Learning soft tissue deformation from incremental simulations
Statistical analysis demonstrated that the spatiotemporal incremental method reduced mean errors compared to the spatial-only incremental method, emphasizing the importance of incorporating temporal information in incremental simulations. Overall, we successfully implemented spatiotemporal incremental learning tailored to simulate soft tissue deformation while substantially reducing simulation time compared to FEM.
Authors
- Han Deng (ORCID: https://orcid.org/0000-0001-9363-1722)
- Tianshu Kuang (ORCID: https://orcid.org/0000-0002-9439-464X)
- Xi Fang (ORCID: https://orcid.org/0000-0001-8938-1732)
- Michael A. K. Liebschner (ORCID: https://orcid.org/0000-0002-7713-1506)
- Nathan Lampen (ORCID: https://orcid.org/0000-0001-6125-6681)
- Pingkun Yan (ORCID: https://orcid.org/0000-0002-9779-2141)
- Jaime Gateño (ORCID: https://orcid.org/0000-0002-7597-6433)
- Xuanang Xu (ORCID: https://orcid.org/0000-0002-6045-8457)
- Daeseung Kim (ORCID: https://orcid.org/0000-0001-6270-5813)
- Jungwook Lee
Institutions
- Houston Methodist (US)
- Rensselaer Polytechnic Institute (US)
- Baylor College of Medicine (US)
- Cornell University (US)
Publication Details
- Journal
- Medical Physics
- Published
- 2024-12-06
- DOI
- https://doi.org/10.1002/mp.17554
- Citations
- 3
- Primary Topic
- Model Reduction and Neural Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.84
Funders
- National Institute of Dental and Craniofacial Research