从磁共振图像中建模肺动脉血压的物理信息神经网络:一个减少顺序的纳维埃-斯托克斯模型
Sebastián Jara1, Julio Sotelo1, David Ortiz-Puerta2,3,4
1Departamento de Informática, Universidad Técnica Federico Santa María, Santiago 8940897, Chile.
Biomedicines
|September 27, 2025
概括
基于物理学的神经网络 (PINNs) 提供一种非侵入性方法,使用4D流MRI数据来估计肺动脉压力. 这种方法避免了危险的导管治疗,并且在心血管诊断方面显示出有前途的结果.
科学领域:
- 心血管成像和建模.
- 计算流体动力学 计算流体动力学
- 医学中的人工智能
背景情况:
- 肺动脉压对于诊断心血管和肺部疾病至关重要.
- 目前通过右心导管测量黄金标准是侵入性的和有风险的.
- 需要使用患者特定成像的非侵入性技术.
研究的目的:
- 开发和实施一个物理信息神经网络 (PINN) 模型来估计肺动脉压力.
- 预测肺动脉分叉的压力,速度和面积变化.
- 为了验证使用4D流MRI数据的非侵入性方法.
主要方法:
- 实现了一个PINN模型,集成了1D减小的纳维埃-斯托克斯物理学.
- 该模型利用了肺动脉分叉中的4D流MRI的速度和面积测量.
- 训练包含了对流量和动量保存的处罚.
主要成果:
- 在健康患者中,PINN模型成功估计了肺动脉压.
- 压力估计与文献值一致,平均动脉压为21.5mmHg.
- 该模型预测了整个分叉的压力,速度和面积变化.
结论:
- 皮恩为肺动脉压估计提供了导管治疗的创新性,非侵入性的替代方案.
- 这是第一个应用PINNs来估计肺动脉分叉的压力的研究.
- 未来的工作包括对较大人群和肺高血压病例的验证.
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