Spatiotemporal Incremental Mechanics Modeling of Facial Tissue Change

Accurate surgical planning for orthognathic surgical procedures requires biomechanical simulation of facial soft tissue changes. Simulations must be performed quickly and accurately to be useful in a clinical pipeline, and surgeons may try several iterations before arriving at an optimal surgical plan. The finite element method (FEM) is commonly used to perform biomechanical simulations. Previous studies divided FEM simulations into incremental steps to improve convergence and model accuracy. While incremental simulations are more realistic, they greatly increase FEM simulation time, preventing integration into clinical use. In an attempt to make simulations faster, deep learning (DL) models have been developed to replace FEM for biomechanical simulations. However, previous DL models are not designed to utilize temporal information in incremental simulations. In this study, we propose Spatiotemporal Incremental Mechanics Modeling (SIMM), a deep learning method that performs spatiotemporally-aware incremental simulations for mechanical modeling of soft tissues. Our method uses both spatial and temporal information by combining a spatial feature extractor with a temporal aggregation mechanism. We trained our network using incremental FEM simulations of 18 subjects from our repository. We compared SIMM to spatial-only incremental and single-step simulation approaches. Our results suggest that adding spatiotemporal information may improve the accuracy of incremental simulations compared to methods that use only spatial information.