232] to generate simulation US RF data. For thesimulation, we use 96 element linear array transducer with 0.2 mm element pitch.The depth was set to 60 mm. Fixed focusing was used in transmission (focal depth60 mm) and dynamic focusing was used in reception. We simulate 256 RF linesfor each of the 32 different ACE values ranging from 0.1 to 1.65 dB/cm/MHz. Theselected ACE range encompasses the observed ACE values in liver at differentNAFLD stages and in placenta. Finally, we divide each RF line into 13 segmentswith 25% overlap, resulting in a training set of 106,496 RF line segments. We alsocreated a separate test dataset of 550 examples, where 50 RF line segments areextracted for each of the 11 different ACE values (0.5-1.5 dB/cm/MHz).3.3.2 Training and TestingIn the training stage, a teacher-forcing procedure is applied where the model per-forms a one-step ahead prediction. The model outputs a sequence of n steps, whichis one time-step shifted version of the input sequence. Therefore, the model istrained using the correct output instead of the predicted output. We use a batch sizeof 64 and use Adam optimizer to minimize mean squared error loss with β=0.9,β2=0.999 and learning rate of 0.001.In contrast to the training stage, the testing stage makes a n-step ahead predictionsequentially by feeding each prediction back into the network at the next time step.3.3.3 RF Data Processing and AnalysisFor ACE computation, the lateral dimension for the RF signal window was keptfixed at 5 scanlines. The axial dimension was varied from 5λ (100 samples) to10λ (200 samples) to study the effect of PredictUS in improving the trade-offbetween resolution and precision. For the power spectrum computation, the Welchmethod has been found to yield more accurate and precise estimation compared torectangular, Hanning, or Hamming windows [132]. Therefore, we used the Welchmethod to estimate the power spectrum from the RF scanlines within each RF590 50 100 150Time step-1-0.500.51Amplitude×104Input RF LineTarget RF LinePredicted RF Line2 4 6 8 10 12Frequency (MHz)-40-30-20-10010Normalized PSD (dB) 10λ Window(True)7.5λ Window10λ Window (Predicted)(a) (b)Figure 3.2: (a) An example of RF line segment prediction using the proposedsequence-to-sequence CNN. (b) Comparison of power spectra estimationusing original large window (10λ ), original small window (7.5λ ) andPredictUS large window (10λ ) predicted from 7.5λ original window.window. According to the Welch method, each RF scanline within a window wassubdivided into overlapping sections, with length equal to 67% of the original RFscanline and with 50% overlap. Each segment was then multiplied with a Hammingwindow. The power spectral density was obtained after averaging the periodogramsobtained from the windowed segments. We considered the -20 dB bandwidth of thereceived power spectrum as the usable frequency range. To compute the ACE, weutilize the RF data with ACE of 0.5 dB/cm/MHz as the reference data.3.3.4 Performance MetricsWe use Mean Absolute Scaled Error (MASE) to measure the performance ofsequence-to-sequence CNN to predict the larger RF window from the smaller one,whereMASE =1T ∑Tt=1 |et |1T−1 ∑Tt=2 |Yt −Yt−1|(3.5)Here, et is the prediction error, defined as the difference between the actual valueand the predicted value and the denominator denotes the in-sample mean absoluteerror from the naive forecast method. A MASE value < 1 indicates a predictionperformance better than the naive forecast method.As a measure of precision of ACE, we report the standard deviation of the600 50 100Time Step00.511.52MASE0.5 1 1.5ACE (dB/cm/MHz)0.50.550.60.650.7MASE(a) (b)Figure 3.3: RF data prediction performance: (a) for n-step ahead predictionwith varying n, and (b) for varying ACE in term of Mean Absolute ScaledError (MASE).computed ACE as a percentage of the actual value. We also report the bias inthe estimated ACE, as the difference between the estimated ACE and actual ACEpresented as a percentage of the actual ACE.3.3.5 ResultsPerformance of RF data PredictionWe apply the trained sequence-to-sequence CNN on the test dataset. An example ofRF data prediction is shown in Figure 3.2a, where a 50-step (2.5λ ) ahead predictionwas made, given a RF line segment of 150 steps (7.5λ ). We can see a preciseprediction for the initial RF samples, which starts to degrade with increasing timestep. We found that the power spectra estimate obtained from the PredictUS windowof length 10λ (generated from 7.5λ segment) gives similar estimates as obtainedfrom a larger window (10λ segment) and outperforms the estimates obtained fromthe original 7.5λ segment (Figure 3.2b).We analyse the performance of the network for n-step prediction for varyingvalues of n. It was found that, on average, the network can make a 16-step predictionwhen MASE < 1. This performance is comparable to the previous work on time-series forecasting using WaveNet model, which reports MASE for a single-step610 50 100 150Time Sample-101Amplitude×104Input RF Line Target RF Line Predicted RF Line0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5ACE (dB/cm/MHz)01020Std. Dev. of ACE (%)Large Window Small Window Large Window (PredictUS)0 50 100 150 200Time step-101Amplitude×1040.6 0.8 1 1.2 1.4ACE (dB/cm/MHz)024681012ACE Error (%)0.5 1 1.5ACE (dB/cm/MHz)01020Std. Dev. of ACE (%)0 50 100 150 200Time step-101Amplitude×1040.6 0.8 1 1.2 1.4ACE (dB/cm/MHz)05101520ACE Error (%)0.6 0.8 1 1.2 1.4ACE (dB/cm/MHz)0510152025Std. Dev. of ACE (%)0 50 100 150 200Time step-5000500Amplitude0.6 0.8 1 1.2 1.4ACE (dB/cm/MHz)05101520ACE Error (%)0.6 0.8 1 1.2 1.4ACE (dB/cm/MHz)0510152025Std. Dev. of ACE (%)(a) (b) (c)Figure 3.4: PredictUS performance for case I (top), case II (middle), and caseIII (bottom). (a) Example RF line segments and their divisions; (b)ACE error for diffirent ACE values; (c) Standard deviation of ACE fordifferent ACE values.ahead prediction [24]. Additionally, we investigated whether the ACE amplitudehas an effect on the prediction performance. We computed MASE for a 15-stepprediction for data with ACE varying from 0.5 to 1.5 dB/cm/MHz. From Figure 3.3,we see that the MASE remains within a range of 0.52-0.58 for ACE≤ 1 dB/cm/MHz,and after that MASE starts to degrade with increasing ACE. This result agrees withprevious finding from the AR based techniques where higher attenuating mediumshowed inferior QUS estimation performance [245].62Performance of ACE ComputationWe investigate the performance of the proposed PredictUs method in extending theresolution-precision trade-off inherent in QUS parameter estimation. According to[132], an axial dimension of 10λ has been defined to be the optimum keeping thevariance and bias of QUS parameter estimation within 10%. Taken these numbersas the baseline, in this study, we examined the limit to which the trade-off can beextended by simulating three cases with increasing difficulty as follows:1. Case I: PredictUS window of length 10λ with 2.5λ predicted using 7.5λwindow;2. Case II: PredictUS window of length 10λ with 5λ predicted using 5λ win-dow;3. Case III: PredictUS window of length 10λ with 7.5λ predicted using 2.5λwindow;Here, PredictUS window refers to the window obtained by concatenating the originalinput window and the predicted window. For each case, the precision as well as thebias in ACE computation have been compared with the original ‘large’ RF windowwith length equal to the PredictUS window, and with the original ‘small’ RF windowwith length equal to the original data used in computing PredictUS window. TheACE computation results for these three cases have been demonstrated in Figure3.4.For case I, the small window (7.5λ ) with original data attains a precision andaccuracy performance where ACE error and the standard deviation remain within4% and 7%, respectively. The large window (10λ ) with original data improves boththe precision and the bias where ACE error and the standard deviation both remainwithin 2% and 6%, respectively. The ACE estimation using PredictUS windowoutperforms the estimation from the small window and achieves performanceequivalent to that obtained from the large window.In case II, the difference between the ACE measures obtained from smallwindow (5λ ) and the large window (10λ ) is more prominent, where small windowresults in a bias up to 7.3% and standard deviation as large as 19.7%. Interestingly,the PredictUS window, using original data of length 5λ only, can attain a precision63within 5.5%, slightly larger than that obtained from the large window (4%). ThePredictUS window gives moderate bias (within 6%) for ACE< 1.1 dB/cm/MHz.Finally, for case III, the PredictUS window only includes 2.5λ original data,however, achieves a standard deviation within 10%. Although, the increasinglyaccumulated error in the prediction of RF data affects the accuracy, resulting in alarger ACE error compared to the small original window.In summary, compared to the optimum trade-off, the PredictUS method canreliably maintain similar precision and accuracy while improving the resolution to75% of the optimum value. Moreover, the proposed method can still achieve com-parable precision and accuracy for low ACE values with a resolution improvementof 50%. However, reducing the resolution to 25% of the optimum exhibit degradedACE measurement, which can be attributed to the error accumulation in the n-stepahead prediction. Unlike the case of RF data prediction, high ACE does not haveany distinct effect on the performance of ACE computation.3.4 ConclusionWe propose a novel QUS parameter estimation method, PredictUS, utilizing ultra-sound RF signal prediction. The method shows promising results by predictinglarger RF windows from the smaller ones. We conduct a proof-of-concept studybased on extensive simulation analysis. The proposed sequence-to-sequence convo-lutional neural network based on WaveNet model was able to predict up to 16 RFsignal samples with MASE < 1, resulting in improved power spectral estimation.A resolution reduction, as high as 50%, while maintaining comparable estimationprecision introduces a paradigm shift by challenging the insistent trade-off betweenprecision and resolution, inherent in ultrasound spectral estimation. Future researchwill address the issue of error accumulation in the n- step prediction by furtherimproving the CNN structure.64Chapter 4Attenuation CoefficientEstimation using A SpatiallyWeighted Regularization (SWTV)Method4.1 IntroductionCurrent challenges towards the clinical translation of QUS imaging include largeestimation bias and variance due to tissue inhomogeneity and precision-resolutiontrade-off [132, 173]. The resolution-precision trade-off is particularly critical for thin(e.g. dermis) and heterogeneous (e.g. placenta) tissue characterization. Availablesmaller windows result in noisy power spectral estimates due to limited spectralresolution and spatial variation noise inherent in ultrasound scattering. Deviationfrom the homogeneity assumption arising from the variation in underlying scattererstatistics [188], on the other hand, is likely to affect most biological tissue to differentextent. Rubert et al. demonstrated that the high variance (with standard deviationbeing around 50% of the mean value) in ACE for ex vivo bovine liver is attributedto the deviation from the Rayleigh distribution, rather to the biological variability[203]. A large variance due to spatial heterogeneity was also reported for a cervix65remodelling assessment study, where the difference in QUS parameters betweenearly and late pregnant cervix were obscured by the intra-class variation [86, 149].The reliability of Controlled Attenuation Parameter (CAP, Fibroscan, Echosens,France), a promising commercial tool to detect and quantify hepatic steatosis, hasbeen reported to be compromised due to the variability in attenuation estimates. Forexample, an interquartile range (IQR)>40dBm was found to be indicative of poordiagnostic accuracy [250].Large axial window lengths and averaging over lateral and elevational RFlines reduce the spatial variation noise due to random scatterer positioning. Theseapproaches offer improved accuracy and precision, assuming uniform scatteringwithin interrogated volume. Averaging also improves the power spectral estimation[132]. Studies show that the Welch method, a refined periodogram that estimatesthe averaged periodogram of overlapped subwindows, was superior compared torectangular, Hamming and Hanning windowing [132]. While these techniques attainan improved estimation at the expense of spatial resolution, more recent works ex-plore the possibility of extending the precision-resolution trade-off. Regularizationincorporating a spatial prior was successful in simultaneously improving the resolu-tion and the precision of QUS estimation for homogeneous regions [40, 196, 235].However, uniform regularization, which was used in [40, 196, 235], might not beappropriate for biological tissue with the presence of inhomogeneities, as it willlead to oversmoothing in homogeneous regions in an attempt to compensate for thelocal inhomogeneities. To address this issue, we propose a Spatially Weighted TotalVariation (SWTV) regularization approach for ACE estimation.Furthermore, the rigorous assessment of the reliability and reproducibility is ofcritical importance for generalization and adoption to the widespread application ofQUS. Repeatability and reproducibility both are the measures of the precision of amethod, where repeatability measures the precision under identical conditions whilereproducibility measures precision for different measurement conditions [227]. Inseveral works, repeatability and reproducibility studies have been conducted onphantoms and human tissue for QUS parameters obtained using reference phantommethod [89, 90]. A similar study will be useful to establish the value and applicabil-ity of the SWTV method comparative to the baseline reference phantom method.Therefore, we evaluate the repeatability and reproducibility of QUS parameters, i.e.,66Attenuation Coefficient Estimate (ACE) and Backscatter Coefficient (BSC) for awell-characterized tissue mimicking phantom obtained using the SWTV method.4.2 Method4.2.1 Spatially Weighted Regularization based ACE (SWTV-ACE)The ultrasound ACE is a measure of ultrasound amplitude dissipation due to thecombined effect of scattering and absorption, whereas the dissipation associatedwith the scattering is small compared to absorption (less than 10% for typicalbiological tissue) [181]. The standard way is to compute ACE using the referencephantom method [255]. According to this method (previously described in section2.2.1, the radiofrequency (RF) data are acquired from both the tissue sample and areference phantom using the same transducer and system settings.ACE is computed in a m×n grid using a frequency band discretized at r points.For a RF signal window centered at (i, j)[i ∈ (1,m), j ∈ (1,n)] location, the ratioof the power spectrum S from the sample to the reference phantom at frequencyfk,k ∈ (1,r) can be written as [51, 235]:RSi, j,k =Ssi, j,kSri, j,k=Asi, j,kBsi, j,kAri, j,kBri, j,k=e−4αsi, j fkzi, je−4αri, j fkzi, j.Bsi, jBri, j. (4.1)Here, the s and r superscript denote sample and reference, respectively. A is thetotal attenuation effect from the transducer surface to the center of the respectiveRF signal window, B is the backscatter coefficient (BSC), z is the axial distancefrom the transducer surface to the center of the corresponding time-gated RF signalwindow, and α is the effective ACE for the total ultrasound propagation path z.After taking the natural logarithm, equation 4.1 reduces to:ln [RSi, j,k] =−4(αsi, j−αri, j) fkzi, j + lnBsi, jBri, j. (4.2)Substituting the following variables in equation 4.2 as: ln [RSi, j,k] = Yi, j,k,αri, j−αsi, j = αi, j, lnBsi, jBri, j= βi, j, we get,67Yi, j,k =−4αi, j fkzi, j +βi, j. (4.3)The above equation can be written in a matrix form: y = Ax+n, where n denotesGaussian noise with zero mean and standard deviation σ , whereA =4z1,1 f1 . . . 0 1 . . . 0.... . ........ . ....0 . . . 4zm,n f1 0 . . . 1..................4z1,1 fr . . . . . . 1 . . . 0.... . ........ . ....0 . . . 4zm,n fr 0 . . . 1,y =Y1,1,1...Ym,n,1...Y1,1,r...Ym,n,r,x =α1,1...αm,nβ1,1...βm,n.We propose to solve the following spatially weighted optimization problem for thereconstruction of x = [α,β ] from the noisy estimation Y :xˆ = argminx{‖(y−Ax)‖22+λ1TV (α)+λ2SWTV (β )}, (4.4)where the first term is the data fidelity term, the second and third terms are the TVbased regularization term, and λ1 and λ2 are the regularization weights. The TVoperator is defined as: TV (α) = ∑i, j |αi+1, j−αi, j|+ |xi, j+1− xi, j|, andSWTV (β ) = ∑i, j Wi, jβ (|βi+1, j−βi, j|+ |βi, j+1−βi, j|).Here we have employed a spatially weighted total variation regularization on theBSC term, β . The reason is that a change in scattering (which occurs due to achange in scatterer size and change in scatterer number density) affects the powerspectrum of the ultrasound RF data. The change in scattering should be accountedinto the BSC term, β . However, the assumption of constant backscatter in thereference phantom method and uniform piece-wise homogeneity in uniform TVregularization fail to account for the change in scattering into the BSC term andconsequently result in inaccurate computation of ACE. For the regions associated68Figure 4.1: Feasibility test of the proposed SWTV-ACE method on a tissue-mimicking phantom. (a) Ultrasound image of the phantom with backscat-ter variation; (b) Spatially weighted matrix formation as a function ofthe envelope SNR deviation map; (c) & (d) ACE and BSC results withand without SWTV.with changes in backscatter, β should be lightly regularized to decrease the penaltyon their variation. Therefore, we propose incorporating a spatially weighted matrixWb into the regularization of β to account for the backscatter variation.4.2.2 Derivation of Spatially Weighted MatrixAccording to a previous study, envelope SNR deviation is a useful criterion toindicate inhomogeneity, i.e., variation in backscatter. Envelope SNR Deviation,∆SNRe is defined as:∆SNRe =|SNRe−SNRopt |SNRopt×100%, (4.5)where SNRe is defined as the ratio of the mean to the standard deviation of the RFsignal envelope. SNRopt = 1.91, which is the average envelope SNR of a Ralyleighdistribution, characteristic distribution of a RF signal spectrum, arising from a largenumber of randomly distributed scatterers of identical sizes.To validate the applicability of ∆SNRe as an indicator of inhomogeneity, weperformed a feasibility analysis of RF data acquired from an ultrasound phantom(Model 040GSE) manufactured by CIRS (Norfolk, VA, USA). The phantom con-tains two different types of targets: scattering targets and gray scale targets (Figure69Figure 4.2: ACE results for simulated phantoms: (a) phantom 1 (uniformACE and BSC); (b) phantom 2 (variable ACE and uniform BSC); and(c) phantom 3 (uniform ACE and variable BSC).4.1a). The scattering targets are made of nylon monofilaments and contain scatterersof different size whereas the gray scale targets contain scatterers with a differentdensity. As we plot the ∆SNRe map, the targets can be distinguished with high∆SNRe values (Figure 4.1b). Ideally, the phantom has a uniform ACE value of 0.5dB/cm/MHZ, and variable BSC values at the locations of the targets (BSC valueswere not reported by the manufacturer). Interestingly, computing the ACE and BSCusing both the reference phantom and the uniform TV regularization yield high ACEerrors at the target locations while both the methods fail to identify the variation inthe BSC term (Figure 4.1c). As a solution to this problem, we propose to form aspatially weighted matrix, Wβ as a function of ∆SNRe, to adaptively regularize theBSC parameter:Wβ (∆SNRe) =a1+ exp[b.(∆SNRe−∆SNRmine )], (4.6)where a and b are constants. ∆SNRmine is a nominal ∆SNRe value for which theassociated regions can be considered to be homogeneous. As ∆SNRe remains muchsmaller than ∆SNRmine , the weighting has little effect on the regularization. On theother hand, the weight will decrease as ∆SNRe increases resulting in relaxationof the regularization effect on the BSC term. By applying the proposed spatiallyweighted TV regularization, the ACE error was significantly reduced, where theBSC term captures the backscatter variation at the target locations.704.2.3 Repeatabiltiy and ReproducibilityThe ultrasound data were acquired with an Ultrasonix SonixTouch machine (Ana-logic, Richmond, Canada). The system was equipped with a motor control module,that provides motor commands to step the motor to one position to another whilecollecting several frames in each position. The motor control module thereforeenables volumetric data acquisition. The phantom used in this study (CIRS, Inc.,Norfolk, VA) has two homogeneous layers (Z1 and Z2) (Figure 4.1). The ACE ofthe two layers are 0.54 dB/cm/MHz and 1.3 dB/cm/MHz, respectively. The logBSC(defined as 10log10BSC) at 3 MHz were reported to be −27.72 dB and −29.07 dB.Phantom data were acquired in four classes with different combinations ofreproducibility conditions including operators (A and B), transducers (4DL14-5/38linear array and m4DC7-3 curved array), system settings (placenta setting andliver setting), and motor control (enabled and disabled). The placenta setting wasoptimized for imaging placenta ex vivo, with an imaging depth of 5 cm [50] and theliver setting was optimized for imaging liver in vivo with 15 cm imaging depth [52].The description of the four classes has been presented in Table 4.1. The last columnshows the total number of data points acquired from both of the phantom layers foreach class.Table 4.1: Description of the categories of the data acquired for repeatabilityand reproducibility test.Class Operator Transducer Motor Motion System Setting No. ofDatasetC1 A curved enabled liver 18C2 B curved disabled liver 12C3 B curved enabled placenta 12C4 B linear enabled placenta 12We compute the QUS parameters using both reference phantom method andSWTV method. For both methods, we empirically set the parameter values (suchas window dimension, regularization parameter, etc.) to optimize the performanceof that particular method. QUS parameter measurements were summarized asmeans and standard deviations for each combination of reproducibility conditions.71Figure 4.3: In-vivo human liver ACE from six patients and their correlationwith proton density fat fraction. The error bars show the standard devia-tion whereas the square represents the mean calculated over a region-of-interest.Coefficients of variation (CoV) were calculated. Box plots of QUS measures wereused to qualitatively represent the distribution of the data, both within and betweenthe combinations of reproducibility conditions.4.3 Experiments and ResultsThe proposed method was implemented in MATLAB 2018a (The MathWorks Inc.,Natick, MA, USA). We evaluated the proposed SWTV-ACE method on severaldatasets including simulations and liver in vivo. We compared the performanceof the proposed method against the reference phantom method [255] and uniformTV regularization. The dimensions of the sliding windows were selected to be 15scanlines (10 wavelengths) laterally and 8.5 mm (10 wavelengths) with an 80%overlap in both directions for all the methods.4.3.1 SimulationsWe used the k-Wave toolbox to simulate three numerical phantoms [231]. Phantom1 consisted of homogeneous medium with uniform ACE (0.6 dB/cm/MHz) anduniform BSC. Phantom 2 had uniform BSC and variable ACE, where the backgroundACE was 0.5 dB/cm/MHz, and the inclusion ACE was 0.7 dB/cm/MHz. Phantom 3had variable BSC and uniform ACE (0.5 dB/cm/MHz), where the inside inclusionhad a higher BSC compared to the background. Also, we simulate a phantom with72Figure 4.4: (a) An example of ACE computation for in-vivo human liver usingthree different methods. (b) Effect of window size on estimation varianceof ACE for a homogeneous region-of-interest (top) and inhomogeneousregion-of-interest (bottom).uniform ACE (0.6 dB/cm/MHz) and uniform BSC, which would be used as thereference phantom to compensate for the system dependence.For phantom 1 and phantom 2, the SWTV-ACE method gives similar perfor-mance as that obtained from the uniform TV regularization as these phantoms haveuniform BSC. In both methods, ACE error is reduced (<1% ) compared to the refer-ence phantom method (10%), whereas the ACE variance is reduced to ∼ 1% from53% obtained using the reference phantom method (Figure 4.2 (a)). For phantom2, both uniform TV and SWTV-ACE method can identify the ACE transition inthe center inclusion (standard deviation ∼ 11%) opposed to the reference phantommethod where the transition remains occluded with high variance of ACE (63% )(Figure 4.2 (b)). Finally, the SWTV-ACE method shows superior performance forphantom 3 where both the reference phantom and the uniform TV regularizationexhibit underestimation and overestimation of ACE centering the high backscatterinclusion (shaded area in Figure 4.2 (c)) [188]. On the contrary, the spatial weight-ing enables the proposed method to reconstruct the expected uniform ACE mapwith 16% error and standard deviation <1%.731 2 3 4Width (cm)2468Depth (cm)Figure 4.5: ACE (dB/cm/MHz) results for CIRS tissue-mimicking phantoms.Top, middle and bottom rows correspond to phantom 1, phantom 2 andphantom 3, respectively. The first two columns show the B-mode imagesand ground truth ACE overlaid on the B-mode images. The last threecolumns show ACE maps obtained from the RPM, unweighted TV andSWTV method, respectively. The dashed lines outline the inclusionsinside phantom 2 and phantom 3.Table 4.2: Performance metrics of ACE measures obtained using referencephantom method (RPM), unweighted total variation (TV) and spatiallyweighted total variation (SWTV) methods for CIRS tissue mimickingphantoms.Mean Absolute Error (%) Standard Deviation (%)Phantom RPM TV SWTV RPM TV SWTV1 47.58 2.64 5.89 58.72 1.57 1.192 (Background) 106.38 10.50 7.20 134.88 16.11 10.522 (Inclusion) 55.07 8.67 8.96 75.07 9.47 4.043 (Background) 103.50 19.23 15.58 132.0 26.07 12.133 (Inclusion) 74.92 20.97 10.23 87.96 28.27 5.0174Figure 4.6: ∆SNRe map (left) and ACE maps obtained from the RPM, TV andSWTV methods for a placenta ex vivo.4.3.2 Tissue Mimicking Phantoms: ValidationUltrasound data were acquired from three custom-built phantoms. Phantom 1 hasuniform ACE and uniform BSC, phantom 2 has an inclusion with higher ACEand similar BSC compared to the background, and phantom 3 has an inclusionwith higher BSC and similar ACE compared to the background. The ACE groundtruth values reported by CIRS (Norfolk, VA, USA) have been shown in Table5.2. Ultrasound radio-frequency data were acquired from these phantoms using anUltrasonix SonixTouch machine (Analogic, Canada) and an m4DC7-3 curved arraytransducer operated at 3.33 MHz. The depth was set to 15 cm with a focus at 10cm. We select the size of sliding windows to be 6 scanlines (5λ ) laterally and 300radio-frequency samples (25λ ) axially, where λ denotes the wavelength. For thereference phantom data, ultrasound data were acquired from a uniform ACE anduniform BSC region with an ACE value of 0.54 dB/cm/MHz with the same systemsetting.The resulting ACE maps obtained from RPM, unweighted TV and SWTVmethods are shown in Figure 4.5 (column 3-5). The mean absolute error andstandard deviation of the ACE error have been reported in Table 4.2 as a percentageof the ground truth values. The high bias and variance obtained from RPM arepartly attributed to the insufficient number of independent samples arising fromthe small window size. Though selecting a larger window size can improve theresults from RPM, we select a window size optimum for the regularization basedmethods and keep the size consistent across the methods for a fair comparison.For phantom 1 (uniform ACE and uniform BSC), both performance metrics forall the methods are better compared to phantom 2 and phantom 3. This result75(a) Reference Phantom Method (b) SWTV MethodFigure 4.7: Boxplots of ACE (top) and logBSC (bottom) for phantom lay-ers Z1 and Z2, measured using reference phantom method and SWTVmethod. The black dashed horizontal lines are ground truth QUS valuesof the respective phantom layer reported by CIRS.understates the fact that the ACE measures are more accurate and precise in thehomogeneous regions-of-interest. For phantom 2 (variable ACE and uniform BSC),the contrast-to-noise ratio for ACE maps obtained using the RPM, TV and SWTVmethods are 0.31, 1.35, and 2.11, respectively. The inclusion can be distinguishedfrom the ACE maps obtained using both TV and SWTV methods, as opposed to theRPM method. However, the SWTV method improves the contrast-to-noise ratio by57% compared to that obtained using the unweighted TV method. For phantom 3(Similar ACE and variable BSC), the unweighted TV method shows underestimationand overestimation of ACE centering at the upper and lower edges of the inclusion,which is a characteristic pattern of ACE error in cases of backscatter variation76[188]. However, the ∆SNRe in this region with backscatter variation is higher than∆SNRmine . Therefore, the proposed method results in improved ACE measure withmean absolute error < 16% and standard deviation < 13% by reducing the weightin the inhomogeneous regions.4.3.3 Ex vivo PlacentaFor the ex vivo placenta, the ultrasound data were acquired using a 4DL14-5/384D linear transducer. The depth was set to 3 cm, comparable to the placentathickness, with a focus at 2 cm. The results for an inhomogeneous region-of-interest(indicated by mean(∆SNRe)> 20%) in placenta is shown in Figure 4.6. The meanACE obtained from the RPM, TV and SWTV methods are 0.77, 0.84 and 0.84dB/cm/MHz, respectively, whereas the standard deviation is 0.43, 0.05, and 0.01dB/cm/MHz, respectively. This preliminary ex vivo result shows that the proposedSWTV method yields improved precision for ACE measurement in case of theheterogeneous tissue such as placenta.4.3.4 In Vivo Liver: Steatosis DetectionAs previously described in section 1.3.2, QUS measures, particularly ACE, could bea clinically useful tool to measure the fat content in hepatic cells and thereby providea validation framework for placental fat imaging. On this premise, we validated theefficacy of the proposed method to measure fat in steatotic patients (n = 6), whounderwent MRI in a 3.0 T system (Philips Achieva, Philips Medical Systems). MRIproton density fat fraction (MRI-PDFF) computed from the MRI data was usedas a gold standard for hepatic steatosis quantification, which strongly correlateswith histological steatosis grading [172]. The patients also underwent ultrasoundexamination with an Ultrasonix SonixTouch machine (Analogic, Canada), the RFdata from which were used to compute ACE. The correlation between MRI-PDFFand ACE values was calculated to evaluate the performance of the SWTV-ACEmethod to detect hepatic steatosis.The correlation between ACE computed using the reference phantom methodto MRI-PDFF was 0.44 (p = 0.3838). The TV method yields a better correlation77performance (r = 0.71, p = 0.1132). The SWTV-ACE method outperforms bothof these methods with a correlation of 0.953 (p =.003). Therefore, SWTV-ACEmethod was demonstrated an improved correlation with MRI-PDFF even with smallwindow size, therefore extending the trade-off between window size and precisioninherent in conventional ACE computation.We also showed an example of ACE computation for in-vivo liver, where aninhomogeneity indicated by high envelope SNR deviation causes large variationof ACE (including negative ACE values) computed using the reference phantommethod and the uniform TV regularization method (Figure 4.4 (a)). However,SWTV-ACE method yields positive ACE values with low estimation variance.Additionally, we investigated the effect of axial window size on the the estimationvariation of ACE for a homogeneous region-of-interest (max(∆SNRe)< 20%) andan inhomogeneous region-of-interest in liver (mean(∆SNRe) > 20%). For thehomogeneous case, both uniform TV regularization and SWTV-ACE methodsmaintain similar standard deviation for different window size, whereas the referencephantom method exhibits the trade-off where estimation precision improves withincreasing window size. For the inhomogeneous case, however, SWTV-ACE methodoutperforms both the reference phantom method and the uniform TV regularizationby maintaining similar standard deviation for different window sizes (Figure 4.4(b)). Therefore, SWTV-ACE effectively improves the quality of ACE computationby reducing the variability in the estimates irrespective of window size.4.3.5 Repeatability and Reproducibility ResultThe boxplots of ACE and logBSC values are shown in Figure 4.7b for each phantommeasured under the four categories. The QUS measures (both ACE and logBSC)obtained using SWTV methods for different conditions were in closer agreement(Figure 4.7b) compared to those obtained using reference phantom method (Figure4.7a). Also, the variance of measurements for a particular condition was smaller forSWTV method compared to that for RPM. Additionally, the variances for differentconditions were comparable in SWTV method. On the contrary, the measures fordifferent conditions were comparatively more dispersed in RPM.Mean and standard deviations of ACE and logBSC measures acquired with78Table 4.3: ACE result in terms of mean (dB/cm/MHz), standard deviation(dB/cm/MHz), and coefficient of variation (%).RPM SWTVLayer Class mean std CoV mean std CoVZ1 C1 0.58 0.04 6.02 0.54 0.01 1.52C2 0.64 0.01 1.53 0.54 0.003 0.59C3 0.50 0.05 10.89 0.54 0.003 0.65C4 0.53 0.02 3.28 0.54 0.004 0.62Z2 C1 1.24 0.13 10.32 1.26 0.01 0.89C2 1.32 0.03 1.93 1.21 0.01 0.63C3 1.18 0.10 8.46 1.24 0.01 0.70C4 1.09 0.10 8.62 1.12 0.04 3.49Table 4.4: logBSC result in terms of mean (dB) and standard deviation (dB)RPM SWTVLayer Class mean std mean stdZ1 C1 -25.48 0.62 -28.21 0.45C2 -28.53 1.55 -27.82 0.67C3 -27.28 0.24 -27.65 0.17C4 -26.81 0.51 -27.72 0.07Z2 C1 -26.35 0.49 -29.03 0.44C2 -30.13 3.97 -29.88 0.33C3 -27.47 0.40 -28.86 0.23C4 -26.26 0.78 -29.11 0.36different conditions of the four classes are presented in Table 4.3 and Table 4.4. Forlayer 1 (ACE = 0.54 dB/cm/MHz), the average of the mean ACE measurementsacross the different reproducibility conditions was 0.56±0.03 dB/cm/MHz (RPM),and 0.54±0.005 dB/cm/MHz (SWTV). For layer 2 (ACE = 1.3 dB/cm/MHz), theaverage of the mean ACE measurements across the different reproducibility condi-tions was 1.21±0.09 dB/cm/MHz (RPM), and 1.21±0.02 dB/cm/MHz (SWTV).The coefficients of variation (not applicable for negative logBSC measures) weresmaller for the SWTV method compared to the RPM for all classes and all phantomlayers.79For layer 1 (logBSC = −27.72 dB), the average of the mean logBSC mea-surements across the different conditions was −27.02± 0.73 dB (RPM), and−27.85± 0.34 dB (SWTV). For layer 2 (logBSC = 29.07 dB), the average ofthe mean logBSC measurements across the different reproducibility conditionswas −27.55± 1.41 dB (RPM), and −29.22± 0.34 dB (SWTV). Therefore, bothintra-class (repeatable) and inter-class (reproducible) variability were smaller forSWTV method compared to the RPM.4.4 ConclusionA new spatially weighted regularization based ACE estimation method has beenproposed. The goal was to modulate the regularization in the inhomogeneousregions using a weight function formulated as a function of envelope signal-to-noise ratio, an indicator of tissue heterogeneity. The proposed method was able toattain improved precision without compromising the resolution of ACE for bothhomogeneous and inhomogeneous regions with high backscatter variation. The QUSmeasures obtained from the proposed method was found to be more repeatable andreproducible compared to the baseline Reference Phantom method. The improvedACE measure serves as a measure of the fat content in hepatic cells, with a strongcorrelation to the current gold-standard MRI-PDFF. The application of ACE for thedetection of liver steatosis provides a validation of the proposed method and offersopportunity for the use in placental tissue characterization and fat quantification.80Chapter 5Multiparametric VolumetricQuantitative Ultrasound Imagingusing 3D QUS5.1 IntroductionAn important factor that might affect the clinical uptake of QUS imaging is theprovision of visual guidance. CAP, which is performed in A-mode, lacks visualguidance, limiting ability to select the optimum region-of-interest [213]. Severalrecent works on liver imaging have addressed the importance of B-mode andparametric image guidance to select an appropriate region-of-interest (ROI) thatexcludes vessels and extrahepatic areas [76, 103]. 3D QUS imaging can further beadvantageous over the 1-D or 2-D counterpart, allowing volumetric reconstruction,multimodal registration along with enhanced visualization and interpretation, aswell as improved estimation quality due to greater data averaging capability. Inthe previous chapter, we presented a spatially adaptive regularization method forreconstruction of a single QUS parameter, limited to a two dimensional region.That proof-of-concept study showed that regularization using homogeneity priorimproves the fundamental precision-resolution trade-off in QUS estimation. In thischapter, the aim is to further enhance the utility of the improved QUS measure by81applying volumetric reconstruction enabling improved QUS estimation and abilityto analyze the spatial distribution of QUS in 3D.Towards this aim, we present a multiparametric 3D weighted QUS (3D QUS)method, incorporating spatially variable regularization parameters. Unlike con-ventional QUS method, we acquire multiple elevational planes of RF data in theproposed method. We perform a three-dimensional adaptive TV regularizationto obtain the volumetric QUS maps. The proposed 3D adaptive total variation(TV) regularization exploits the spatial consistency information while preservingthe variation in scattering properties in all three directions, thus would provide arich spatial description of the tissue properties. Previous studies used differentultrasound compounding techniques, such as angular compounding and deformationcompounding to reduce the standard deviation in QUS estimation [78, 94]. In thischapter, We study the effect of the proposed compounding using three dimensionalregularization by using varying number of planes and evaluating the resulting preci-sion of QUS estimates. We also investigate whether compounding obtained by threedimensional regularization has any advantage over the two-dimensional counterpart.Furthermore, the proposed method simultaneously generates multiple QUS maps,including ACE, IBC, and ESD, and therefore provides complementary informationregarding the underlying tissue structure. The multiparametric approach couldimprove the class separability between normal and pathological tissue in the featurespace. Toward this goal, we evaluate the benefit of a multiparametric approach overthe single QUS counterpart in an in vivo liver study including healthy volunteersand patients with hepatic steatosis. The liver QUS study would later provide thefoundation for placental tissue characterization using the proposed algorithm.5.2 Methodology5.2.1 3D QUS: Proposed Volumetric Quantitative UltrasoundParameter Estimation AlgorithmIn our previous work [52], we presented a single QUS parameter estimation methodlimited to a Two Dimensional (2D) region-of-interest. In the problem formulation,we utilized a simplified model for the backscatter coefficient, ignoring the frequency82dependent term. Herein, we introduce a 3D multiparametric QUS method, usingan adaptive Three Dimensional (3D) total variation regularization method (section5.2.1). As the adaptive regularization parameter, we utilize a 3D spatially weightedmatrix as a function of envelope SNR (signal-to-noise ratio) deviation (section 5.2.1).We further adopt an improved model of the backscatter coefficient considering itsfrequency dependence. Using this model, we formulate equations to extract scattererstatistics, such as effective scatterer diameter and effective acoustic concentration(section 5.2.1).Attenuation Coefficient Estimate and Integrated Backscatter CoefficientMeasurementA pulse-echo ultrasound system, with the assumption of weak scattering (i.e. theBorn approximation) and negligible effect of diffraction and refraction, can bemodeled as a simple frequency domain multiplication of pulse characteristics andbackscatter characteristics [233]. The backscattered signal intensity, S( f ,z) obtainedfrom a time-gated radio frequency signal window centered at depth z from thetransducer surface at frequency f can be written as:S( f ,z) = G( f )A( f ,z)B( f ), (5.1)where G( f ) represents the transducer transfer function and transmit pulse. A( f ,z)represents the total attenuation effect from the transducer surface to the center ofthe respective time-gated window at depth z and B( f ) is the backscatter term.The ultrasound pulse is attenuated as an exponential decay function of thepropagation depth. Therefore, the attenuation term, A( f ,z) can be written as:A( f ,z) = e−4α0( f )z = e−4α f z, (5.2)where α0( f ) is the attenuation coefficient, which is approximately linearly propor-tional to the frequency, for most soft tissue [243] and α is the effective amplitudeattenuation coefficient estimate (ACE) for the round trip of the depth z.The backscatter term, B( f ), in its simplest form, can be expressed as a scalarparameter, as has been adopted in the scattering model used by Field II [102] andprevious ACE computation works [40, 52]. However, a more appropriate model is83required to capture the non-linear frequency dependence of the BSC term [233, 235]:B( f ) = β f n, (5.3)where β and n are backscatter parameters.The reference phantom method [255], a widely adopted method for QUS estima-tion, removes the system dependence (e.g. cancels out the system dependent term,G( f )) by normalizing the signal intensity backscattered from the tissue sample uti-lizing the one obtained from a well-characterized reference phantom under the sametransducer and system settings. Therefore, the ratio of the intensity backscatteredfrom the tissue sample to that from the reference at (i, j,k) location at frequency fpcan be written as:RS( f ,z) =Ss( f ,z)Sr( f ,z)=As( f ,z)Bs( f )Ar( f ,z)Br( f )=βs f nsβr f nre−4(αs−αr) f z,(5.4)where s and r denote sample under experiment and the reference, respectively.Taking the natural logarithm of equation 5.4 leads tolnRS( f ,z) =−4(αs−αr) f z+ ln βsβr +(ns−nr) ln f (5.5)Substituting the following variables in equation 5.5 aslnRS( f ,z) = y, 4 f z = φ , αr−αs = α, ln βsβr = β ,ns−nr = n, (5.6)we obtain,y = φα+β +n ln f (5.7)The system is defined in a N1×N2×N3; (N = N1N2N3) spatial grid and a fre-quency band discretized at M points is considered. We can represent the volu-metric variables in vector format, for any variable a ∈ RN with elements ai, j,k(i ∈ [1,N1], j ∈ [1,N2],k ∈ [1,N3]), reducing the forward model in equation 5.7 to84y = Ax+N (0,σN), (5.8)whereA =φ 1 I ln f1Iφ 2 I ln f2I.........φM I ln fMI ,y =y1y2...yM ,x =αβn .Here, φm ∈ RN×N is a diagonal matrix, with the diagonal elements evaluated atpropagation depth z ∈ RN for frequency fm (m ∈ [1,M]) is given byφm = 4 fmdiag(z). (5.9)The vector y ∈ RNM concatenates M sub-vectors ym ∈ RN , with each ym represent-ing the volumetric power spectra ratio term evaluated at frequency fm and I ∈RN×Nis a identity matrix. N (0,σN) is the Gaussian noise present in the measurement ofy.QUS parameters α , β , and n can abruptly change at the interface between twodifferent tissue types, but these parameters will undergo gradual change (i.e. sparsespatial variation) within each tissue type. Therefore, with the piece-wise continuousassumption in all three spatial direction, we propose a 3D total variation regular-ization approach for the reconstruction of x = [α,β ,n] from the noisy estimationy:xˆ = argminx‖y−Ax‖22+λ1||α||TV+λ2||β ||TV+λ3||n||TV, (5.10)where the first term is the data fidelity term, the last three terms are the anisotropic3D total variation regularization terms, and λ1, λ2 and λ3 are the regularizationweights.85The 3D spatially weighted TV regularizer for a variable η is defined as||η ||TV = ∑i, j,kWi, j,k(1(hi+1,i) jk|ηi+1, j,k−ηi, j,k|+1(h j+1, j)ik|ηi, j+1,k−ηi, j,k|)+ 1(hk+1,k)i j|ηi, j,k+1−ηi, j,k|), (5.11)where the 3D weighted matrix Wi, j,k is uniform for α , and spatially variable forβ and n. hi+1,i,h j+1, j,hk+1,k are distances between the adjacent axial, lateral andelevational pixels respectively at corresponding spatial positions. The TV regularizer[204] minimizes the sum of the gradients by imposing sparsity promoting l1- penaltyin all three directions of the reconstructed QUS volume. By promoting signalswith sparse gradients, TV minimization recovers piecewise-constant volumetricparameters.Solving (5.10) and (5.6) will give the values of volumetric αs, β s and ns. Fromthe effective attenuation coefficient estimate, α obtained at depth z, the local attenu-ation coefficient estimate, α local can be computed as:α locali, j,k =αi, j,kzi, j,k−αi−1, j,kzi−1, j,kzi, j,k− zi−1, j,k .The integrated backscatter coefficient (IBC) at (i, j,k) location can be computedas:IBC =1fM− f1∫ fMf1β f nd f .Selection of the Spatially Weighted Regularization ParameterSpectral-based methods, such as the reference phantom method, make the assump-tion that RF signal generation is a stationary scattering process, arising from a largenumber of randomly distributed scatterers identical in size. The RF envelope ofsuch speckle pattern (namely fully developed speckle) can be modelled using theRayleigh distribution [81]. There will be large measurement error and variancein QUS estimation when the underlying assumption for Rayleigh distribution isviolated. Such conditions occur for ROIs with varying backscatter properties orfinite scatterer density (< 10 scatterers per resolution cell) [188, 203]. Therefore an86(a) (b)(c)(d)Figure 5.1: Efficacy of the adaptive regularization parameter ∆SNRenv to iden-tify BSC variation within the ROI. (a) B-mode image with dashed whiteline outlining the three inclusions. Four color-coded ROIs are selected atthe boundaries of the inclusions and the background. Inclusion 1 - red,inclusion 2 - green, inclusion 3 - blue, and background - gray. (b) Theadaptive regularization parameter ∆SNRenv map. (c) Rayleigh distribu-tion fit associated to the selected ROIs. (d) IBC and ∆SNRenv along thecurve shown in (b). The dashed lines correspond to the center point ofthe selected ROIs.adaptive regularization parameter is required, which is sensitive to the conditionsleading to the underlying assumption for Rayleigh distribution.Envelope SNR (SNRenv), defined as the ratio of the mean to the standard devia-tion of the RF signal envelope, is constant for a Rayleigh distribution and equalsto SNRopt =1.91. The SNRenv value deviates from the Rayleigh level of 1.91 forconditions involving finite scatterer density (< 10 scatterers per resolution cell) orreduced degree of randomness (due to the presence of periodicity or clustering).Therefore, SNRenv can be used to identify media with low scatterer density, a sourceof large variance in ACE measurement [203]. Though SNRenv cannot discriminatemedia with different backscatter characteristic for large scatterer densities (> 10scatterers per resolution cell), it can identify the boundary of two such media, as the87Rayleigh assumptions of identical diameter and random distribution do not hold atthe boundary.We define a normalized SNRenv parameter, envelope SNR deviation (∆SNRenv),given at a spatial location (i, j,k) by∆SNRenvi, j,k =|SNRenvi, j,k−SNRopt |SNRopt×100%. (5.12)Previous works qualitatively demonstrated the association between ∆SNRenv andbackscatter variation from the visual inspection of inhomogeneity, lacking thequantitative description of BSC parameters [50, 52]. In this work, we investigatethe dependence of ∆SNRenv on the quantitative backscatter parameters (i.e. IBC)and therefore validate the efficacy of ∆SNRenv to detect the backscatter variationwithin the Region-of-interest (ROI).We perform a feasibility analysis on RF data acquired from a custom builtultrasound phantom (Table 5.1) with three inclusions of similar ACE and differentbackscatter characteristic, manufactured by CIRS (Norfolk, VA, USA). Inclusion 1(with low scatterer density) can be identified from the background with significantlyhigh ∆SNRenv, whereas inclusion 2 and inclusion 3 have similar ∆SNRenv as thebackground (Figure 5.1b). However, the boundaries for all three inclusions arehighlighted with increased ∆SNRenv. This trend is also evident in the Rayleighdistribution fit to the RF envelope amplitude for selected ROIs, where the envelopeamplitude in the ROIs on the boundaries deviate from the Rayleigh distribution(Figure 5.1c). The spatial variation in ∆SNRenv correlates with the change in decibelscaled IBC (dB) (Figure 5.1d). Therefore, ∆SNRenv is a suitable regularization pa-rameter which can identify backscatter variation as well as sufficiently low scattererdensity.We propose a 3D spatially weighted matrix, {Wi, j,k} ∈ RN as a function of∆SNRenv to adaptively regularize the backscatter parameters, n and β :Wi, j,k(∆SNRenvi, j,k)=a1+ exp[b.(∆SNRenvi, j,k−∆SNRenvmin)] , (5.13)where a and b are constants and ∆SNRenvmin is a nominal ∆SNRenv value for homo-geneous region. For ∆SNRenv << ∆SNRenvmin, the weighting has little effect on the88regularization. As the weight decreases with the increase in ∆SNRenv, the regular-ization effect on the backscatter terms is relaxed.Effective Scatterer Diameter (ESD) MeasurementThe backscatter coefficient, when expressed in terms of an acoustic intensity formfactor, can be used to extract the ESD and Effective Acoustic Concentration (EAC).We utilize a Gaussian form factor Fgauss = exp(−0.827k2a2e f f ) [99], which assumesscattering sources with continuously varying acoustic properties randomly sus-pended in a fluid-like media. The backscatter coefficient, B can be written as:B( f ) =19k4a6e f f nγ¯20 Fgauss. (5.14)Here, k = 2pi f/c is the wave number, c is the sound speed, and ae f f is the effectivescatterer radius. nγ¯20 is the net scattering strength, which is the product of theconcentration of scatterers n times the relative impedance difference between thescatterers and surrounding tissues, γ¯20 . B( f ) (= β f n) is the backscatter coefficientmeasured using the 3D adaptive regularization method (section 5.2.1).Dividing by f 4 and then taking the natural logarithm on both sides of (5.14) weget:lnBs( f ) · f−4 = ln(19(2pic)4a6e f f nγ¯20)−0.827(2pi fc)2a2e f f (5.15)We can estimate ae f f and nγ¯20 by fitting a regression line, y = mx+C, wherey = lnBs( f ) · f−4, x = f 2, andm =−0.827(2pic)2a2e f f , (5.16)C = ln(19(2pic)4a6e f f nγ¯20). (5.17)The effective scatterer diameter (ESD= 2ae f f ) can be estimated from (5.16) andeffective acoustic concentration (EAC = 10log(nγ¯20 )) can be obtained solving (5.16)and (5.17) [199]. The ‘Model Inversion’ technique [175] of measuring scattering89Zone 2Zone 1Zone 2ACE: 1.3 dB/cm/MHzIBC : ‐29.96 dBESD: 51 µm Zone 1ACE: 0.54 dB/cm/MHzIBC : ‐28.60 dBESD: 51 µm (a)3.5 cmBackgroundACE: 0.84dB/cm/MHzIBC : ‐29.67 dBESD: 51 µm Inclusion 1ACE: 0.37 dB/cm/MHzIBC : ‐30.83 dBESD: 51 µm Inclusion 2ACE: 0.74 dB/cm/MHzIBC: ‐29.43 dBESD: 51 µm Inclusion 3ACE: 1.18 dB/cm/MHzIBC : ‐30.42 dBESD: 51 µm 3 cm4 cm4 cm 4 cm4 c 4 cm(b)3.5 cmBackgroundACE: 0.72dB/cm/MHzIBC : ‐35.40 dBESD: 90 µm Inclusion 1ACE: 0.70 dB/cm/MHzIBC : ‐35.11 dBESD: 342 µm Inclusion 2ACE: 0.65 dB/cm/MHzIBC: ‐31.01 dBESD: 158 µm Inclusion 3ACE: 0.77dB/cm/MHzIBC : ‐28.72 dBESD: 51 µm 3 cm4 cm4 cm 4 cm4 c 4 cm(c)Figure 5.2: Diagram of the custom-made CIRS phantoms.(a) Phantom 1 withtwo vertical layers; and (b) phantom 2 with three spherical inclusions(variable ACE and similar backscatter (IBC) and scatterer diameter (ESD)property) and (c) phantom 3 with three spherical inclusions (similar ACEand variable backscatter (IBC) and scatterer diameter (ESD) property)90Table 5.1: Ground truth values for the phantomsPhantom ACE IBC ESD(dB/cm/MHz) (dB) (µm)Phantom 1Zone 1 0.54 -28.60 51Zone 2 1.3 -29.96 51Phantom 2Background 0.84 -29.67 51Inclusion 1 0.37 -30.83 51Inclusion 2 0.74 -29.43 51Inclusion 3 1.18 -30.42 51Phantom 3Background 0.72 -35.40 90Inclusion 1 0.70 -35.11 342Inclusion 2 0.65 -31.01 158Inclusion 3 0.77 -28.72 51properties by comparing measured backscatter spectrum to the theoretical one andfitting a regression line has been utilized in several previous works [171, 199].This technique is computationally efficient over the traditional Minimum AverageSquared Deviation (MASD) technique [99]. However, both of these techniquesemploy single time-gated window-based backscatter coefficient measures, whichare prone to large measurement error. A previous work showed an improvementin QUS estimation using a nearest neighborhood averaging, which exploits thesimilarity among QUS measures of neighbouring windows [171]. In the proposedmethod, ESD is derived from a TV regularized backscatter measure and thereforeexploits the sparse spatial variation of QUS parameters.5.2.2 Data SetsPhantomsThe method was validated on three custom-built phantoms manufactured by CIRS(Norfolk, VA, USA). All three phantoms consisted of proprietary Zerdine hydrogelpolymer and glass bead scatterers (Potter Industries, Malvern, PA, USA). Thephantoms were specifically designed to cover the wide range of QUS parameters tobe expected in human liver with different stage of steatosis and human placenta withdifferent pathological states. Phantom 1 (as sketched in Figure 5.2a) has two vertical91ACE (dB/cm/MHz)Axial Distance (cm)2.5 5 7.500.20.40.60.813DQUS2D QUSRPMGround TruthACE (RPM)B‐mode ImageACE (2D QUS) ACE (3D QUS)0.511.5(dB/cm/MHz)ACE (Ground Truth)0.511.5(dB/cm/MHz)1020304050 (%)SNRenv Deviation(a) (b)Figure 5.3: (a) ∆SNRenv map, ground truth ACE map and reconstructed ACEmap using different methods for inclusion 1 in phantom 2 (variable ACEand approximately uniform backscatter and scatterer size property) and(b) the profile of the reconstructed ACE along the axially central linethrough the inclusion.homogeneous zones with similar backscatter properties and variable ACE. Zone 1of phantom 1 is used as the reference phantom. Phantom 2 and phantom 3 (sketchedin Figure 5.2b) both include three spherical inclusions. The acoustic propertieswithin the inclusion and the background were adjusted by adding soda lime glassmicrospheres (Spheriglass®, Potters Industries Inc., PA, USA) of varying diameterand concentration. The inclusions in phantom 2 have variable ACE properties andsimilar backscatter and scatterer size properties, whereas the inclusions in phantom3 have similar ACE with variable backscatter and scatterer size properties. Theground truth values for the QUS parameters reported by CIRS have been listed inTable 5.1. For all the experiments, zone 1 of phantom 1 was used as the referencephantom.In vivo LiverA cross-sectional study was performed, which included a cohort of twenty-one adultparticipants, with 13 healthy controls (defined as MRI-PDFF<5%) and 8 NAFLDpatients (MRI-PDFF>5%). This study ( H14-01964) was performed under writteninformed consent after approval by the The University of British Columbia (UBC)Clinical Research Ethics Board. All the participants underwent MRI and ultrasound92examination between May 2017 and May 2018. MRI was performed on a 3.0 Tsystem (Philips Achieva, Philips Medical Systems) with a Dual-Dual-Echo (DDE)sequence [146], where images are acquired in two dual-echo sequences at threetime-points corresponding to nominal out-of-phase and in phase time (images atfirst in-phase time are acquired in both sequences). The first sequence acquirestwo images at sequential echo times of 1.15/2.3 msec and the second sequenceacquires two more images at sequential echo times of 2.3/4.6 msec. A three-pointDixon method was used to determine the MRI-PDFF (proton density fat fraction).MRI-PDFF computed from the MRI data has been found to be strongly correlatedwith histological steatosis grading [172]. Therefore, MRI-PDFF was used as a goldstandard for hepatic steatosis detection, where an MRI-PDFF> 5% was definedas presence of hepatic steatosis. The ultrasound data were acquired from the rightlateral intercostal window of the volunteer, laid in a supine position on a shakerboard propagating the mechanical excitation.5.2.3 Data Acquisition and Processing3D QUS Method OverviewThe proposed volumetric QUS imaging technique was implemented on a standard3D ultrasound system, which comprises an Ultrasonix SonixTouch ultrasoundmachine (Analogic, Richmond, BC, Canada), and a m4DC7-3/40 4D transducer.The transducer center transmit frequency was set to 3.33 MHz whereas the imagingdepth was set to 15 cm with a focus at 13 cm. The customized motor control moduleallows the acquisition of volumetric RF data consisting of 15 planes spaced at 1◦angular distance, with 25 frames acquired at each plane location at an effectiveframe rate of 250 Frame-Per-Second (FPS). Each 2-D frame in the 3-D volumeconsists of 64 RF scanlines covering a lateral field-of-view of 79◦. The samplingrate was 20 MHz, resulting in 3870 samples in each scanline.RF data processing and analysisThe proposed method was implemented in MATLAB 2018a (The MathWorksInc., Natick, MA, USA). The optimization problem was solved using the convex93optimization toolbox CVX in MATLAB [85]. We compare the performance of theproposed 3D QUS method against the baseline reference phantom method (RPM)and a uniform 2D QUS method utilizing 2D uniform total variation regularization[40], applied on each of the elevational planes separately.We manually selected ROIs within the tissue or phantom, excluding the near-field data suffering from ringdown and other artifacts associated with transducer-to-phantom interface. Each ROI included 25 temporal frames. Each frame withinthe ROIs were divided into time-gated windows with 80% overlap in both lateraland axial direction. The optimum dimensions for the time-gated windows for RPMwere found to be 20 scan lines laterally and 50 wavelength (λ ) (600 samples or 2.3cm) axially. Here, the axial extent of one wavelength is approximately 0.46 mm.On the other hand, the dimensions for the time-gated windows for the regularizationmethods were selected as 5 scanlines laterally and 1.2 cm (300 samples or 25λ )axially. The Welch method [248] was used to obtain the power spectrum fromthe RF scan lines within each time-gated window. We considered the −20 dBbandwidth of the received power spectrum as the usable frequency range.The power spectra obtained from a single realization suffers from large vari-ance due to statistical fluctuations arising from a random media. Compounding nstatistically independent but equivalent realizations have been found to improve thepower spectral estimate by reducing the standard deviation proportional to 1/√n.Previously, different spatial and angular compounding based on spatial translationsor rotations have been adopted to decorrelate the signal allowing independent real-izations of RF data [78, 94, 128]. In the proposed method, we perform averaging ofthe periodograms spatially and temporally, using 5 lateral scanlines and 25 temporalframes, to compute the power spectral density. To further improve the QUS estima-tion using RPM, we average the periodograms over the elevational planes resultingin a single QUS plane. The 2D QUS method applies 2D uniform regularization oneach of the elevational planes separately, whereas the 3D QUS method applies 3Dadaptive regularization using all the elevational planes. Specifically, the use of RFdata from different elevational planes in 3D QUS allows independent realizations ofsignal data to be averaged. Several previous studies have utilized the correlationcoefficient to measure the degree of change in the speckle pattern between differ-ent realizations, and consequently the effectiveness of ultrasound compounding94[94, 132, 202]. We compute the correlation, ρ between two planes separated by ∆Nplanes:ρ(∆N) =∑N1i=1∑N2j=1(Xi, j,k−Xk)(Xi, j,k+∆N−Xk+∆N)√∑N1i=1∑N2j=1(Xi, j,k−Xk)2(Xi, j,k+∆N−Xk+∆N)2,where Xi, j,k denotes the ith axial RF data point of the jth scanline in the kth planefor a volume-of-interest with N1, N2 and N3 data points in the axial, lateral andelevational directions, respectively. Two planes, for which the correlation is low(< 0.2), are considered to be sufficiently independent required for improving QUSestimation precision [94, 202].The initial guesses for parameters a and b in equation 5.13 were determines asfollows: first we plotted the IBC error for phantom 3 (similar ACE and variable IBC)as a function of ∆SNRenv and fit a curve with an equation form given in equation5.13. The curve fitting gives an estimate of the parameters a and b, which werefurther tuned to match the ground truth QUS values of the phantoms. We tunethe regularization weights λ1, λ2 and λ3 to obtain optimum QUS reconstructionresults for the homogeneous phantom (phantom 1) and the variable ACE phantom(phantom 2). The regularization weights used for all QUS results presented in thischapter using the 2D QUS method were λ1 = 20,λ2 = 2−2,λ3 = 2−3, whereas λ1 =20.5,λ2 = 20,λ3 = 2−0.5 were used for the 3D QUS method. Also, we empiricallyselect a = mean(∆SNRenv) and b = 0.09 in equation 5.13 for all experiments in thischapter.5.2.4 Evaluation MetricsThe mean and standard deviation of ACE, IBC and ESD were measured and reported.The accuracy and precision were assessed using the mean absolute percentageerror and standard deviation of absolute percentage error, respectively. Absolutepercentage error of a quantity a ∈ RN , is defined as:APE =| aestimated−aactual |aactual×100%, (5.18)where APE is the absolute percentage error, aestimated and aactual represent thereconstructed and the ground truth measure of a QUS quantity, respectively.95The detectability of an inclusion in a QUS map was evaluated using Contrast-to-Noise Ratio (CNR) ratio:CNR =| µinc−µbg |√σ2inc+σ2bg, (5.19)where µ and σ are the mean and standard deviation of a QUS quantity within theinclusion and the background denoted by the subscripts inc and bg, respectively.The inclusions were delineated from the B-mode images, refined using the positionand dimension information provided by the manufacturer.To evaluate steatosis detection performance for in vivo liver, the correlationbetween MRI-PDFF and QUS measures were calculated using Spearman’s rank cor-relation coefficient. Also, the classification accuracy and Area Under the Receiver-Operating-Characteristics curve (AUROC) were measured to evaluate the classifica-tion performance using different combination of QUS parameters.5.3 Experiments and Results5.3.1 Phantom ValidationThe ACE reconstruction results for phantom 2 (variable ACE and similar backscatterand scatterer size properties) are shown in Figure 5.3. From the ∆SNRenv map, it isevident that the variation in ACE does not cause deviation of SNRenv values fromSNRopt . With ∆SNRenv < ∆SNRenvmin, the spatial weighting has little effect on theregularization. Therefore, QUS reconstruction results from the 2D QUS methodwith uniform regularization and the 3D QUS with adaptive regularization are similar,where both methods clearly identify the low attenuation inclusion from the highattenuation background. The RPM, on the other hand, detects the variation in ACEwith a poorer resolution compared to the regularization methods.Figure 5.4 shows the results for phantom 3 (approximately uniform ACE andvariable backscatter and scatterer size properties). As previously discussed in section5.2.1, the variation in backscatter characteristic is associated with a variation in∆SNRenv map. The 3D QUS method, that incorporates the ∆SNRenv information forenabling adaptive regularization, yields more accurate reconstruction of the the IBC96(b)IBC (Ground Truth)-40-35-30-25(dB)ACE (3D QUS)0.511.5(dB/cm/MHz)B‐mode ImageIBC (RPM)ACE (RPM)1020304050 (%)SNRenv DeviationIBC (2D QUS)(a)ACE (2D QUS)(c)(dB)-40-35-30-25IBC (3D QUS)(d)Figure 5.4: (a) ∆SNRenv map, ground truth IBC map and reconstructed IBCmaps using different methods for phantom 3 (similar ACE and variablebackscatter and scatterer size property). Inclusion 1, 2, and 3 have beenannotated in all the maps. (b) The profile of the reconstructed IBCalong the axial central line through inclusion 2. (c) Reconstructed ACEmaps using different methods for phantom 3 and (d) the profile of thereconstructed ACE along the axial central line through inclusion 2.map compared to its uniform regularization counterpart, 2D QUS (Figure 5.4(a)).The RPM failed to generate a reasonable IBC result. The ACE maps obtainedfrom the three methods have also been shown in Figure 5.4 (c). Both the RPMand the 2D QUS method exhibit consecutive undershoot and overshoot in ACEmeasurement at the boundary between medium with different backscatter properties,a trend described in previous literature [52, 188]. The proposed 3D QUS method,on the contrary, was able to overcome the issue, yielding accurate ACE results.The ESD map for phantom 3 has been shown in Figure 5.5. The RPM and the2D QUS method using uniform regularization were not successful to reconstructthe ESD map, as were expected from the inaccurate estimation of IBC map. Theproposed 3D QUS method results in improved ESD reconstruction.97Table 5.2: Estimated mean and standard deviation of absolute percentage errorfor the reconstructed ACE and IBC maps for the experimental phantoms.PhantomACE (dB/cm/MHz)Mean absolute percentage error (%)RPM 2D QUS 2D adaptive QUS 3D QUS1 7.43 2.97 4.34 2.962 23.05 8.23 10.62 9.683 61.15 19.93 9.61 7.34Std. of absolute percentage error (%)RPM 2D QUS 2D adaptive QUS 3D QUS1 4.61 0.35 0.80 0.712 16.75 6.77 6.92 6.063 37.52 8.90 0.65 2.3e-6PhantomIBC (dB)Mean absolute percentage error (%)RPM 2D QUS 2D adaptive QUS 3D QUS1 6.30 24.94 9.45 23.292 135.97 34.15 20.72 24.153 2247.2 64.11 31.69 22.83Std. of absolute percentage error (%)RPM 2D QUS 2D adaptive QUS 3D QUS1 3.55 3.61 5.83 0.372 125.17 7.39 12.60 10.663 866.25 2.42 9.73 10.48For the quantitative comparison of the QUS parameters, we compare the meanand standard deviation of the ACE in phantom 2 and IBC and ESD in phantom 3for the inclusions and the background. For IBC, we compute the standard deviationof the IBC error expressed in dB. The results are shown in Figure 5.6.We also report the mean absolute percentage error and standard deviation ofabsolute percentage error results in Table 5.2 and 5.3. We include the resultsobtained using 2D adaptive QUS along with 2D QUS and 3D QUS to highlightthe effect of 3D regularization compared to the 2D regularization and adaptiveregularization compared to the uniform regularization. In case of ACE, both the 2DQUS method and the 3D QUS method reach close to the ground truth value with highprecision with mean and standard deviation of absolute percentage error below 10%,98ESD (Ground Truth) (μm)B‐mode ImageRPM ESD (2D QUS)(a)ESD (3D QUS)50100150200250(μm)50100150200250(b)Figure 5.5: (a) ∆SNRenv map and reconstructed ESD maps using differentmethods for phantom 3 (approximately uniform ACE and variablebackscatter and scatterer size property) with inclusion 1, 2, and 3. (b)The profile of the reconstructed ESD along the axial central line throughinclusion 2.BackgroundInclusion 1Inclusion 2Inclusion 300.20.40.60.811.2ACE (dB/cm/MHz)Ground TruthRPM2D QUS3D QUSBackgroundInclusion 1Inclusion 2Inclusion 3-40-35-30-25-20IBC (dB)Ground TruthRPM2D QUS3D QUSBackgroundInclusion 1Inclusion 2Inclusion 350100150200250300350ESD (m)Ground TruthRPM2D QUS3D QUSFigure 5.6: Mean and standard deviation of ACE in phantom 2 (left), IBC(middle) and ESD (right) in phantom 3 using different methods.enabling accurate ACE characterization. However, for IBC and ESD estimation, theproposed 3D QUS method consistently outperforms the other methods, where themean absolute percentage error in IBC remained below 25% and standard deviationof absolute percentage error in IBC was below 11% for 3D QUS. 2D adaptive QUSyield improved result compared to its uniform counterpart (2D QUS), however, wasoutperformed by the 3D counterpart (3D QUS).We evaluate the CNR of the QUS parameters for different methods. Figure5.7 shows the mean and standard deviation computed over the three inclusions inrespective phantom. The mean CNR values obtained using the proposed 3D QUSmethod were larger than the other two methods. Compared to the baseline RPM, the3D QUS method results in around 4.2 fold, 1.8 fold and 2.9 fold increase in the CNR99Table 5.3: Estimated mean and standard deviation of absolute percentageerror for the reconstructed ESD map for phantom 3.RegionMean absolute percentage error (%)RPM 2D QUS 2D adaptive QUS 3D QUSInclusion 1 51.71 51.14 46.62 28.35Inclusion 2 8.11 3.78 32.38 18.36Inclusion 3 192.18 213.16 106.09 45.08Background 154.76 111.19 82.02 30.95RegionStandard deviation of absolute percentage error (%)RPM 2D QUS 2D adaptive QUS 3D QUSInclusion 1 1.56 0.34 3.85 6.44Inclusion 2 7.36 0.62 6.42 4.78Inclusion 3 55.81 0.19 65.02 33.80Background 36.58 11.24 32.53 40.32ACE IBC ESD00.511.522.53CNRRPM2D QUS3D QUSFigure 5.7: CNR for ACE, IBC and ESD measures obtained from differentmethods.for ACE, IBC, and ESD, respectively. Also, the 3D QUS improves the CNR by 1.4times and 1.9 times compared to the 2D QUS method (uniform regularization) forIBC and ESD, respectively.1005.3.2 Effect of Compounding using three-dimensional regularizationUnlike conventional QUS method, we acquire multiple elevational planes of RF datain the proposed method. We perform a three-dimensional adaptive TV regularizationto obtain the volumetric QUS maps. Previous studies used different ultrasound com-pounding techniques, such as angular compounding and deformation compoundingto reduce the standard deviation in QUS estimation, such as scatterer size [3] andbackscatter coefficient [4]. To study the effect of the proposed compounding usingthree dimensional regularization, we vary the number of planes and evaluate the pre-cision by computing standard deviation of absolute percentage error for ACE, IBCand ESD measures. We also investigate whether compounding obtained by threedimensional regularization has any advantage over the two-dimensional counterpart,two-demensional regularization followed by averaging over elevational planes).Therefore, we perform 2-dimensional adaptive regularization after averaging theestimated power spectra from multiple planes. The results have been shown inFigure 5.8. Also, the 1/√N curves for IBC and ESD have been superimposed onthe graphs, where N represents the number of planes used in the computation. Foruncorrelated planes, the standard deviation of absolute percentage error curves theo-retically follow the 1/√N curve. The ACE variance remains unaffected (standarddeviation of absolute percentage error< 0.5%) as the number of planes increasesfor this particular example of homogeneous phantom. For IBC and ESD measures,increasing the number of planes improve the estimation variance for compoundingelevational planes using both 2D and 3D regularization methods. However, thecompounding using 3D regularization not only outperforms the 2D regularizationestimation precision, but the reduction in standard deviation of absolute percentageerror measure improves over the 1/√N rate, which is the theoretical rate obtainedby averaging N uncorrelated realizations.Figure 5.9 shows that the radio-frequency data in subsequent elevatonal planesobtained from in vivo liver are more decorrelated compared to the one obtained fromthe phantom. Improvement in QUS precision requires compounding of independentrealizations. Ultrasound planes are considered to be sufficiently independent whenthe corresponding correlation is low (< 0.2) [94, 202]. Therefore, the improveddecorrelation between subsequent planes is an effective indicator of improvement1012 4 6 8 10Number of Planes05101520SDAPE (%)IBC (3D QUS)ESD (3D QUS)IBC (2D adaptive QUS)ESD (2D adaptive QUS)IBC (1/ N curve)ESD (1/ N curveFigure 5.8: Standard deviation of absolute percentage error in IBC and ESDmeasure obtained using three-dimensional TV regularization and two-dimensional TV regularization vs. the number of planes used in thecomputation. The dashed lines represent the theoretical (1/√N) curvesobtained by averaging N completely uncorrelated planes.0 5 10 15Plane Separation ( N)00.20.40.60.81Correlation CoefficientPhantomIn vivo LiverFigure 5.9: Correlation coefficient between two 2-D radio-frequency (RF) sig-nals separated by ∆N planes for a phantom and an in vivo liver example.102in QUS measurement for in vivo liver compared to phantom.5.3.3 Effect of Spatial Weighting in Total Variation RegularizationThe phantom study demonstrates that the spatial weighting in the proposed 3DQUS method improves the QUS estimation, even in regions with high backscattervariation. In this section, we investigate the effect of spatial weighting on theselection of regularization parameter and QUS estimation performance. In 5.10 (a)and (b), we compare the cost function value for different values of regularizationparameters obtained at the ground truth QUS measures for the 3D adaptive QUSand 2D uniform QUS method. As it is difficult to visualize a 4-dimensional costfunction, we set the values of λ2 and λ3 equal. We found that the spatial weightingkeeps the optimum cost comparatively less variable for different λ2 and λ3 valuesfor variable ACE region-of-interest (homogeneous as dictated by low ∆SNRenv).The effect is more pronounced for phantom 3 with variable backscatter property(high ∆SNRenv).Figure 5.10 (c) demonstrates that the error in QUS parameter estimation can beoptimized by choosing a suitable regularization parameter set for both the 3D QUSand the 2D QUS method. However, for the 3D QUS, the optimum ranges for regu-larization parameter coincide, whereas for the 2D QUS with uniform regularization,the ranges do not overlap. Therefore, it is possible to select an optimum regular-ization parameter for the 3D QUS method to optimize QUS parameter estimationirrespective of homogeneity (backscatter variation). On the contrary, the 2D QUSwith uniform regularization would require different parameters set depending on theunderlying tissue characteristic, which would not be feasible for clinical application.5.3.4 In vivo Liver: Steatosis Detection PerformanceThe correlation results between MRI-PDFF and QUS measures have been displayedin Figure 5.11. The ACE and IBC measures demonstrated significant positiveassociations with MRI-PDFF, whereas ESD was negatively associated to MRI-PDFF. The proposed 3D QUS method yields stronger correlation between ACE andMRI-PDFF (r = 0.877, p < 0.001) compared to the RPM (r = 0.415, p = 0.062)and the 2D QUS (r = 0.645, p = 0.002) method. The IBC measure had similar1032D QUS 3D QUSVariable ACE ROIVariable BSC ROI(a) (b)(c)Variable Backscatter ROIVariable ACE ROI2D QUS3D QUS2D QUS3D QUSFigure 5.10: Effect of spatial weighting. (a) Cost at ground truth QUS param-eter values for regularization parameter values (upper surface: 2D QUS;lower surface: 3D QUS). (b) Cost at ground truth values for differentregularization parameter values, λ2; λ1 = 2−4 and λ3 = λ2. Shadedregions: range of ∆SNRenv, Mid line: mean ∆SNRenv, upper and lowerlines: standard deviation. (c) Error in QUS estimation for differentregularization parameter values.104RPM 2D QUS 3D QUSFigure 5.11: In vivo human liver QUS measures from twenty-one patientsand their correlation with proton density fat fraction. The vertical andhorizontal error bars show the standard deviation in QUS measure andPDFF measure, respectively, whereas the square represents the meancalculated over a region-of-interest.Table 5.4: Steatosis classification results for different QUS feature combina-tion.FeaturesAccuracy AUROCRPM 2D QUS 3D QUS RPM 2D QUS 3D QUSACE 0.62 0.71 0.95 0.70 0.68 0.93IBC 0.67 0.81 0.95 0.40 0.97 1.00ESD 0.67 0.81 0.76 0.53 0.75 0.76ACE & IBC 0.67 0.81 0.95 0.72 0.97 1.00IBC & ESD 0.57 0.81 0.86 0.77 0.85 0.93ACE & ESD 0.67 0.81 0.95 0.52 0.97 1.00ACE & IBC 0.76 0.86 1.00 0.70 0.97 1.00& ESDand significantly strong association with MRI-PDFF for both the 2D QUS (r =0.914, p < 0.001) and the 3D QUS method (r = 0.912, p = 0.004) and a moderate1052D QUS3D QUSRPMFigure 5.12: Scatter plot of QUS measures representing normal and steatosiscases.106correlation for the RPM (r = 0.602, p < 0.001). On the other hand, the 2D QUS(r =−0.731, p < 0.001) and 3D QUS method (r =−0.51, p = 0.02) yield strongto moderate negative association, whereas the noisy ESD results obtained by theRPM (r =−0.265, p = 0.53) almost obscures the correlation.From the correlation analysis, the 2D QUS and the 3D QUS method exhibitcomparable performance. However, a feature analysis plot including all three QUSfeatures in Figure 5.12 shows that the 3D QUS results in an improved separabilitybetween the steatosis and non-steatosis group compared to the 2D QUS method.We utilize a Quadratic Discriminant Analysis (QDA) classification to classifythe cases into steatosis and non-steatosis group. The classification accuracy and theAUROC [56] were reported in Table 5.4. IBC, as a single feature, exhibits superiorability to discriminate while using the regularization methods: 2D QUS and 3DQUS, with AUROC close to 1 and statistically significant (p < 0.05) improvementcompared to the RPM. Among different feature combination, ACE and IBC, ACEand ESD, and all three feature combination yield comparable performance, forwhich AUROC were significantly different compared to the one obtained from RPM.Compared to 2D QUS, 3D QUS yields better accuracy and AUROC for all featurecombinations, which were, however, not statistically significant, except for ACE.5.4 DiscussionAdopting a total variation regularization scheme extends the trade-off betweenestimation accuracy, precision, and spatial resolution. A window dimension of 30scanlines (10 uncorrelated scanlines) and 35λ (13 pulse lengths) was found to be theminimum reliable dimension without incorporating regularization. This findings liewithin the range of 7-15 pulse lengths and 10-20 uncorrelated scanlines, which arepresented as the dimensions offering the best trade-off between spatial resolution,accuracy, and precision [202] for QUS estimation. In contrast, incorporating the totalvariation regularization into the 2D and the 3D QUS methods results in a dramaticimprovement in precision of both ACE and IBC, where standard deviation ofabsolute percentage error remains below 5% for a window dimension of 10λ (' 3.75pulse lengths) and 25λ (' 9.40 pulse lengths) axially and 5 (' 2 uncorrelated)scanlines, laterally. The mean of the absolute error of ACE and IBC remained below1076% and 40%, respectively for both the regularization methods. We can compare theaccuracy performance with the previous work [235] using a regularization method,which was able to achieve a mean percentage error (MPE) of 0.46% in ACE and327% MPE and 19.7% MPE in estimation of backscatter parameters, b and n.Our results remain consistent with the previous studies [246], where backscatterestimations have been found to suffer from significant measurement error, rangingwithin 2 orders of magnitude.Both 2D QUS and 3D QUS methods yield similar performance for the homoge-neous phantom (phantom 1) and variable ACE phantom (phantom 2), as can be seenin Fig. 5.3 and Fig. 5.4. 2D adaptive QUS also show similar results for phantom 1and phantom 2. With a similar scatterer properties (including IBC and ESD), the∆SNRenv values of both of these phantoms remain below ∆SNRenvmin, with little effectof spatial weighting. Therefore, the uniform QUS and the weighted QUS methodsare essentially similar when the tissue under experiment is homogeneous in termsof backscatter properties. The 3D QUS and 2D adaptive QUS method outperformthe 2D QUS when the underlying tissue has variable backscatter properties, as isthe case of phantom 3. Figure 5.4 shows that the ∆SNRenv map was able to iden-tify the boundaries where backscatter properties vary. Incorporating the ∆SNRenvinformation in the 3D QUS (as well as in 2D adaptive QUS) enables adaptiveregularization, and therefore results in more accurate reconstruction of the ACE andIBC map compared to the uniform regularization in 2D QUS. It should be notedthat both the RPM and the 2D QUS (with uniform regularization) method exhibitan underestimation and an overestimation of ACE centring the boundary betweenmedium with different backscatter properties, a trend described in previous literature[52, 188]. Both 3D QUS and 2D adaptive QUS method were able to overcome theissue. Additionally, the 3D regularization in 3D QUS further improves the QUSestimation quality compared to the 2D adaptive QUS, yielding accurate ACE andIBC results with close agreement to the values reported by the manufacturer.For ESD computation, a Gaussian form factor has been used as it can success-fully model the scattering properties of many soft tissues, including liver [99, 169].However, a spherical shell form factor is more appropriate for spherical glass beads,as were used in the phantoms in our experiments [98, 99]. The Gaussian form factorwould consistently overestimate the sphere size, resulting in an effective diameter108greater than the physical diameter [98, 132]. The effective diameter of the glassbeads can be found by comparing the Gaussian form factor to the spherical shellform factor and computing the conversion factor for the particular physical diameterwithin the usable frequency range [132]. The physical diameters for inclusion 1,inclusion 2, inclusion 3 and the background medium in phantom 3 are: 220 µm, 120µm, 40 µm, and 70 µm, respectively. With conversion factors 1.27, 1.31, 1.56, and1.28, respectively, the effective diameters have been found to be : 342, 158, 51, and90 µm. The accuracy of ESD estimation depends on the estimation accuracy of thebackscatter parameter, n. Therefore, the accurate backscatter parameter estimationobtained by the 3D QUS further is translated to accurate ESD estimation as can beseen in Fig. 5.5.The CIRS phantom was specifically designed to cover the wide range of QUSparameters to be expected in human liver in vivo with different stage of steatosis. the2D QUS method and the 3D QUS method both reach close to the ground truth valuewith high precision for the entire range of ACE from 0.37 to 1.18 dB/cm/MHz. Theaccuracy and precision of ESD estimation further depends on the range of kae f f ,the product of the wave number k and the effective scatterer radius ae f f , whichshould be within 0.5 to 1.2 for optimum results [99]. For the frequency range of2 MHz to 3.5 MHz used in our experiments, the kae f f values for the scatterers ininclusion 1, 2, 3 and the background are: 1.40−2.44, 0.64−1.13, 0.21−0.36, and0.37−0.64, respectively. Accordingly, we obtain high estimation error and variancefor inclusion 1 and 3, as the corresponding kae f f values are well outside of theoptimal range. The high variance in the ESD estimation for background scattererscan partly be attributed to the boundary artifacts. Finally, the kae f f for the scatterersin inclusion 2 is within the optimal range, resulting in an accurate and precise ESDestimation (Fig. 5.5, Fig.5.6c and Table 5.3).The advantage of using spatial weighting in the proposed 3D QUS over theuniform QUS and the baseline RPM is further evident from the CNR study. The3D QUS improves the contrast significantly for all three QUS parameters comparedto the 2D QUS and baseline RPM. This improved CNR might translate into betteridentification of abnormality in clinical application. The processing is done using astandard personal computer (Intel(R) Core(TM) i5-10210U CPU at 1.6 GHz with16 GB RAM). The computation time for the proposed algorithm depends on the109ROI size. For a volume-of-interest with lateral dimension of 35 scanlines, axialdimension of 6 cm (1450 samples or 122 λ ) and elevational dimension of 5 planes,the computation time was 203.30 seconds. The bulk of the computation time (180.67seconds) was used for computing power spectrum estimates, whereas the rest (22.6seconds) was used for 3D TV regularization.The proposed QUS method utilizes the volumetric RF data acquired by amotorized 3D transducer. The use of a 3D system allows the volumetric QUSreconstruction, using a three-dimensional total variation regularization. Figure 5.8shows that 3D regularization improves the estimation variance at a faster rate thanthe theoretical 1/√N curve obtained from averaging N uncorrelated realizations.The advantage of utilizing volumetric data could be further enhanced in case of invivo liver data. The correlation between two radio-frequency signal data separatedby ∆N planes, gives a measure of effectiveness of spatial compounding [94], wherea low normalized correlation coefficient value (< 0.2) indicates independent datarealizations. Figure 5.9 shows that the radio-frequency data obtained from in vivoliver get decorrelated at a faster rate with the increasing plane separation comparedto the one obtained from the phantom. As the ground truth values for QUS variancein in vivo liver is unknown, we cannot obtain the exact measures of improvementwith increasing data realizations. Nevertheless, the improved decorrelation betweensubsequent planes is an effective indicator of improvement in QUS measurementfor in vivo liver compared to phantom.Our QUS estimation results for liver in vivo were comparable to the MRI-PDFF performance, the current gold standard. The presence of hepatic steatosis wasdefined as MRI-PDFF> 5% [31]. From the correlation analysis, it was found that theACE and IBC obtained using the proposed method have strong positive associationwith MRI-PDFF, whereas the ESD was found to be negatively correlated to MRI-PDFF (Fig. 5.11). The ACE and IBC correlation results are in agreement withprevious studies on human liver in vivo [11, 228], additionally with an improvedcorrelation. On the other hand, this is the first work to report the correlationbetween ESD of human liver and MRI-PDFF. Nevertheless, previous studies onrabbit fatty liver found negative correlation between ESD and fat content of the liver[169]. Using different combination of these three QUS parameters estimated usingthe proposed method with a simple QDA classifier, we were able to discriminate110between the steatosis and the non-steatosis class with an accuracy up to 100% andarea under the receiver operating characteristic (AUROC) of 1. Among the QUSfeatures, ESD was least effective to classify the two groups. However, the highcontrast achievable using ESD (Fig. 5.7) suggests that ESD, though exhibit inferiorperformance to identify steatosis which is a diffuse condition, might be effective incharacterizing focal diseases.Using Youden index, the optimal threshold for ACE, IBC and ESD to detectthe presence of steatosis were found to be 0.77 dB/cm/mHz, 8.5e−4 1/(sr.cm), and146.3 µm. The ACE and IBC cut-off agree with the previous findings, where theACE and IBC cut-off values were equal to 0.76 dB/cm/MHz and 2.7e−3 1/(sr.cm)[11]. Among the limitations of our study is the ESD values obtained for steatosisparticipants, which partially lie within non-optimal kae f f < 0.5 range. Future workwill involve application of comparatively higher frequency range to mitigate theproblem. Also, we observe high variance in the ESD estimation for the liver study,which can be due to the presence of coherent component in the backscatter RF data.Therefore, we will further explore different signal decomposition techniques toseparate the diffuse component from the coherent component [171]. Also, insteadof using a Gaussian form factor, tissue-specific form factors can be derived fromthe impedance maps of underlying tissue and could be used for improved ESDcomputation [143]. Another limitation of the in vivo liver study is that the MRI-PDFF images and the ultrasound images are not registered. Though care has beentaken to ensure that the planes-of-interest in MRI and ultrasound roughly coincide,a registration scheme would allow direct comparison with possible identificationof focal lesions. Finally, we have demonstrated through phantom studies that theproposed method is able to attain improved reconstruction of QUS parameters forheterogeneous regions-of-interest. However, validation of current study is limitedto in vivo liver, which involves large homogeneous parenchyma. Further studies arerequired for validating the efficacy of the proposed method for heterogeneous andthin tissue, such as the placenta, kidney and dermis.1115.5 ConclusionIn this chapter, we present a multiparametric 3D weighted QUS method, whichsimultaneously measures three biomarkers for tissue characterization including ACE,IBC and ESD. The method incorporating a spatially adaptive regularization wasable to achieve improved precision-resolution trade-off in QUS reconstruction in thepresence of underlying tissue homogeneity. The three-dimensional regularizationfurther improves the QUS estimation precision with a standard deviation reductionfaster than the theoretical 1/√N rate achievable by averaging of N uncorrelatedrealizations. As the initial study for validation, We applied the proposed methodto detect hepatic steatosis, that demonstrated strong correlation to the current goldstandard MRI-PDFF with each of the three QUS parameters. The combination ofthe QUS parameters further improve the steatosis classification performance. Theproposed volumetric QUS method therefore shows promise for improved tissuecharacterization and disease detection for liver in vivo, the current organ of study.In the following chapters, we explore the applicability of the proposed QUS methodfor tissue characterization and disease detection in the placenta.112Chapter 6An Overview of SWAVE 2.06.1 IntroductionThe objective of the second part of the thesis (chapters 6, 7 and 8) is to identifynon-invasive imaging features as a measure of the placental health, with the un-derstanding that placental evaluation should constitute a critical component of aneffective pregnancy screening. To attain this goal, a multimodal placenta imagingstudy, SWAVE 2.0, was designed and conducted. We explore the application of thespectral based Quantitative Ultrasound algorithm (particularly, 3D QUS algorithmin chapter 5) for global predictor of pregnancy outcome using the clinical data asthe ground truth as well as for the characterization of local features correspondingto the lesions in pathology slides. Besides spectral QUS parameters, we also investi-gate elastographic QUS parameters. Finally, this part also presents initial data andanalysis obtained from the MRI and pathology data.Project SWAVE 2.0 was initiated in 2018 with the objective of identifyingquantifiable measures of placental health using a multimodal correlative approach.The first phase, Project SWAVE (shear wave absolute vibro-elastography), initiatedin 2015, was designed to measure the mechanical properties of placenta ex vivousing the namesake elastography technology [2]. The ultrasound data acquired inthe first phase were later successfully implemented for characterizing and estab-lishing normative QUS values for normal placentas [51]. The initial phase wasfollowed by SWAVE 2.0, where a multimodal image acquisition protocol was de-113signed to acquire and align data from ex vivo placenta specimens derived from bothhealthy pregnancies and pregnancies with preeclampsia and/ or intrauterine growthrestriction. This unique project, which is first of its kind, involved extensive dataacquisition, including novel multiparametric quantitative ultrasound (such as elas-ticity, attenuation coefficient estimate, backscatter coefficient and effective scatterdiameter), multiparametric MRI, and histopathology. A co-registration methodwas proposed using surface markers and external fiducials to enable alignmentand fusion of multimodal data. Though explained within the framework of projectSWAVE 2.0, the proposed co-registration approach is versatile and can be adoptedin a wide range of experimental setups involving multimodal imaging of ex vivoplacentas. Table 6.1 presents a possible (non-exhaustive) list of commercial USand MR imaging modes, which can be readily acquired with available packagesfrom vendors and aligned using the proposed method. The proposed protocol couldfurther be used for quantitative pathology analysis. A few possible applicationsof quantitative pathology, which are currently being investigated by different re-search groups have been enlisted. We refer to the quantitative modes obtained usingultrasound as US+, MR as MR+ and pathology as pathology+.In this chapter, we present an overview of the data acquisition protocol withthe aim of supporting multimodal and multiscale placental research. We describea unique approach for multimodal alignment using surface marker and externalfiducial. Finally, we report the alignment accuracy of the proposed method. We alsopresent sample correlative imaging analysis to show our capabilities and ongoingchallenges.6.2 Methods6.2.1 SubjectsIn this case-control study, placentas (n=46) were collected from a group of women(n=40) who delivered via cesarean delivery at BC Women’s Hospital, Vancouver,Canada. We included 26 placentas from pregnancies affected by preeclampsia(PE) and/or intrauterine growth restriction (IUGR). Preeclampsia was defined asthe presence of gestational hypertension with proteinuria and a pregnancy was114Table 6.1: Example of commercial imaging modalities for which proposedalignment protocol is applicable.Modality Technique Manufacturing NameUS+ Elastography ShearWaveTM Elastography(SWE) - Super Sonic ImagineShear wave elastography - CanonVirtual TouchIQ - SiemensElastQ and ElastPQ imaging -Philips2D Shear Wave Elastography -GE HealthcareAttenuation Attenuation Imaging (ATI) -CanonUltrasound-Guided AttenuationParameter (UGAP) - GE Health-careMRI+ Inversion Recovery FLAIR - GE, Philips, Hitachi(long τ) TIRM - SiemensFastFLAIR - CanonSusceptibility SWAN - GE Healthcare-Weighted SWI - SiemensSWIp - PhilipsFSBB - CanonSpoiled Gradient SPGR - GE HealthcareEcho FLASH - SiemensT1-Fast Field Echo (FFE) -Philips, CanonUltrafast Gradient 3D Fast SPGR - GE HealthcareEcho 3D MPRAGE - Siements, Hitachi3D TFE - PhilipsFastFE 3D - CanonFat-water IDEAL - GE Healthcareseparation Dixon TSE - Siemens HealthcaremDixon TSE - PhilipsWFS - Canon115Table 6.2: Example of histopathology algorithms for which proposed align-ment protocol is applicable.Modality Technique Manufacturing NameMRI+ Diffusion- DWI - GE, Siemens,weighted Philips, Canon, HitachiFat & iron IDEAL-IQ - GE Healthcareevaluation LiverLab - SiemensmDixon Quant - PhilipsMR Elastography MR-Touch - GEElastography - Siemens, Philips,ResoundantPathology+ Cellular Cell classification into classes:Phenotyping cytotrophoblast, fibroblast,Hofbauer, syncytiotrophoblast,vascular [70]Gestational age GestAltNet: deep learning forestimation GA estimationfrom whole-slide images [156].Lesion detection Decidual vasculopathy identifica-tion in whole slide images [39]considered to be complicated by IUGR if the fetal abdominal circumference isbelow the 10th percentile measured from routine ultrasound examination. Of the 26placentas complicated by PE and/ or IUGR, there were 16 IUGR placentas, 1 PEplacenta and 9 placentas affected by both IUGR and PE. Among all cases affectedby IUGR, 15 were categorized as early onset (diagnosed at <32 weeks), 2 werelate onset (diagnosed at >32 weeks) and the rest were not recorded. Among thePE cases for which the gestational age at diagnosis were recorded, 6 were earlyonset (diagnosed at <32 weeks) and one was late onset. Four of these placentaswere from pregnancies additionally complicated by gestational diabetes mellitus.We included 10 controls from singleton pregnancies and 10 control twin placentas(from 5 dichorionic twin pregnancies) that were uncomplicated by PE and IUGR.Among our control placentas, one singleton and four twin placentas were frompregnancies affected by gestational diabetes mellitus.The exclusion criteria included any gestational abnormality identified prior to116m4DC7‐3/40 TransducerSWAVE GUI inSonixTouch MonitorVoice Coil MotorMarked Placenta Oil PillsWater Bath(b) QUS Imaging(a) ROI identification and marking(c) MR Imaging(d) Histologic sectioningFigure 6.1: Overview of project SWAVE 2.0.delivery and treatment with investigational medication. The study (H17-00331)was performed under written informed consent of all participants after approvalby the University of British Columbia Children’s and Women’s Research EthicsBoard. Study data were collected and managed using REDCap electronic datacapture tools hosted at British Columbia Women’s and Children’s Hospital. Clinicaldata extracted from the participants health record (maternal and neonatal charts)included maternal age, parity, risk factors for placental disease (history of smoking,hypertension, diabetes), birth date and time, gestational age at diagnosis, gestationalage at birth, birth weight, cord blood pH, and admission to neonatal intensive care.All 46 placentas underwent the steps in the proposed protocol as was described inthe following sections (6.2.2–6.2.6). However, the digital photographs were notavailable for 5 placenta cases.6.2.2 Placenta PreparationPlacentas were stored at 4◦C until the examination. Examinations was initiatedbetween approximately 6 - 48 hours after delivery and completed within 5 days of117delivery. At the beginning of the examination, a perinatal pathologist (Dr. JeffersonTerry) removed the umbilical sac and identified externally appreciable placentaldisc lesions. Layers of acoustic absorbing pad (Aptflex F28, Precision Acoustics,UK) were placed beneath the maternal surface of the disc to reduce reverberationartifacts. The placenta was secured using two rubber bands at the edges to theabsorbing pad layers and then placed in a rigid plastic container to minimize anydeformation while transporting from one imaging modality to another.6.2.3 Region-of-Interest Identification and Annotation/ MarkingThe placenta was scanned using a SonixTouch ultrasound machine (Analogic Corp.,Richmond, BC) and an L14-5/38 linear transducer at the Perinatal Research andImaging Evaluation (PRIME) centre, located at BC Women’s Hospital and HealthCentre. A sonographer (Victoria Lessoway) scanned the placenta to locate tworegions-of-interest: i) a homogeneous region without macroscopic or sonographi-cally visible abnormalities, and ii) an inhomogeneous region with suspected lesionsidentified during gross examination or ultrasound scan, such that both regions lie onthe same axial-lateral plane. The suspected lesions, if found, were further annotated,measured and recorded using the ultrasound built-in interface. Water, instead of ul-trasonic gel, was used as the conductive medium to prevent smearing of ink used formarking the identified locations. India ink was applied using a paint brush to markthe identified regions and the corresponding plane-of-interest on the fetal surfaceof the placenta. Specifically, while holding the ultrasound probe at each identifiedregion, we marked the location of the probe. For each identified region, we thendrew an in-plane line connecting the marks indicating probe edges, drew anotherline perpendicular to the in-plane line at the mark indicating probe-center and finallyextended the two in-plane lines for the two regions (Figure 6.1(a)) indicating theplane-of-interest. We then placed cod liver oil pills, considered as MRI-visible fidu-cials, at the edges of the placenta along the in-plane line and a flexible ruler along thein-plane line marking the plane-of-interest on the fetal surface. Photographs of thefetal surface of the placenta, with the ruler, markings and the fiducials, were takenfor future reference (6.1(a)). The simple but powerful approach, ensuring alignmentamong multiple modalities, can be adopted to a range of different modalities and118T1 FSE T2 FSE T1 FLAIR PD FSESWAN: Magnitude3D FSPGR DWI T2 FLEX: FatSWAN: Magnitude SPGR: TE = 1.9 ms SPGR: TE = 1.9 msT2 FLEX: Water246810(kPa)0501001502002500.511.5(dB/cm/MHz)-35-30-25-20(dB)Elas�cityB-modeA�enua�onBacksca�er CoefficientScatterer Size(a) QUS Images(b) MRI Images(c) Pathology ImageFigure 6.2: Multi-modality images obtained from SWAVE 2.0.imaging of different organs.6.2.4 Magnetic Resonance ImagingThe MRI protocol was reviewed and approved by the BC Children’s Hospital(BCCH) MRI Research Facility’s Protocol Review Committee. MRI scans wereperformed on a GE Discovery MR750 3.0T MRI scanner (GE Healthcare, Milwau-kee, Wisconsin) using an 8-channel cardiac array. The placenta was positioned suchthat the laser beam localizer aligns to the mark on the fetal surface indicating theplane-of-interest (Figure 6.3).Three-plane-localiser using a standard spin-echo pulse sequences were firstperformed to visualize the overall structure of the placenta and to facilitate the sub-sequent acquisitions. The placenta was then scanned using the following sequences:1. T1-weighted fast-spin-echo (T1 FSE): TE 8.16 ms, TR 850 ms, flip angle111◦, slice thickness 2 mm, FOV 220 mm.2. T2-weighted fast-spin-echo (T2 FSE): TE 110 ms, TR 2500 ms, flip angle142◦, slice thickness 2 mm, FOV 220 mm.3. T2-weighted fluid-attenuation-inversion-recovery (T2 FLAIR): TE 120 ms,TR 12000 ms, TI 2708 ms, flip angle 160◦, slice thickness 2 mm, FOV 220 ×220 mm.4. Proton Density fast-spin-echo (PD FSE): TE 9.3 ms, TR 3000 ms, flip angle119111◦, slice thickness 2 mm, FOV 220 mm.5. 3D Susceptibility-weighted angiography (SWAN) using spoiled gradient echo:TE 32.6 ms, TR 40.6 ms, flip angle 15◦, slice thickness 2 mm, FOV 240 mm.Slices were acquired for coverage of the entire placenta.6. 3D Fast spoiled gradient (FSPGR): TE 3.2 ms, TR 7.9 ms, TI = 450 ms flipangle 12◦, slice thickness 2 mm, FOV 240 mm. Slices were acquired forcoverage of the entire placenta.7. Six-echo spoiled gradient echo (SPGR): TE = 1.9. 2.6, 3.3, 4.0, 4.7, 5.4 ms,TR 125 ms, flip angle 10◦, slice thickness 4 mm, FOV 210 mm. Slices wereacquired for coverage of the entire placenta.8. T2-weighted fast-spin-echo (FSE) FLEX: TE 100 ms, TR 8961 ms, flip angle111◦, slice thickness 2 mm, FOV 220 mm.9. Diffusion weighted imaging (DWI) using spin echo: TE 62.4 ms, TR 6000ms, flip angle 90◦, slice thickness 4 mm, FOV 240 mm, b values: 0, 1000s/mm2.Except for 3D SWAN and 3D FSPGR, the other sequences were performedin 2D and slices were acquired for covering the pills in axial direction. The 3Dsequences, 3D SWAN and 3D FSPGR acquired slices to cover the entire placenta.6.2.5 Quantitative Ultrasound ImagingThe quantitative ultrasound (QUS) imaging of the placenta was performed using aSWAVE (shear wave absolute vibro-elastography) imaging system [2]. Elastographyis a non-invasive technique where an external force is applied to measure themechanical properties of tissue, such as shear wave speed and Young’s modulus.The ultrasound radio frequency (RF) data acquired in a SWAVE system can furtherbe processed to compute complementary acoustic properties, such as attenuationcoefficient estimate, backscatter coefficient and effective scatterer size [49–51] (aswere previously defined in section 1.2.1 and 1.2.4). Young’s modulus provides ameasure of the tissue stiffness by computing the speed of the shear wave propagation.120Figure 6.3: The co-ordinate systems involved in the multimodality imagingsystem: world coordinate system W , ultrasound coordinate system U ,pathology coordinate system P, MRI coordinate system M and the trans-formations between them.Attenuation coefficient, backscatter coefficient and effective scatterer size quantifythe acoustic properties of the underlying tissue. Attenuation coefficient measuresthe ultrasound energy loss with propagation depth due to scattering and absorption.Backscatter coefficient, on the other hand, measures the ultrasound intensity that isscattered in the backward direction. Backscatter coefficient can further be used toestimate the effective size of the dominant scatterer, with the effective scale beingdetermined by the incident ultrasound frequency.The SWAVE system in our project consists of an Ultrasonix SonixTouch ultra-sound machine (Analogic, Richmond, BC, Canada), with a m4DC7-3/40 curvedarray transducer (Ultrasonix, Richmond, BC, Canada). The placenta fixed withinthe container was placed on a custom-built plexiglass shaker platform. The shakerplatform, along with the placenta container, was then submerged in a constant-temperature water bath (Cole-Parmer, Montreal, QC, Canada) with temperatureset at 37◦C, to ensure a sound of speed value of 1540 m/s. The shaker platformis connected to a voice coil motor through a rod, that generates a multi-frequency121(between 40 Hz to 200 Hz) excitation. A set of four springs are affixed to theplatforms corner that allow its vibration along the vertical direction. The excitationfrequencies and amplitude are controlled by a custom-built control unit. Duringdata acquisition, the vibrating shaker platform generates shear waves within theplacenta. The transducer was submerged in the water bath with a few millimetresbetween the transducer face and the fetal surface unlike traditional contact ultra-sound acquisition to avoid deformation. The transducer was positioned to imagethe placenta at the marked location while being parallel to the platform and thewater bath. Volumetric ultrasound radio-frequency data were captured and storedfor offline processing to reconstruct QUS maps, including elasticity, attenuationcoefficient estimate, backscatter coefficient, and effective scatterer size. The timerequirement for the data acquisition process were 6.3 seconds.6.2.6 HistopathologyAfter imaging, each placenta underwent pathological examination including themeasurement of the placental weight and dimensions. The placenta was sampled insuch a way that the full-thickness sections included the volumes of interest acquiredwith SWAVE, and aligned with the plane-of-interest marked during placental prepa-ration and imaged with ultrasound and MRI. Up to 4 sections (2µm each) wereobtained per placenta. After routine formalin fixation, processing, and hematoxylinand eosin staining, the slides were scanned with the Aperio ScanScope system(Aperio, Vista, CA) at a 400× microscope resolution at the BC Children’s HospitalDepartment of Pathology. The pathologic slides were reviewed by an experiencedperinatal pathologist (J.T.) according to a routine protocol [107].6.2.7 Multimodal RegistrationThe multimodal imaging system involved three coordinate systems, including world(W ), MRI (M), ultrasound (U) and pathology (P) coordinate system (Figure 6.3).A point pA in A coordinate system is transformed to the B coordinate system asfollows:pB = BTA pA,122Figure 6.4: Possible sources of registration error during data acquisition. Left:ultrasound probe alignment process during QUS imaging using SWAVEand right: full-thickness placenta sampling, both along the line indicatingthe plane-of-interest.where BTA defines the rigid transformation matrix that transforms from coordinatesA to B.The digital photograph (Figure 6.5 (b)) of the fetal surface along with themarking, ruler and MRI-visible fiducials, taken at the PRIME, provides the locationsof ROI 1 and ROI 2 with respect to the world (W ) coordinate system. The relativeposition of the regions-of-interest (ROI 1 and ROI 2) with respect to US (U) andpathology (P) coordinates are known, as the US volumes and pathology imagesare centred around the ROI locations marked in the PRIME center. To attainthe co-registration of the ROIs in different modalities, their relative location withrespect to the MRI coordinate system (M) has to be identified. The MR coronalslice corresponding to the fetal surface shows the fiducials, as well as the placentaboundary. Therefore, the locations of the ROIs with respect to MRI coordinates (M)can be identified by registering the digital photograph and the coronal MRI plane.Finally, the multimodal co-registration can be attained by performing US-MRI andpathology-MRI registration. According to our proposed image acquisition protocol,the center plane in the US volume, MRI volume and histopathology image volumesare aligned. This reduces the multi-modal registration to a 2D registration problem,123Lesion NormalLesion Normal(a) Digital Photograph (moving)Coronal Plane(reference) Axial Plane(b) (c) (e) (d) Figure 6.5: Co-registration between digital photograph taken at the PRIMEcentre and the coronal slice of the MRI localiser sequence. (a) Pointclouds before and after registration. Co-ordinates of the cod liver oilpills as well as a portion of the placenta boundary were used to generatethe point cloud in the moving and reference images. (b) The digitalphotograph was used as the moving image. The line connecting thepills indicates the plane-of-interest. Two other lines perpendicular to thisline mark the two regions-of-interest to be imaged in US and pathology.The dot indicates the location with suspected lesion. (c)The coronalslice corresponding to the fetal surface acquired during 3D localisersequence was used as the reference image. (d) Registration result where’pink’ denotes the moving point cloud and ’green’ denotes the fixed pointcloud. Magnified views show the overlapping of the oil pills and the fetalsurface boundaries between the registered images. The distance betweenthe centroids of the pills in the registered digital photograph (130.6 mm)closely matches to the measure indicated by the ruler (132 mm). (e) Thecorresponding regions-of-interest in axial MRI have been localized usingthe registration result.124i.e. registering the ultrasound central plane and 2D pathology image to the MRIcentral axial plane.The US-to-world (W TU ) transform is known as the coordinates for ultrasoundand the pathology are defined using India ink marking in the PRIME centre. Toattain the co-registration among the multimodal coordinates, the MRI-to-world(W TM) transform has to be estimatedThe steps for the registration between the MRI and the digital photograph are asfollows:1. The coronal slice that contains the fiducials, was identified in the MR localisersequence. This is typically the fetal surface captured in the digital photograph.2. On the fetal surface, two point sets were created, the reference point setY = (y1, ...,yM)T (a M× 2 matrix) from the MR image and the movingpoint set X = (x1, ...,xM)T from the digital photograph. The point sets werecreated using the MATLAB function drawfreehand by outlining the fiducialsand corresponindg placenta edges (Figure 6.5 (a)) in the MRI and digitalphotograph. To perform the alignment between the two point sets accordingto the coherent point drift (CPD) algorithm [161], we construct a GaussianMixture Model (GMM) by considering the points in Y as the centroids ofthe GMM, and fit the model to the points X by maximizing the likelihoodfunction. An expectation-maximization optimization is performed to solvethe registration problem, i.e., compute the transform matrix MTW .3. The digital photograph was registered to the MRI using MTW computed duringthe previous step.4. The registration performance was evaluated by measuring Fiducial Registra-tion Error (FRE) and Target Registration Error (TRE). FRE is calculated asthe Euclidean distances between the corresponding fiducials (that were usedfor registration) in the reference MRI and the registered digital photograph.TRE, on the other hand, is the Euclidean distances between correspondingpoints (which were not used for registration) in the reference MRI and theregistered digital photograph. Two points for each placenta were selected125by identifying anatomic landmarks visible in both modalities, such as largevessels and cord insertion site.5. The ROI locations along the axial MRI plane corresponding to the US andhistopathology slides are computed by measuring the distance between thefiducials and the ink marks in the registered digital photograph.6. The registration of the center plane in ultrasound volume and the histopathol-ogy plane to the MRI central plane are then fine-tuned using anatomic land-marks that were clearly identifiable in all modalities. These landmarks in-cluded the specimen contour along the maternal and fetal surface and lesionboundaries. The MatchPoint Registration plug-in in MITK was used to findthe rigid transformation matrix [249].Possible sources of error include the (i) alignment of ultrasound probe duringQUS imaging, (b) alignment of laser guide in MR, and (iii) sampling the placentaduring histopathology (Figure 6.4). Though these alignments are carried out manu-ally, the alignment error is approximated to be limited to a few millimetres in eachcase.6.2.8 Statistical AnalysisThe registration algorithm implementation and the statistical analysis were per-formed in MATLAB 2020a (The MathWorks Inc., Natick, MA, USA). Comparisonof demographic and clinical characteristics between controls and diseased caseswere performed with the non-parametric Mann-Whitney U-test. We consider a pvalue < 0.05 as indicating statistical significance.6.3 Results6.3.1 Registration Performance EvaluationFigure 6.5 shows qualitative registration results for a representative placenta case.Figure 6.6 presents the quantitative performance, where TRE and FRE and theirmean values are shown for 41 placenta cases. The registration method using the126Figure 6.6: Fiducial registration error (FRE) (left) and target registration error(TRE) (right) of the MRI/digital photograph registration algorithm of 41placenta samples. The lines indicate the mean FRE (left) and mean TRE(right).proposed approach was able to achieve a mean FRE of 2.66±1.44 mm and meanTRE of 5.49± 2.65 mm between MRI and digital image (the marking on whichspecifies the ultrasound and pathology coordinates). The TRE were comparativelylarger which can be contributed to the large point localization error as a result ofuncertainty in identifying corresponding features in the MRI and digital photograph.For example, the intersection of two vessels selected from slightly different plane(arising from the placenta height variation along the fetal surface) in the digitalphotograph and the coronal MRI would correspond to two different locations andthereby result in error.6.3.2 Correlative Multi-modality ImagingWe present a qualitative demonstration of co-registration among MRI, ultrasoundand histopathology images for a placenta specimen (Figure 6.7(a)). The particularcase included a retroplacental hematoma, which was visible in all the imagingmodalities. According to the attending radiologist, the lesion was hypointense in T2-weighted sequence, whereas hyperintense in T1 FSE and DWI sequences[131]. Inultrasound, an acute hematoma appears slightly hyperechoic, becoming hypoechoicand echolucent with time [127]. In our case, the lesion appears to be hypoechoic,with echogenic border.The aligned multimodal data allows for correlative analysis among different127(a) (b)Figure 6.7: Correlative imaging of a retroplacental hematoma for an exampleplacenta. (a) Registered MRI, ultrasound, and histopathology imagescontaining retroplacental hematoma at the maternal side of the disc(indicated by the arrows). (b) Profiles corresponding to multiparametricQUS and MRI measures along the white line showing in (a). The shadedarea highlights the hematoma region.parameters obtained from MRI and quantitative US. As an illustrative example, weshow the correlation among quantitative ultrasound and MRI parameters along a ver-tical line through the hematoma (Figure 6.7(b)). Among the quantitative ultrasoundparameters, the hematoma region had a smaller scatterer size and elasticity valueand larger backscatter coefficient value compared to the surrounding tissue. Threeexample MRI sequences showed similar trends along the vertical line shown inFigure 6.7(a). Further analysis will investigate patterns from the correlative analysisof the quantitative parameters and relationship to diseases.6.4 DiscussionMultimodal multi-scale imaging of the placenta can potentially provide insightinto placental structure and function. The integration of multiple modalities cancreate a composite and complementary view of the placenta under study. Multi-modality imaging also allows the validation of the quantifications obtained fromany single modality. The combination of different in vivo modalities measuringdifferent properties of the placenta could be particularly informative. For example,128the acoustic and mechanical tissue properties measured in quantitative ultrasoundcan be related to the the functional measures such as diffusion measured in MRdiffusion weighted imaging. However, to reap the benefit of multimodal imaging, thedevelopment of a structured workflow enabling sample preparation, data acquisition,data processing, and deposition and dissemination of large datasets is critical.This chapter presents the standard workflow designed for the data acquisition forthe project SWAVE 2.00. The workflow essentially lays the foundation of thesubsequent data management, analysis and visualization.One central problem of multimodality imaging is attaining the alignment amongdifferent modalities for enabling correlative analysis. The placenta, unlike manyother organs such as the prostate [80], does not contain sufficient anatomic land-marks visible in different modalities. We present a surface-marker and externalfiducial (cod liver oil capsule) based method to facilitate the registration process.We attain a mean fiducial registration error (FRE) < 3 mm, which was the meandistance of the corresponding capsule position in the digital photograph and theMRI. This amounts for 1.9% of the placenta dimension along the line connecting thefiducials. We also evaluated the mean target registration error, which was < 6 mm.The target registration error (TRE) was computed by identifying similar landmarkson the fetal surface of the placenta.One source of error in registration arises from the assumption that the centralplanes in different modalities are perfectly aligned. Considering the thicknessof the india ink marking (in the range of a few millimeter) and the best effortsduring aligning the laser guide in MR, probe positioning during US and slicingduring pathology, the alignment error can be approximated to a maximum of afew millimetres. Also, rigid registration assumes that there were no deformationsinvolved between different modalities. While this assumption holds true for US andMR imaging, the sectioning in pathology may induce deformation and shrinking ofthe sampled tissue. Another source of error was due to the assumption that the fetalsurface is contained in one coronal plane in the MRI. However, the variable depth ofplacentas led to different vertical position of fiducials, resulting in slightly differentcross-section of fiducials in the selected MRI coronal plane compared to the onein the digital photograph. This limitation could be mitigated in future experimentsby recording the placenta height at different points and registering to the plane129computed by interpolating the MR coronal planes obtained from the correspondingheights. For evaluating the target registration error, ambiguity in identification oflandmarks led to error in localizing points for a few cases (Figure 6.6(b)). However,with the current performance metric, we can attain excellent alignment with lessthan 6 mm TRE among different modalities.The proposed protocol could serve as a simple yet practical tool for the placentascientific community to replicate and extend ex vivo placenta imaging using a widerange of commercial and research imaging modalities. The protocol attains thegoal of capturing US, MR and pathology images on placentas. The co-registrationprocess with india ink markings and MRI-compatible pills is developed in a waythat does not disrupt imaging using any of the three modalities. The alignmentprotocol includes readily available tools, such as ink markings, oil pills, laserguides, containers, ruler and camera as well as an open source registration algorithm.Therefore, the protocol offers many researchers worldwide a promising tool formultimodal placenta image alignment, a crucial step for correlative imaging ofplacenta.A major strength of the project SWAVE 2.0 is that it provides multiple quanti-tative ultrasound and MRI parameters. Unlike their qualitative counterpart, qMRIand QUS extracts tissue properties which can be expressed as a number with unitsor relative to a reference material [105]. The scope of this chapter does not coverthe detailed analysis of this quantitative aspect. However, we present an example ofplacenta specimen with a lesion visible in all modalities. We present the correlationof a few representative modalities, including backscatter coefficient, scatterer size,elasticity, DWI, and proton density water fraction. The multi-modality approachwill open new opportunities for characterizing placental tissue under normal andpathologic conditions (i.e. IUGR and/or PE) and therefore identifying subtle changein placenta microstructure indiscernible in qualitative counterparts.130Chapter 7SWAVE 2.0 Imaging of PlacentalElasticity and Viscosity7.1 IntroductionBesides spectral QUS parameters, Project SWAVE 2.0 acquires elastography data.In the elastography study for placental imaging [2], low-frequency steady-statemechanical waves are used to excite tissue and provide quantitative measure of pla-cental stiffness. Low frequency shear waves have the capability of deep penetration(and thus the ability to access both anterior and posterior located placentas) andreduced safety concerns. Previously, Project SWAVE demonstrated the applicationof the SWAVE system on clinically normal placentas ex vivo and established abaseline elasticity. In the current SWAVE 2.0 project, we extend our previousbaseline study to a clinical study that includes multimodal image acquisition fromex vivo placenta specimens from clinically normal pregnancies and pregnanciescomplicated with PE and intrauterine growth restriction. Besides elasticity, wealso acquired other quantitative ultrasound (such as attenuation coefficient estimate,backscatter coefficient and effective scatter diameter), multiparametric MRI, andhistopathology data. The current chapter will primarily investigate the elastographyof placenta.SWAVE 2.0 elastography incorporates several improvements over the previouselastography method, Project SWAVE [2]. First, Project SWAVE utilized single-131frequency excitation and therefore, required repeated acquisitions for measurementat different frequencies. This involved increased acquisition time, inconsistencybetween region-of-interest probed at different frequencies, and inability to utilizemulti-frequency elasticity reconstruction approach required for improved perfor-mance. Second, the Project SWAVE utilized a linear array transducer with a depthlimit of 9 cm, which would be unable to access the posterior-lying placentas. Third,a bandpass sampling scheme was utilized [64], achieving a frame rate of 72 Hz,which is not sufficient for in vivo applications in the presence of breathing andfetal motions. Finally, a single-frequency 2D local frequency estimation (LFE)algorithm was utilized for elasticity reconstruction, potentially leading to relativelynoisy and less accurate estimation. In the current work, we address the above-mentioned limitations. We perform hardware and software modifications to enablemulti-frequency excitation, thereby improving acquisition time and elasticity re-construction performance. We utilize a curved array transducer, with an imagingdepth of up to 24 cm, as a preparation for the future in vivo study. We utilize asector-based sampling approach [14], which resulted in a 11-fold increase with asampling rate of 800 Hz. Finally, we apply a multi-frequency 3D LFE algorithmfor accurate elasticity reconstruction. Novel contributions of this chapter are: (1)the application of the improved elastography system to acquire data from a studycohort including normal control placentas and placentas from pregnancies affectedby PE and IUGR; (ii) establishing a stiffness threshold to distinguish the diseasedplacentas from the control ones, and (3) viscoelastic characterization of placentasusing Voigt’s rheological model. We perform comparison among different elasticitymeasurement methods (2D LFE vs. 3D LFE, single frequency vs. multi-frequency)to identify the most suitable SWAVE parameters measurement approach. Finally,we perform a comprehensive analysis of the diagnostic performance of SWAVEparameters for predicting clinical findings associated to placental health (healthy,IUGR or PE). We also analyse the correlations of SWAVE parameters with thepregnancy outcome in terms of admission of neonates into intensive care.1327.2 Materials and Methods7.2.1 Study Population, Scanning System Overview, DataAcquisition and Data Processing46 placentas were collected (detailed description of the study population has beengiven in the previous chapter in section 6.2.1). Placenta preparation and region-of-interest identification were conducted, which were described in the previous chapter(section 6.2.2 and 6.2.3). The elastography data of the placenta were acquired usinga SWAVE (shear wave absolute vibro-elastography) imaging system [2], equippedwith a m4DC7-3/40 curved array transducer (Ultrasonix, Richmond, BC, Canada).The transducer was set to operate at a transmit frequency of 3.33 MHz to perform afeasibility test for the future in vivo experiments. According to American Instituteof Ultrasound in Medicine [44], while transmit frequency between 3-5 MHz issufficient to achieve an optimum trade-off between penetration depth and resolution,a lower frequency (≈ 3 MHz) transducer might be required for abdominal imagingin obese patients. We acquired two sets of multi-frequency elasticity data at [80, 100,120] (n = 46) and [40, 50, 60, 70] Hz (n = 41) for the feasibility test of prospectivein vivo study applicable for entire range of pregnant population. The imaging depthswere set to 3 cm to capture the full-thickness of the full-term placentas. Volumetricultrasound RF data were captured and stored for offline processing to measure tissuedisplacements and reconstruct elasticity maps. The ultrasound volume consists of15 elevation planes spaced at 0.95◦ angular positions. The volume acquisitions werecompleted within 6.3 seconds. More details regarding the experimental setup of ourSWAVE 2.0 system are provided in chapter 6.For tissue displacement tracking, a sector subdivision method [14] was utilized,where each imaging plane was divided into 8 sectors. Each sector is imaged40 times before moving to the imaging of the next sector, enabling the motionestimation to be computed at the sector repetition frequency. The effective framerate achieved in this method was 800 Hz. The relative tissue displacement along theaxial direction between consecutive ultrasound frames was estimated using a time-domain cross-correlation with prior estimates (TDPE) method [259]. Accordingto this method, the RF data were divided into blocks of 63 data samples with a13368% overlap, the normalized cross-correlation function was computed, and theposition of the maximum peak of the normalized cross-correlation was measuredusing cosine interpolation resulting in the steady-state tissue displacement at therespective block’s location and time instance. From the steady-state displacementestimates, displacement phasors at each excitation frequency were computed usingleast-square fitting. To obtain synchronous displacement phasors required forsubsequent elasticity measurement, the time delay between scanning points on asingle scan line at different depth, between scan-lines, and between sectors werecompensated in the frequency domain [14] based on the assumptions of periodicmotion, constant ultrasound speed of sound within the region-of-interest and stilltransducer with respect to the tissue. The synchronized phasor volumes are thenscan-converted and interpolated to a grid with voxel size of 0.6×0.6×0.6 mm3 inCartesian coordinates.LFE was used to estimate the elasticity of the tissue in terms of Young’s modulus(E) [111], which describes the resistance of the tissue to deform under unilateralcompression. For a homogeneous, isotropic, linear elastic material, the shearmodulus µ is related to E asE = 2µ(1+ν) (7.1)Most soft tissues are nearly incompressible, therefore the Poisson’s ratio is assumedto be approximately equal to 0.5. Therefore, Young’s modulus E reduces to E = 3µ .Now, LFE estimates the local shear wave speed at each excitation frequency usingfilter banks operating on the displacement phasors. According to this method, pairsof log-normal quadrature filters (defined as a product between radial and directionalcomponents) are used, where the ratio of the pairs of filters provides the solution ofthe Helmholtz equation, where the Helmholtz equation describes the tissue motionwith assumptions of incompressibility and homogeneity [111]. A set of filter pairswith different center frequency and bandwidths are utilized to obtain the wavenumber over a broad band of frequencies.In LFE method, first, the phasor volume at each excitation frequency is translatedinto k-space (spatial frequency domain) using Fast Fourier Transform and filteredusing a 4th order butterworth band-pass filter with cut-off frequencies associatedto elasticity of E ∈ [1,100] kPa. Six k-space directional filters along axial, lateral134and elevational directions (±x,±y,±z) were applied on the entire 3D volume. Weutilized filter banks with 11 frequencies spaced an octave apart, where the highestfrequency was associated to the wavelength of 2 pixels, to obtain the estimate of thewavenumber over a broadband frequency range. The local wave number km at eachexcitation frequency ωm is estimated as the weighted summations of the estimatesfrom the pair of filters. Finally, the elasticity estimate over all excitation frequenciesis computed as a weighted average where the estimate at each frequency is weightedby the displacement phasor amplitude, U f igm:E = 3ρc2s =3ρnn∑m=1Umω2mk2m, (7.2)where ρ is tissue density and is assumed to be equal to 1000 kg/m3. cs is the localshear wave speed evaluated over all excitation frequencies. Here, the LFE algorithmcomputes the real part of wave number, with the simplifying assumption that thetissue is not significantly viscous.Rheological ModellingIn our proposed 3-D multi-frequency SWAVE method, data were acquired at mul-tiple frequencies: 40, 50, 60, 70, 80, 100 and 120 Hz using the curved arraytransducer. In presence of viscosity, the shear modulus (µ∗) and shear wave number(k∗ =√ρω2µ∗ ) would be complex. Shear wave speed at each excitation frequencycs(ω), can be computed as:cs(ω) = ω/k, (7.3)where k is the real part of k∗. This results in the single frequency elasticity measure,E(ω) = 3ρcs(ω)2. Analysing the shear wave speed dispersion, i.e., the change ofspeed cs as a function of frequency ω , both tissue elasticity and viscosity can bequantified [36].The shear wave speed can be related to the complex shear modulus usingcs =√2(Re(µ∗)2+ Im(µ∗)2)ρ(Re(µ∗)+√Re(µ∗)2+ Im(µ∗)2). (7.4)Rhelogical models can be used to relate the viscoelastic properties to the complex135shear modulus, where the shear modulus can be expressed in terms of equivalentmechanical impedance, Z asµ∗ = jωZ, (7.5)where the impedance is given as the ratio of shear stress and shear strain. Among therheological models, Voigt’s model has previously been used for the characterizationof viscoelastic properties of normal placentas [2]. According to this model, thetissue is a parallel arrangement of a spring of shear modulus µ and a dashpot ofviscosity η , resulting in µ∗ = µ+ jωη . Also, the model results in the followingexpression for the relation between dispersion of shear wave speed with µ and η :cs(ω) =√2(µ2+ω2η2)ρ(µ+√(µ2+ω2η2)). (7.6)The parameters µ and η were computed using a least square fit between the shearwave speed measures obtained from the model and that obtained using equation 2.We also measured the root mean square error as a metric of goodness of the fit. Weapply the Voigt model fitting on the 41 placentas for which data at all 7 frequencies,[40,50,60,70,80,100,120] Hz were available.7.2.2 Classifier Training and EvaluationWe train and evaluate a linear discriminant analysis (LDA) classifier to assess thepotential of elasticity parameters to discriminate between tissue from control anddiseased placentas. Classifier performance was assessed using AUROC (area underthe receiver operator characteristic curve) values obtained from Receiver OperatingCharacteristics (ROC) analyses, sensitivity, specificity, and accuracy obtained with5-fold cross validation. We also reported the cut-off values of correspondingparameters.Statistical AnalysisStatistical analyses were performed using MATLAB R2020b (The MathWorks,Natick, MA, USA) and IBM SPSS statistics for Windows, Version 27.0 (IBM Corp,Armonk, NY, USA). We summarized continuous variables using mean and standard136deviation or median and interquartile range. Nominal or ordinal variables weredescribed as frequencies with percentages. Because the sample size was relativelysmall and the distributions of the estimated quantitative ultrasound measures werenot strictly Gaussian, the Wilcoxon rank-sum test was used to compare the statisticalsignificance of the difference between placentas from different groups. A p value< 0.05 was considered to indicate statistical significance.7.3 ResultsSubjectsThe present study included 46 placentas. As mentioned, these placentas were dividedinto two groups: control and PE and/or IUGR. Of the 26 placentas complicated byPE and/ or IUGR, there were 16 IUGR placentas, 1 PE placenta and 9 placentasaffected by both IUGR and PE. Among all cases affected by IUGR, 15 werecategorized as early onset (diagnosed at ≤32 weeks), 2 were late onset (diagnosedat >32 weeks) and the rest were not recorded. Among the PE cases for which thegestational age at diagnosis were recorded, 4 were early onset (diagnosed at ≤32weeks) and one was late onset. Among our control placentas, one singleton andfour twin placentas were from pregnancies affected by gestational diabetes mellitus.Table 7.1 presents the clinical characteristics of the study cohort by group. Themean gestational age at delivery for the pregnant women in the IUGR/PE groupwas 33.1 weeks (standard deviation = 14.2 weeks), which was significantly lower(p < 0.05) compared to the control group, with a mean gestational age of 38.6weeks (standard deviation = 1.2 weeks). Also, the pregnant women in the IUGR/PEgroup had significantly higher occurrences of hypertension compared to with thecontrol group (p < 0.05). The birth weight (1560 ± 717 gm) and the placentaweight (234 ± 110 gm) associated to the IUGR/PE group were also significantlylower compared to the birth weight (3379 ± 610 gm) and the placenta weight (464± 102 gm) in the control group (p < 0.05). 20 out of 26 IUGR/PE affected neonateswere admitted into the Neonatal Intensive Care Unit (NICU).137Table 7.1: Clinical characteristics of pregnancies for placentas studied.Mat. Parameter Controls (n = 15) IUGR or PE (n = 24)Gravidity Median = 2 Range = 1-8 Median = 2 Range = 1-6Parity Median = 1 Range = 0-3 Median = 0 Range = 0-3Gestational Age(weeks) Mean = 38.6 SD = 1.2 Mean = 33.1 SD = 4.2Maternal Age(years) Mean = 34.5 SD = 5.7 Mean = 32.5 SD = 6.4Smoking Status Ex-smoker Non-smoker Ex-smoker Non-smokern = 0 n = 12 n = 3 n = 18Hypertension Yes, No, Yes, No,n =0 n = 15 n = 12 n = 8Diabetes Yes, No, Yes, No,n = 3 n = 12 n = 4 n = 16Neonat. Param. Controls (n = 20) IUGR or PE (n = 26)Birth weight(gms) Mean = 3379 SD = 610 Mean = 1560 SD = 717Placenta weight(gms) Mean = 464 SD = 102 Mean = 234 SD = 110Gender Female, Male, Female, Male,n= 9 n= 10 n = 15 n = 9NICU Yes, No, Yes, No,Admission 0 20 20 67.3.1 Elasticity of Placenta ex vivoSWAVE imaging examples of ex vivo placental elasticity along with the histologyoverlays are shown in Figure 7.1. Both representative examples for control andPE/IUGR group do not contain any focal lesions as can be seen in the histologyimages. While B-mode and histology represent similar appearances for both cases,the elasticity results obtained from both 2D LFE and 3D LFE show that PE/ IUGRplacentas are associated with elevated elasticity. However, the elasticity for 2DLFE is much dispersed than 3D LFE. Several previous studies showed that 3DLFE provides more accurate estimation of tissue stiffness [93, 257]. 2D LFEtends to overestimate the tissue stiffness as it computes the wavelength from theprojection of the 3D wave onto the 2D axial-lateral plane. Figure 7.1(b) shows138that the shear waves for PE/IUGR case have longer wavelengths compared to thosefrom control case. Also, the projected waves on the axial-lateral plane are longercompared to those in other two planes. Therefore, elasticity measures obtained from2D LFE using the projected waves on the axial-lateral plane yields high stiffnessartifacts and consequently high mean elasticity and high variance. The elasticitymeasures from 3D LFE were comparatively homogeneous as were expected fromthe histology image in this particular case. Also, Figure 7.2(a) shows that theelasticity measures obtained using 2D LFE were higher from those obtained using3D LFE, which were statistically significant for both control (p = 1.25e−6) andPE/IUGR (p = 2.48e−5) groups. Figure 7.2(b) shows the comparison of elasticitymeasures for control, IUGR and PE groups. The elasticity measures obtained using3D LFE for both IUGR (15.30 ± 2.96 kPa, p = 3.35e−5) and PE (12.33 ± 4.88kPa, p = 0.017) groups were significantly higher compared to the control group(8.32 ± 3.67 kPa). On the other hand, the elasticity obtained using 2D LFE for theIUGR (23.40 ± 9.56 kPa, p = 0.75) and PE (23.80 ± 8.73, p = 0.46) groups wereslightly higher compared to the control group (22.46 ± 10.02 kPa), which didn’treach statistical significance. For the subsequent analysis, we present only the 3DLFE multi-frequency measurements.We also evaluate the elasticity for placentas grouped according to the clinicaloutcome (i.e. NICU admission). The elasticity measure in the placentas corre-sponding to the neonates required to be admitted into NICU (13.69 ± 3.67) wassignificantly higher compared to those which were not admitted to the NICU (9.37± 4.80 kPa, p=0.0013). Finally, we measure the elasticity in placentas in late versusearly onset of IUGR and PE. The stiffness of placentas in early (12.82 ± 5.22kPa) and late IUGR (13.57 ± 3.50 kPa) were similar (p = 0.72). The stiffnessof placentas in late PE (17.13 kPa) was higher compared to those in early onset(10.65± 2.73 kPa), however the sample size was too small to compute a meaningfulstatistical significance.7.3.2 Viscoelastic CharacterizationFigure 7.3(a) shows that the shear wave speed, and hence the resulting elasticity Eincreases monotonically with excitation frequency ω . The slopes of the elasticity139510152025(kPa)B-mode Histology 2D LFE 3D LFEControlPE/ IUGR(a)(b)ControlPE/ IUGRFigure 7.1: SWAVE imaging of ex vivo placental elasticity for representativeexamples from the control and the PE/IUGR group. (a) Elasticity resultsobtained using 2D LFE and 3D LFE are shown as overlays. The ROIsare selected from the registered histological sections. (b) The phasorplanes corresponding to the slices shown on the B-mode images havebeen shown.140(a) (b)2D LFE 3D LFEFigure 7.2: (a) Elasticity results obtained using 2D LFE vs. elasticity resultsobtained using 3D LFE. 2D LFE overestimates the stiffness for mostplacenta examples from both control and PE/ IUGR class (indicated bythe points on the right side of the identity line). The histograms of thedifferences between E measures obtained using 2D LFE and 3D LFE forboth classes also shows this overestimation. (b) Violin plots of placentasfrom the control, IUGR and PE group obtained using 2D LFE (left)and 3D LFE (right). All violin and boxplots indicate the 1st, 2nd and3rd quartiles; the whiskers indicate the minimum and maximum values.∗p < 0.05;∗∗ p < 0.01;∗∗∗p < 0.001dispersion for the placentas in the IUGR and PE group are steeper compared to thosefrom the control group, indicating higher viscosity values associated to pregnancycomplication. After fitting the Voigt model, we obtain the shear modulus and theviscosity for all the placentas (n = 41), for which data at all excitation frequencieswere available. We found increased shear modulus and viscosity in placentas fromIUGR (n = 13) and PE (n = 10) group compared to measures obtained from thecontrol group (n = 17) (Figure 7.3(b)). The placentas in the control group haveshear modulus value of 379.04 Pa ± 306.58 Pa (mean ± standard deviation), whichwere slightly lower that those in IUGR (466.77 Pa ± 120.53 Pa, p = 0.24) and PE(382.86±170.94 Pa, p = 0.22). However, the differences didn’t reach statisticalsignificance. On the other hand, we found significantly increased viscosity in theplacentas in IUGR group (3.5 Pa.s ± 0.57 Pa.s, p = 0.002) and in PE group (3.17Pa.s ± 1.0 Pa.s, P = 0.0015), compared to the viscosity of placentas in controlgroup (2.38 Pa.s ± 0.87 Pa.s). The mean Root Mean Square (RMS) error obtainedfor the Voigt fit for the control, IUGR and PE group were 0.07 (4.94% of average141Table 7.2: Thresholds, AUROC, sensitivity and specificity for receiver operat-ing characteristic analyses of elasticity for control and PE/IUGR Placentas.Measure AUROC Cut-off (kPa) Sens. Spec. Acc.(95% CI)70 Hz 0.65 (0.47-0.83) 5.1 0.74 0.44 0.6180 Hz 0.70 (0.52-0.89) 5.9 0.83 0.56 0.73100 Hz 0.78 (0.62-0.93) 8.8 0.78 0.61 0.73120 Hz 0.84 (0.7-0.98) 13.6 0.91 0.78 0.85Multi-frequency 0.90 (0.8-0.99) 10.52 0.87 0.78 0.83([80, 100, 120] Hz)viscosity (Pa.s) 0.77 (0.62-0.92) 2.7 0.87 0.59 0.75shear modulus +viscosity 0.73 (0.56-0.90) N/A 0.56 0.92 0.73shear wave speed), 0.17 (10.85 % of average shear wave speed), and 0.22 (14.33%of average shear wave speed), respectively.Diagnostic Performance Evaluation of SWAVEFigure 7.4 represents the ROC curves showing the classification performance of dif-ferent SWAVE parameters to discriminate between control and PE/IUGR placentasusing a LDA classifier. Corresponding AUROC, sensitivity, specificity, accuracy andcut-off values obtained for the minimum misclassification cost have been reported inTable 7.2, except for the parameters with AUROC< 0.5 (indicating an incorrect in-verse relationship between SWAVE parameters and placenta disease state). Amongthe single frequency elasticity measures obtained using 3D LFE (Figure 7.4 (left)),low frequency measures corresponding to 40 Hz to 60 Hz were unable to detectthe increase in stiffness corresponding to placental abnormality. Higher frequencyelasticity measures tend to attain increasingly improved classification performance,where elasticity at 120 Hz achieved 91% sensitivity, 85% accuracy and an AUROCof 0.84. The difference among the area under the ROC curves for the elasticitymeasures for 70, 80, 100 and 120 Hz didn’t reach statistical significance.Multi-frequency elasticity measure improved classification performance whereas2D LFE demonstrated a performance of random classifier, with an AUROC of 0.42(Figure 7.4 (middle)). The multi-frequency elasticity measures computed using 3D142NS NS****(a)Shear Modulus(c)(b)ViscosityFigure 7.3: Viscoelastic characterization of the placentas from control, IUGRand PE groups. (a) Elasticity measures as a function of frequency (n =41). The line and the shaded regions indicate the mean elasticity andthe standard deviation at corresponding frequency. (b) Violin plots andboxplots of shear modulus (top) and viscosity (bottom) of placentas fromthe control (n = 17), IUGR (n = 13) and PE (n = 10) group. All violinand boxplots indicate the 1st, 2nd and 3rd quartiles; the whiskers indicatethe minimum and maximum values. ∗p < 0.05;∗ ∗ p < 0.01;∗ ∗ ∗p <0.001. (c) Shear wave speed dispersion and the fitted Voigt modelobtained by averaging the actual shear wave speed and estimated shearwave speed from the Voigt model for the placentas in control, IUGR andPE group.143LFE, showed similar classification performance (p = 0.18). Finally, classificationanalyses on Voigt parameters reveal that shear wave modulus had a poor predictivevalue (AUROC = 0.42) to distinguish between control and diseased placentas. How-ever, viscosity and the combination of viscosity and shear modulus demonstrateimproved and similar (p = 0.14) classification performance for identifying placentatissue from complicated pregnancies. Among all the parameters, multi-frequencyelasticity measure obtained using 3D LFE was found to yield the best classificationperformance, with AUROC significantly different from most other parameters ex-cept the linear transducer counterpart and single frequency measures for 100 and120 Hz.Figure 7.5 represents the classification performance of the SWAVE parameters,that were selected based on superior discriminating abilities. Multi-frequencyelasticity measure produced the best result with an AUROC of 0.78, which was onlystatistically different from the viscosity performance (p = 0.013). The accuracy,sensitivity and specificity obtained with this parameter were 0.71, 0.70 and 0.71,respectively. The multi-frequency elasticity measure with the linear transducer, onthe other hand, yields accuracy, sensitivity and specificity of 0.59, 0.6 and 0.57,respectively. The single-frequency elasticity measure attained 0.65 and 0.76 assensitivity and specificity, with an accuracy of 0.71. Viscosity measure was foundto be correlated with the NICU outcome, with sensitivity of 0.65 and specificity of0.55, resulting in an accuracy of 0.6.7.4 DiscussionAmong different system settings and methods we investigated in the current work,the SWAVE elasticity measures obtained using a multi-frequency 3D LFE approachdemonstrate the best performance to distinguish between placentas ex vivo fromnormal and diseased pregnancies. Our findings agree with the previous reports in theliterature showing that placentas from pregnancies affected by PE (12.33±4.88 kPa)and IUGR (15.30 ± 2.96 kPa) are significantly stiffer, corresponding to an increaseof 48% and 84% increase respectively, compared to those from normal pregnancies(8.32 ± 3.67 kPa). However, due to difference in elastography techniques used inthe previous works, the quantitative measure of placenta stiffness in any disease144Figure 7.4: Receiver operating characteristic curves for elasticity estimatesbetween control and PE/IUGR placentas using single frequency and 3DLFE technique (left), elasticity estimates using multi-frequency (middle),and for Voigt parameters (right). The dashed line indicate an AUROC of0.5.Figure 7.5: Receiver operating characteristic (ROC) curves and correspondingAUC values with 95% confidence intervals for elasticity estimates be-tween cases with and without NICU admission. All elasticity measureswere obtained using 3D LFE. The dashed line indicate an AUROC of0.5.145group involves large variation. Table 7.3 presents the range of elasticity measures,sample size, elasticity techniques and study type in previous studies and our studies.We observe that the Point Shear Wave Elastograhy (PSWE) techniques for in vivostudies yield a mean/median Young’s modulus value within 2.28 kPa and 3.85 kPafor the subjects in control group. 2D shear wave elastography techniques, on theother hand, are associated with higher Young’s modulus ranging from 5.8 kPa to11.06 kPa for the similar placenta group. The elasticity measures obtained usingthe proposed SWAVE 2.0 method fall within the ranges obtained with those usingSWEI (2D), TE and the previous SWAVE method.Additionally, a fraction of the previous studies performed receiver operatingcharacteristic analyses to assess the efficacy of elasticity measure as a predictor ofplacental abnormality [7, 75]. [75] reported a cut-off elasticity of 4.2 kPa to predictPE, resulting in area under the ROC curve of 0.91. [7] identified a cut-off maximumelasticity measure of 9.08 kPa with an PE diagnostic performance of AUROC=0.883. Similarly, [224] found a cut-off of 16.3 kPa to identify the presence of PEwith a classification performance of AUROC = 0.82. On the other hand, our studyfound an optimum cut-off elasticity measure of 10.52 kPa resulting in an AUROC of0.9 to predict PE and IUGR cases, which was consistent with the previous findings.We also report the efficacy of the proposed method to predict the pregnancyoutcome in term of NICU admission. The multi-frequency elasticity measure using3D LFE obtained for the neonatal group requiring NICU admission was significantlyhigher compared to the one not requiring NICU admission (13.69 ±3.67 kPa vs.9.37 ± 4.80 kPa, p = 0.0013). Also, the elasticity measure was able to attain anAUROC of 0.78 (CI: 0.63−0.93) to predict NICU admission (Figure 7.5).We investigate the effect of 3D LFE reconstruction method compared to the2D counterpart. We found that 2D LFE results in higher elasticity measures thanthose from 3D LFE (23.04 kPa vs. 11.25 kPa). In our experiments, we selectedthe sonologically homogeneous regions-of-interest to obtain relatively uniformelasticity measures suitable for stiffness comparison among different disease groups.However, the average standard deviation obtained using 2D LFE was 8.80 kPafor intra-placenta elasticity measurement (38.2% of the mean value) and 2.56 kPa(22.76% of the mean value) for 3D LFE method. This agrees with previous findingwhich demonstrated that 2D LFE is prone to overestimating the tissue stiffness146and results in high-stiffness artifacts [93, 257] . Simple planar wave imaging using2D LFE fails to capture the complex shear wave propagation, specifically at theboundary and low SNR regions [257]. Furthermore, unlike 3D LFE counterpart, 2DLFE could not statistically differentiate between different disease groups.We estimate elasticity measures using single-frequency and multi-frequencytechniques. We also analyse the frequency dependence of placenta elasticity andperform a viscoelastic characterization of placenta belonging to different groupsusing a Voigt rheological model fitting. Previous studies revealed the presence of asignificant viscous component [2, 28], by fitting rheological model to fit the shearwave speed dispersion with frequency variation, which were limited to the studyof clinically normal placentas. Our current study confirms the previous finding,and further identifies an increased values of viscosity in diseased placenta group,which is evident through a steeper slope of shear wave speed dispersion (Figure7.3(a)). Viscosity was found to be a discriminating feature to classify normalplacentas from PE and IUGR affected placentas with significantly different meanvalues (2.38 ± 0.87 Pa.s vs. 3.17±1.0 Pa.s, 3.5 ± 0.57 Pa.s). Viscosity alsoshowed superior diagnostic performance with an AUROC of 0.77 (CI: 0.62−0.92)to differentiate between normal and PE/IUGR cases and an AUROC of 0.78 (CI:0.63−0.93). We further notice that the RMS error in the Voigt fit for the PE (14.33%of average shear wave speed) and IUGR(10.85% of average shear wave speed)groups were higher compared to the control group (4.95% of average shear wavespeed). This could be an indication that the diseased group increasingly becomesnon-linear and therefore deviates from the linear Voigt model. Future studieswill investigate different rheological models to capture the non-linear frequencydependence of shear wave speed, particularly important to characterize diseaseplacental tissue. Additionally, we found an inverse relationship of stiffness withthe placental disease group (low elasticity for PE/IUGR group compared to thecontrol group) at lower frequency range (40 Hz - 70 Hz). This could be due tothe placental dimension not being sufficiently large to capture a half wavelengthof the larger shear wave for PE/IUGR affected stiffer placentas, a requirement forelasticity estimation using LFE [144]. Therefore, the single frequency elasticityestimates at lower frequencies have greater errors. This is further evidenced bythe poor ROC performance of the associated elasticity features (Figure 7.4). On147the contrary, elasticity measures at higher frequencies (70 - 120 Hz) demonstrateincreasingly improved AUROC, i.e. better diagnostic performance (Table 7.2 andFigure 7.4). On the other hand, multi-frequency acquisition further improves theelasticity estimate by reducing artifacts arising due to noisy displacement at one ofthe frequencies. In our study, the multi-frequency elasticity obtained by averagingthe single frequency measures weighted by the displacement amplitude was foundto outperform the single-frequency counterpart.Our placenta study has several limitations. First, the ex vivo nature of the currentstudy limits its ability to capture the variation in stiffness due to the altered bloodperfusion due to onset of PE or IUGR. Also, this post-delivery study does notidentify the time of disease onset, and therefore it is not clear whether SWAVE2.0 is able to detect the early symptoms of the placental abnormality required foreffective intervention. While this study aims to detect the global variation due toplacental abnormality, we do not incorporate the histopathological findings, andtherefore some of the elasticity measures can arise from the presence of local lesions.To address these limitations, we will design and perform a longitudinal in vivostudy. We would also incorporate histopathological information from the registeredpathology slides to identify the source of elasticity variation. A second limitation ofour study was the relatively small population.Nevertheless, our sample size achievedan overall of 99% power to reject the null hypothesis that the population mean is thesame for all three groups when the group means are 8.32 kPa, 12.33 kPa and 15.30kPa and pooled standard deviation is 3.74 kPa, with a significance level of 0.050using a one-way ANOVA. However, a pair-wise power analysis found that the powerattained for rejecting the equal mean hypothesis between the control and PE groupwere 61% while achieved a power of 100% for the control and the IUGR group.The analyses underpin the importance of a larger sample size for the PE group.Finally, the current study lacks a formal safety analysis of the SWAVE technologyto be applied on pregnant women and fetus. We argue that the mechanical vibrationapplied in SWAVE is similar to the vibration using mechanical massager, whichhas not been found to have deleterious effects on pregnant women and produced arelaxation response [58]. However, a formal safety analysis will be conducted infuture studies to establish the safety of the proposed method.148Table 7.3: Comparison between the elasticity (E) obtained in this study andthose obtained in previous studies.Elasticity (kPa)Study Method Control PE IUGR[224] SWE (2D)(in vivo)10.45 ± 7.6 (n =18)26.36±14.1 (n =18)NA[6] pSWE (invivo)3.85 ± 1.2 (n =81)NA 5.51±2.09(n=66)[75] pSWE (invivo)3.18(1.22− 6.48) (n= 181)5.96(3.56 − 17.14)(n=13)NA[28] TE 9.57 ±0.75 NA NA(ex vivo) (n = 20)[88] SWE (2D) 6 (4.38−7.45) NA 28 (16.8−35)(in vivo) (n = 42) (n=42)[2] SWAVE (exvivo)111.18±1.76 (n= 61)NA NA[104] pSWE (invivo)2.48 ± 0.2 (n =38)6.40±0.69(n=35)NA[147] SWE (2D)(ex vivo)11.06 ± 0.01 (n= 11)NA NA[7] pSWE (invivo)3.43 (3.0−3.90)(n = 42)5.80(5.23 − 7.02)(n=44)NA[38] pSWE (invivo)2.28(2.02− 2.58) (n= 101)6.67(3.60 − 12.34)(n=28)NA[108] SWE (2D) 25.8 (2.5−13) 225 (2.4−66.5) NA(in vivo) (n = 27) (n=23)149Elasticity (kPa)Study Method Control PE IUGR[178] pSWE (invivo2.88±0.13 (n =143)37.68±0.61(n=15)4.92±0.46(n=21))[226] pSWE (exvivo)5.15±0.37 (n =74)36.66±0.81(n=17)11.29±1.64(n=24)[129] SWE (2D) 7.84±1.68 NA NA(in vivo) (n = 30)Ours SWAVE 2.0(ex vivo)8.32±3.67 (n =20)12.33± 4.88(n=10)15.30±2.96(n=16)1average of single frequency measures at 80, 100 and 100 Hz.2expressed as median (maximum - minimum).3 E measures for PIH (pregnancy-induced hypertension) group has been classifiedunder PE group.Elasticity measurements are expressed as mean (± standard deviation) or median(25% - 75% quartile) of Young’s modulus (E). The Young’s modulus (E) wereconverted from shear wave speed (cs) using the equation, E = 3ρc2s , whennecessary.7.5 ConclusionsIn this chapter, we present SWAVE 2.0, three-dimensional multi-frequency imple-mentation of shear wave vibro-elastography for the application of ex vivo placentalimaging. Using the proposed method, we compute Young’s modulus of 46 placentas,which can separate placentas from normal pregnancies and pregnancies affected byPE and intra-uterine growth restriction. We identified a threshold of 10.52 kPa forthe optimum system setting, to classify placentas into control and diseased groupwith a accuracy of 83%, sensitivity of 87%, specificity of 78% and AUROC of 0.9.Moreover, the proposed elasticity measure was able to predict pregnancy outcomein terms of NICU admission with an AUROC of 0.78, and accuracy, sensitivity,and specificity of 0.71, 0.70 and 0.71, respectively. A viscoelastic characteriza-tion established viscosity as a discriminating feature, able to differentiate betweenplacentas from control and PE/IUGR group as well as to correlate with the NICUadmission outcome. This work demonstrates the feasibility of SWAVE 2.0 in an invivo pregnancy screening system to identify placental abnormality and to predict150adverse outcome.151Chapter 8Applications of Placental QUS8.1 IntroductionIn this chapter, we explore two applications of placental QUS. The first applicationinvolves the detection of placenta-mediated diseases using the combination ofplacental QUS parameters. In the second application, we showed that placentalACE could be used for quantifying placental fat.8.2 QUS for Placenta-Mediated Disease DetectionCurrently, multiple multinational research initiatives have been undertaken to de-velop non-invasive and in utero placenta imaging techniques [220]. Compared toother imaging techniques, such as MRI, the role of QUS in placenta imaging is lessstudied. The few QUS studies on the placenta are limited to the characterizationof normal placental tissue [51]. The objective of this proof-of-concept study is toinvestigate the ability of QUS to differentiate between placentas from normal andcomplicated pregnancies.In this study, 46 placentas were collected from a group of women who deliveredvia cesarean delivery at BC Women’s Hospital, Vancouver, Canada. The detailof the study and the subjects are discussed in the previous chapter (section 6.2.1).Volumetric ultrasound radio-frequency data were captured and stored for offlineprocessing to reconstruct the QUS maps: ACE, IBC, and ESD. To compute ACE,152Figure 8.1: ACE, IBC and ESD maps overlaid on ultrasound B-mode imagefor an example placenta.Figure 8.2: Scatter plot of QUS measures representing control, IUGR and PEcases.BSC and ESD, the data acquired from the tissue were normalized by the data froma well-characterized reference phantom, manufactured by CIRS (Northfolk, VA,USA), acquired using the same transducer and system settings. With a piece-wisecontinuous assumption in all the three directions, we applied a 3D total variationregularization approach to reconstruct ACE and IBC maps [55]. We utilized aGaussian form factor [98] to express the backscatter coefficient as a function ofESD and measure the ESD map. Finally, we applied a fine Gaussian support vectormachine classifier using the mean of the three QUS maps as the feature.We show example QUS maps overlaid on the B-mode images for a placentaex vivo in Figure 8.1. The combination of QUS parameters can distinguish among153Figure 8.3: Cross validation matrix for Fine Gaussian Support Vector Machine(SVM) classifier.Figure 8.4: Receiver operating characteristic curve.154different placenta groups. We demonstrate the scatter plot of QUS measures forcontrol, IUGR and PE group (Figure 8.2). Using a fine Gaussian support vectormachine classifier with a multi-class classification approach, we were able to attainan overall accuracy of 87%, while the area under the receiver operating characteristiccurve (AUROC) was equal to 1.0 (Figure 8.4). Specifically, the classifier was ableto correctly identify 100%, 93.3% and 44.4% cases in the control, IUGR and PEgroup, respectively (Figure 8.4).8.3 Ultrasonic ACE of Placenta: A measure of PlacentalFatThe association of ultrasonic attenuation coefficient estimate (ACE) and MRI Proton-Density-Fat-Fraction (PDFF) has been widely researched in relation with the pres-ence of fat in hepatic cells [52, 172, 184]. While MRI PDFF is the current goldstandard to quantify the fat content in hepatic cells [172], it is not feasible for routinescreening due to its prohibitive cost. On the other hand, ultrasonic ACE providesan indirect measure of fat. Fat droplets in the fatty liver cause cellular ballooning,which affects the ultrasonic scattering process, resulting in an increase in ACE. Witha strong correlation to MRI PDFF, ACE is being recognized as a more accessiblealternative to MRI PDFF for fatty liver disease detection [55, 172, 184].The form, type and size of fatty acid molecules deposited in the placenta, the un-derlying mechanisms and the implications are very different and also relatively lessstudied compared to the fat contents in liver. The placenta, being the primary facilita-tor for the transfer of nutrients from mother to fetus, regulates the availability of fattyacids, adapting to constant changes in demands for the developing fetus as well asfor itself [214]. Recent research found that placentas from pregnancies complicatedwith preeclampsia had significantly higher lipid content than healthy placentas (40%higher triacyglycerol and 33% higher cholesteryl ester) [25]. Hypoxia, which refersto the insufficient utero-placental oxygenation, is a key component of placentaldysfunction, and is associated with intra-uterine growth restriction and preeclampsia.It was showed that placental tissue cultured in hypoxic environment deposit lipidsin the form of lipid droplet [214]. A separate study hypothesized that the increasein lipid trapping occurs into the placenta due to reduced utilization, rather than the155increased storage [137]. Therefore, increased lipid storage in the placenta couldbe an indicator of inefficient lipid transport to the fetus, altered placental energystorage and utilization, or protection against lipotoxicity.Fat in the placenta accumulate as droplets with size ranging between 1—100micrometer in the endoplasmic reticulum. Fat in the placenta requires powerfuloptical modalities, such as fluorescent microscope and exogenous absorbers to labellipid droplets for visualization [214]. Optical imaging, while providing sub-atomicresolution, is limited by low penetration depth. The need for exogenous agentsfurther limits the application of most optical modalities to in vitro and ex vivospecimens. There is an unmet need to identify appropriate imaging modalities thatdo not require exogenous labels and can attain sufficient penetration depth for thevisualization of placental fat deposition in vivo. Based on our previous in vivoliver study, we propose two modalities namely MRI-PDFF and ultrasonic ACE fornon-invasive in vivo imaging of the placental fat. Previously, only the ultrasonicACE of normal placentas have been studied [51], while the role of ACE in placentalabnormality detection is yet to be established. In the current work, we collectultrasound ACE and MRI PDFF data from placenta ex vivo from both normal anddiseased placentas. We also acquire histopathology data. We designed and appliedan data acquisition and alignment protocol to register images from these differentmodalities. From the registered dataset, we investigate the spatial variation of fatin the placenta with MRI-PDFF and ultrasound ACE. Additionally, we analyze thecorrelation between these two parameters using phantom and ex vivo placentas. Wealso investigate the spatial variation of ACE and PDFF measures in focal lesions,namely chorangioma for an example case.8.3.1 DataFor validation, a set of 4 oil-in-gelatin phantoms were manufactured with 5%, 10%,20% and 40% fat (oil) by weight [168]. The placentas used in this study have beenacquired and prepared according to the protocol discussed in the previous chapter.MR PDFF imaging was performed on 40 placentas using a T2-weighted fast-spin-echo FLEX sequence, resulting in fat-only (F) and water-only (W ) images.Fat fraction was computed from these two images using the following equation:156Figure 8.5: Set of 4 phantoms with the actual fat percentage annotated (top)and the MRI proton-density-fat-fraction (PDFF) map (bottom).Figure 8.6: Ultrasonic ACE versus MRI PDFF for phantom (left) and ACEversus PDFF for ex vivo placental tissue.157Figure 8.7: Left: Multimodal registration for an example placenta case: MRIPDFF image (top), ultrasound overlaid on MRI (middle), and pathologyimages overlaid on MRI (bottom). Middle: Ace map overlaid on theMRI. The mean ACE value for two regions-of-interest have been shown.Right: PDFF map showing mean PDFF for the corresponding regions-of-interest.PDFF = FW+F ×100%. Volumetric ultrasound radio-frequency data were acquiredfrom the placenta. A 3D total variation regularization approach was used to re-construct ACE maps [55]. After imaging, each placenta underwent pathologicalexamination. The center plane in the ultrasound, MRI and pathology image volumeswere registered using the method described in the previous chapter.8.3.2 ResultsThe PDFF values obtained for the reference phantoms were 4.8%, 10.5%, 20.0% and36.5%, respectively (Figure 8.5). The obtained ACE measures were well-correlatedto the reference MRI PDFF values (R2 = 0.87), showing the reliability of ACEmeasures. The ACE and PDFF measures obtained for the ex vivo placentas werefound to be moderately correlated (R2 = 0.44). The correlation results have beenshown in Figure 8.6. We also study the ACE and PDFF measures observed in focallesions, namely chorangioma and compare with those found in a homogeneousregion without any lesion. With this objective, we first register the multimodalimage data (Figure 8.7 (left)). The ACE and PDFF maps both showed lower valuesin the chorangioma region compared to those in the normal region. The PDFF valuein the lesion region was 1.8%, whereas the normal section was associated to a PDFFvalue of 5.4%. The ACE values in the lesion region and normal regions were 0.1dB/cm/MHz and 0.41 dB/cm/MHz, respectively. These results have been shown inFigure 8.7. The decrease in PDFF and ACE could be a result of relatively increased158water content in the infarcted chorangioma.8.4 ConclusionIn this chapter, we show the application of quantitative ultrasound to classifyplacenta-mediated diseases. In this work, we found that the combination of attenua-tion coefficient, integrated backscatter coefficient and effective scatterer diametercould distinguish the placentas from normal pregnancies from those affected in-trauterine growth restriction and preeclampsia, with moderate accuracy. Furtherstudy is needed to relate QUS to patho-physiological changes in the placenta andimproved classification performance.We also showed the spatial variations of MRI PDFF and ultrasonic ACE forthe placenta obtained from normal and diseased placentas. We investigated thecorrelation of MRI PDFF and ultrasonic ACE for focal placental lesions using asingle case study. This study unveils a new research avenue for the understandingof placental pathology using non-invasive imaging techniques. Future research iswarranted to understand the complex relationship between the fat distribution andplacenta-mediated disorders.159Chapter 9Conclusions and Future WorkThe majority of maternal deaths occur in developing countries, primarily due tothe inaccessibility to regular pregnancy screening. As a low-cost, portable, andsafe imaging modality, ultrasound can offer optimal point-of-care solutions forpregnancy screening. However, the subjective nature of conventional ultrasoundposes a significant obstacle in a low-resource setting due to the lack of necessaryexpertise required for image interpretation. The user interpretation is further af-fected by the image quality, largely varying from the lower end hand-held scannersto the hospital-grade systems. The system- and user-dependence, a fundamentallimitation of traditional ultrasound, limits the benefit of accessible pregnancy screen-ing offered by the point-of-care ultrasound. Furthermore, the current pregnancyscreening system does not include adequate evaluation of the placenta. Placentalinsufficiency leads to the development of severe pregnancy complications, includingpreeclampsia and intrauterine growth restriction. The placenta-mediated diseasescause changes in placental tissue architecture. With the paucity of non-invasivetools for assessing placental pathology, current clinical practice rather relies onsymptom-based abnormality identification, which often fails to distinguish theunderlying pathophysiology between normal and abnormal pregnancies. Addition-ally, most placenta-mediated diseases are late clinical manifestations of chronicprocesses precedented by long subclinical phases. Clinical manifestation-basedscreening, often marred by delayed and inaccurate detection, therefore lacks efficacyin identifying pregnancy complications.160To address these issues, the vision of my PhD research is to develop the keytools for an accessible and effective ultrasound-based pregnancy screening systemthat would provide an early, accurate, and objective assessment of placental health.The first part of my thesis aims at developing accurate quantitative ultrasoundalgorithms with potential application in placental tissue characterization towards thevision of achieving accessible pregnancy screening. The second part of my thesisfocuses on the understanding of the placenta structure in healthy and complicatedpregnancies using multimodal imaging approach including ultrasound, MRI andpathology towards the vision of achieving an effective pregnancy screening tool.9.1 Thesis ContributionsThe first part of the thesis contributes toward the theoretical development of novelQUS algorithms. Here we have addressed the fundamental limitations in QUS esti-mations, namely the high estimation variability arising due to precision-resolutiontrade-off and effect of tissue heterogeneity. The second part of the thesis contributestowards the validation of the proposed QUS methods in clinical applications, througha multimodal imaging study, Project SWAVE 2.0, which incorporated pathology andMRI and clinical diagnosis. The main contributions of the thesis are summarized asfollows.9.1.1 Attenuation Coefficient Estimation using a Region-of-InterestSelection ApproachIn chapter 2, we proposed a robust and accurate ACE measurement method and es-tablished a baseline ACE for normal placental tissue. Traditional ACE methods, suchas reference phantom method, assume that the region-of-interest is homogeneouswith same scatterer density and same scatterer size. Violation of the homogeneityassumptions leads to inaccurate estimation of ACE. Selecting a suitable attenuationestimation regions (AERs) and thereby estimating ACE with low variance and highaccuracy within matured placenta is particularly challenging due to the presence ofirregularities and prominent indentations. To mitigate the limitation,• We proposed a new Attenuation Estimation Region Of Interest (AEROI) se-lection method. In this method, homogeneous attenuation estimation regions161(AERs) were selected by setting thresholds for the corresponding AEROIselection parameters: (i) envelope signal-to-noise ratio deviation and (ii)coefficient of variation of the transmit pulse bandwidth.• Through the largest ex vivo placental study including 59 clinically normalplacentas, we reported a baseline placenta ACE of 0.77 dB/cm/MHz.• The proposed AEROI selection method enabled a 46% reduction in intra-subject standard deviation of ACE from 0.72 to 0.39 dB/cm/MHz.The limitations of the proposed method includes the possible loss of clinicallyimportant information while discarding inhomogeneous regions.9.1.2 Quantitative Ultrasound Imaging using PredictUSIn chapter 3, we adopted an alternative approach to address the resolution-precisiontrade off in QUS estimation algorithms. Recently, deep neural network, such asWaveNet, have been successfully used to forecast non-stationary and non-linearprocesses, such as speech and financial time-series. Inspired by these successes,we present PredictUS, a QUS estimation technique to predict ultrasound radio-frequency signal using a sequence-to-sequence convolutional neural network. Givena small windowed RF signal, this method predicts the next data points, resulting ina larger window. Therefore, the method yields better estimates of power spectra aswell as can characterize local tissue properties with high resolution by essentiallyextending the property to a larger region. The contributions of this chapter are asfollows:• A novel approach is proposed which models the spectral estimation problemin QUS as a forecasting problem.• The PredictUS method can reliably maintain similar precision and accuracywhile reducing the window size by 25% of the optimum value (10λ ). Forlow ACE, the PredictUS method could still achieve comparable precision andaccuracy with a window size 50% less than the optimum value.PredictUS results showed the promise to extend the precision-resolution trade-off,162which, in turn, would have implications in small lesion detection or heterogeneoustissue characterization.9.1.3 Attenuation Coefficient Estimation using A Spatially WeightedRegularization (SWTV) MethodIn chapter 4, we address the limitation of the method proposed in the previouschapter. According to the traditional spectral difference and spectral shift methods,there will be large measurement error and variance in QUS estimation in the case ofrapid change in the scatterer characteristics. Therefore an adaptive regularizationparameter is required, which is sensitive to the conditions leading to the underlyingassumption for homogeneity conditions. Based on this hypothesis, we proposed anadaptive regularization based method to improve the accuracy and precision of ACEfor a given window dimension in presence of tissue heterogeneity. We demonstratedthe application of the proposed method for hepatic steatosis detection and placentaltissue characterization.The contributions of this chapter are as follows:• We proposed a novel spatially weighted total variation regularization method,where the amount of regularization was modulated depending on the localinhomogeneity.• We derived a spatially weighted regularization parameters as a function ofthe tissue inhomogeneity indicator: envelope signal-to-noise ratio.• We validated the efficacy of SWTV method through simulation and phantomstudies for improved ACE computation by reducing the variability in theestimates, irrespective of window size and presence of heterogeneity.• We demonstrated a strong correlation between the ACE computed usingSWTV method with the MRI proton density fat fraction (r = 0.953, p =0.003) based on a small population (n = 6).• We also evaluated the repeatability and reproducibility of the proposedmethod, where the proposed SWTV method showed lower intra- and inter-class variability compared to the state-of-the-art ACE methods.1639.1.4 Multiparametric Volumetric Quantitative Ultrasound Imagingusing 3D QUSIn chapter 5, we further extend the scope of the previously proposed proof-of-concept study, which was limited to the reconstruction of a single QUS parameterfor a two-dimensional region. Here, we present a multiparametric 3D weightedQUS (3D QUS) method, incorporating the reconstruction of three QUS parameters:attenuation coefficient estimate (ACE), integrated backscatter coefficient (IBC) andeffective scatterer diameter (ESD). The contributions of this chapter are as follows:• The proposed 3D QUS method accurately reconstructs QUS parameters,resulting in high reconstruction contrast compared to the 2D QUS and baselinereference phantom method.• We showed that the 3D regularization involving N planes results in a highQUS estimation precision, with an improvement of standard deviation overthe theoretical 1/√N rate achievable by compounding N independent realiza-tions.• We demonstrated that the ACE and IBC measures were positively correlatedto MRI-PDFF (surrogate biomarker for fat content) while ESD was negativelyassociated to MRI-PDFF.• Compared to the 2D QUS, 3D QUS showed an improved separability betweenthe steatosis and non-steatosis group. We attained a perfect classificationperformance to distinguish between normal and fatty liver tissue using acombination of three QUS parameters and a quadratic discriminant classifier.9.1.5 An overview of SWAVE 2.0In chapter 6, we present the protocol for the project SWAVE 2.0, designed toacquire and align multimodal images obtained from 46 placentas ex vivo fromboth healthy pregnancies and pregnancies with preeclampsia and/ or intrauterinegrowth restriction. This unique project, which is first of its kind, involved extensivedata acquisition, including novel multiparametric quantitative ultrasound (suchas elasticity, attenuation coefficient estimate, backscatter coefficient and effective164scatter diameter), multiparametric MRI, and histopathology. The contributions ofthis chapter are as follows:• We described the protocol to capture ultrasond, MRI and histopathologyimages of placentas.• We proposed a co-registration method using surface markers and externalfiducials to enable alignment and fusion of multimodal data. The proposedco-registration approach is versatile and can be adopted in a wide range ofexperimental setups involving multimodal imaging of ex vivo placentas.9.1.6 SWAVE 2.0 Imaging of Placental Elasticity and ViscosityIn chapter 7, we present SWAVE 2.0 imaging of placental elasticity and viscosityas a non-invasive technology for detection of placental abnormalities as well ascorrelates of pregnancy outcomes. The contributions of this chapter are as follows:• We applied an improved elasticity acquisition system and reconstruction al-gorithm, compared to the previously proposed SWAVE for placenta elasticityimaging. We incorporated a multi-frequency acquisition system and used athree-dimensional local frequency estimation (LFE) algorithm for elasticityreconstruction. We used a Voigt’s rheological model for the viscoelasticcharacterization of the placenta.• We showed that the IUGR and PE placentas were significantly stiffer com-pared to the control placentas. The viscosity measures in IUGR and PEplacentas were also significantly higher compared to those in normal placen-tas.• We attained classification sensitivity, specicity, and accuracy of 86%, 78%,and 83%, and area under the receiver operating curve of 0.90, to classifybetween healthy and diseased placentas.9.1.7 Applications of Placental QUSIn chapter 8, we applied the QUS approach presented in chapter 5 to detect placenta-mediated diseases. We also explore the association of ultrasonic ACE and MRI165PDFF in the placental tissue. The contributions of this chapter are as follows:• We showed that the combination of QUS features could identify 100%, 93.3%,and 44.4% cases in the control, IUGR and PE group, respectively.• We demonstrated that ultrasonic attenuation coefficient estimate (ACE) isstrongly correlated to the MRI PDFF in the phantoms. In the ex vivo placentalstudy, we demonstrated that the spatial variation in MRI PDFF map andultrasonic ACE map are correlated.9.2 Future WorkWhile pushing the frontiers in QUS for the application of soft tissue in general,the current thesis has identified novel biomarkers of placenta-mediated diseases,establishing a breakthrough in placenta research. The thesis has opened up multipleresearch directions, that includes the exploration of QUS algorithms and understat-ing of disease mechanisms in the placenta. The following research and developmentareas are suggested to investigate:• Quantitative Ultrasound: This thesis explores different methods for QUSestimation. While 3D QUS has been investigated comprehensively for tissuecharacterization and disease detection, PredictUS was utilized only for simu-lation data. Future study can further explore PredictUS, by trying improveddeep learning networks as well as by applying the method on phantom andbiological tissue.• Radiology-Pathology Correlation: The immediate envisioned future direc-tion for the current thesis would be development of an exhaustive radiology-pathology correlation framework based on the multimodal placental data.While the current thesis focused on the application of the quantitative ul-trasound data as a global predictor of pregnancy outcome using the clinicaldata as the ground truth, the multiparametric MRI and the histopathologydata are yet to be explored. Furthermore, the co-registration frameworkwould facilitate the identification of local features in the ultrasound and MRIcorresponding to the lesions in the pathology slides.166• Automated Annotation of Pathology Slides: A particularly interesting ap-proach would involve deep learning, integrating information from differentmodalities, and predicting the local and global pathology states. Besides, theautomated identification of different cells and lesions in the pathology slidescould be another immediate application of our acquired dataset. 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Role of color doppler imaging in diagnosing and managing196pregnancies complicated by placental chorioangioma. Journal of ClinicalUltrasound, 30(5):264–269, 2002. → page 20197Appendix ASpectral QUS Parameters:Theoretical BackgroundAttenuation Coefficient Estimate (ACE)The attenuation coefficient, α ′ has an approximately linear frequency dependence(between 0.8 and 1.2 for most biological tissue) in the frequency range of a typicalultrasound scanner.A(ω,z) = e−4α′(ω)z = e−4αωz, (A.1)where α is the effective amplitude attenuation coefficient estimate (ACE), in unitsof Np/cm/MHz, for the round trip of the depth z.Backscatter Coefficient (BSC)The theoretical formulation of the backscatter coefficient of a random, heterogeneousmedium, according to the model developed by Insana et al. [99], has been revisitedin this section. According to this model, the medium is assumed to contain numerousscattering sites, with density and comperssibility values that vary relative to thesurrounding medium. Further, the medium is considered to be weakly scatteringand the scatterers are very small ( ultrasound wavelength), required conditions forBorn approximation. Born approximation imply that the effect of multiple scatteringis negligibly small. Under these assumptions, the backscattered pressure field for198each particle (i.e., scatterer site) at an observation point r, far from the scatteringvolume, behaves as a spherical wave and thus is given by [99, 158]ps(r) = (eikr/r)Φ(K), (A.2)where the factor Φ(K) is the angle distribution function:Φ(K) =k24pi∫Vγ(r0)e2ikr0d3r0. (A.3)Here, V represents the scattering volume and K is the scattering vector. For backscat-ter, K = 2kiˆ (the angle of scattering between the directions of incident plane andthe observer is 1800). The relative impedance function γ(r) is given byγ(r) = γκ(r)− γρ(r), (A.4)whereγκ(r) =κ(r)−κ0κ0= fractional variation in medium compressibility, (A.5)andγρ(r) =ρ(r)−ρ0ρ0= fractional variation in medium density. (A.6)Here, ρ(r) and κ(r) are the density and the compressibility of the scattering sites atposition r, while ρ0(r) and κ0(r) are the average values for the surrounding medium.Therefore, these two terms in equation A.4 describe the interaction between thepressure field and the fluctuations in compressiblity and density.Now, the scattered field from a collection of identical, randomly positionedparticles is the sum of the individual pressure fields,ps(r) =eikrrN∑j=1Φ j(K)eiK·r j , (A.7)where r j is the position of the jth particle with respect to the origin and N is thetotal number of particles within the scattering volume V . The scattered pressurefield can be considered to be a random function, that is the sum of the average filed(coherent field) and the fluctuating field (incoherent filed). For a random scattering199medium, the coherent filed is negligible and the incoherent filed dominates. Forincoherent scattering, the scattered pressure field is related to the average scatteredintenisty 〈I〉 as〈I〉= 1ρ0c0〈|ps|2〉= 〈|Φ|2〉/ρ0c0r2, (A.8)where c0 is the average longitudinal sound speed in the medium.The average differential cross section per unit volume, σd for large observationdistances, is defined as the power scattered into a unit solid angle divided by theproduct of the incident intensity and the scattering volume, V .σd =r2V〈I〉I0=r2V〈I〉I0=1V〈|Φ(K)|2〉, (A.9)where the incident intensity for plane waves of unit amplitude, I0 = 1/ρ0c0. Bycombining equation A.3 and A.9, we can writeσd =k416pi2V∫V∫V〈γ(r1)γ(r2)〉e−iK·(r1−r2)d3r1d3r2=k416pi2∫V〈γ(r1)γ(r2)〉e−iK·(r1−r2)d3∆r,(A.10)where γ(r) is a random function describing the distribution of scatterers involume V . The overbar represents the volume-average value. Now the randomfunction can be written as the sum of its average and fluctuating components.Neglecting the coherent component, which is zero for random medium, we canwriteσd =k416pi2∫V〈(γ1−〈γ1〉)(γ1−〈γ2〉)〉e−iK·∆rd3r1d3r2, (A.11)where ∆r = r1− r2, γ(r1) = γ1, and γ(r2) = γ2. The autocovariance function can bewritten as the product of variance and the autocorrelation function,〈(γ1−〈γ1〉)(γ1−〈γ2〉)〉= 〈|γ−〈γ〉|2〉Rγ(∆r). (A.12)The variance in γ,〈|γ−〈γ〉|2〉 = n¯Vsγ20 , the mean sqaure variation in acousticimpedance per particle γ20 , times the fractional volume of scatterers, n¯Vs. Therefore,equation A.11 reduces to200σd =k4〈γ2〉16pi2∫VRγ(∆r)e−iK·∆rd3∆r =k4Vs16pi2n¯γ20∫VRγ(∆r)e−iK·∆rd3∆r. (A.13)Therefore, in spherical coordinates, backscatter coefficient, σb, defined as differen-tial cross section per unit volume for an angle of 1800, is given as,σb =k3Vs8pin¯γ20∫ ∞0Rγ(∆r)sin(2k∆r)∆rd∆r. (A.14)According to the reference phantom method [255], the BSC of a sample is estimatedbySs(ω,z)Sr(ω,z)=BSCs(ω)BSCr(ω)e−4z(α′s(ω)−α ′r(ω)), (A.15)Effective Scatterer Diameter (ESD)The effective scatterer diameter (ESD) is a quantitative ultrasound measure, thatgives an estimate of average particle size in random media. The ESD can bedirectly related to the correlation coefficient of inhomogeneties in the medium.The correlation coefficient, Rγ(∆r), a measure of similarity between the impedanceat positions separated by ∆r, would tend to zero for a distance ∆r D, as thecorrelation distances γ1−〈γ1〉 and γ2−〈γ2〉 at those positions would be statisticallyindependent. Therefore, scatterer diameter can be estimated from the zeros of thecorrelation function [34] or from the acoustic intensity form factor (FF), which is theFourier transform of the autocorrelation function[99]. In this thesis, we will focuson the methods where the ESD is measured by comparing the measured data to theappropriate form factor that describes the geometrical properties and organizationof scattering particles in frequency space.Among different models to compute ESD from form factor data, fluid sphere andspherical shell models assume discrete scatterers with an average diameter, D withina uniform fluid-like background. Gaussian and exponential functions, on the otherhand, describe the spherical scattering sources with continuously varying impedanceas a function of position, where D refers to the effective scatterer diameter [98]. Forthe Gaussian model, a widely used model to study the structure of biology tissue201[99, 174, 199], the correlation coefficient is given byRγ(∆r) = e(−∆r2/2d), (A.16)where d is the characteristic dimension of correlation coefficient. Using the Gaussianmodel, the equation A.14 becomes,σb =k4V 2s16pi2n¯γ20 e−2k2d2 , (A.17)where V 2s = (2pid2)3 = 43pia3 and a is the average scatterer diameter for discretemedium. In the long-wavelength limit where λ pid (ka 1), the above equationreduces to the backscatter coefficient for Rayleigh scattering:σ0 =k4V 2s16pi2n¯γ20 . (A.18)The Form factor is defined as the ratio of the BSC for a test material with finite sizescattereres, σb to that of a similar material with Rayleigh (point) scatterers, σ0. Theform factor, therefore, isolates any deviation of the frequency dependence of σbfrom k4 (i.e., f 4), characteristic frequency dependence of Rayleigh scattering. ForGaussian model, the form factor is given byF(k,d) =σbσ0= e−2k2d2 , (A.19)which only depends on the scatterer dimension, d. The effective scatterer diameter,2ae f f can be computed by setting the scatterer volume equal to the volume of asphere with radius, ae f f (2ae f f = (12√2pi)1/3d = 3.11d). This results in the formfactor for the Gaussian model asF(2k) = e−0.827k2a2e f f . (A.20)Using the above equation, the ESD is calculated from the experimental form factordata, which is measured from the estimates of the power spectrum of the receivedecho signals.202Reference Phantom MethodThe speckle pattern generated during ultrasound imaging is a function of both thesystem-dependent factors and the tissue properties. To extract tissue-dependentinformation of the sub-resolution scatterers, researchers have adopted different ap-proaches to account for the system effects. To estimate the spectral bases parameters,the average power spectrum is computed from the gated rf echo signals. The averagepower spectrum obtained from the considered region-of-interest is then calibratedand compensated for attenuation and diffraction effects. Previous research effortswere directed to design accurate and appropriate calibration techniques for differ-ent types and geometries of ultrasound transducers, ranging from single-elementtransducer to clinical array transducer.For single-element sources, the substitution method using a planar rigid reflectorhas been commonly adopted [121, 133]. However, the planar reflection calibrationtechniques, valid only in the focal plane, suffer from degraded accuracy in theregion-of-interest far from the focal plane. While different research groups haveattempted to design appropriate diffraction correction terms, particularly for dis-tances away from the focus, large differences were found in the BSC estimates andtheir derivatives depending on the focus, aperture and scatterer size [121]. Yao et al.proposed a reference phantom method that calibrates the sample data with the dataacquired from a well-characterized reference material with known acoustic proper-ties [255], with an assumption of equal sound of speed in the reference phantomand the sample under study. The method is applicable for any transducer geometry,including single-element focused and unfocused transducers and array transducers.Below we will outline the theoretical derivation of QUS parameters using referencephantom method that provides the foundation for the methods proposed in thisthesis.Consider the case when a pulse-echo transducer insonifies a scattering mediumwith randomly distributed scatterers. Under the Born approximation, the scatteredRF echo signal (voltage) at time t is given by [34]V (t) =∫ ∞−∞dωT (ω)B(ω)ω2e−iωt ×∫ ∫ ∫Ωdrγ(r)A2(r,ω). (A.21)203In this equation, T (ω) is a complex force-to-voltage transfer function of thetransducer, B(ω) is a complex superposition coefficient corresponding to the insoni-fying pulse, A(r,ω) represents the value of the ultrasonic beam profile at point r,and Ω is the volume containing the scatterers. γ(r) is the relative impedance func-tion, i.e., variation in acoustic impedance between a scatterer and the surroundingmedium at location r,The Fourier transform of a segment of the scattered RF echo signal defined by atime gate is given byV (ω) =∫ ∞−∞dω1T (ω1)B(ω1)ω2W (ω−ω1)×∫ ∫ ∫Ωdrγ(r)A2(r,ω1), (A.22)where W (ω−ω1) is the Fourier transform of the gating function.If the gated segment is much longer than the characteristic length of the spatialautocorrelation function, equation A.22 simplifies toV (ω) = T (ω)B(ω)ω2∫ ∫ ∫∆Ωdrγ(r)A2(r,ω), (A.23)where ∆Ω is the volume of the scattering medium with significant contribution tothe gated echo signal.Now, the echo signal power spectrum, obtained from the average of severalsignal segments can be written asS(ω,z) = 〈V (ω)V ∗(ω)〉= |T (ω)B(ω)|2ω4∫ ∫ ∫∆Ωdr1∫ ∫ ∫∆Ωdr2×A2(r1,ω)A∗2(r2,ω)Rγ(r1,r2),(A.24)where z is the axial distance to the center of the gated region from the transducersurface, and Rγ(r1,r2) = 〈γ(r1)γ∗(r2)〉 is the autocorrelation function of γ(r). Fora isotropic and stationary medium,the autocorrelation function reduces to Rγ(∆r)(∆r = r1− r2), which will not vary with position r, rather depends only on thedifference.The transducer beam, A(r,ω) is computed by summing the contributions from204each point on the transducer surface to the pressure at field point r. In the mostgeneral form, the field integrals can be written asA(r,ω) =∫ ∫sdse(ik−α ′(ω))|r−r’|r− r’ , (A.25)where k is the wave number (k = ω/c), c is the sound of speed, pointing in thedirection of wave propagation, α ′(ω) is the attenuation coefficient of the propagatingmedium, and r′ points to element ds on the transducer surface. The attenuation termin the expression of the beam profile can be separated using the assumption that thedistance of the transducer from the field point is greater than the effective transducerdiameter [255]. This allows writing the beam profile asA(r,ω)≈ e−α ′(ω)zA0(r,ω). (A.26)where α ′(ω) is the attenuation coefficient of the medium and A0(r,ω) is A(r,ω)with α ′(ω) = 0. Now, with ‘local plane wave’ assumption (|A(r2,ω)| ≈ |A(r1,ω)|)and far-field approximation (|∆r| |r1| ≈ |r2|), equation A.24 can be written asS(ω,z)≈ |T (ω)B(ω)|2ω4∫ ∫ ∫∆Ωdr|A20(r,ω)|4× e−4α ′(ω)z∫ ∫ ∫∆Ωd∆re2ik·∆rRγ(r1,r2).(A.27)Comparing with equation A.13, equation A.27 can be rewritten in the formS(ω,z)≈ 16pi2c4BSC(ω)e−4α ′(ω)z|T (ω)B(ω)|2ω4∫ ∫ ∫∆Ωdr|A20(r,ω)|4,(A.28)The representation in equation A.28 effectively separates the tissue dependentfactors (backscatter and attenuation terms) from the instrumentation dependentfactors (other terms). According to the reference phantom method [255], theinstrumentation dependent factors can be eliminated by taking ratios of the RFecho signal power spectrum obtained from the sample Ss to that obtained from thesame depth in a well-characterized reference phantom Sr, given that same system205settings have been used for data acquisition and the speed of sound is similar in bothmaterials. The equation relating the power spectrum, attenuation and the backscatterterms according to the reference phantom method is given bySs(ω,z)Sr(ω,z)=BSCs(ω)BSCr(ω)e−4z(α′s(ω)−α ′r(ω)), (A.29)where the subscripts s and r denote sample and reference, respectively. Aftermeasuring the power spectra from both the sample and the reference, differentapproaches are adopted for accurate estimation of the attenuation and the backscatterterms.This quantitative ultrasound parameter estimation method in this thesis buildsupon the classical reference phantom (i.e., spectral difference) method, which isdescribed here. Equation (A.15), after taking natural logarithm on both sides,reduces toln[RS(ω,z)] = lnSs(ω,z)Sr(ω,z)=−4(α ′s(ω)−α ′r(ω))z+ lnBSCs(ω)BSCr(ω). (A.30)This is the basic equation of the reference phantom method. Using this equation, theACE, BSC, and ESD of a sample can be computed, if the corresponding parametersin the reference material are known.206
Thesis/Dissertation
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Electrical and Computer Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Rohling, Robert N.
A multimodal approach for placenta characterization : towards an objective and effective pregnancy screening system
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