FEM-augmented image registration improved aortic valve tracking accuracy by 40% compared to direct registration, reducing the mean distance error from 3.70 mm to 2.23 mm.
Does an FEM-augmented image registration framework improve aortic valve tracking accuracy compared to direct registration in patients undergoing 4D echocardiography and CT?
An FEM-augmented image registration framework improves aortic valve tracking accuracy by 40% over direct registration, enabling more precise patient-specific biomechanical strain analysis.
Absolute Event Rate: 2.23% vs 3.7%
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.
Nakhaei et al. (Tue,) conducted a other in Aortic valve disease (n=20). FEM-augmented image registration vs. Direct image registration was evaluated on Mean distance between propagated closed-state segmentation and manual ground truth (mm). FEM-augmented image registration improved aortic valve tracking accuracy by 40% compared to direct registration, reducing the mean distance error from 3.70 mm to 2.23 mm.