Learning soft tissue deformation from incremental simulations.

Journal: Medical physics
PMID: 39642013

Abstract

BACKGROUND: Surgical planning for orthognathic procedures demands swift and accurate biomechanical modeling of facial soft tissues. Efficient simulations are vital in the clinical pipeline, as surgeons may iterate through multiple plans. Biomechanical simulations typically use the finite element method (FEM). Prior works divide FEM simulations into increments to enhance convergence and accuracy. However, this practice elongates simulation time, thereby impeding clinical integration. To accelerate simulations, deep learning (DL) models have been explored. Yet, previous efforts either perform simulations in a single step or neglect the temporal aspects in incremental simulations.

Authors

  • Nathan Lampen
    Department of Biomedical Engineering and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.
  • Daeseung Kim
  • Xuanang Xu
    Department of Biomedical Engineering and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.
  • Xi Fang
    Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA.
  • Jungwook Lee
    Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.
  • Tianshu Kuang
    Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, TX, USA.
  • Hannah H Deng
    Department of Oral and Maxillofacial Surgery, Houston Methodist Research Institute, Houston, TX, 77030, USA.
  • Michael A K Liebschner
  • Jaime Gateno
  • Pingkun Yan
    Philips Research North America, Briarcliff Manor, NY 10510, USA.

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