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Cotton, Paul

University of British Columbia

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.

Text

2022-08-29

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/82570

Health and Exercise Sciences

University of British Columbia

UBCO

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