Ann Biomed Eng. 2026 Jun 23. doi: 10.1007/s10439-026-04245-z. Online ahead of print.
ABSTRACT
PURPOSE: 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.
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 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 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.
CONCLUSION: 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.
PMID:42334707 | DOI:10.1007/s10439-026-04245-z