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 ADEPT: A noninvasive method for Determining Elastic Parameters of valve Tissue. 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.
@article{arxiv.2409.19081,
title = {ADEPT: A Noninvasive Method for Determining Elastic Parameters of Valve Tissue},
author = {Wensi Wu and Mitchell Daneker and Christian Herz and Hannah Dewey and Jeffrey A. Weiss and Alison M. Pouch and Lu Lu and Matthew A. Jolley},
journal= {arXiv preprint arXiv:2409.19081},
year = {2025}
}