Collaborative Research: An Integrated Experimental and Computational Twin Approach to Characterize Blood Vessel Mechanics

Coronary blood vessels supply oxygen and nutrients to the heart muscle, and they are essential for healthy heart function. Blood flow through these vessels is governed not only by blood pressure but also by the heart contraction of the surrounding heart tissue. Current knowledge is largely based on studies of vessels themselves and ignores the interplays with muscles. As a result, the mechanisms of coronary vascular disease remain incompletely understood. This project will advance fundamental science on how blood vessels and muscles in the heart work together to regulate blood flow under healthy and diseased conditions. The findings will deepen our understanding of the coronary vessels and inform future approaches for diagnosing and treating heart disease. More broadly, the project will generate computational tools to study blood flow in other organs, integrate research findings into biomechanics education, and engage K-12 students and public to promote awareness of cardiovascular health and biomedical engineering. This project will establish an integrated experimental and computational framework to characterize coronary vascular mechanics and determine how they are altered by reduced blood supply to the heart muscle. The research will incorporate vascular impedance analysis and wave intensity analysis into a multiscale model that couples coronary blood flow with cardiac mechanics. Experimental measurements obtained from healthy animal models will be used to validate the computational framework. The validated model will then be applied to quantify how reduced blood supply alters vascular wave propagation, pressure transmission, pulse wave energy, and the mechanical interactions between coronary vessels and the myocardium. By integrating vascular mechanics with cardiac-coronary coupling, the project will provide the first comprehensive framework to predict the coronary vascular mechanical responses to physiological and pathological perturbations. The anticipated outcome is a fundamental mechanistic understanding of how myocardial contraction influences coronary perfusion in various conditions, thereby advancing the fields of cardiovascular biomechanics and mechanobiology. The resulting computational framework will also provide a foundation for future development of machine learning approaches that can accelerate the identification of biomechanical signatures of heart disease and enable predictive models of coronary vascular function across physiological and pathological conditions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.