ADEPT: A Noninvasive Method for Determining Elastic Parameters of Valve Tissue

TL;DR

This paper introduces ADEPT, a noninvasive technique that uses image registration and physics-informed neural networks to accurately determine the elastic properties of valve tissue, improving computer simulation precision for valve repair planning.

Contribution

ADEPT is the first noninvasive method to estimate patient-specific valve tissue elastic parameters using 3D echocardiogram data and physics-informed neural networks.

Findings

Achieved less than 1 mm mean symmetric distance in model alignment

Doubled the accuracy of simulations compared to generic parameters

Successfully applied to a child's tricuspid valve

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 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.