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

Purpose: 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. However, existing imaging and computational methods often fail to capture rapid valve motion and complex patient-specific features, limiting precise biomechanical assessment. Methods: 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 derived 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. Results: In 20 patients, FEM-augmented registration improved tracking accuracy by 40% compared with direct registration. This improvement enabled more reliable strain estimation by measuring leaflet deformation directly from imaging and reducing uncertainties associated with 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, revealing distinct deformation patterns across valve groups. Convergence in mean deviatoric strain between adult trileaflet and pediatric valves suggests volumetric deformation underlies age- and size-related differences in AV mechanics. Conclusion: Overall, this FEM-augmented registration framework enhances geometric tracking and biomechanical evaluation accuracy, providing clinically relevant insights into patient-specific AV deformation to support individualized medical and intervention planning.