Rapid left ventricle mesh prediction by adaptive deformable model fitting

Objective.Accurate three-dimensional left ventricular mesh reconstruction from medical imaging plays a pivotal role in critical clinical applications such as cardiac biomechanical simulations, myocardial strain quantification, and pathological characterization. This study aims to overcome key limitations in existing approaches including the computational complexity of conventional finite element modeling, the heavy reliance on large-scale paired training data in deep learning methods, and limited generalizability across diverse cardiac pathologies.Approach.We present a novel adaptive deformable model fitting framework for rapid and training-free ventricular mesh prediction, incorporating two core components: (1) an adaptive mesh module leveraging proper orthogonal decomposition-derived basis functions, and (2) a two-stage fitting scheme that independently optimizes endocardial/epicardial surfaces through shared modal components. Our methodology eliminates dependence on annotated datasets through adaptive mesh basis functions derived via proper orthogonal decomposition, dynamically scaled across orthogonal spatial dimensions to accommodate inter-patient morphological variations in adaptive mesh module. The two-stage fitting scheme independently optimizes endocardial and epicardial surfaces using shared modal components while preserving anatomical topology and addressing myocardial wall thickness heterogeneity. The overall framework integrates differentiable voxelization and polyharmonic spline interpolation to achieve gradient-driven alignment between predicted meshes and segmentation masks.Main Results.Comprehensive validation across three cardiac magnetic resonance imaging datasets ,which demonstrated performance with a mean Dice coefficient of 0.85. In the clinical data set of dilated cardiomyopathy diseases, the dice value of our method averaged 0.78, which demonstrated 16% higher than that of other methods.Significance.This work further improves the accuracy of three-dimensional left ventricle fitting and enhances inference speed. The proposed approach demonstrates significant advantages by eliminating the need for additional training datasets while maintaining strong generalizability across various cardiac pathologies.