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This paper introduces an end-to-end framework for reconstructing 4D whole-heart meshes from sparse cine MRI sequences, addressing the challenges of limited 2D slice coverage and the complex dynamics of cardiac motion. By employing a differentiable contour renderer and a multi-scale temporal modeling module, the method achieves significant improvements in both reconstruction accuracy and temporal consistency, yielding a mean absolute error of 1.68 mm and a motion jitter of 0.77 mm/frame鲁. The results not only surpass existing techniques but also enhance 2D contour alignment and facilitate electrophysiological simulations, marking a substantial advancement in cardiac imaging.
Achieving a mean absolute error of just 1.68 mm in 4D heart mesh reconstruction could revolutionize cardiac digital twin applications.
Accurate 4D whole-heart mesh reconstruction from sparse cine MRI is critical for creating cardiac digital twins, but remains challenging due to limited 2D slice coverage and the complex coupling between cardiac shape and motion. Existing methods often rely on intermediate contour fitting and typically reconstruct static, single-phase, or partial cardiac geometries, limiting their ability to capture full-chamber dynamics. We propose a novel end-to-end framework for reconstructing temporally resolved whole-heart meshes from multi-view 2D cine MRI sequences by learning an image-to-mesh mapping. The framework incorporates a differentiable contour renderer inspired by the Beer-Lambert attenuation principle, enabling anatomy-aware supervision of 3D+t mesh deformation through contour-based projection losses. To improve temporal consistency across the cardiac cycle, we further introduce a multi-scale temporal modeling module that integrates global cycle-level dynamics with local inter-frame coherence to generate smooth and physiologically plausible mesh trajectories. The proposed method achieved a whole-heart mean absolute error of 1.68 $\pm$ 0.31 mm and a motion jitter of 0.77 $\pm$ 0.17 $\mathrm{mm}/\mathrm{frame}^{3}$, outperforming existing methods with lower reconstruction error and substantially improved motion smoothness. It also improved 2D contour alignment across multiple cine MRI views and supported downstream proof-of-concept electrophysiological simulation. The code will be released publicly upon acceptance of the manuscript for publication.
