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arXiv · 2510.06578

A geometric feature tracking approach for noninvasive patient specific estimation of leaflet strain from 3D images of heart valves

Wensi Wu·Matthew Daemer·Jeffrey A. Weiss·Alison M. Pouch·Matthew A. Jolley
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Abstract

Valvular heart disease is prevalent and a major contributor to heart failure. Valve leaflet strain is a promising metric for evaluating the mechanics underlying the initiation and progression of valvular pathology. However, generalizable methods for noninvasively quantifying valvular strain from clinically acquired patient images remain limited. To address this limitation, we developed a geometric feature-tracking framework to quantify in vivo leaflet strain from 3DE images. The method integrates a cohort-derived geometric reference atlas to establish geometric correspondence and introduces a novel distance-weighted coherent point drift algorithm for non-rigid registration. We evaluated performance against a finite element benchmark model and compared the approach with conventional point-based tracking methods. The framework was applied to pediatric and adult patient datasets (N = 31) across variable valve morphologies. The proposed method demonstrated greater accuracy in quantifying anatomical alignment and leaflet strain than conventional point-based approaches. Validation against the finite element benchmark confirmed improved strain estimation. The framework achieved reliable inter-phase tracking of valve deformation across diverse morphologies in pediatric and adult patients. Analysis identified a consistent distribution pattern of the 1st principal strain associated with leaflet billow (prolapse). This feature-tracking framework provides a generalizable method for noninvasive quantification of atrioventricular valve leaflet strain from clinical 3DE images. Characterization of biomechanical strain patterns may improve prognostic assessment and support longitudinal evaluation of valvular heart disease. Further investigation of the biomechanical signatures of heart valve disease has the potential to enhance prognostic assessment and longitudinal evaluation of valvular heart disease.

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Wensi Wu, Matthew Daemer, Jeffrey A. Weiss, Alison M. Pouch, Matthew A. Jolley. 2026-02-24. A geometric feature tracking approach for noninvasive patient specific estimation of leaflet strain from 3D images of heart valves. https://arxiv.org/abs/2510.06578

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