基于物理的图形神经网络来解决血流的1D方程
Ahmet Sen1, Elnaz Ghajar-Rahimi2, Miquel Aguirre3
1Mines Saint-Etienne, Univ Jean Monnet, INSERM, U 1059, Sainbiose, F-42023, France.
Computer methods and programs in biomedicine
|September 26, 2024
概括
这项研究将1D血流方程与物理信息图神经网络 (PIGNNs) 集成在一起,以准确估计动脉脉冲波. 这种新的方法使用最小的数据来预测血液流动速度和流明面积,为心血管分析提供了具有成本效益的工具.
科学领域:
- 计算流体动力学 计算流体动力学
- 生物医学工程 生物医学工程
- 机器学习在医疗保健中的应用
背景情况:
- 计算血动力学模型有助于手术规划和理解心血管疾病.
- 机器学习对于降低这些模型中的计算成本越来越重要.
研究的目的:
- 提出一种新的方法,将1D血流方程与物理信息图形神经网络 (PIGNNs) 集成在一起.
- 估计动脉中的血流速度和光线区域脉冲波传播.
- 为了降低计算成本,同时保持血液动力学模拟的准确性.
主要方法:
- 开发了一个基于图形的动脉拓,边缘代表1D流线,节点代表分析点.
- 利用PIGNNs来解码节点之间的数学关系,预测速度和流明面积波形.
- 将物理流体动力学原理纳入PIGNN损失函数,以利用速度和流明面积测量优化学习.
主要成果:
- 在各种动脉网络中实现了高精度的 (R2 > 0.99),与传统的数值方法相比.
- 通过体内数据证明有效性,达到R2>0.80用于流量和流明动力学预测.
- 展示了使用最小输入数据预测动脉脉冲波的能力,特别是血液流速测量.
结论:
- 成功地将1D血流动力学与PIGNNs集成在一起,以计算动脉网络内的流量和流量.
- 介绍了PIGNNs与血流模拟的经典物理信息神经网络 (PINNs) 的首次比较.
- 强调了这种具有成本效益的PIGNN方法在实时动脉脉冲波估计中的潜力.
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