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Deeba, Farah

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.

Text

2022-07-19

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/82166

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/