Anatomy-Aligned Surface Field Learning for Myocardial Reconstruction from Sparse Short-Axis Cine MRI

2026-09-24

AI Summary

This study addresses the challenge of achieving dense, anatomically consistent myocardial surface modeling from sparse short-axis cardiac MRI. We propose an anatomy-aligned surface learning framework that introduces a novel shared UV parameterization approach, reformulating 3D reconstruction as coordinate field completion within the UV domain. By integrating coverage-aware sampling with topology-distortion-aware learning, the framework ensures explicit anatomical correspondence across subjects and cardiac phases. Extensive evaluations on three public datasets demonstrate that our method significantly outperforms existing baselines in terms of Chamfer distance while yielding minimal errors in ventricular function assessment. The source code has been made publicly available to facilitate future research.

Abstract

Patient-specific 4D myocardial reconstruction from cine MRI supports quantitative functional assessment, regional motion analysis, and simulation-based modeling. However, routinely acquired short-axis (SAX) cine MRI is sparsely sampled along the through-plane direction, making dense and anatomically consistent surface reconstruction challenging. In this study, we propose an anatomy-aligned surface learning framework that parameterizes the epicardial and endocardial surfaces on a shared circumferential-longitudinal UV domain. This formulation converts irregular 3D reconstruction into structured coordinate-field completion with explicit correspondence across subjects and cardiac phases. Sparse SAX contours are encoded as UV observation fields, coverage-aware sampling improves robustness to incomplete slice coverage, and topology- and distortion-aware learning preserves circumferential continuity and local surface quality. Experiments on three public cine MRI datasets showed that the proposed method consistently outperformed representative mesh-based and implicit reconstruction approaches, achieving overall Chamfer distances of $2.887$~mm on ACDC, $2.641$~mm on M\&Ms, and $2.810$~mm on M\&Ms-2. The reconstructed sequences also preserved ventricular function, with end-diastolic volume and ejection fraction errors of $3.3$~mL and $1.1 \%$, respectively. These results demonstrate that anatomy-aligned UV learning provides an accurate, efficient, and correspondence-aware representation for sparse cine MRI reconstruction and myocardial modeling. The source code will be available at https://github.com/yuan-xiaohan/SAX2MyoSurf.

Problem

• Myocardial Reconstruction
• Cine MRI
• Sparse Sampling
• Short-Axis
• 4D Surface Reconstruction

Innovation

• Anatomy-Aligned Surface Learning
• UV Parameterization
• Sparse Cine MRI
• Myocardial Reconstruction
• Coordinate-Field Completion