Lampen Nathan, Kim Daeseung, Xu Xuanang, Fang Xi, Lee Jungwook, Kuang Tianshu, Deng Hannah H, Liebschner Michael A K, Gateno Jaime, Yan Pingkun
Department of Biomedical Engineering and the Center for Biotechnology and Interdisciplinary Studies at Rensselaer Polytechnic Institute, Troy, New York, USA.
Department of Oral and Maxillofacial Surgery at Houston Methodist Research Institute, Houston, Texas, USA.
Med Phys. 2025 Mar;52(3):1914-1925. doi: 10.1002/mp.17554. Epub 2024 Dec 6.
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.
This study investigates the use of spatiotemporal incremental modeling for biomechanics simulations of facial soft tissue.
We implement the method using a graph neural network. Our method synergizes spatial features with temporal aggregation using DL networks trained on incremental FEM simulations from 17 subjects that underwent orthognathic surgery.
Our proposed spatiotemporal incremental method achieved a mean accuracy of 0.37 mm with a mean computation time of 1.52 s. In comparison, a spatial-only incremental method yielded a mean accuracy of 0.44 mm and a mean computation time of 1.60 s, while a spatial-only single-step method yielded a mean accuracy of 0.41 mm and a mean computation time of 0.05 s.
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.
正颌手术的手术规划需要对面部软组织进行快速准确的生物力学建模。高效的模拟在临床流程中至关重要，因为外科医生可能会反复制定多个方案。生物力学模拟通常使用有限元方法（FEM）。先前的研究将有限元模拟划分为多个增量以提高收敛性和准确性。然而，这种做法延长了模拟时间，从而阻碍了临床应用。为了加速模拟，人们探索了深度学习（DL）模型。然而，以往的研究要么是单步进行模拟，要么在增量模拟中忽略了时间因素。
本研究探讨时空增量建模在面部软组织生物力学模拟中的应用。
我们使用图神经网络实现该方法。我们的方法通过在17名接受正颌手术的受试者的增量有限元模拟上训练的深度学习网络，将空间特征与时间聚合相结合。
我们提出的时空增量方法平均精度达到0.37毫米，平均计算时间为1.52秒。相比之下，仅空间增量方法的平均精度为0.44毫米，平均计算时间为1.60秒，而仅空间单步方法的平均精度为0.41毫米，平均计算时间为0.05秒。
统计分析表明，与仅空间增量方法相比，时空增量方法减少了平均误差，强调了在增量模拟中纳入时间信息的重要性。总体而言，我们成功实现了针对软组织变形模拟的时空增量学习，同时与有限元方法相比大幅减少了模拟时间。
