Date of Award
Summer 8-18-2025
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Biomedical Engineering
First Advisor
Lei Fan
Second Advisor
Ranjan Dash
Third Advisor
Roger Guillory
Abstract
Cardiovascular function arises from the complex interactions among the heart, the arterial system, and the coronary circulation. Questions such as how variations in left ventricular (LV) loading conditions affect LV and aortic functions through LV–aorta interactions; and how coronary artery diseases, such as stenosis, impact cardiac function and myocardial perfusion including left and right coronary perfusion via LV–coronary interactions, remain understudied. To answer these questions, this thesis focuses on two core objectives: (1) investigate the effects of LV loading conditions on LV and aortic functions through LV–aortic interactions by performing ex-vivo beating rat heart experiments, and (2) elucidate the impact of coronary artery stenosis on coronary perfusion in the left and right coronary arteries (LCA and RCA) by developing a Distributive Model and a one-dimensional Navier-Stokes Model. Understanding LV–aorta interaction is essential for revealing how alterations in preload and afterload affect cardiac output and hemodynamic efficiency. To investigate these interactions, we conducted ex-vivo beating heart experiments that allowed independent modulation of preload and afterload to quantify their effects on LV and aortic functions. In the ex-vivo beating heart experiments, LV and aortic functions including LV pressure and volume waveforms, aortic pressure waveforms, aortic flow rate, were measured by independently varying LV preload and afterload. Our results demonstrated that increased afterload led to reduced aortic flow and elevated LV developed pressure (DP), whereas increased preload enhanced aortic flow and was accompanied by a rise in DP. These findings indicate that aortic pressure predominantly governs aortic flow under varying afterload conditions, whereas LV DP plays a dominant role in regulating aortic flow under different preload conditions. In parallel, a Distributive Model and a one-dimensional Navier-Stokes Model were developed to investigate cardiac–coronary interactions, focusing on how coronary artery stenosis impacts myocardial perfusion, in both LCA and RCA. Specifically, a one-dimensional Distributive Model and Navier–Stokes Model, incorporating anatomical LCA and RCA networks, vessel compliance, and fractional flow reserve (FFR), were developed to simulate coronary functions and regional perfusion under varying degrees of stenosis in a closed-loop of systemic circulation. The model predicts that as stenosis severity increased, coronary flow decreased significantly in both the LCA and RCA, demonstrating the nonlinear nature of hemodynamic impairment. Notably, coronary flow reduction in the RCA is more pronounced than that in the LCA, indicating a vessel-specific susceptibility to stenotic effects. These results highlight two key findings: (1) stenosis severity exerts a nonlinear impact on coronary pressure and flow, and (2) the RCA exhibits greater perfusion loss compared to the LCA under equivalent levels of stenotic conditions. Together, the ex-vivo beating heart experiments not only demonstrate mechanistic insights into how ventricular loading and aortic properties collectively shape cardiovascular function but also provide valuable information for computational modeling (e.g., measured hemodynamics serve as boundary conditions and reconstructed contractility-flow relationship) and rigorous model validation. The developed computational modeling can predict detailed hemodynamics in coronary vessels and elucidates how ventricular loading and coronary artery stenosis influence myocardial perfusion and overall cardiovascular performance, which is difficult to measure in experiments, overcoming the limitations in pure experimental studies. These findings have important implications for advancing the diagnosis, monitoring, and management of cardiovascular diseases such as heart failure, aortic stenosis, and coronary artery disease. The developed integrated experimental and computational tools can be applied to understand the underlying mechanisms of a wide range of cardiovascular diseases in future.