Rapid left ventricle mesh prediction by adaptive deformable model fitting.

Area of Science:

• Biomedical Engineering
• Medical Imaging Analysis
• Computational Cardiology

Background:

• Accurate 3D left ventricular mesh reconstruction is crucial for cardiac simulations and diagnostics.
• Existing methods face challenges with computational cost, data requirements, and limited generalizability.

Purpose of the Study:

• To develop a rapid, training-free framework for predicting left ventricular meshes.
• To overcome limitations of conventional finite element modeling and deep learning approaches.

Main Methods:

• A novel adaptive deformable model fitting framework utilizing proper orthogonal decomposition (POD)-derived basis functions.
• A two-stage fitting scheme optimizing endocardial and epicardial surfaces independently using shared modal components.
• Integration of differentiable voxelization and polyharmonic spline interpolation for gradient-driven mesh alignment.

Main Results:

• Achieved a mean Dice coefficient of 0.85 across three cardiac MRI datasets.
• Demonstrated a 16% improvement in Dice score (0.78) for dilated cardiomyopathy cases compared to other methods.
• Validated strong performance and generalizability across diverse cardiac pathologies.

Conclusions:

• The proposed framework enhances the accuracy and speed of 3D left ventricle reconstruction.
• This training-free approach offers significant advantages, eliminating the need for extensive annotated datasets.
• The method shows broad applicability across various cardiac conditions.