Wu Wensi, Daneker Mitchell, Herz Christian, Dewey Hannah, Weiss Jeffrey A, Pouch Alison M, Lu Lu, Jolley Matthew A
Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA, USA; Cardiovascular Institute, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Department of Statistics and Data Science, Yale University, New Haven, CT, USA; Department of Chemical and Biochemical Engineering, University of Pennsylvania, Philadelphia, PA, USA.
Acta Biomater. 2025 Jun 15;200:283-298. doi: 10.1016/j.actbio.2025.05.021. Epub 2025 May 26.
Computer simulation of "virtual interventions" may inform optimal valve repair for a given patient prior to intervention. However, the paucity of noninvasive methods to determine in vivo mechanical parameters of valves limits the accuracy of computer prediction and their clinical application. To address this, we propose a noninvasive method for determining elastic parameters of valve tissue using physics-informed neural networks. In this work, we demonstrated its application to the tricuspid valve of a child. We first tracked valve displacements from open to closed frames within a 3D echocardiogram time sequence using image registration. Physics-informed neural networks were subsequently applied to estimate the nonlinear mechanical properties from first principles and reference displacements. The simulated model using these patient-specific parameters closely aligned with the reference image segmentation, achieving a mean symmetric distance of less than 1 mm. Our approach doubled the accuracy of the simulated model compared to the generic parameters reported in the literature.
“虚拟干预”的计算机模拟可以在干预前为特定患者提供最佳瓣膜修复方案。然而，用于确定瓣膜体内力学参数的非侵入性方法匮乏，限制了计算机预测的准确性及其临床应用。为了解决这个问题，我们提出了一种使用物理信息神经网络来确定瓣膜组织弹性参数的非侵入性方法。在这项工作中，我们展示了其在一名儿童三尖瓣上的应用。我们首先使用图像配准在三维超声心动图时间序列中跟踪瓣膜从开放到关闭帧的位移。随后应用物理信息神经网络从第一原理和参考位移估计非线性力学特性。使用这些特定患者参数的模拟模型与参考图像分割紧密对齐，平均对称距离小于1毫米。与文献中报道的通用参数相比，我们的方法使模拟模型的准确性提高了一倍。
