用物理约束神经网络，从心肌变形数据反推心脏电激活过程。
Non-Invasive Reconstruction of Cardiac Activation Dynamics Using Physics-Informed Neural Networks
- 结合力学物理规律与神经网络，直接嵌入真实心脏行为约束。
- 在噪声和低分辨率数据下仍能准确还原电活动时空动态。
- 适合心血管建模、心律失常分析与个性化医疗研究者。
心脏心律失常由复杂的电-机械耦合机制驱动，这些过程在活体中无法直接观测，因此亟需非侵入式计算方法来重建三维电激活动态。本文提出一种物理信息神经网络框架，基于可测量的形变数据，在简化左心室几何结构中恢复心脏电激活模式、主动张力传播、变形场及静水压力。该方法融合非线性各向异性本构模型、异质纤维取向、控制方程的弱形式及基于有限元的损失函数，将物理约束直接嵌入训练过程。结果表明，该框架在不同噪声水平和降低的空间分辨率下均能准确重建时空电激活动态，同时保持全局传播模式与激活时间一致性。通过结合机理建模与数据驱动推断，本方法为患者特异性非侵入式心脏电激活重构提供了新路径，具有数字表型分析与心律失常评估计算支持的潜在应用价值。
原文摘要 · Abstract (English)
Cardiac arrhythmogenesis is governed by complex electromechanical interactions that are not directly observable in vivo, motivating the development of non-invasive computational approaches for reconstructing three-dimensional activation dynamics. We present a physics-informed neural network framework for recovering cardiac activation patterns, active tension propagation, deformation fields, and hydrostatic pressure from measurable deformation data in simplified left ventricular geometries. Our approach integrates nonlinear anisotropic constitutive modeling, heterogeneous fiber orientation, weak formulations of the governing mechanics, and finite-element-based loss functions to embed physical constraints directly into training. We demonstrate that the proposed framework accurately reconstructs spatiotemporal activation dynamics under varying levels of measurement noise and reduced spatial resolution, while preserving global propagation patterns and activation timing. By coupling mechanistic modeling with data-driven inference, this method establishes a pathway toward patient-specific, non-invasive reconstruction of cardiac activation, with potential applications in digital phenotyping and computational support for arrhythmia assessment.
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