Authors: Daniel H. Pak (Yale University)*; Minliang Liu (Georgia Institute of Technology); Shawn Ahn (Yale University); Andres Caballero (Georgia Institute of Technology); John A Onofrey (Yale University); Liang Liang (University of Miami); Wei Sun (Georgia Institute of Technology); James S Duncan (Yale University)
Abstract: Finite Element Analysis (FEA) is useful for simulating Transcather Aortic Valve Replacement (TAVR), but has a significant bottleneck at input mesh generation. Existing automated methods for imaging-based valve modeling often make heavy assumptions about imaging characteristics and/or output mesh topology, limiting their adaptability. In this work, we propose a deep learning-based deformation strategy for producing aortic valve FE meshes from noisy 3D CT scans of TAVR patients. In particular, we propose a novel image analysis problem formulation that allows for training of mesh prediction models using segmentation labels (i.e. weak supervision), and identify a unique set of losses that improve model performance within this framework. Our method can handle images with large amounts of calcification and low contrast, and is compatible with predicting both surface and volumetric meshes. The predicted meshes have good surface and correspondence accuracy, and produce reasonable FEA results.