Distortion Energy for Deep Learning-Based Volumetric Finite Element Mesh Generation for Aortic Valves

Abstract

Volumetric meshes with hexahedral elements are generally best for stress analysis using finite element (FE) methods. With recent interests in finite element analysis (FEA) for Transcatheter Aortic Valve Replacement (TAVR) simulations, fast and accurate generation of patient-specific volumetric meshes of the aortic valve is highly desired. Yet, most existing automated image-to-mesh valve modeling strategies have either only produced surface meshes or relied on simple offset operations to obtain volumetric meshes, which can lead to undesirable artifacts. Furthermore, most recent advances in deep learning-based meshing techniques have focused on watertight surface meshes, not volumetric meshes. To fill this gap, we propose a novel volumetric mesh generation technique using template-preserving distortion energies under the deep learning-based deformation framework. Our model is trained end-to-end for image-to-mesh prediction, and our mesh outputs have good spatial accuracy and element quality. We check the FEA-suitability of our model-predicted meshes using a valve closure simulation. Our code is available at https://github.com/danpak94/Deep-Cardiac-Volumetric-Mesh.