为4D血液动力学预测提供物理信息的神经网络 (PINNs):基于血管形态学的最佳框架的调查
Xuelan Zhang1, Baoyan Mao2, Yue Che1
1School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, 100083, China.
Computers in biology and medicine
|August 3, 2023
概括
这项研究引入了一个新的框架,将计算流体动力学和深度学习结合起来,以准确,实时的血液动力学预测. 该方法通过在患者特定的模型中模拟血液流动来增强心血管疾病诊断.
科学领域:
- 心血管工程 心血管工程
- 计算流体动力学 (CFD) 是一种计算流体动力学.
- 人工智能在医学中的应用
背景情况:
- 准确的血液动力学参数获取对于心血管疾病的诊断和治疗至关重要.
- 目前获取血液动力学数据的方法通常是侵入性的或缺乏实时准确性.
- 需要先进的计算方法来克服非侵入性血液动力学监测的局限性.
研究的目的:
- 开发和验证一种用于非侵入性,实时4D血液动力学预测的新型框架.
- 将计算流体动力学与基于物理的神经网络 (PINNs) 和深度学习相结合.
- 评估框架在患者特异性大动脉模型中的性能.
主要方法:
- 将计算流体动力学与使用多属性点云数据的定制分析框架集成在一起.
- 物理信息神经网络 (PINNs) 辅助深度学习模块的应用,用于流场建模.
- 在对主动脉和腹部主动脉的患者特定模型中生成流域数据集.
主要成果:
- 该框架成功地模拟了88000个病例的血液动力学中的时空异构性.
- 证明了PINNs在预测时空流场行为的有效性.
- 确定了不同船型的最佳深度学习框架,平衡准确性和计算成本.
结论:
- 与现有方法相比,拟议的框架提供了优越的计算成本,准确性和可视化.
- 该方法显示了对各种船舶位置和形态变异的概括潜力.
- 这项工作将4D血液动力学预测推向实时临床应用.
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