Biomechanically Informed Image Registration for Patient-Specific Aortic Valve Strain Analysis

Area of Science:

• Cardiovascular Biomechanics
• Medical Imaging Analysis
• Computational Biology

Background:

• Aortic valve (AV) biomechanics are crucial for cardiac function, but pathological variations, like in bicuspid aortic valves (BAVs), accelerate disease.
• Accurate, patient-specific characterization of AV geometry and deformation is vital for predicting disease progression and guiding repair.
• Current imaging and computational methods struggle to capture rapid valve motion and patient-specific features.

Purpose of the Study:

• To develop and validate a Finite Element Method (FEM)-augmented image registration framework for enhanced aortic valve tracking and biomechanical assessment.
• To improve the accuracy of patient-specific AV deformation characterization using combined imaging and computational modeling.
• To enable more reliable strain estimation and provide clinically relevant insights for individualized intervention planning.

Main Methods:

• Combined image registration with Finite Element Method (FEM) to enhance aortic valve (AV) tracking and biomechanical assessment.
• Utilized patient-specific valve geometries from 4D transesophageal echocardiography (TEE) and CT within FEM simulations.
• Developed a registration algorithm to correct mismatches between FEM-simulated deformation states and actual imaging data.

Main Results:

• FEM-augmented registration improved accuracy by 40% compared to direct registration (33% for TEE, 46% for CT) across 20 patients.
• Enabled more reliable strain estimation directly from imaging, reducing uncertainties from boundary conditions and material assumptions.
• Observed distinct deformation patterns: uniform in trileaflet adults, asymmetric in BAVs, and low mean strain with high variability in pediatric valves.

Conclusions:

• The FEM-augmented registration framework significantly enhances geometric tracking and biomechanical assessment of the aortic valve.
• This improved accuracy provides clinically relevant insights into patient-specific AV deformation, supporting individualized treatment strategies.
• Findings suggest volumetric deformation influences age- and size-related differences in AV biomechanics, with potential implications for understanding disease progression.