# Personalized 4D Whole-Heart Mesh Reconstruction from Cine MRI via Multi-Scale Temporal Modeling and Differentiable Contour Rendering arXiv: https://arxiv.org/abs/2607.01952 · HTML: https://mlsanity.com/paper/2607.01952/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Builds on 31 of its 69 references that are in the map. - First posted on arXiv 2 Jul 2026. ## Builds on (most cited first, 30 of 31) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017) - [Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images](https://mlsanity.com/paper/1804.01654/) (2018) - [Generating 3D faces using Convolutional Mesh Autoencoders](https://mlsanity.com/paper/1807.10267/) (2018) - [PU-Net: Point Cloud Upsampling Network](https://mlsanity.com/paper/1801.06761/) (2018) - [WHAM: Reconstructing World-grounded Humans with Accurate 3D Motion](https://mlsanity.com/paper/2312.07531/) (2023) - [Boundary loss for highly unbalanced segmentation](https://mlsanity.com/paper/1812.07032/) (2018) - [Exphormer: Sparse Transformers for Graphs](https://mlsanity.com/paper/2303.06147/) (2023) - [Physical Inertial Poser (PIP): Physics-aware Real-time Human Motion Tracking from Sparse Inertial Sensors](https://mlsanity.com/paper/2203.08528/) (2022) - [Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation](https://mlsanity.com/paper/1705.08302/) (2017) - [Deep learning cardiac motion analysis for human survival prediction](https://mlsanity.com/paper/1810.03382/) (2018) - [DARNet: Deep Active Ray Network for Building Segmentation](https://mlsanity.com/paper/1905.05889/) (2019) - [Learning Whole Heart Mesh Generation From Patient Images For Computational Simulations](https://mlsanity.com/paper/2203.10517/) (2022) - [Towards Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference](https://mlsanity.com/paper/2307.04421/) (2023) - [Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow](https://mlsanity.com/paper/1811.03433/) (2018) - [End to End Trainable Active Contours via Differentiable Rendering](https://mlsanity.com/paper/1912.00367/) (2019) - [4D Myocardium Reconstruction with Decoupled Motion and Shape Model](https://mlsanity.com/paper/2308.14083/) (2023) - [Solving the Inverse Problem of Electrocardiography for Cardiac Digital Twins: A Survey](https://mlsanity.com/paper/2406.11445/) (2024) - [Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRI](https://mlsanity.com/paper/2311.13706/) (2023) - [Mesh-based 3D Motion Tracking in Cardiac MRI using Deep Learning](https://mlsanity.com/paper/2209.02004/) (2022) - [CardiacFlow: 3D+t Four-Chamber Cardiac Shape Completion and Generation via Flow Matching](https://mlsanity.com/paper/2509.05754/) (2025) - [Cobiveco: Consistent biventricular coordinates for precise and intuitive description of position in the heart -- with MATLAB implementation](https://mlsanity.com/paper/2102.02898/) (2021) - [A comprehensive and biophysically detailed computational model of the whole human heart electromechanics](https://mlsanity.com/paper/2207.12460/) (2022) - [Improved 3D Whole Heart Geometry from Sparse CMR Slices](https://mlsanity.com/paper/2408.07532/) (2024) - [Modeling 3D cardiac contraction and relaxation with point cloud deformation networks](https://mlsanity.com/paper/2307.10927/) (2023) - [An Automated Computational Pipeline for Generating Large-Scale Cohorts of Patient-Specific Ventricular Models in Electromechanical In Silico Trials](https://mlsanity.com/paper/2503.03706/) (2025) - [CineMesh4D: Personalized 4D Whole Heart Reconstruction from Sparse Cine MRI](https://mlsanity.com/paper/2605.13994/) (2026) - [UltraTwin: Towards Cardiac Anatomical Twin Generation from Multi-view 2D Ultrasound](https://mlsanity.com/paper/2506.23490/) (2025) - [Cardiac Mesh Flow: One-Step Generation of 3D+t Cardiac Four-Chamber Meshes via Flow Matching](https://mlsanity.com/paper/2605.01884/) (2026) ## Built on by (most cited first, 1 of 1) - [A Strong Linear Baseline for Whole-Heart Cardiac Shape Completion on CT, with an Open Eleven-Structure Statistical Shape Model](https://mlsanity.com/paper/2608.19932/) (2026) ## Abstract (arXiv) 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 ± 0.31 mm and a motion jitter of 0.77 ± 0.17 mm/frame3, 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.