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

Daniel H. Pak, Minliang Liu, Theodore Kim, Liang Liang, Raymond McKay, Wei Sun, James S. Duncan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2021 - 24th International Conference, Proceedings
EditorsMarleen de Bruijne, Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert
PublisherSpringer Science and Business Media Deutschland GmbH
Pages485-494
Number of pages10
ISBN (Print)9783030872304
DOIs
StatePublished - 2021
Event24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online
Duration: Sep 27 2021Oct 1 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12906 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
CityVirtual, Online
Period9/27/2110/1/21

Keywords

  • 3D image to volumetric mesh
  • Aortic valve modeling
  • ARAP energy
  • CNN-GCN
  • Finite element analysis

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint

Dive into the research topics of 'Distortion Energy for Deep Learning-Based Volumetric Finite Element Mesh Generation for Aortic Valves'. Together they form a unique fingerprint.

Cite this