用图神经网络模拟乳腺压缩变形，速度更快精度高。
Graph Neural Networks for modelling breast biomechanical compression
- 基于物理的图神经网络融合网格结构信息，支持非规则网格的归纳学习。
- 在有限元模拟数据上训练，预测节点位移与真实结果误差小。
- 适合需要快速仿真乳腺变形的临床影像配准场景。
乳腺压缩仿真对从三维模态到乳腺摄影等X射线检查的精确图像配准至关重要。它能反映组织形状和位置因压缩产生的变化，确保精准对齐与分析。尽管有限元分析（FEA）在软组织变形近似中可靠，但难以兼顾精度与计算效率。近期研究采用基于数据驱动的模型，利用FEA结果加速变形预测。本文提出探索基于物理的图神经网络（PhysGNN）用于乳腺压缩仿真。该方法首次将PhysGNN应用于乳腺变形预测，相比传统数据驱动模型，其结合了网格结构信息，支持对非规则网格的归纳学习，更适于捕捉复杂乳腺组织几何形态。模型在增量式FEA模拟生成的变形数据上训练，并通过比较预测节点位移与FE模拟结果评估性能。该深度学习框架展现出高精度、快速的乳腺变形近似能力，显著提升实际应用中的计算效率。
原文摘要 · Abstract (English)
Breast compression simulation is essential for accurate image registration from 3D modalities to X-ray procedures like mammography. It accounts for tissue shape and position changes due to compression, ensuring precise alignment and improved analysis. Although Finite Element Analysis (FEA) is reliable for approximating soft tissue deformation, it struggles with balancing accuracy and computational efficiency. Recent studies have used data-driven models trained on FEA results to speed up tissue deformation predictions. We propose to explore Physics-based Graph Neural Networks (PhysGNN) for breast compression simulation. PhysGNN has been used for data-driven modelling in other domains, and this work presents the first investigation of their potential in predicting breast deformation during mammographic compression. Unlike conventional data-driven models, PhysGNN, which incorporates mesh structural information and enables inductive learning on unstructured grids, is well-suited for capturing complex breast tissue geometries. Trained on deformations from incremental FEA simulations, PhysGNN's performance is evaluated by comparing predicted nodal displacements with those from finite element (FE) simulations. This deep learning (DL) framework shows promise for accurate, rapid breast deformation approximations, offering enhanced computational efficiency for real-world scenarios.
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