Purpose: 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. This study aims to develop a robust feature tracking framework that enables accurate shape matching across variable valve morphologies and quantification of in vivo atrioventricular leaflet strain from three-dimensional echocardiographic (3DE) images in pediatric and adult patients. Methods We developed a geometric feature tracking framework to quantify in vivo leaflet strain from 3DE images and to assess anatomical deformation across the cardiac cycle. Our approach integrates a cohort-derived geometric reference atlas to establish geometric correspondence and introduces a novel distance-weighted coherent point drift algorithm within a Gaussian mixture model framework 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) to assess robustness across variable valve morphologies. Results 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 <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$1^{\text {st}}$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <msup> <mn>1</mn> <mrow> <mi mathvariant="italic">st</mi> </mrow> </msup> </mrow> </math></EquationSource> </InlineEquation> principal strain associated with leaflet billow (prolapse). Conclusion 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.

A Geometric Feature Tracking Approach for Noninvasive Patient-Specific Estimation of Leaflet Strain from 3D Images of Heart Valves

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2026年7月11日

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

摘要

Purpose:

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. This study aims to develop a robust feature tracking framework that enables accurate shape matching across variable valve morphologies and quantification of in vivo atrioventricular leaflet strain from three-dimensional echocardiographic (3DE) images in pediatric and adult patients.

Methods

We developed a geometric feature tracking framework to quantify in vivo leaflet strain from 3DE images and to assess anatomical deformation across the cardiac cycle. Our approach integrates a cohort-derived geometric reference atlas to establish geometric correspondence and introduces a novel distance-weighted coherent point drift algorithm within a Gaussian mixture model framework 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) to assess robustness across variable valve morphologies.

Results

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 $1^{\text {st}}$ $1^{st}$ principal strain associated with leaflet billow (prolapse).

Conclusion

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.