Biomechanically Informed Image Registration for Patient-Specific Aortic Valve Strain Analysis
Mohsen Nakhaei, Alison Pouch, Silvani Amin, Matthew Daemer, Christian Herz, Natalie Yushkevich, Lourdes Al Ghofaily, Nimesh Desai, Joseph Bavaria, Matthew Jolley, Wensi Wu

TL;DR
This study introduces a biomechanically informed image registration framework that combines finite element modeling with imaging data to improve patient-specific aortic valve deformation analysis, aiding better diagnosis and treatment planning.
Contribution
We developed a novel FEM-augmented image registration method that enhances tracking accuracy and biomechanical assessment of aortic valves from imaging data.
Findings
FEM-augmented registration improved accuracy by 40% over direct registration.
Reliable strain estimation was achieved, revealing distinct deformation patterns.
Convergence in deviatoric strain suggests volumetric deformation differences across age groups.
Abstract
Aortic valve (AV) biomechanics play a critical role in maintaining normal cardiac function. Pathological variations, particularly in bicuspid aortic valves, alter leaflet loading, increase strain, and accelerate disease progression. Accurate patient-specific characterization of valve geometry and deformation is therefore essential for predicting disease progression and guiding durable repair. Current imaging and computational methods often fail to capture rapid valve motion and complex patient-specific features, limiting precise biomechanical assessment. To address these limitations, we developed an image registration framework coupled with the finite element method (FEM) to improve AV tracking and biomechanical evaluation. The valve geometries derived from 4D echocardiography and CT were used to simulate AV closure and generate intermediate deformation states. These FEM-generated…
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Taxonomy
TopicsCardiac Valve Diseases and Treatments · Aortic Disease and Treatment Approaches · Cardiovascular Function and Risk Factors
