Learning reduced-order models for cardiovascular simulations with graph neural networks.
Learning reduced-order models for cardiovascular simulations with graph neural networks.
复制标题
使用图神经网络学习心血管模拟的降阶模型。
DOI：
10.1016/j.compbiomed.2023.107676
复制
发表时间：
2024
影响因子：
7.7
通讯作者：
Marsden,AlisonL
中科院分区：
文献类型：
--
作者：
Pegolotti,Luca;Pfaller,MartinR;Rubio,NataliaL;Ding,Ke;BrugarolasBrufau,Rita;Darve,Eric;Marsden,AlisonL
基于物理学的降阶模型由于其效率而成为心血管建模中的流行选择，但当处理包含许多连接或病理条件的解剖结构时，它们可能会损失准确性。我们开发了一维降阶模型，该模型使用在三维血液动力学模拟数据上训练的图神经网络来模拟血流动力学。给定系统的初始条件，网络迭代地预测血管中心线节点处的压力和流速。我们的数值结果表明，我们的方法在生理几何形状，包括各种解剖结构和边界条件的准确性和普遍性。我们的研究结果表明，只要有足够的训练数据，我们的方法可以实现压力和流量的误差低于3%。因此，我们的方法表现出上级性能相比，基于物理的一维模型，同时保持高效率的推理时间。
Reduced-order models based on physics are a popular choice in cardiovascular modeling due to their efficiency, but they may experience loss in accuracy when working with anatomies that contain numerous junctions or pathological conditions. We develop one-dimensional reduced-order models that simulate blood flow dynamics using a graph neural network trained on three-dimensional hemodynamic simulation data. Given the initial condition of the system, the network iteratively predicts the pressure and flow rate at the vessel centerline nodes. Our numerical results demonstrate the accuracy and generalizability of our method in physiological geometries comprising a variety of anatomies and boundary conditions. Our findings demonstrate that our approach can achieve errors below 3% for pressure and flow rate, provided there is adequate training data. As a result, our method exhibits superior performance compared to physics-based one-dimensional models while maintaining high efficiency at inference time.