{"@context":{"@language":"en","Affiliation":"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool","AggregatedSourceRepository":"http:\/\/www.europeana.eu\/schemas\/edm\/dataProvider","Campus":"https:\/\/open.library.ubc.ca\/terms#degreeCampus","Creator":"http:\/\/purl.org\/dc\/terms\/creator","DateAvailable":"http:\/\/purl.org\/dc\/terms\/issued","DateIssued":"http:\/\/purl.org\/dc\/terms\/issued","Degree":"http:\/\/vivoweb.org\/ontology\/core#relatedDegree","DegreeGrantor":"https:\/\/open.library.ubc.ca\/terms#degreeGrantor","Description":"http:\/\/purl.org\/dc\/terms\/description","DigitalResourceOriginalRecord":"http:\/\/www.europeana.eu\/schemas\/edm\/aggregatedCHO","FullText":"http:\/\/www.w3.org\/2009\/08\/skos-reference\/skos.html#note","Genre":"http:\/\/www.europeana.eu\/schemas\/edm\/hasType","GraduationDate":"http:\/\/vivoweb.org\/ontology\/core#dateIssued","IsShownAt":"http:\/\/www.europeana.eu\/schemas\/edm\/isShownAt","Language":"http:\/\/purl.org\/dc\/terms\/language","Program":"https:\/\/open.library.ubc.ca\/terms#degreeDiscipline","Provider":"http:\/\/www.europeana.eu\/schemas\/edm\/provider","Publisher":"http:\/\/purl.org\/dc\/terms\/publisher","Rights":"http:\/\/purl.org\/dc\/terms\/rights","RightsURI":"https:\/\/open.library.ubc.ca\/terms#rightsURI","ScholarlyLevel":"https:\/\/open.library.ubc.ca\/terms#scholarLevel","Supervisor":"http:\/\/purl.org\/dc\/terms\/contributor","Title":"http:\/\/purl.org\/dc\/terms\/title","Type":"http:\/\/purl.org\/dc\/terms\/type","URI":"https:\/\/open.library.ubc.ca\/terms#identifierURI","SortDate":"http:\/\/purl.org\/dc\/terms\/date"},"Affiliation":[{"@value":"Health and Social Development, Faculty of (Okanagan)","@language":"en"},{"@value":"Health and Exercise Sciences, School of (Okanagan)","@language":"en"}],"AggregatedSourceRepository":[{"@value":"DSpace","@language":"en"}],"Campus":[{"@value":"UBCO","@language":"en"}],"Creator":[{"@value":"Cotton, Paul","@language":"en"}],"DateAvailable":[{"@value":"2022-08-29T17:37:34Z","@language":"en"}],"DateIssued":[{"@value":"2022","@language":"en"}],"Degree":[{"@value":"Master of Science - MSc","@language":"en"}],"DegreeGrantor":[{"@value":"University of British Columbia","@language":"en"}],"Description":[{"@value":"During maximal or near maximal exercise, cardiac reserve is diminished and there is evidence that respiratory and locomotor muscles compete for a finite blood supply. However, human data describing blood flow distribution between locomotor and respiratory muscles during exercise is limited and the current method of measuring respiratory muscle blood flow (near infrared spectroscopy in combination with indocyanine green; NIRS-ICG) has produced contradictory results. Interpretation of these conflicting results is difficult for two reasons. First, respiratory muscle perfusion has only been measured in secondary respiratory muscles and perfusion of the primary respiratory muscle, the diaphragm, has not been considered. Second, NIRS-ICG is limited in its spatial resolution and depth of penetration. Ultrasound inherently provides improved spatial resolution and depth control over NIRS-ICG. Thus, the purpose of this thesis was to explore the possibility of using contrast enhanced ultrasound (CEUS) to measure indices of blood flow and volume from the intercostal and costal diaphragm muscles during increases in respiratory muscle work. We collected data in six participants under varying circumstances with the goal of offering the optimal methodology and image processing workflow for future research. We demonstrated that it is feasible to quantify CEUS-derived indices of blood flow\/volume from the chest wall, diaphragm and liver regions during increases in respiratory work. The feasibility of our blood flow analysis was improved in the presence of apnea. On average, frame by frame angle correction of the ultrasound images improved the quality of data produced from the chest wall and diaphragm regions but the effect of angle correction was variable. We acknowledge that the feasibility of the destruction-replenishment CEUS technique was not explored in this thesis and speculate that the development of an automated deep learning approach to image segmentation may offer utility in the advancement\/refinement of our approach. In conclusion, our recommendations for future research are to (1) investigate the use of an automated deep learning approach for image segmentation, (2) compare the utility of the destruction-replenishment versus bolus-injection CEUS techniques for the application of CEUS in the costal diaphragm region, and (3) utilize apnea during the CEUS protocol.","@language":"en"}],"DigitalResourceOriginalRecord":[{"@value":"https:\/\/circle.library.ubc.ca\/rest\/handle\/2429\/82570?expand=metadata","@language":"en"}],"FullText":[{"@value":"DEVELOPMENT AND PROOF OF CONCEPT OF A NOVEL METHODOLOGY UTILIZING CONTRAST ENHANCED ULTRASOUND TO ASSESS PERFUSION OF THE COSTAL DIAPHRAGM MUSCLE IN HUMANS  by Paul Cotton  B.H.K., University of British Columbia, 2019  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Health and Exercise Sciences)  THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) August 2022  \u00a9 Paul Cotton, 2022  ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis\/dissertation entitled: Development and Proof of Concept of a Novel Methodology utilizing Contrast Enhanced Ultrasound to assess perfusion of the costal Diaphragm muscle in Humans submitted by Paul Cotton in partial fulfillment of the requirements of the degree of Master of Science.   Dr. Glen Foster, School of Health and Exercise Sciences Supervisor Dr. Neil Eves, School of Health and Exercise Sciences Supervisory Committee Member Dr. Paolo Dominelli, University of Waterloo Supervisory Committee Member Dr. Jordan Guenette, School of Kinesiology University Examiner s    iii Abstract During maximal or near maximal exercise, cardiac reserve is diminished and there is evidence that respiratory and locomotor muscles compete for a finite blood supply. However, human data describing blood flow distribution between locomotor and respiratory muscles during exercise is limited and the current method of measuring respiratory muscle blood flow (near infrared spectroscopy in combination with indocyanine green; NIRS-ICG) has produced contradictory results. Interpretation of these conflicting results is difficult for two reasons. First, respiratory muscle perfusion has only been measured in secondary respiratory muscles and perfusion of the primary respiratory muscle, the diaphragm, has not been considered. Second, NIRS-ICG is limited in its spatial resolution and depth of penetration. Ultrasound inherently provides improved spatial resolution and depth control over NIRS-ICG. Thus, the purpose of this thesis was to explore the possibility of using contrast enhanced ultrasound (CEUS) to measure indices of blood flow and volume from the intercostal and costal diaphragm muscles during increases in respiratory muscle work. We collected data in six participants under varying circumstances with the goal of offering the optimal methodology and image processing workflow for future research. We demonstrated that it is feasible to quantify CEUS-derived indices of blood flow\/volume from the chest wall, diaphragm and liver regions during increases in respiratory work. The feasibility of our blood flow analysis was improved in the presence of apnea. On average, frame by frame angle correction of the ultrasound images improved the quality of data produced from the chest wall and diaphragm regions but the effect of angle correction was variable. We acknowledge that the feasibility of the destruction-replenishment CEUS technique was not explored in this thesis and speculate that the development of an automated deep learning approach to image segmentation may offer utility in the advancement\/refinement of our approach. In conclusion, our recommendations for future research are to (1) investigate the use of an iv automated deep learning approach for image segmentation, (2) compare the utility of the destruction-replenishment versus bolus-injection CEUS techniques for the application of CEUS in the costal diaphragm region, and (3) utilize apnea during the CEUS protocol.    v Lay Summary During exercise, the energy requirements of respiratory and locomotor muscles are elevated, and these two muscle groups compete for a finite blood supply. However, it is unclear how blood flow during exercise is distributed between respiratory and locomotor muscles due to the limitations of the current technique used to measure respiratory muscle blood flow. In our study we applied an existing ultrasound technique in a novel way to explore the possibility of measuring blood flow to the diaphragm muscle. We showed that it is feasible to obtain indices of blood flow and volume from the diaphragm muscle during normal spontaneous breathing and during heavy breathing. We found that the feasibility of our approach was improved when participants held their breath during the perfusion measurement. We also identified future directions for researchers looking to further investigate the ultrasound approach we outlined in this thesis.   vi Preface This thesis contains original data collected in the Cardiopulmonary Laboratory for Experimental and Applied Physiology by Paul Cotton and Dr. Glen Foster. Dr. Glen Foster assisted with phlebotomy, contrast administration and placement of esophageal and gastric balloon catheters. Tyler Vermeulen assisted with contrast administration while Brooke Shafer and Lindsey Boulet operated data recording software. Paul Cotton and Dr. Glen Foster were responsible for the design of the experiment with input from Dr. Paolo Dominelli and Dr. Neil Eves. During the COVID-19 pandemic, data collection for this project was halted, and research progress was hindered as a result. During this time Dr. Paolo Dominelli and Dr. Neil Eves assisted with the revision of our research proposal as we adapted to the research curtailments imposed as a result of the COVID-19 pandemic. All data analysis, software development and writing were completed by Paul Cotton with guidance and editing provided by Dr. Glen Foster. In addition, Paul Cotton was responsible for participant recruitment, screening, instrumentation and performing ultrasound measurements. Ethical approval for this project was provided by the University of British Columbia Clinical Research Ethics Board (H19-00717).   vii Table of Contents Abstract ................................................................................................................................ iii Lay Summary ........................................................................................................................ v Preface .................................................................................................................................. vi Table of Contents ................................................................................................................ vii List of Tables ......................................................................................................................... x List of Figures ...................................................................................................................... xi Acknowledgements ........................................................................................................... xiii Dedication ........................................................................................................................... xiv Chapter 1: Introduction ........................................................................................................ 1 1.1 Blood flow Distribution during Exercise ................................................................. 1 1.2 Challenges in Data interpretation .......................................................................... 5 1.2.1 Methodological Considerations ................................................................... 5 1.2.2 Technical Considerations ............................................................................ 5 1.2.1 Physiological Considerations ...................................................................... 6 1.3 Introduction to Contrast Enhanced Ultrasound ..................................................... 7 1.3.1 Measuring Respiratory Muscle Blood Flow ............................................... 12 1.4 Purpose ............................................................................................................... 14 Chapter 2: Methodology and Data Analysis Workflow .................................................... 15 2.1 Ethical Approval .................................................................................................. 15 2.2 Participants ......................................................................................................... 15 2.2.1 Inclusion Criteria ........................................................................................ 15 2.2.2 Exclusion Criteria ...................................................................................... 15 2.3 Experimental Procedures .................................................................................... 16 2.3.1 General Procedures .................................................................................. 16 2.3.2 Maximal Inspiratory Pressure Testing ....................................................... 16 viii 2.3.3 Inspiratory Threshold Loading ................................................................... 17 2.3.4 Contrast Enhanced Ultrasound ................................................................. 19 2.3.5 Pulmonary Function Testing ...................................................................... 20 2.3.6 Respiratory and Cardiovascular Measurements ....................................... 21 2.4 Experimental Conditions ..................................................................................... 22 2.4.1 Increasing Respiratory Muscle Work ......................................................... 22 2.4.2 Free Breathing versus Apnea .................................................................... 23 2.4.3 Non-contrast Control ................................................................................. 23 2.5 Ultrasound Image Processing ............................................................................. 23 2.5.1 Angle Correction and Cropping ................................................................. 23 2.5.2 Extraction of Time-Intensity Curves .......................................................... 25 2.6 Blood Flow Analysis ............................................................................................ 26 2.6.1 Blood Flow Index ....................................................................................... 27 2.6.2 Indicator Dilution Modelling ....................................................................... 28 2.7 Data Analysis ...................................................................................................... 29 Chapter 3: Results and Discussion ................................................................................... 31 3.1 Influence of Region of Interest ............................................................................ 31 3.2 Influence of Inspiratory Threshold Loading ......................................................... 33 3.2.1 Diaphragm Region .................................................................................... 35 3.2.2 Liver Region .............................................................................................. 40 3.2.3 Chest Wall Region ..................................................................................... 44 3.3 Influence of Apnea .............................................................................................. 45 3.4 Influence of Angle Correction .............................................................................. 46 3.5 Influence of Contrast ........................................................................................... 50 Chapter 4: Conclusion ........................................................................................................ 51 4.1 Limitations ........................................................................................................... 51 ix 4.2 Recommendations for Future Research ............................................................. 52 Bibliography ........................................................................................................................ 54    x List of Tables Table 1-1: Summary of studies using the NIRS-ICG method to measure locomotor and respiratory muscle blood flow. ................................................................................................ 2 Table 3-1: Mean cardiorespiratory data from one subject. ................................................... 34 Table 3-2: Relative changes in indices of blood flow and volume between breathing conditions. ............................................................................................................................. 40    xi List of Figures Figure 1-1: Example of a sternocleidomastoid NIRS-ICG curve in an individual subject. ...... 3 Figure 1-2: Example destruction-replenishment TIC. ............................................................. 8 Figure 1-3: Example of CEUS signal obtained from an occlusive stress-perfusion imaging protocol. .................................................................................................................................. 9 Figure 1-4: Example of a CEUS signal derived from non-occlusive stress-perfusion imaging. .............................................................................................................................................. 10 Figure 1-5: Example of a CEUS TIC obtained using the bolus injection technique. ............. 11 Figure 2-1: Inspiratory threshold loading setup. .................................................................... 17 Figure 2-2: Relationship between mouth pressure and loading mass. ................................. 18 Figure 2-3: Annotated diaphragm ultrasound image. ............................................................ 20 Figure 2-4: Image processing and angle correction. ............................................................. 24 Figure 2-5: CEUS image analysis schematic. ....................................................................... 25 Figure 2-6: Example of a CEUS TIC and a BFI calculation. ................................................. 27 Figure 3-1: Example of individual TICs obtained from a free breathing subject during 20% ITL. ........................................................................................................................................ 32 Figure 3-2: Data from the diaphragm region in subject 04 (free breathing). ......................... 36 Figure 3-3: Data from the diaphragm region in subject 05 during apnea. ............................ 37 Figure 3-4: Data from the diaphragm region in subject 01 (free breathing). ......................... 39 Figure 3-5: Relative changes in indices of blood volume and flow within the liver region relative to the resting breathing condition. ............................................................................ 41 Figure 3-6: Data from the liver region in subject 05 (apnea). ................................................ 42 Figure 3-7: Data from the chest wall region in subject 05 (apnea). ...................................... 44 Figure 3-8: Average NRMSE of individual subjects across all regions and conditions. ........ 46 Figure 3-9: Percent change in NRMSE after angle correction. ............................................. 47 xii Figure 3-10: Average NRMSE of individual subjects across all regions and conditions by contrast. ................................................................................................................................ 50    xiii Acknowledgements I would like to acknowledge my supervisor Dr. Glen Foster and committee members Dr. Paolo Dominelli and Neil Eves for their guidance and support throughout my studies. I would also like to acknowledge Courtney Brown, Brooke Shafer, Lindsey Boulet, and Tyler Vermeulen for their unconditional willingness to help me succeed as a new master\u2019s student. Furthermore, I would like to acknowledge Lindsey Boulet for mentoring me in the development of my computer programming and problem-solving abilities. Finally, I would like to acknowledge the school of Health and Exercise Sciences for providing a supportive, inclusive, and stimulating work environment.   xiv Dedication This thesis is dedicated to the late Gabriel Ursus Dix. A person with exceptional character, work ethic, and humility. Gabe\u2019s ambition and discipline inspired me throughout my academic journey while his quick wit and compassion kept me sane. He was a dear friend, a great scholar, and an average snooker player. His memory will forever be in our hearts.   1 Chapter 1: Introduction 1.1 Blood flow Distribution during Exercise During exercise, the respiratory and locomotor muscles of healthy male athletes demand up to 15% and 70% of cardiac output respectively, and blood flow to active muscles is determined by the net effect of local vasodilatory and systemic vasoconstrictor mechanisms (Calbet et al., 2007; Harms et al., 1997). Vessels in active muscles dilate in response to accumulation of metabolites, increased temperature, metabolic acidosis, and hypoxia, while metabolically and mechanically sensitive afferents originating in locomotor (exercise pressor reflex) and respiratory (respiratory muscle metaboreflex) muscles induce intensity-dependant increases in sympathetic output and resultant vasoconstriction (Crisafulli et al., 2015; Fisher et al., 2015; Galbo et al., 1975; Hill, 2000; Rodman et al., 2003). During exercise, the interaction of these two opposing mechanisms, termed functional sympatholysis, helps distribute a limited amount of blood to tissues that need it the most (e.g., respiratory and locomotor muscles). Furthermore, at some critical exercise intensity, where an individual\u2019s cardiac reserve is diminished, the locomotor and respiratory muscles must compete for a finite blood supply and one muscle group will \u201csteal\u201d blood flow from the other (i.e., respiratory versus locomotor muscle steal) (Sheel et al., 2018). In humans, data describing blood flow distribution between locomotor and respiratory muscles is conflicting (see Table 1-1). Thus, a novel tool to measure respiratory muscle blood flow is needed to help resolve this issue.      2 Table 1-1: Summary of studies using the NIRS-ICG method to measure locomotor and respiratory muscle blood flow. Authors Population Intervention(s) Outcome Variable(s) Result Evidence in support of Vogiatzis et al., (2009). Competitive cyclists (n = 10 males; Age = 35\u00b110; VO2,max = 62\u00b18.).1  1) Graded discontinuous exercise test to maximal work rate (intermittent protocol).                                                                        2) Resting isocapnic hyperpnoea (ventilation matched to exercising levels).  Absolute IC and VL muscle BF via NIRS-ICG.  1) IC BF reduced to resting or below resting levels at max work rate while VL BF remained elevated.                                                              2) IC BF increased up to max ventilation. VL BF unchanged across all ventilations.  Locomotor muscle steal.  Athanasopoulos et al. (2010). Healthy (n = 8 males; Age = 27\u00b112).2  High intensity constant load cycling exercise with and without (control) an expiratory flow limitation.  IC and VL muscle BFI via  NIRS-ICG.  Compared to the control condition, IC BFI was increased and VL BFI decreased in response to expiratory flow limitation.  Respiratory muscle steal.  Henderson et al. (2012). Healthy (n = 6 males\/1 female; Age = 28\u00b16; BMI = 22.2\u00b11.6).2  Incremental exercise test to exhaustion (continuous protocol).  IC and VL muscle BFI via  NIRS-ICG.  IC BFI unchanged across all exercise intensities. VL BFI significantly reduced at max exercise intensity.  Respiratory muscle steal.  Dominelli et al.  (2017). Healthy (n = 5 male\/3 females; Age = 25\u00b12; BMI = 21.8\u00b11.6; VO2,max = 54\u00b17).1 Manipulated work of breathing (reduced normal and elevated WOB) during high intensity cycling exercise. SCM and VL muscle BFI via  NIRS-ICG. Changes in SCM BFI were proportional to changes in WOB. Changes in VL BFI were inversely proportional to WOB.  Respiratory muscle steal. Definition of abbreviations: IC = intercostal; VL = vastus lateralis; SCM = sternocleidomastoid; BF = blood flow; BFI = blood flow index; NIRS-ICG = Near infrared spectroscopy in combination with indocyanine green; BMI = body mass index; WOB = work of breathing; VO2,max = maximal oxygen uptake; EFL = expiratory flow limitation. Values are reported as either mean \u00b1 standard deviation1 or mean \u00b1 standard error of the mean 2. Units of values reported: Age = years; VO2,max = mL\u2219 kg-1\u2219 min-1; BMI = kg \u2219 m-2. For the purpose of this summary, a \u2018healthy\u2019 population is defined as having normal pulmonary function and\/or normal BMI (18.5 - 24.9) and\/or a VO2, max greater than or equal to 40 mL\u2219 kg-1\u2219 min-1.   3  1.1.1 Conflicting Data in the Literature The current method used to measure respiratory muscle blood flow utilizes near infrared spectroscopy in combination with a bolus injection of indocyanine green (NIRS-ICG) to provide an index of blood flow. The blood flow index (BFI) is a relative measure of blood flow derived from the wash-in curve of a transcutaneous NIRS-ICG signal in a tissue of interest.   Figure 1-1: Example of a sternocleidomastoid NIRS-ICG curve in an individual subject. The thin line represents the raw NIRS-ICG signal, and the thick line represents the filtered NIRS-ICG signal. Modified from Guenette et al. (2011).   The BFI is calculated as the maximal change in indocyanine green concentration (DICGmax) from baseline to peak concentration divided by the rise time. Rise time is calculated as the time interval between 10 and 90% of DICGmax. In contrast to measures of absolute blood flow utilizing the NIRS-ICG method, calculation of BFI does not require arterial cannulation. Thus, utilization of the BFI proves a less invasive alternative to NIRS-ICG derived measures of absolute blood flow. Furthermore, BFI has been validated against 4 absolute measures of blood flow utilizing the NIRS-ICG technique. However, investigations of respiratory muscle blood flow utilizing the NIRS-ICG method have produced conflicting results (see Table 1-1). During constant load high intensity cycling exercise, increased WOB (via expiratory flow limitation or resistive loading) has been reported to increase intercostal (IC) and sternocleidomastoid (SCM) muscle BFI and decrease locomotor muscle (vastus lateralis; VL) BFI. In contrast, reducing WOB (via proportional assist ventilator) resulted in reductions in SCM muscle BFI and increases in locomotor muscle (VL) BFI compared with a control condition (e.g., spontaneous breathing) (Athanasopoulos et al., 2010; Dominelli et al., 2017). These results are in accordance with an investigation by Henderson et al. (2012) whom measured respiratory (IC) and locomotor muscle (VL) BFI during an incremental exercise test to exhaustion. In this study, respiratory muscle BFI was unchanged throughout the exercise protocol, while locomotor muscle BFI was reduced at maximal exercise intensity - suggesting a possible redistribution of blood flow away from locomotor muscles. Taken together these data support the hypothesis that respiratory muscles steal blood flow away from locomotor muscles during intense exercise.  Other data in the literature provide evidence in support of locomotor muscle steal. During incremental exercise tests to maximal work rate Vogiatzis et al. (2009, 2010) reported reductions in respiratory muscle (IC) blood flow at maximal and near-maximal work rates while locomotor muscle (VL) blood flow remained elevated at peak levels \u2013 suggesting redistribution of blood flow from the respiratory muscles to the locomotor muscles. Furthermore, during a voluntary bout of isocapnic hyperpnea where ventilation was matched to exercising levels, the reductions in respiratory muscle blood flow observed during the exercise trial were not present, and instead respiratory muscle blood flow increased with ventilation from resting to maximal levels. Possible explanations for these contradictory data are discussed next 5  1.2 Challenges in Data interpretation The divergent results presented in Table 1-1 are challenging to reconcile because of methodological, technical, and physiological considerations. 1.2.1 Methodological Considerations Human respiratory muscle perfusion has only been measured in secondary respiratory muscles (e.g., intercostal and sternocleidomastoid muscles), and it is unknown whether perfusion of these muscles accurately represents human respiratory muscle blood flow during exercise. Presumably, perfusion of the primary respiratory muscle, the diaphragm, would more accurately describe respiratory muscle blood flow demand. Animal studies report blood flow (ml\/min\/100g) levels to the diaphragm twice that of flow to secondary respiratory muscles (e.g., intercostal, serratus and scalenus) and on par with locomotor muscle blood flow during exercise (Fixler et al., 1976; Manohar, 1986a). These data highlight the significant metabolic demand of the diaphragm during exercise and suggest that diaphragm blood flow may provide a more accurate representation of global respiratory muscle blood flow compared with secondary respiratory muscles. 1.2.2 Technical Considerations The current technique used to measure respiratory muscle blood flow (NIRS-ICG) is limited in its spatial resolution and depth of penetration, making measures of IC blood flow susceptible to movement artifact as the chest wall moves throughout the respiratory cycle. More specifically, the depth at which the NIRS-ICG recording probes placed over the intercostal space record their signal is fixed. This may prove problematic if IC muscle position (in relation to the NIRS-ICG recording probe) is dynamic \u2013 concurrently changing with the alterations in lung volume and chest wall distortions seen during progressive exercise (Grimby et al., 1968). If this is the case, the NIRS-ICG signal may periodically 6 include deeper tissues (e.g., lung, liver or spleen) and may not accurately reflect perfusion of the IC muscles. This may explain reports of IC blood flow at or below resting levels during maximal and near-maximal exercise ventilations (Vogiatzis et al., 2009, 2010) and why investigations of SCM muscle blood flow during exercise yield contrasting results (Dominelli et al., 2017).  1.2.1 Physiological Considerations The reductions in IC muscle blood flow to resting levels during intense exercise reported previously (Vogiatzis et al. 2009, 2010) do not agree with the principle tenets of exercise physiology in which the blood flow garnered by an exercising muscle is matched closely with metabolic demand (Joyner & Casey, 2015). The observed reduction in IC muscle blood flow occurred despite increases in the WOB, at near maximal levels of ventilation, and without change in mean arterial pressure (MAP). Presumably, a large reduction in blood flow to an actively exercising muscle (i.e., intercostals) would be accompanied by a significant cardiovascular response (i.e., increased MAP), an increase in muscle fatigue (i.e., decreased ventilation) and an increase in blood flow to other tissues with elevated metabolic demands (i.e., locomotor muscles).  It is possible that changes in respiratory muscle recruitment patterns during progressive exercise may contribute to a reduction in IC muscle blood flow. Specifically, if the relative contribution of the IC muscles to ventilation is reduced, blood may be shunted to other respiratory muscles. This suggests that IC blood flow would become uncoupled from WOB\/ventilation at high exercise intensities as observed by Vogiatzis et al. (2009, 2010). Direct measures of respiratory muscle blood flow in animal models and indirect measures of respiratory muscle activity in humans do not support this hypothesis. In the canine model, Fixler et al. (1976) observed significant increases in both diaphragm and intercostal blood flow during mild and moderate exercise compared with resting values. Similarly, Manohar, (1986a, 1986b, 1988) reported significant increases in diaphragm (~20 fold increase) and 7 intercostal (10 \u2013 20 fold increase) blood flow in maximally exercising ponies compared with rest. In humans, it has been shown that pressure generation by non-diaphragmatic ribcage muscles (e.g., intercostals) and electromyography activity of accessory inspiratory muscles is increased during high intensity exercise compared with rest (Aliverti et al., 1997; Mitchell et al., 2018). Collectively, these data suggest that IC muscle activity and blood flow should remain coupled with WOB during exercise.  1.3 Introduction to Contrast Enhanced Ultrasound The aforementioned challenges in respiratory muscle blood flow data interpretation warrant development of a novel measurement technique able to provide spatial feedback and, thus, verification that the recorded signal is obtained from the tissue of interest and not surrounding tissue(s). Contrast enhanced ultrasound (CEUS) may be useful in this setting due to the visual nature of ultrasound imaging. CEUS utilizes ultrasound enhancing agents (UEA) to increase the echogenicity of blood. UEA\u2019s are comprised of microbubbles (1 \u2013 6 \u00b5m in diameter) containing inert high-molecular weight gas cores surrounded by a lipid monolayer or albumin shell (Lindner, 2021). This combination of size and chemical composition ensures that the microbubbles are large enough to be confined to the vascular space, produce a strong non-linear signal, and are stable under low mechanical index imaging conditions (Lindner, 2021; Rafailidis et al., 2020). Furthermore, non-linear expansion and compression of the microbubbles during insonation generates a unique frequency profile which improves the signal-to-noise ratio. Wei et al. (1998) were the first to investigate the validity of CEUS as a measure of muscle perfusion in the canine myocardium. Subjects were injected with a constant venous infusion of microbubbles whilst ultrasound images of the myocardium were simultaneously collected. Once the microbubble signal within the myocardium plateaued at a steady state, all the microbubbles in the insonation plane were destroyed with a high-powered ultrasound 8 pulse. Subsequent low mechanical index images then captured the signal of newly arriving microbubbles as they reperfused the myocardium before another high powered pulse was emitted. Wei et al. (1998) recorded the microbubble signal intensity at the end of each pulsing interval (i.e., immediately before microbubble destruction) and varied the length of the pulsing interval from ~0 s up to a pulsing interval which permitted the microbubble signal to reach a steady state. The microbubble signal intensity was then plotted as a function of pulsing interval \u2013 generating a reperfusion curve (aka time-intensity curve; TIC). This CEUS technique is known as the destruction-replenishment technique and is advantageous as it allows for the collection of multiple data points at each pulsing interval and in quick succession. Subsequent modelling of TICs generated using the destruction-replenishment technique (see Figure 1-2) yield measures of blood flow and volume that correlate strongly with measures of muscle blood flow derived from venous occlusion plethysmography and radiolabelled microspheres (Krix et al., 2003, 2005; Wei et al., 1998).   Figure 1-2: Example destruction-replenishment TIC. Modified from Wei et al. (1998).  Stress-perfusion imaging is another CEUS technique similar in principle to the destruction-replenishment technique. During stress-perfusion imaging the UEA is infused at a constant rate, and perfusion of the target tissue is monitored before, during and after a stressor (i.e., arterial occlusion, isometric exercise) is applied. In the case of arterial occlusion or submaximal isometric contraction (\u00b3 25% max), blood flow is reduced, and 9 alleviation of the stressor induces a reactive hyperemia in the tissue of interest (see Figure 1-3). Thus, a reperfusion curve is generated and measures of blood volume (peak signal intensity, area under the curve) and flow (inflow slope, time to peak) can be extracted to assess perfusion reserve. This technique has been used to successfully characterize perfusion reserve deficits in subjects with peripheral arterial disease compared to healthy controls (Amarteifio et al., 2011, 2012; Krix et al., 2011).   Figure 1-3: Example of CEUS signal obtained from an occlusive stress-perfusion imaging protocol. Modified from Krix et al. (2011).  In the case of a non-occlusive stressor (e.g., 10% max isometric exercise; see Figure 1-4), no reactive hyperemia is invoked and thus no assessment of perfusion reserve can be made. However, changes in signal intensity and area under the curve can be used to assess real-time changes in relative blood volume (Krix et al., 2009).  10           Figure 1-4: Example of a CEUS signal derived from non-occlusive stress-perfusion imaging. Modified from Krix et al. (2009).  The bolus-injection technique is another CEUS imaging technique and differs from the destruction-replenishment and stress-perfusion techniques in that it does not utilize a constant infusion of the UEA. Consequently, less product may be needed for the duration of the protocol, and the UEA is concentrated in a bolus rather than diluted for constant-infusion. This is advantageous as the cost of examination may be lower and a higher signal-to-noise ratio may be achieved using the bolus versus constant infusion techniques (Nguyen & Davidson, 2019). Similar to constant infusion techniques, measures of blood flow and volume utilizing the bolus-injection technique can be derived from the perfusion kinetics of the microbubble bolus as it arrives in the tissue of interest (Duerschmied et al., 2009; Krix et al., 2005; Weber et al., 2006). Indicator dilution modelling can also be used in combination with the bolus-injection technique (see Figure 1-5) (Strouthos et al., 2010). More specifically, upon the modelling of a TIC generated from the injection of an indicator bolus, quantities related to blood flow and volume can be derived from the fitted model. These quantities are the mean transit time (MTT), the time to peak intensity (Tp) and the area under the curve (AUC). The inverse of the mean transit time (1\/MTT) and time to peak intensity (1\/ Tp) are 11 proportional to blood flow, while the AUC is proportional to blood volume (Gauthier et al., 2011; Tiemann et al., 2000).                      Figure 1-5: Example of a CEUS TIC obtained using the bolus injection technique. The fitted curve represents an indicator-dilution model. Modified from Strouthos et al. (2010).  CEUS has recently been used to characterize tumor, organ, and muscle perfusion (D\u2019Onofrio et al., 2015; Dunford et al., 2018; Kaffas et al., 2017; Thomas et al., 2015b) and, CEUS derived perfusion parameters have been validated against measures of blood flow and volume utilizing the thermodilution, venous occlusion plethysmography and radiolabeled microspheres techniques (Herold et al., 2015; Krix et al., 2005, 2009; Wei et al., 1998). In addition, recent data suggests that CEUS can reliably assess muscle perfusion under optimal conditions (i.e., applied to static superficial muscles). Thomas et al. (2015) reported moderate to excellent intra-observer reliability (r = 0.77 \u2013 0.93) of CEUS derived perfusion parameters in the static gastrocnemius muscle. Similarly, Kunz et al. (2020) reported excellent (r = 0.9) intra-observer, good (r = 0.84) inter-observer, and moderate (r = 0.60) inter-observer\/inter-machine reliability of CEUS derived perfusion parameters in the static deltoid muscle. These reports of excellent reliability of CEUS derived perfusion parameters are encouraging, however, it is important to note that good reliability under these conditions cannot be extrapolated to suggest good reliability under more challenging imaging conditions (e.g., applied to the dynamic and relatively deep diaphragm muscle). 12 1.3.1 Measuring Respiratory Muscle Blood Flow Human respiration is a complex process involving approximately 63 different pairs of muscles (Pilarski et al., 2019). Hence, the concept of respiratory muscle blood flow is challenging to define let alone measure experimentally. In animal models, the use of radio-labelled microspheres in combination with post-mortem dissection of tissues of interest allow for the quantification of blood flow in multiple different respiratory muscles. Investigations of respiratory muscle blood flow in the canine, pony, and rat model report blood flows to the diaphragm muscle significantly higher than flow to secondary respiratory muscles (e.g., intercostal, scalenus, and transverse abdominus muscles) during moderate, heavy and near-maximal exercise (Fixler et al., 1976; Manohar, 1988, 1990; Poole et al., 2000; Sexton & Poole, 1995; Smith et al., 2017). Similarly, blood flow to the rat, pony and canine diaphragm at rest and during exercise is heterogeneously distributed, with the costal region of the diaphragm muscle receiving significantly more blood flow than the crural region (Johnson et al., 2002; Manohar, 1988; Poole et al., 2000; Sexton & Poole, 1995). Furthermore, distribution of blood flow within the costal and crural regions of the canine diaphragm is also heterogenous. Johnson et al. (2002) reported a dorsal-ventral gradient in perfusion whereby the ventral crural, ventral costal and midcostal regions of the canine diaphragm muscle receive more blood flow relative to the dorsal regions of the crural and costal diaphragm muscle. These data are corroborated by reports of a similar dorsal-ventral gradient in muscle power, mechanical advantage, and mass within the canine diaphragm whereby regions with the most perfusion (e.g., ventral crural, ventral costal and midcostal regions) exhibit greater muscle thickness, mechanical advantage, and power compared to lesser perfused regions (e.g., dorsal crural and dorsal costal diaphragm regions) (Brancatisano et al., 1991; Johnson et al., 2002; Margulies, 1991; Wilson et al., 1998). The aforementioned preferential distribution of blood flow to regions of the diaphragm with the greatest mechanical advantage and muscle power makes sense from a ventilatory 13 perspective in order to optimize the efficiency of the diaphragm muscle. It should be noted that similar patterns of dorsal-ventral blood flow have been reported in the costal rodent diaphragm. However, the direction of the gradient is opposite to that seen in dogs with higher blood flow rates observed in the dorsal versus ventral regions of the rodent diaphragm (Sexton & Poole, 1995, 1998). These data suggest that heterogeneity of blood flow to the mammalian diaphragm may be a common trait, but the pattern may be variable between species. Measures of respiratory muscle blood flow in unanesthetized humans rely on non-invasive methodology and have been limited to superficial respiratory muscles. To date, the NIRS-ICG technique is the only methodology that has been used to obtain measures of respiratory muscle blood flow in the exercising human (Athanasopoulos et al., 2010; Dominelli et al., 2017; Henderson et al., 2012; Vogiatzis et al., 2009, 2010) while doppler ultrasound has been used to measure intercostal artery blood flow during rest and loaded breathing (de Bisschop et al., 2017). However, the utility of doppler ultrasound for the measurement of respiratory muscle blood flow during exercise has not yet been investigated. In this section, we will focus on the possibility of using CEUS to measure respiratory muscle blood flow and compare CEUS to the established NIRS-ICG technique. In principle, the CEUS and NIRS-ICG techniques are similar. For both techniques an indicator bolus is injected intravenously, and this indicator enters systemic circulation with hemodynamics similar to that of red blood cells (CEUS) or plasma proteins (NIRS-ICG) (Cherrick et al., 1960; Jayaweera et al., 1994; Lindner et al., 2002; Muckle, 1975). A recording probe is simultaneously positioned over the tissue of interest and emits either light or sound waves. As the blood-indicator mix perfuses this tissue, the indicator either reflects sound waves (CEUS) or absorbs light waves (NIRS-ICG) at a unique frequency, and the number of waves reflected\/absorbed is proportional to the indicator concentration beneath the probe (Lampaskis & Averkiou, 2010; Landsman et al., 1976). These reflected\/absorbed 14 waves are recorded, and, as a result, the intensity or magnitude of the recorded signal at any point in time is proportional to the amount of indicator beneath the probe (Boushel et al., 2000; Guenette et al., 2008, 2011; Strouthos et al., 2010). Since the indicator is constrained to the vascular compartment and exhibits a flow profile similar to that of human blood (Alander et al., 2012; Lindner et al., 2002), determining the kinetics of this signal provides an index of blood flow in the target tissue. For CEUS, the injected indicator is an ultrasound enhancing agent comprised of gas-filled microbubbles engineered to reflect the sound waves emitted by the ultrasound probe (Lee et al., 2017). Similarly, the NIRS-ICG method utilizes a fluorescent dye indicator (indocyanine green) that absorbs the near-infrared light emitted by the NIRS-ICG probe at specific wavelengths (Landsman et al., 1976). Hence, the main differences between the two techniques are (1) the type of indicator used, (2) the presence\/absence of visual feedback of the tissue(s) of interest and (3) the need for a trained sonographer when performing CEUS.  1.4 Purpose The purpose of these thesis was to explore the possibility of using contrast enhanced ultrasound to measure blood flow from the diaphragm and intercostal muscles during increases in respiratory muscle work. In order to do this, we sought to develop imaging processing strategies which would permit us to quantify the blood flow index and the quantities 1\/MTT, 1\/Tp and AUC from the acoustic intensity from three regions of interest \u2013 the chest wall, costal diaphragm, and liver. We selected to use the bolus-injection approach to align with the approach used with NIRS-ICG. We collected pilot data in six participants under varying circumstances with the goal of offering the optimal methodology and image processing workflow for future research.    15 Chapter 2: Methodology and Data Analysis Workflow 2.1 Ethical Approval Ethical approval was obtained from the University of British Columbia Clinical Research Ethics Board (H19-00717). All participants provided written informed consent before visiting the lab for data collection. 2.2 Participants Six male volunteers participated in our protocol with a mean (\u00b1 standard deviation) age, height, weight, BMI and FEV1\/FVC of 25 \u00b1 5 years, 176 \u00b1 7 cm, 76.1 \u00b1 10.6 kg, 24.5 \u00b1 2.8, and 0.78 \u00b1 0.05 respectively. 2.2.1 Inclusion Criteria Inclusion in this study required volunteers to be healthy males or females between the ages of 18 and 40, normotensive (<130\/80 mmHg), have normal pulmonary function (FEV1\/FVC above 0.75), free from cardiovascular and respiratory diseases, not currently taking any medications (prescribed or over the counter, other than oral contraceptives) and able to hold their breath for thirty seconds.  2.2.2 Exclusion Criteria Volunteers were excluded if they had a known or suspected hypersensitivity to perflutren or had a history of hypertension, heart failure, angina, myocardial infarction, coronary artery disease, stroke, diabetes, chronic obstructive pulmonary disease, asthma, chronic bronchitis, cystic fibrosis, obstructive or central sleep apnea, or smoked within the past year. Volunteers were also excluded if they were allergic to latex, lidocaine or antihistamines or had any COVID-19 symptoms in the 7 days prior to testing.  16 2.3 Experimental Procedures 2.3.1 General Procedures Subjects underwent CEUS imaging of the costal diaphragm muscle, chest wall and liver regions at rest and during two incremental increases in respiratory muscle work for a total of three breathing stages. Each stage lasted 3 minutes to ensure that respiratory muscle blood flow had reached a steady state. During the final minute of each stage, contrast (Definity; Lantheus Medical Imaging, North Billerica, Massachusetts) was administered intravenously and the arrival of the contrast bolus was simultaneously visualized in the diaphragm, chest wall and liver regions via B-mode ultrasound images recorded on one ultrasound machine (Vivid E9; GE Healthcare). For the duration of the protocol, participants were seated in the upright position with their forearms resting on a table and breathing through a standard mouthpiece (connected to the inspiratory threshold loading device; see Figure 2-1) with a nose clip on. All data from each participant was collected during a single visit to the lab. 2.3.2 Maximal Inspiratory Pressure Testing Maximal inspiratory pressure (MIP) was measured in order to set the inspiratory threshold loads detailed in section 2.3.3 Inspiratory Threshold Loading and conducted in accordance with the guidelines from the American Thoracic Society (\u201cATS\/ERS Statement on Respiratory Muscle Testing,\u201d 2002). Subjects wore a nose clip and breathed normally through a 3-way valve (Hans Rudolph, Kansas City, MO, USA). For MIP, subjects exhaled passively to functional residual capacity, then the valve closed, and subjects inhaled maximally, drawing only small amounts of air in through a pinhole (21 gauge) in the valve. This allowed the subject to generate large negative pressure within the thorax whilst preventing the glottis from closing. Mouth pressure was measured from a port in the mouthpiece connected to a differential pressure transducer (model PA-1; Hans Rudolph, 17 Kansas City, MO, USA). Subjects received strong encouragement from researchers to inhale with maximum effort (Mueller maneuver) for at least 1.5 seconds, and the maximum pressure sustained for 1 second was taken as the maximum pressure for that maneuver. The maximum pressure of at least 3 maneuvers that varied by less than 20% was then recorded as MIP. 2.3.3 Inspiratory Threshold Loading Loading of the respiratory muscles was achieved using an inspiratory threshold loading (ITL) device used previously in our lab (Dominelli et al., 2018b; see Figure 2-1). The device consists of a plastic tube with three airflow openings (inspiration, expiration, and participant). The inspiratory end is sealed with a plunger that is held in place with weights. One-way valves ensure all airflow during inspiration occurs through the plunger, and that expiration is unimpeded by any external loading.  Figure 2-1: Inspiratory threshold loading setup.  18 To initiate inspiratory airflow, the participant must generate sufficient negative pressure to overcome the gravitational pull of the weights. Thus, a calibration study was run to determine the relationship between mouth pressure and the mass of the attached weight. Simple linear regression was used to model mouth pressure as a function of mass, and adjusting the weights allows for the fine-tuning (within 2 cm H2O) of the minimal pressure needed to generate inspiratory airflow for each breath.   Figure 2-2: Relationship between mouth pressure and loading mass.  Five subjects inspired against threshold loads set at 20% and 40% of MIP while one subject inspired against threshold loads set at 15% and 30% of MIP. We changed the relative load for one subject as it became clear that the lesser load would provide sufficient increases in respiratory work while minimizing the chance that loading could become occlusive exercise impacting our assessment of blood flow. In addition, two subjects followed a 0.7 duty cycle while breathing on the ITL device. This combination of ITL and duty cycle ensures that the workload placed on the diaphragm would be sufficient to y = -0.17x - 8.49R\u00b2 = 0.98-50-45-40-35-30-25-20-15-10-500 50 100 150 200 250Mouth Pressure (cm H2O)Mass (g)19 increase O2 demand, but not so high that the muscular contractions of the diaphragm impede its own blood flow (i.e., time-tension index < 0.3) (Bark et al., 1987; Bellemare et al., 1983).  2.3.4 Contrast Enhanced Ultrasound For the duration of CEUS imaging subjects were seated in an upright position (see Figure 2-1). One 10 ml syringe was connected to a 22-guage cannula placed in the left antecubital vein. The syringe was filled with a diluted bolus of activated Definity (1.3 ml) and sterile saline (8.7 ml). Over the course of the experimental day, 3 Definity injections (3.3 ml each) were administered. All bolus injections were made on the left side to in order to keep the right side clear for CEUS imaging. One ultrasound machine (Vivid E9; GE Healthcare) in combination with a two-dimensional linear probe (9L-D; GE Healthcare), and one sonographer were used for all examinations. The sonographer completed diaphragm-specific ultrasound training via the Michener Institute (IG814; Diaphragm Ultrasound: Physiology and Technique). All ultrasound data was collected with the ultrasound probe placed on the mid-axillary line of the participant\u2019s right side in the 9th intercostal space parallel to ribs 9 and 10. This imaging setup provides a transverse cross-section of the chest wall, costal diaphragm muscle and liver. After initial placement of the ultrasound probe, the orientation of the probe within the intercostal space and ultrasound settings were adjusted in order to produce an image where the pleural and peritoneal membranes were clearly defined. Image depth was set to include liver tissue deep to the diaphragm muscle (~3.5 cm), dynamic range was set at 35, the mechanical index was set at 0.08, and the focus depth was set to the depth of the peritoneum. Gain was set as low as possible to reduce noise, but not so low that the visibility of the pleura and peritoneum were compromised. Harmonic imaging at a frequency of 2.5 MHz\/5.0 MHz (send\/receive) and a vascular contrast application (GE Healthcare) were utilized to enhance the signal-to-noise ratio. Once 20 the image was optimized, the probe position was marked, and measures of diaphragm muscle thickness were recorded. These markings and thickness measures were then used to verify proper probe positioning at the start of each breathing condition. The start of all CEUS videos was synchronized with the injection of the Definity bolus, and recordings were made for as long as the ultrasound machine\u2019s buffer size would allow (~40 s).  Figure 2-3: Annotated diaphragm ultrasound image.  2.3.5 Pulmonary Function Testing Spirometry, lung volumes and diffusion capacity tests were conducted in agreement with the American Thoracic Society and European Respiratory Society\u2019s joint guidelines (MacIntyre, 2005; Miller, 2005). Forced vital capacity (FVC) and forced expired volume in one second (FEV1) was assessed using an FVC maneuver that involves a full inspiration followed by a forced expiration. A minimum of three repeatable maneuvers were performed and the largest FEV1 and FVC value was selected. Vital capacity was assessed through a maneuver that involves a full inspiration followed by a full slow expiration; a minimum of three maneuvers were performed and the largest vital capacity value selected. A single breath carbon monoxide test was used to quantify diffusion capacity (DLCO) for each 21 individual. Body plethysmography was used to assess lung volumes, specifically total lung volume, functional residual capacity (FRC) and residual volume; panting maneuvers were performed until three tests were obtained with values \u00b1 5% of the mean value. For each test, participants sat within the body plethysmography box (V62J, Vmax Sensormedics, Yorba Linda, CA, USA) with a rigid upright posture and their feet flat on the ground, whilst breathing through a spirometer and bacteriological filter with nose clamped. All pulmonary function measurements were compared against appropriate population-based predictions (Crapo & Morris, 1981; Goldman & Becklake, 1959; Morris et al., 1971, 1973). 2.3.6 Respiratory and Cardiovascular Measurements Non-invasive measurements of heart rate (lead II ECG; FE 132, ADInstruments, Colorado Springs, CO, USA) and beat by beat blood pressure with finger plethysmography (Finometer PRO; Finapress Medical Systems, Amsterdam, Netherlands) were continuously recorded for one participant. All respiratory and cardiovascular parameters were acquired using an analog-to-digital converter (Powerlab\/16SP ML 880; ADInstruments, Colorado Springs, CO, USA) interfaced with a personal computer. Commercially available software was used to analyze ventilatory and cardiovascular variables (LabChart V7.1, ADInstruments, Colorado Springs, CO, USA).  Throughout all procedures, all subjects breathed through a mouthpiece and ITL device while wearing a nose clip.  Respired gas pressures were sampled at the mouth and analyzed for PETO2 and PETCO2 (ML206; ADinstruments, Colorado Springs, CO, USA).  Respiratory flow was also measured near the mouth using a pneumotachograph (HR 800L, HansRudolph, Shawnee, KS, USA) and a differential pressure amplifier (model PA-1; Hans Rudolph, Kansas City, MO, USA). 2.3.7 Esophageal and Gastric Pressure Measurements Measures of esophageal and gastric pressures were made for one subject. A topical anesthetic was applied to the subject's nares and nasal conchae (Xylocaine, Lidocaine 22 Hydrochloride) before passing two conventional balloon catheters through the nose. As previously described (Dominelli et al., 2018a), the balloons were positioned in the lower third of the esophagus and in the stomach to measure esophageal and gastric pressure respectively. The subject was asked to perform a brief Valsalva maneuver while the catheters were open to the atmosphere to empty the balloons and then 1 and 2 ml of air was administered into the esophageal and gastric balloons respectively using a glass syringe. The validity of the balloon position was assessed using a dynamic occlusion before being secured in place. The dynamic occlusion test consists of measuring the ratio of change in esophageal pressure to the change in airway opening pressure during three to five spontaneous breaths against a closed airway. A pressure ratio between 0.8 and 1.2 indicates that the esophageal balloon is in the correct position (Baydur et al., 1982). The balloon catheters were connected to piezoelectric pressure transducers. The signal from the pressure transducer was converted to a digital signal using a data acquisition system and sampled at 200Hz. The pressure transducer was calibrated at baseline and before and after each test using a digital manometer. Trans diaphragmatic pressure was calculated as the difference between gastric and esophageal pressures. The pressure-time products of esophageal and trans diaphragmatic pressure was calculated as described previously (Dominelli & Sheel, 2012) and used as an index of respiratory muscle work. 2.4 Experimental Conditions 2.4.1 Increasing Respiratory Muscle Work All subjects underwent CEUS imaging while breathing at rest and during two increases in respiratory muscle work via ITL. The purpose of modulating respiratory muscle work was to investigate if BFI measures would be (1) sensitive to increases in work of breathing and (2) feasible at multiple ITL intensities. 23 2.4.2 Free Breathing versus Apnea One subject was studied under conditions of apnea. For this subject, injection of the contrast bolus and collection of CEUS images was initiated at the end of each three-minute breathing condition. In addition, the subject performed a 30 second end-expiratory apnea initiated at the onset of CEUS data collection. The purpose of this condition was to determine if the BFI could be determined during free breathing or if a static apnea is required.  2.4.3 Non-contrast Control One subject completed the protocol in the absence of any contrast injections. This trial was conducted to act as a non-contrast control to identify how the acoustic intensity within the three regions of interest (i.e., intercostals, chest wall, diaphragm) changes over time in the absence of a contrast signal. 2.5 Ultrasound Image Processing 2.5.1 Angle Correction and Cropping Each inspiratory loading condition produced a two-dimensional ultrasound video, and these videos were loaded into an analysis script written in the Python programming language (Python Software Foundation. Python Language Reference, version 3.8.5. Available at http:\/\/www.python.org). First, each video was parsed into F constituent static 2D images (i.e., frames) where each image is made up of m rows and n columns of pixels (see Figure 2-4; panel A). Next, each frame was angle corrected on a frame-by-frame basis so that the pleural and peritoneal membranes in each frame were parallel to the horizontal plane. This is done by isolating the peritoneal membrane from the rest of the image and extracting the angle of the edge in relation to the horizontal plane. First a bilateral filter (see Figure 2-4; Panel B) is used to de-noise each frame and then a binary threshold filter (see 24 Figure 2-4; Panel C) is used to filter out any remaining artifact(s). Now only the peritoneal membrane remains in the frame and the Canny edge detection algorithm (see Figure 2-4; Panel C) is used to detect an outline of the peritoneal membrane. Taking the outline as a series of data points, a simple linear regression line is fit through it and the peritoneum\u2019s angle to the horizontal plane is calculated trigonometrically using the slope of the regression line (see Figure 2-4; Panel D). Each frame is then rotated by an equal and opposite amount to the previously calculated angle \u2013 tilting the original image off-axis. A new frame is then generated by cropping the rotated frame to the size of the largest possible rectangle within the rotated frame. After every frame has been angle corrected and cropped all frames are resized to the smallest frame size generated by the cropping process. This ensures that all angle-adjusted frames are the same size.  Figure 2-4: Image processing and angle correction. Panel A shows an individual ultrasound frame before any processing. Panel B shows the original frame after application of a bilateral filter, and the red rectangle represents the cropping applied. Panel C shows the cropped image from panel B after application of a binary filter and a subsequent edge detection algorithm. Panel D shows a regression line fit through the outline generated from the edge detection algorithm and the calculation of correction angle (q). 25 2.5.2 Extraction of Time-Intensity Curves Next, on a frame-by-frame basis, the mean pixel intensity is calculated for each of the m rows. As a result, each frame is condensed into a single column (i.e., m by 1 matrix) where each row represents the mean pixel intensity across the original row of n pixels (see Figure 2-5; panel B).   Figure 2-5: CEUS image analysis schematic. The data presented are from the resting breathing condition of a pilot subject. Panel A depicts the angle correction of four representative frames of a CEUS video. Panel B depicts the averaging of each frame\u2019s m rows into a single m by 1 column, and the horizontal concatenation of said frames. Panel C shows the image resulting from concatenation all F frames, and the three ROIs derived from it. The chest wall, diaphragm and liver ROIs are shown in blue, red and green respectively. Panel D shows the TICs obtained from the image shown in panel C. The dotted lines represent the raw pixel intensity data, and the solid lines represent the lognormal model fit to the raw data. Abbreviations: m, the number of pixel rows within each frame of a CEUS cine loop; n, the number of pixel columns within each frame of a CEUS cine loop; F, the total number of static frames within a CEUS cine loop; f, the frame number within a set of F frames; \ud835\udf03, the correction angle (degrees); TIC, time-intensity curve; ROI, region of interest.   + + +!!C DFm0 2010 30Time (s)AmmfConcatenateAverage Row ValueAverage Row ValueAverage Row ValueB02040600 10 20 30Time (s)Average Pixel Intensity (dB) Chest WallDiaphragmLivern f26 Thus, when each frame is averaged across the horizontal plane, the angle correction ensures that the mean pixel values (e.g., rows) within the diaphragm, chest wall, and liver ROIs reflect only the average pixel intensity within their respective region and are not contaminated by the by the hyperechoic pleural and peritoneal membranes. After all F frames have been condensed, they are then concatenated in sequential order resulting in a m by F matrix that represents the mean pixel intensity at each pixel depth (y-axis) over time (x-axis) (see Figure 2-5; panel C). In this way, researchers can visualize the movement of the diaphragm throughout the entire ultrasound video in one image. The aforementioned m by F matrix will be referred to as the \u201cmatrix image\u201d in subsequent chapters. Next, the researcher traces the pleural and peritoneal membranes through the middle of each structure with a 1-pixel width line and the image is divided into three different ROIs. If the pleura and peritoneum are thicker than 1 pixel, their hyperechoic edges are excluded from these regions via an adjustable \u201cexclusion\u201d buffer which excludes pixels above and below the researcher-delineated trace. After the researcher defines the pleura and peritoneum, the matrix image can be separated into three distinct ROI\u2019s. The chest wall ROI which encompasses everything superficial (i.e., \u201cabove\u201d) to the pleura. The diaphragm region, encompassing everything in between the pleura and peritoneum, and, lastly, the liver region which includes everything deep (i.e., \u201cbelow\u201d) to the peritoneum (see Figure 2-5; panel C). The average pixel intensity within each ROI is then calculated and a low-pass filter of 0.05 Hz is applied to the raw signal to remove high-frequency noise. Plotting the average pixel intensity within each ROI as a function of time (see Figure 2-5; panel D) produces a time-intensity curve. 2.6 Blood Flow Analysis All blood flow analysis was completed within an interactive app developed by Paul Cotton. The app was written using R (R Core Team, 2020), RStudio (RStudio Team, 2020), 27 and the shiny web application framework for R (Chang et al., 2021). The application is open source and is available to use as a web application at https:\/\/cottonp.shinyapps.io\/blood_flow_analysis\/.  2.6.1 Blood Flow Index BFI was calculated using the same methodology developed by Guenette et al. (2011) to analyze NIRS-ICG data. BFI was derived from CEUS TICs (see Figure 2-6) and calculated as the maximal change in acoustic intensity from baseline to peak intensity (i.e., intensity range) divided by the rise time. Rise time was calculated as the time interval between 10 and 90% of the intensity range. Baseline was calculated as the average acoustic intensity within a user-defined range. The baseline range was set from 0 \u2013 10 seconds by default and adjusted as needed to exclude artifact. Time zero was calculated by back-extrapolating the slope of the data within the rise time range to the point of intersection with the baseline estimate via simple linear regression (see Figure 2-6). The time zero estimate was not used in the BFI analyses but was used in the indicator dilution modelling analysis as an initial estimate of the time offset value during non-linear regression (see 2.6.2 Indicator Dilution Modelling).  Figure 2-6: Example of a CEUS TIC and a BFI calculation. The grey points and red line represent the raw data and filtered data respectively. The dotted black line represents a linear model utilized to provide an initial estimate of the time-offset value for the fitting of an indicator dilution model. 28 2.6.2 Indicator Dilution Modelling Indicator dilution modelling was applied to CEUS TIC data utilizing non-linear least-squares regression with the Levenberg-Marquardt algorithm (Padfield & Matheson, 2020; Strouthos et al., 2010). Fitted indicator dilution models can be interpreted as probability density functions describing indicator transit times through an ROI. Thus, the expected value (i.e., \u201cmean\u201d) of an indicator dilution model represents the expected or mean transit time (MTT) of an indicator bolus through an ROI. If the mass of the indicator m is known, and the concentration of the indicator as a function of time is measured, then blood flow (F) and volume can be calculated in terms of area under the curve (AUC) and MTT (Profant et al., 1978). (a) \ud835\udc39 = \ud835\udc5a \u2219 (\ud835\udc34\ud835\udc48\ud835\udc36)!\" (b) \ud835\udc49 = \ud835\udc39 \u2219 \ud835\udc40\ud835\udc47\ud835\udc47 Together, equations (a) and (b) are known as the Stewart-Hamilton relations and can be applied to a variety of indicator dilution models (Strouthos et al., 2010). For this study, the gamma-variate indicator dilution model (c) was used for all analyses. The control parameters of the gamma-variate model are AUC, t0, C, a and b respectively representing the area under the curve, the time offset value, the baseline acoustic intensity, and model-specific parameters alpha and beta. Once the model is fit, MTT\tand the time to peak acoustic intensity of the TIC (Tp)\tcan be calculated in terms of a and b.  (c) \ud835\udc3c(\ud835\udc61) = \ud835\udc34\ud835\udc48\ud835\udc36\ud835\udefd#$\"\u0393(\ud835\udefc + 1) (\ud835\udc61 \u2212 \ud835\udc61%)#\ud835\udc52!('!'!) )\u2044 + \ud835\udc36 (d) \ud835\udc40\ud835\udc47\ud835\udc47 = \t\ud835\udefd(\ud835\udefc + 1) (e) \ud835\udc47+ = \ud835\udefc \u2219 \ud835\udefd 29 Referencing equation (b) we can see that the MTT value is used to calculate blood volume. However, in CEUS the acoustic intensity of microbubbles within an ROI is measured as a surrogate for indicator concentration. Thus, absolute measures of blood flow (a) and subsequently volume (b) are not possible as the relationship between microbubble concentration and acoustic intensity within an ROI has not been defined. Still, quantities proportional to blood volume (AUC) and flow (1\/MTT, 1\/Tp) can be derived (Gauthier et al., 2011; Tiemann et al., 2000). The normalized root mean squared error (NRMSE) was used to quantify the amount of error of the fitted indicator dilution models. The calculation for NRMSE is shown in (f), where \ud835\udc5b\tis the number of raw data points within a given TIC, \ud835\udc66, \t is the i-th raw data point, \ud835\udc669 is the predicted value given the fitted indicator dilution model, and \ud835\udc66: is the average of the raw data points. (f) \ud835\udc41\ud835\udc45\ud835\udc40\ud835\udc46\ud835\udc38 = \t!\u2211 ($%&$'))*%+, *\"#  2.7 Data Analysis  Indices related to blood flow (BFI, 1\/MTT, 1\/Tp) and volume (AUC) were qualitatively compared between individual subjects and breathing conditions. Note that the parameters 1\/MTT and 1\/Tp characterize bolus kinetics in terms of time, while the parameters BFI and AUC characterize bolus kinetics as a function of signal intensity and time. Thus, when discussing the indices of flow and volume presented in this thesis, parameters will be characterized as either signal-intensity-dependent (BFI and AUC) or signal-intensity-independent (1\/MTT and 1\/Tp). The average NRMSE of fitted indicator dilution models across all conditions and regions was qualitatively compared between an individual subject who completed end-expiratory apneas during their trials versus individual subjects who were free breathing. The average NRMSE of fitted indicator dilution models across all conditions and regions was qualitatively compared between the no-contrast control subject and the 30 other individual subjects. In addition, the percent change in the NRMSE of fitted indicator dilution models after angle correction relative to the NRMSE value without angle correction was qualitatively compared across all subjects (who were administered contrast) and conditions and between regions.   31 Chapter 3: Results and Discussion 3.1 Influence of Region of Interest A total of 45 TICs were generated across five subjects where contrast was present. In 18 (40%) of the 45 TICs, the indicator signal was indistinguishable from noise and the indices of blood flow and volume (BFI, 1\/MTT, 1\/Tp and AUC) generated from these TICs were discarded from discussion (see Figure 3-1 for example TICs). Out of the 18 TICs with inadequate signal-to noise ratios, 12 (67%) were obtained from the chest wall region, 6 (33%) from the diaphragm region, and zero from the liver region. It makes sense that the largest proportion of inadequate TIC curves were obtained from the chest wall region as this region contains multiple different tissues and hyperechoic structures (e.g., adipose, muscle, and connective tissue) among which is a muscle group active in respiration (intercostal muscles). Thus, the heterogeneity of the chest wall region in combination with the presence of respiratory muscles (intercostal muscles) within the region makes it susceptible to artifact as the chest wall distorts throughout the respiratory cycle (Grimby et al., 1968). Increased artifact in the chest wall region relative to the diaphragm and liver regions could then manifest as lower signal-to noise ratios in the TICs extracted from the chest wall region and this would explain the large proportion of inadequate TICs generated from the chest wall region. Similarly, the diaphragm region is susceptible to movement artifact as the costal diaphragm muscle contracts and relaxes throughout the respiratory cycle. However, in contrast to the chest wall region, the diaphragm region only encompasses the diaphragm muscle, and it is not impacted by the presence of multiple different tissues. Thus, it makes sense that 100% more TICs with inadequate signal-to noise ratios were generated from the chest wall region relative to the diaphragm region.  32  Figure 3-1: Example of individual TICs obtained from a free breathing subject during 20% ITL. The chest wall TIC in this figure was discarded from BFI analysis due to an inadequate signal-to noise ratio. The diaphragm and liver region TICs presented adequate signal-to noise ratios for analysis. The grey points and red line represent the raw data and filtered data respectively. The dotted black line represents a linear model utilized to provide an initial estimate of the time-offset value for the fitting of an indicator dilution model.  Furthermore, the liver region only contains liver tissue which is passive throughout the respiratory cycle. Thus, the liver region is not affected by a heterogenous distribution of tissue within the region, and any movement artifact within the liver region is likely secondary to movement of the diaphragm muscle as it applies and subsequently releases pressure to the liver during inspiration and expiration respectively. In addition, the human liver receives a large proportion of cardiac output (~25%) relative to its size (~2.5% of total body weight) resulting in a relative blood flow of ~100 ml\/min\/100g liver (Greenway & Lautt, 2011). For context, this figure (100 ml\/min\/100g) is ~600% higher than and equal to blood flow values in the canine and pony diaphragm during rest and moderate exercise respectively (Fixler et RegionChest WallDiaphragmLiver33 al., 1976; Manohar, 1988). Taken together, the relatively large amount of blood flow to the liver and absence of artifact within the liver region results in a higher signal-to noise ratio compared to the chest wall and diaphragm regions. The higher signal-to noise ratio in the liver manifests in the fact that 100% of the TICs generated from the liver region were adequate for blood flow analysis while only 20% and 60% of the TICs generated from the chest wall and diaphragm regions respectively were adequate for blood flow analysis. 3.2 Influence of Inspiratory Threshold Loading  The average pressure-time product generated in one subject (05) was increased by 376% and 552% compared to rest during the 20% ITL and 40% ITL respectively. In addition, the time-tension index was kept below the critical threshold of 0.3 during all three breathing conditions (see Table 3-1). Thus, our protocol was successful in increasing the work of breathing whilst preserving the time-tension index in at least one subject. In addition, 7 (39%) of the 18 TICs with inadequate signal-to noise ratios were obtained during the resting breathing condition, 4 (22%) during the 20% ITL condition, and 7 (39%) during the 40% ITL condition. There was no difference in the number of discarded TICs generated from the chest wall and liver regions between the three breathing conditions - suggesting no impact of loading on TIC quality in these regions. However, within the diaphragm region, an equal number of discarded TICs were generated from the resting and 40% ITL conditions while zero TICs generated from the 20% ITL condition were discarded. The higher proportion of discarded diaphragm TICs obtained from the resting and 40% ITL conditions relative to the 20% ITL condition may be explained by a suboptimal balance of indicator signal strength versus noise during the former conditions.    34 Table 3-1: Mean cardiorespiratory data from one subject. Condition fb TV (L) VE, (L\/min) Duty Cycle TTI PTP (cm H2O\u00b7s) MAP (mm Hg) Resting breathing 12.2 \u00b1 2 0.6 \u00b1 0.3 7.7 \u00b1 3.7 0.7 \u00b1 0.1 0.05 \u00b1 0.01 17 \u00b1 12.9 93 \u00b1 6.1 20% ITL 12.1 \u00b1 1.1 1.4 \u00b1 0.2 16.9 \u00b1 2 0.7 \u00b1 0.1 0.12 \u00b1 0.02 80.9 \u00b1 18.4 90.9 \u00b1 9.3 40% ITL 12 \u00b1 1 1.4 \u00b1 0.2 16.6 \u00b1 2.3 0.7 \u00b1 0 0.15 \u00b1 0.03 110.9 \u00b1 21.7 88.9 \u00b1 9.4 All data are presented as means \u00b1 standard deviation. Abbreviations: fb, breathing frequency; ITL, inspiratory threshold loading; MAP, mean arterial pressure; PTP, pressure time product; TTI, tension-time index of the diaphragm muscle; TV, tidal volume; VE,, minute ventilation.  More specifically, the work of breathing, and thus blood flow demands of the diaphragm muscle, is lowest during the resting breathing condition, increased during 20% ITL, and highest during the 40% ITL condition (see Table 3-1). Thus, the fraction of total cardiac output sent to the costal diaphragm muscle is likely lowest during the resting breathing condition, increased during 20% ITL and highest during the 40% ITL condition (Fixler et al., 1976; Manohar, 1988). Therefore, because the indicator used in this study is injected into the systemic venous circulation, the strength of the indicator signal observed within the diaphragm region should be proportional to the fraction of cardiac output reaching the costal diaphragm muscle. Consequently, we expect that the strength of the indicator signal is lowest during the resting breathing condition, increased during the 20% ITL condition, and highest during the 40% ITL condition. In addition, we expect that the degree of costal diaphragm muscle movement during free-breathing protocols is influenced by ITL. One measure of costal diaphragm muscle movement is the thickening fraction. Thickening fraction describes the largest change in costal diaphragm muscle thickness as it contracts and relaxes throughout the respiratory cycle. Ultrasound derived measures of thickening fraction utilizing the same imaging window as the one used in this study are positively correlated with work of breathing (Poulard et al., 2022; Vivier et al., 2012). Hence, we 35 expect that movement of the costal diaphragm muscle, and thus movement artifact, within the diaphragm region would be lowest during the resting breathing condition, increased during the 20% ITL condition, and highest during the 40% ITL condition. Based on these expectations, we speculate that during the 20% ITL condition a larger increase in signal strength versus movement artifact resulted in a primarily signal-dependent increase in the signal-to noise ratio compared to the resting breathing condition. Similarly, we speculate that during the 40% ITL condition a smaller increase in signal strength versus movement artifact resulted in a primarily noise-dependent reduction in the signal-to noise ratio compared to the 20% ITL condition. This would explain the larger proportion of discarded diaphragm TICs generated from the resting breathing and 40% ITL conditions compared to the 20% ITL condition.  In summary, calculation of BFI, 1\/MTT, 1\/Tp and AUC was feasible across all three breathing conditions with greater success in the 20% ITL condition relative to the other two conditions. We speculate that more TICs from the 20% ITL condition relative to the other two conditions were adequate for analysis due to a superior balance of indicator signal strength versus movement artifact within the costal diaphragm region. 3.2.1 Diaphragm Region Individual data from the diaphragm region are presented in figures Figure 3-2, Figure 3-3 and Figure 3-4, and relative changes in indices of blood flow and volume are summarized in Table 3-2. During subject 04\u2019s 20% ITL condition (free breathing; see Figure 3-2), costal diaphragm muscle BFI, 1\/MTT and 1\/Tp were increased by 42%, 28%, and 89% respectively while AUC was 15% lower compared to the resting breathing condition. An increase in all three quantities proportional to blood flow may be indicative of elevated blood flow to the costal diaphragm muscle during the 20% ITL condition compared to the resting breathing condition in one free-breathing subject. The change in the AUC value between the 36 resting breathing and 20% ITL conditions may be reflective of a decrease in blood volume in the costal diaphragm muscle between the resting breathing and 20% ITL conditions in one free-breathing subject.   Figure 3-2: Data from the diaphragm region in subject 04 (free breathing). Panel A depicts the BFI analyses of individual TICs from the resting breathing (bottom) and 20% ITL (top) conditions. The grey points and red line represent the raw data and filtered data respectively. The dotted black line represents a linear model utilized to provide an initial estimate of the time-offset value for the fitting of an indicator dilution model. Panel B includes a table of blood flow parameters (top) and a graph of the two TICs from panel A plotted together (bottom). Raw TIC data is represented as points and the indicator dilution models fit to the raw data are represented as solid lines. Abbreviations: BFI, blood flow analysis; AUC, area under the curve; MTT, mean transit time; Tp, time to peak; t0, time-offset value derived from the fitted indicator dilution model; NRMSE, normalized root mean squared error; 1\/MTT, the inverse of the mean transit time; 1\/Tp, the inverse of the time to peak.  Similarly, during the 20% ITL condition from subject 05 (apnea: see Figure 3-3), 1\/MTT and 1\/Tp were increased 59% and 44% respectively while BFI and AUC were reduced by 12% and 45% respectively compared to the resting breathing condition. The aforementioned increase in signal-intensity-independent indices of blood flow (1\/MTT and 1\/Tp) suggest an increase in blood flow to the costal diaphragm muscle region while the reductions in BFI and AUC (signal-intensity-dependent values) imply a reduction in costal diaphragm muscle blood flow and volume. Thus, the signal-intensity-independent indices of flow (1\/MTT and 1\/Tp) are trending in the expected direction (i.e., increased flow in response to increased WOB) while the signal-intensity-dependant indices of flow (BFI) and volume ConditionA B37 (AUC) are trending in the direction opposite to what was expected (i.e., reduced flow and volume in response to increased WOB).    Figure 3-3: Data from the diaphragm region in subject 05 during apnea. Panel A depicts the BFI analyses of individual TICs from the resting breathing (bottom) 20% ITL (middle) and 40% ITL (top) conditions. The grey points and red line represent the raw data and filtered data respectively. The dotted black line represents a linear model utilized to provide an initial estimate of the time-offset value for the fitting of an indicator dilution model. Panel B includes a table of blood flow parameters (top) and a graph of the three TICs from panel A plotted together (bottom). Raw TIC data is represented as points and the indicator dilution models fit to the raw data are represented as solid lines. Abbreviations: BFI, blood flow analysis; AUC, area under the curve; MTT, mean transit time; Tp, time to peak; t0, time-offset value derived from the fitted indicator dilution model; NRMSE, normalized root mean squared error; 1\/MTT, the inverse of the mean transit time; 1\/Tp, the inverse of the time to peak.  The discrepancy between the signal-intensity-independent and signal-intensity-dependent indices of flow and volume is likely related to the fact that the maximal change in acoustic intensity (a.k.a intensity range) during the 20% ITL condition is 30% lower relative to the intensity range during the resting breathing condition. Thus, skewing the signal-intensity-dependent indices of flow\/volume in the opposite direction compared to the signal-intensity-independent indices of flow. However, it is unclear why the intensity range and AUC observed in the 20% ITL condition is reduced relative to the resting breathing ConditionA B38 condition. In the same subject (05) during the 40% ITL condition, BFI, 1\/Tp and AUC were increased 129%, 28%, and 105% respectively while 1\/MTT decreased 4% relative to the 20% ITL condition. Increases in BFI, 1\/Tp and AUC suggest a possible increase in blood flow and volume while a reduction in 1\/MTT implies a reduction in blood flow to the costal diaphragm muscle in the 40% ITL condition compared to the 20% ITL condition. However, the magnitude of relative change observed in the signal-intensity-dependent indices of blood flow (BFI) and volume (AUC) during the 40% versus 20% ITL conditions in subject 05 may be skewed by the unexpectedly low AUC and BFI values observed during the 20% ITL condition. Indeed, the magnitudes of change in the signal-intensity-independent indices of flow (1\/MTT and 1\/Tp; see Table 3-2) are larger relative to the magnitudes of change in the signal-intensity-dependent indices of flow and volume (BFI and AUC) between the 20% and 40% ITL conditions in subject 05. As a result, the signal-intensity-dependent indices of flow and volume imply a large change (~100%) while the changes in signal-intensity-independent indices of flow between the 40% and 20% ITL conditions are indicative of a relatively smaller change (28% increase in 1\/Tp) or possibly no change (4% decrease in 1\/MTT). Similarly, during the 40% ITL condition in subject 01\u2019s trial (free breathing; see Table 3-2), costal diaphragm muscle 1\/MTT and 1\/Tp were decreased 10% and 10% respectively while AUC and BFI increased 25% and 107% respectively relative to the 20% ITL condition. All three indices of blood flow trended down suggesting a possible reduction in blood flow while the increase in AUC implies an increase in blood volume to the costal diaphragm muscle during the 40% ITL condition relative to the 20% ITL condition. However, due to the small magnitudes of change observed in subject 01\u2019s BFI, 1\/MTT, and 1\/Tp values between the 20% and 40% ITL conditions it is unclear if these changes are reflective of changes in physiology or the result of normal measurement error.  In summary, all indices of blood flow from subject 04 during the 20% ITL condition and subject 05 during the 40% ITL condition trended upwards compared to the resting 39 breathing condition. In addition, signal-intensity-independent indices of blood flow (1\/MTT and 1\/Tp) from subject 05 during the 20% ITL condition also trended upwards compared to the resting breathing condition (see Table 3-2). In contrast, all indices of blood flow from subject 01 were reduced by a small amount while indices of flow from subject 05 trended in diverging directions during the 40% ITL condition compared to the 20% ITL condition. Thus, it seems plausible that indices of blood flow derived from the diaphragm region (BFI, 1\/MTT, 1\/Tp) were sensitive to changes in WOB between the resting breathing and ITL conditions but not sensitive to changes in WOB between the two ITL conditions.   Figure 3-4: Data from the diaphragm region in subject 01 (free breathing). Panel A depicts the BFI analyses of individual TICs from the 20% ITL (bottom) and 40% ITL (top) conditions. The grey points and red line represent the raw data and filtered data respectively. The dotted black line represents a linear model utilized to provide an initial estimate of the time-offset value for the fitting of an indicator dilution model. Panel B includes a table of blood flow parameters (top) and a graph of the two TICs from panel A plotted together (bottom). Raw TIC data is represented as points and the indicator dilution models fit to the raw data are represented as solid lines. Abbreviations: BFI, blood flow analysis; AUC, area under the curve; MTT, mean transit time; Tp, time to peak; t0, time-offset value derived from the fitted indicator dilution model; NRMSE, normalized root mean squared error; 1\/MTT, the inverse of the mean transit time; 1\/Tp, the inverse of the time to peak.  In addition, AUC in subjects 04 and 05 trended down during the 20% ITL condition and up (subject 05 only) during the 40% ITL condition compared to resting breathing, while AUC values during the 40% ITL condition trended up relative to the 20% ITL condition in subjects 01 and 05. Thus, the observed changes in AUC values in response to increases in ConditionA B40 WOB are inconsistent. This could be interpreted to mean that the AUC values derived from the diaphragm region were not sensitive to changes in WOB. However, it is important to note that the previous statements are speculative, and more data is needed to reach any meaningful conclusions about the sensitivity of BFI, 1\/MTT, 1\/Tp and AUC values in response to changes in WOB.  Table 3-2: Relative changes in indices of blood flow and volume between breathing conditions.  3.2.2 Liver Region In contrast to the chest wall and diaphragm regions, all TICs generated from the liver region presented adequate signal-to noise ratios for blood flow analysis. Indices of blood flow and volume generated from the liver region across all conditions and subjects are presented in Figure 3-5. The median (\u00b1 interquartile range) change in liver region BFI values from the 20% and 40% ITL conditions expressed as a percent change relative to the resting breathing condition are 191 \u00b1 200% and 58 \u00b1 101% respectively. The median change in liver region 1\/MTT values from the 20% and 40% ITL conditions relative to the resting condition are -25 \u00b1 29% and -27 \u00b1 37% respectively. The median change in liver region 1\/Tp values from the 20% and 40% ITL conditions relative to the resting condition are 2 \u00b1 50% and -13 \u00b1 52% respectively. The median change in liver region AUC values from the 20% and 40% ITL conditions relative to the resting condition are 73 \u00b1 360% and 58 \u00b1 111% respectively. 41    Figure 3-5: Relative changes in indices of blood volume and flow within the liver region relative to the resting breathing condition. Abbreviations: 1\/MTT, the inverse of the mean transit time; 1\/Tp, the inverse of the time to peak intensity; AUC, area under the curve; BFI, blood flow index.  There are two obvious outliers within the 1\/Tp and AUC categories in Figure 3-5. Both data points belong to subject 05 and the associated TICs are presented in Figure 3-6. The large change in AUC in subject 05 during the 20% ITL condition relative to the resting breathing condition is likely because the liver TIC from the 20% does not appear to reach a clear plateau in acoustic intensity. As a result, the AUC value derived from the fitted indicator dilution model is skewed higher during the 20% ITL relative to the resting breathing and 40% ITL conditions. Similarly, the large change in 1\/Tp in subject 05 during the 40% ITL condition compared to the resting breathing condition is likely because the Tp value from the resting breathing condition is unrealistically large at 139.80 seconds. Thus, the relative change in the 1\/Tp value between the resting breathing and 40% ITL condition is skewed to an extreme value. Similar to the resting breathing condition, the Tp value from the 20% ITL condition appears to be unrealistically large at 70.27 seconds. Therefore, the percent 42 change (from rest) in the 20% ITL 1\/Tp value is not as affected by the large resting breathing 1\/Tp value compared to the percent change in the 40% ITL 1\/Tp value.   Figure 3-6: Data from the liver region in subject 05 (apnea). Panel A depicts the BFI analyses of individual TICs from the 20% ITL (bottom) and 40% ITL (top) conditions. The grey points and red line represent the raw data and filtered data respectively. The dotted black line represents a linear model utilized to provide an initial estimate of the time-offset value for the fitting of an indicator dilution model. Panel B includes a table of blood flow parameters (top) and a graph of the two TICs from panel A plotted together (bottom). Raw TIC data is represented as points and the indicator dilution models fit to the raw data are represented as solid lines. Abbreviations: BFI, blood flow analysis; AUC, area under the curve; MTT, mean transit time; Tp, time to peak; t0, time-offset value derived from the fitted indicator dilution model; NRMSE, normalized root mean squared error; 1\/MTT, the inverse of the mean transit time; 1\/Tp, the inverse of the time to peak.  Irrespective of the aforementioned outliers, variability of the signal-intensity-dependent indices of blood flow (BFI) and volume (AUC) appears to be greater relative to the signal-intensity-independent indices of blood flow (1\/Tp and 1\/MTT). In addition, BFI and AUC values are elevated during the 20% and 40% ITL conditions relative to the resting breathing condition and do not appear to change between the 20% and 40% ITL conditions. Similarly, 1\/Tp and 1\/MTT values do not appear to change between the 20% and 40% ITL conditions. In contrast, 1\/MTT and 1\/Tp values during the ITL conditions trend lower and do not appear to change respectively compared to the resting breathing condition.  ConditionA B43  We expect no influence of ITL on liver blood flow and volume because, although respiration induces phasic changes in portal vein pressure and flow (Rabinovici & Navot, 1980), acute changes in portal vein flow are buffered by inverse changes in hepatic artery flow (Eipel et al., 2010). Furthermore, portal flow within the liver is not governed by intrahepatic resistance but is determined by the arteriolar resistance of the splanchnic organs (Greenway & Lautt, 2011). Thus, any influence of ITL induced pressure swings within the abdominal cavity on intrahepatic pressure\/resistance should not translate into changes in liver blood flow. While respiration may not influence liver blood flow, an acute bout of moderate exercise transiently increases splanchnic vascular resistance and reduces liver blood flow (Heinonen et al., 2014; Perko et al., 1998; Rowell et al., 1964; Shephard & Johnson, 2015). In addition, ITL at 60% MIP but not 30, 40 or 50% MIP has been shown to induce an increase and decrease in limb vascular resistance and limb blood flow respectively (Sheel et al., 2002). Thus, if ITL at a sufficient intensity causes systemic cardiovascular changes, it seems plausible that ITL induced respiratory muscle metaboreflex activation could also result in an increase in splanchnic vascular resistance and a consequential reduction in liver blood flow. However, it seems unlikely that the intensity of ITL utilized in this study is sufficient to cause a reduction in liver blood flow as ITL at 40% MIP did not elicit any systemic cardiovascular effects in a previous study (Sheel et al., 2002).  The lack of change in liver region 1\/Tp values between all three breathing conditions in five subjects seems to align with the hypothesis that liver blood flow will not change in response to 20 and 40% ITL relative to rest. Similarly, given the large amount of variability in liver region 1\/MTT values, it seems plausible that the changes observed in 1\/MTT values between all three breathing conditions may be reflective of normal measurement error and not real physiological changes. On the contrary, the changes in BFI and AUC values between the resting breathing condition and the ITL conditions suggest an increase in blood 44 volume and flow to the liver in response to ITL. This trend in BFI and AUC values between the resting breathing condition and the ITL conditions does not align with the hypothesis that liver blood flow and volume will not change in response to 20 and 40% ITL relative to rest. However, with the limited data presented in this thesis it is impossible to reach any meaningful conclusions about the influence of ITL on liver blood flow and volume. 3.2.3 Chest Wall Region The three chest wall TICs with signal-to noise ratios adequate for analysis were collected from subject 05 and are presented below in Figure 3-7.    Figure 3-7: Data from the chest wall region in subject 05 (apnea). Panel A depicts the BFI analyses of individual TICs from the 20% ITL (bottom) and 40% ITL (top) conditions. The grey points and red line represent the raw data and filtered data respectively. The dotted black line represents a linear model utilized to provide an initial estimate of the time-offset value for the fitting of an indicator dilution model. Panel B includes a table of blood flow parameters (top) and a graph of the two TICs from panel A plotted together (bottom). Raw TIC data is represented as points and the indicator dilution models fit to the raw data are represented as solid lines. Abbreviations: BFI, blood flow analysis; AUC, area under the curve; MTT, mean transit time; Tp, time to peak; t0, time-offset value derived from the fitted indicator dilution model; NRMSE, normalized root mean squared error; 1\/MTT, the inverse of the mean transit time; 1\/Tp, the inverse of the time to peak.  During the 20% ITL condition 1\/MTT, BFI and AUC values from subject 05 were decreased by 16, 34, and 44% respectively while the 1\/Tp value was increased by 1708% ConditionA B45 relative to the resting breathing condition. During 40% ITL condition 1\/MTT, BFI and 1\/Tp values from subject 05 were increased by 160, 351, and 300% respectively while the AUC value was decreased by 40% relative to the 20% ITL condition. The extreme relative change in the 1\/Tp value between the resting breathing condition and the 20% ITL condition is skewed upward by an unrealistically large Tp value (315 seconds) in the resting breathing condition. Therefore, the change in the 1\/Tp value from the resting breathing condition to the 20% ITL condition is omitted from further discussion.  Measures of 1\/MTT, AUC, and BFI values during the 20% ITL condition were reduced by 16 to 44% relative to the resting breathing condition suggesting a decrease in blood flow and volume to the chest wall region in response to ITL. In contrast, the 1\/MTT, 1\/Tp, and BFI values during the 40% ITL condition increased by a range of 160 to 351% relative to the 20% ITL condition suggesting an increase in blood flow to the chest wall region in response to ITL. Together, these observations could be interpreted to mean that measures of 1\/MTT, 1\/Tp, and BFI within the chest wall region in one subject were not sensitive to the increase in WOB from the resting breathing condition to the 20% ITL condition but were sensitive to the increase in WOB in the 40% ITL condition relative to the other two conditions. However, this conclusion is speculative and more data is needed to properly define the relationship between measures of 1\/MTT, BFI, 1\/Tp and AUC derived from the chest wall region and WOB. 3.3 Influence of Apnea The average normalized root mean squared error (NRMSE) of fitted indicator dilution models across all regions and conditions for subjects 01, 02, 03, 04, and 05 are 1.59, 1.18, 2.00, 1.10, and 0.28 respectively (see Figure 3-8). Subject 05 was the only subject who performed end-expiratory apneas and is also the only subject where all the TICs collected were adequate for analysis. Thus, all 18 TICs with inadequate signal-to noise ratios were 46 derived from free-breathing subjects. Additionally, end-expiratory apnea appears to improve the quality of indicator dilution model fits across the 45 TICs where contrast was present. The mean NRMSE from one subject who performed end-expiratory apneas is 81% lower compared to the average NRMSE of free-breathing subjects. We interpret this to mean that the presence of end-expiratory apnea improved the feasibility of calculating the blood flow\/volume parameters BFI, 1\/MTT, 1\/Tp and AUC during resting breathing and ITL. Therefore, we recommend the use of end-expiratory apneas for future research investigating CEUS within the costal diaphragm region.  Figure 3-8: Average NRMSE of individual subjects across all regions and conditions. Abbreviations: NRMSE, normalized root mean squared error. 3.4 Influence of Angle Correction The percent change in the NRMSE of indicator dilution models as a result of angle correction in the 27 TICs with adequate signal-to noise ratios is presented in Figure 3-9. The median (\u00b1 interquartile range) change in the NRMSE of indicator dilution models as a result of angle correction was -9 \u00b1 30%, -7 \u00b1 35%, and 7 \u00b1 18% within the chest wall, diaphragm and liver regions respectively.  47  Figure 3-9: Percent change in NRMSE after angle correction. Abbreviations: NRMSE, normalized root mean squared error.  Thus, angle correction appeared to be most effective in reducing the fitting error of indicator dilution models within the diaphragm and chest wall regions while angle correction negatively affected the fits of indicator dilution models within the liver region. In addition, the effectiveness of angle correction across all three regions is highly variable. It is difficult to reach any meaningful conclusions about the efficacy of our angle correction approach with the limited data presented in this thesis.  Successful application of CEUS to the costal diaphragm region is dependent on the quality of the underlying ultrasound images and the segmentation of those images. More specifically, the effectiveness of our angle correction approach and the quality of the TICs produced utilizing the methodology outlined in this thesis is dependent on the ability of our algorithm to identify and segment out the pleural and peritoneal membranes superficial and deep to the costal diaphragm muscle respectively (see Figure 2-3). Greater precision within the segmentation process will better isolate the indicator signal within a region of interest and consequentially reduce the amount of noise within the resulting TICs. Deep learning has recently been utilized to develop a fully automated approach to measuring fascicle 48 length, pennation angle and muscle thickness in musculoskeletal ultrasound images (Cronin et al., 2020). The aforementioned deep learning approach has produced results comparable to manual human analysis. This is significant, as the success of the deep learning approach in measuring fascicle length, pennation angle and muscle thickness depends on the model\u2019s ability to identify and segment out the aponeurosis and individual muscle fascicles of a skeletal muscle. We face a similar challenge while processing CEUS images of the costal diaphragm muscle for the generation of TICs. Therefore, it is exciting to see the success of a deep learning approach applied to a similar segmentation problem. If the deep learning approach outlined by (Cronin et al., 2020) was successful in segmenting the aponeurosis of skeletal muscles within a musculoskeletal ultrasound image it may also be able to segment the pleural and peritoneal membranes from the ultrasound images collected during CEUS imaging of the costal diaphragm muscle. Thus, there is rationale to compare the precision of a deep learning segmentation approach to our filter and crop approach (see 2.5.1 Angle Correction and Cropping) for the purpose of improving the effectiveness of angle correction. Furthermore, if researchers develop a deep learning approach that is capable of consistently segmenting both the pleural and peritoneal membranes from individual CEUS frames, segmentation of ROI\u2019s could be done on a frame-by-frame basis rather than ROI segmentation based off researcher-delineated traces drawn onto the matrix image discussed in section 2.5.2 Extraction of Time-Intensity Curves. An approach capable of segmenting the chest wall, diaphragm and liver regions on a frame-by-frame basis would eliminate the need to compress all the individual frames of a CEUS video into one matrix image and eliminate researcher bias from the segmentation process. This may be significant as less information about the acoustic intensity within the chest wall, diaphragm, and liver regions would be lost during the image processing phase. Thus, we recommend the investigation of a deep learning segmentation approach for the purpose of (1) improving 49 our existing angle correction process and (2) to explore the possibility of frame-by-frame segmentation of the chest wall, diaphragm and liver regions. However, it important to note that the successful application of a deep learning algorithm for the segmentation of muscle aponeurosis and fascicles in musculoskeletal ultrasound images (Cronin et al., 2020) may not produce the same results when applied to CEUS images of the costal diaphragm muscle. It is more difficult to segment CEUS images relative to musculoskeletal ultrasound images due to (1) the low mechanical index required for CEUS imaging and (2) the presence of contrast within CEUS images. Both of these factors reduce the contrast between the target of segmentation (e.g., pleural and peritoneal membranes) and the surrounding tissue (e.g., liver, costal diaphragm muscle). Consequentially, it is likely that a larger amount of data would be needed to train a robust deep learning algorithm to segment CEUS images relative to musculoskeletal ultrasound images. In addition, it would be more expensive to collect a CEUS data set for the training of a deep learning model relative to musculoskeletal ultrasound imaging due to the added cost of an ultrasound enhancing agent. Nonetheless, a deep learning approach appears promising in its ability to segment the chest wall, diaphragm and liver regions within CEUS images of the costal diaphragm muscle. In summary, angle-correction of CEUS images of the costal diaphragm muscle improved the quality of indicator dilution model fits within the chest wall and diaphragm regions and reduced the quality of indicator dilution model fits within the liver region. Despite improvements in the fits of indicator dilution models within the chest wall and diaphragm regions, the effect of angle correction was inconsistent within these regions. More data is needed to further test our angle correction approach and we speculate that a deep learning segmentation approach may offer utility in the advancement of our methodology. 50 3.5 Influence of Contrast The average normalized root mean squared error (NRMSE) of fitted gamma-variate indicator dilution models across all regions and conditions for subjects 01, 02, 03, 04, 05, and 06 are 4.78, 3.55, 6.00, 3.31, 0.84, and 16.75 respectively.   Figure 3-10: Average NRMSE of individual subjects across all regions and conditions by contrast. Abbreviations: NRMSE, normalized root mean squared error.  The mean NRMSE in one subject where no contrast was administered is 400% higher than the mean NRMSE from the five subjects where an ultrasound enhancing agent was administered. A higher amount of error within the indicator dilution models fit during trials where no contrast was present makes sense as there was no indicator signal present within the TICs the models were fit to. We interpret this to mean that the indicator dilution models fit to TICs where contrast was present are reflective of the contrast bolus\u2019 signal as it passed through the region of interest as opposed to unrelated noise.  51 Chapter 4: Conclusion Measuring respiratory muscle blood flow in humans is challenging due to (1) the variety of muscles involved, (2) the anatomical orientation of the primary respiratory muscle (the diaphragm muscle) and (3) the dynamic nature of the respiratory muscles and thorax during respiration. The method previously used to measure respiratory muscle blood flow does not provide visual feedback of the tissue of interest and has produced inconsistent results. Thus, in this thesis we sought to explore the possibility of using contrast enhanced ultrasound to measure blood flow from the costal diaphragm and intercostal muscles during controlled increases in respiratory muscle work. We provided proof of concept for the application of CEUS within the chest wall, diaphragm and liver region and demonstrated that it is feasible to quantify the blood flow index and the blood flow\/volume related quantities 1\/MTT, 1\/Tp, and AUC from all three regions during increases in respiratory work. The performance of an apnea during our CEUS protocols improved the quality of the time-intensity curves generated and the feasibility of performing blood flow analysis on those time-intensity curves. Angle correction improved the quality of our model fits within the chest wall and diaphragm region on average, but the effectiveness of our angle correction was variable.  4.1 Limitations  This thesis is limited by the amount of data collected. As a result, we could not utilize statistical inference to investigate any potential differences between groups (i.e., apnea versus free breathing), ITL conditions, or regions of interest. In addition, only male subjects participated in our testing. Females are more resistant to diaphragmatic fatigue and exhibit a blunted inspiratory muscle metaboreflex compared to males (Geary et al., 2019; Welch et al., 2018). Therefore, it seems plausible that redistribution of blood flow to the male diaphragm as a result of ITL induced inspiratory muscle metaboreflex activation may be 52 exaggerated relative to the female diaphragm \u2013 resulting in less blood flow to the female versus male diaphragm in response to the same relative load. Thus, the ITL intensity needed to elicit a strong CEUS signal within the costal diaphragm muscle may be higher for females compared to males. However, Smith et al. (2017) reported no difference in rat diaphragm muscle blood flow between males and females in response to moderate and near-maximal exercise. The results reported by Smith et al. (2017) suggest that the same relative stimulus for males and females may be adequate to elicit a strong CEUS signal within the diaphragm muscle. Thus, it is unclear if the optimal ITL intensity needed to elicit a strong CEUS signal within the costal diaphragm region is influenced by sex and this thesis does address that possibility. In addition, we collected our data in a controlled setting, and it is unclear if the CEUS approach outlined in this thesis is feasible under more natural conditions. More specifically, ITL was utilized in this thesis to increase WOB in a way that reduces the amount of thorax and diaphragm movement typically associated with ventilatory driven increases in WOB. Thus, it is unclear if our CEUS approach is feasible under more natural breathing conditions such as cycling exercise or voluntary hyperpnea. Lastly, we did not investigate the use of the destruction-replenishment CEUS technique as an alternate to the bolus-injection approach. Thus, we cannot comment on the effect of CEUS technique on CEUS feasibility within the costal diaphragm region.  4.2 Recommendations for Future Research Our recommendations for future research are to (1) utilize apnea during CEUS protocols, (2) compare the utility of the destruction-replenishment versus bolus-injection CEUS techniques for the application of CEUS to the costal diaphragm region, and (3) investigate the use of an automated deep learning approach for image segmentation. The presence of apnea within our protocol improved the feasibility of CEUS applied to the costal diaphragm region in one subject compared to four free-breathing subjects. Thus, we 53 recommend researchers test the apnea approach repeatedly in multiple subjects to investigate the quality and consistency of the data produced. We also recommend researchers explore the utility of the destruction-replenishment technique for the application of CEUS to the costal diaphragm region. The destruction-replenishment technique permits researchers to collect multiple data points in quick succession which may improve the efficiency of CEUS data collection in the costal diaphragm region. In addition, the ultrasound image analysis procedures detailed in this thesis can be easily modified to be used in combination with the destruction-replenishment technique. The only necessary adjustment is the addition of different modelling curves specific to the destruction-replenishment technique in place of the indicator-dilution models described in this thesis. Lastly, we recommend the investigation of a deep learning approach for the segmentation of CEUS images obtained from the costal diaphragm region. Deep learning has recently been used to segment musculoskeletal ultrasound images with precision comparable to manual human analysis (Cronin et al., 2020) and the refinement of our image segmentation approach may be important for further development of the methodology presented in this thesis. Namely, we speculate that a deep learning approach to CEUS image segmentation may (1) improve the consistency of angle-correction and (2) offer utility in the development of a frame-by-frame approach to segmenting the chest wall, diaphragm and liver regions.  54 Bibliography  Alander, J. T., Kaartinen, I., Laakso, A., P\u00e4til\u00e4, T., Spillmann, T., Tuchin, V. V., Venermo, M., & V\u00e4lisuo, P. (2012). A Review of Indocyanine Green Fluorescent Imaging in Surgery. International Journal of Biomedical Imaging, 2012, 940585. https:\/\/doi.org\/10.1155\/2012\/940585 Aliverti, A., Cala, S. J., Duranti, R., Ferrigno, G., Kenyon, C. M., Pedotti, A., Scano, G., Sliwinski, P., Macklem, P. 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