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

Aortic valve (AV) biomechanics play a critical role in maintaining normal cardiac function. Pathological variations, particularly in bicuspid valves, alter leaflet loading, increase strain, and accelerate disease progression. Accurate patient-specific characterization of valve geometry and deformation is essential, but existing imaging and computational methods often fail to capture rapid valve motion, discontinuous deformation 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. Patient-specific valve geometries from 4D echocardiography and CT were used to simulate AV closure and generate intermediate deformation states. These FEM-generated states facilitated leaflet tracking, while image registration corrected misalignment between simulations and imaging data.

In 20 patients, FEM-augmented registration improved tracking accuracy by 40% compared with direct registration. This improvement enabled bounded-uncertainty strain estimation by aligning leaflet geometry with patient imaging, partially compensating for uncertainties in boundary conditions and material assumptions. Using the improved tracking results, areal, Green-Lagrange, and deviatoric strains were quantified in adult trileaflet and bicuspid valves, as well as pediatric patients. Exploratory comparisons across valve groups suggest that age- and size-related differences in total strain between adult trileaflet and pediatric valves may be driven primarily by volumetric rather than deviatoric components.

This FEM-augmented registration framework improves geometric tracking of the aortic valve and yields bounded-uncertainty leaflet strain estimates with potential to inform patient-specific AV deformation for individualized intervention planning and generation of complementary training data for learning-based methods.