A noninvasive method for determining elastic parameters of valve tissue using physics-informed neural networks.

Journal: Acta biomaterialia
Published Date: May 26, 2025

Abstract

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.

Authors

  • Wensi Wu
    Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
  • Mitchell Daneker
    Department of Statistics and Data Science, Yale University, New Haven, CT, 06511, USA.
  • Christian Herz
    Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
  • Hannah Dewey
    Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
  • Jeffrey A Weiss
    Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA; Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.
  • Alison M Pouch
    Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
  • Lu Lu
    China School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, 510515, China.
  • Matthew A Jolley
    Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.

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