A MOCK CIRCULATION LOOP FOR IN VITRO HEMODYNAMICS IN HUMAN ARTERIAL SYSTEMS WITH APPLICATIONS

Cardiovascular diseases, persistent as a dominant global health burden, demand diagnostic and therapeutic approaches that extend beyond anatomical imaging and delve into functional insight. Although modalities such as computed tomography angiography skillfully delineate vascular morphology, they often fall short in capturing the nuanced dynamics of blood flow that critically inform disease progression and clinical decisions. This dissertation introduces a Mock Circulation Loop (MCL), a sophisticated in vitro platform engineered to emulate physiological pulsatile flow within patient-specific vascular geometries. Integrating a pulsatile pump, compliance chambers, resistive modules, and finely printed 3-dimensional (3D) arterial models, the MCL enables controlled, repeatable investigation of pressure-flow relationships under pathological conditions. Two focused applications underscore the system’s translational promise. The first examines compliance mismatch in arterial grafting, where 3D-printed polymer conduits demonstrated superior mechanical compatibility with native vessels, suggesting a path forward for personalized vascular reconstruction, though long-term durability and biological response remain to be explored. The second application centers on the development of a novel hemodynamic index for stenosis severity, grounded in experimentally derived trans-stenotic pressure gradients. Through an optimization-based algorithm, this work defines two critical thresholds—demarcating mild, moderate, and severe disease states—thus reducing diagnostic subjectivity and enabling physiology-driven intervention guidance. As the algorithm evolves into a computational toolbox, its integration into broader research and clinical frameworks appears increasingly viable. Ultimately, the MCL emerges as more than an experimental device—it stands as a bridge connecting engineering precision to medical relevance, with future extensions poised to redefine disease modeling, surgical planning, and functional diagnostics.