在弹性血管网络中重建in-vitro和in-vivo信号和参数,使用物理信息的神经网络
J Orera1, J Mairal1, L Sánchez-Fuster1
1Aragón Institute of Engineering Research, University of Zaragoza, C. de Mariano Esquillor Gómez, S/N, Zaragoza, 50018, Spain.
Computers in biology and medicine
|January 23, 2026
概括
物理信息神经网络 (PINNs) 可以重建动脉波形并从实验数据中推断隐藏的参数. 这种0D-PINN方法准确地模拟了弹性血管中的血流,在心血管研究中推进了物理建模.
科学领域:
- 生物医学工程 生物医学工程
- 计算流体动力学的流体动力学.
- 人工智能在医学中的应用
背景情况:
- 在弹性动脉网络中精确物理建模血液流动是必不可少的,但由于无法测量的机械性质而具有挑战性.
- 重建波形和隐藏参数对于理解稳定和短暂的流动至关重要.
研究的目的:
- 调查物理信息神经网络 (PINNs) 在重建压力和流量信号方面的潜力.
- 从弹性容器网络中的实验数据中推断参数.
主要方法:
- 在一个神经网络 (0D-PINN) 中,将描述弹性容器流动的零维 (0D) 交联微分方程系统纳入神经网络 (0D-PINN).
- 使用各种测试案例进行评估,包括实验性模拟动脉网络和人胸前动脉中的体内MRI数据.
主要成果:
- 从实验数据中证明了从实验数据中成功恢复参数和波形.
- 在体外和体外数据集上验证了0D-PINN方法,包括一个复杂的37血管模拟动脉系统.
- 展示了从健康成年人胸前大动脉的临床数据的适用性.
结论:
- 将0D模型与PINN (0D-PINN) 合在一起,是参数恢复和波形重建的有效方法.
- 这种方法利用实验数据推进了在弹性动脉网络中血流的物理建模.
- 0D-PINN方法在体外和体外心血管应用方面表现有前途.
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