AUTOMATIC OPTIMIZATION METHODS FOR PATIENT-SPECIFIC TISSUE-ENGINEERED VASCULAR GRAFTS

Abstract

Surgical intervention is sometimes necessary in cases of Coarctation of the Aorta (CoA). The post-repair geometry of the aorta can result in sub-optimal hemodynamics and can have long-term health impacts. Patient-specific designs for tissue-engineered vascular grafts (TEVGs) allow greater control over post-repair geometry. This thesis proposes a method for automatically optimizing patient-specific TEVGs using computational fluid dynamics (CFD) simulations and the ANSYS Fluent adjoint solver. Our method decreases power loss in the graft by 25-60% compared to the native geometry. As patient-specific graft design can be challenging due to incomplete or uncertain flow and geometry data, this thesis also quantifies the robustness of the optimal designs with respect to CFD boundary conditions derived from imaging data. We show that using velocity conditions that deviate by more than 20% of the measured peak systolic velocity, our method produces grafts with deviations on the order of 5% in predicted power loss performance. Lastly, as one way to accelerate the optimization process, we demonstrate and compare how some established machine learning models (K Nearest Neighbors and Kernel Ridge Regression) predict reasonable starting points for an optimizer on a 2D bifurcated pipe dataset.