Advancing TAVR precision: validation of fluid-structure interaction models against 4D flow MRI data
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Accurate Quantities of Interest (QoIs) are pivotal for optimal outcomes in Transcatheter Aortic Valve Replacement (TAVR). Standard imaging often lacks precision in measuring these quantities, while in-silico models have the potential to enhance QoI accuracy, influencing clinical decision-making. Despite the extensive literature on Fluid-Structure Interaction (FSI) models of AV dynamics, a significant gap persists, particularly in validation against in-vivo data. We introduce a study validating a blood-AV FSI model against in-vivo data. Our model is constructed from patient-specific aorta and AV meshes derived from CT data. The FSI model consists of an immersed boundary method within a finite element framework, implemented in a high-performance computing multi-physics simulation software. Comparing computational results with 4D flow MRI measurements, our investigation showcases FSI's efficacy in replicating in-vivo dynamics, and accurately reproducing blood flow patterns and kinetic energy variation. Additionally, we contrast FSI outcomes with standard CFD simulations, demonstrating FSI's superiority in validating aortic flow and computing essential biomarkers crucial for optimal TAVR outcomes. Finally, we underscore the role of in-silico models as invaluable cross-checks, offering a robust method to mitigate noise and erratic behavior in in-vivo data. This study advances FSI validation methodologies and highlights the potential of in-silico models to enhance the reliability of in-vivo observations in the context of TAVR.
