用图神经网络实现软硬组织交互的实时高精度仿真。
MIXPINN: Mixed-Material Simulations by Physics-Informed Neural Network
- 构建带虚拟节点和边的图神经网络，显式建模软硬组织交互。
- 推理速度达实时，误差小于1毫米，计算成本降低十倍。
- 适合手术训练、机器人辅助手术等需要实时反馈的场景。
模拟软组织与刚性解剖结构之间的复杂相互作用，在手术训练、规划和机器人辅助干预中至关重要。传统基于有限元法（FEM）的仿真虽准确但计算成本高昂，难以满足实时需求。基于学习的方法虽能加速预测，却难以有效建模软-刚交互。我们提出MIXPINN，一种基于物理信息图神经网络（GNN）的混合材料仿真框架，通过图结构增强显式捕捉软-刚交互。方法引入虚拟节点（VNs）和虚拟边（VEs），在保持计算效率的同时强化刚体约束。利用生物力学结构的图表示，MIXPINN从FEM生成数据中学习高保真形变，实现亚毫米级精度的实时推理。在真实临床场景中验证表明，其性能优于基线GNN模型和传统FEM方法。结果表明，MIXPINN将计算成本降低一个数量级，同时保持高物理准确性，为实时手术仿真和机器人辅助操作提供了可行方案。
原文摘要 · Abstract (English)
Simulating the complex interactions between soft tissues and rigid anatomy is critical for applications in surgical training, planning, and robotic-assisted interventions. Traditional Finite Element Method (FEM)-based simulations, while accurate, are computationally expensive and impractical for real-time scenarios. Learning-based approaches have shown promise in accelerating predictions but have fallen short in modeling soft-rigid interactions effectively. We introduce MIXPINN, a physics-informed Graph Neural Network (GNN) framework for mixed-material simulations, explicitly capturing soft-rigid interactions using graph-based augmentations. Our approach integrates Virtual Nodes (VNs) and Virtual Edges (VEs) to enhance rigid body constraint satisfaction while preserving computational efficiency. By leveraging a graph-based representation of biomechanical structures, MIXPINN learns high-fidelity deformations from FEM-generated data and achieves real-time inference with sub-millimeter accuracy. We validate our method in a realistic clinical scenario, demonstrating superior performance compared to baseline GNN models and traditional FEM methods. Our results show that MIXPINN reduces computational cost by an order of magnitude while maintaining high physical accuracy, making it a viable solution for real-time surgical simulation and robotic-assisted procedures.
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