mlsanityData to 14 Oct 2026

CineMesh4D: Personalized 4D Whole Heart Reconstruction from Sparse Cine MRI

arXiv 2605.13994 ↗First posted 13 May 2026cs.CV cs.AIcited by 3

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Abstract (arXiv)

Accurate 3D+t whole-heart mesh reconstruction from cine MRI is a clinically crucial yet technically challenging task. The difficulty of this task arises from two coupled factors: inherently sparse sampling of 3D cardiac anatomy by 2D image slices and the tight coupling between cardiac shape and motion. Current cardiac image-to-mesh approaches typically reconstruct only a subset of cardiac chambers or a single phase of the cardiac cycle. In this work, we propose CineMesh4D, a novel end-to-end 4D (3D+t) pipeline that directly reconstructs patient-specific whole-heart mesh from multi-view 2D cine MRI via cross-domain mapping. Specifically, we introduce a differentiable rendering loss that enables supervision of 3D+t whole-heart mesh from multi-view sparse contours of cine MRI. Furthermore, we develop a dual-context temporal block that fuses global and local cardiac temporal information to capture high-dimensional sequential patterns. In quantitative and qualitative evaluations, CineMesh4D outperforms existing approaches in terms of reconstruction quality and motion consistency, providing a practical pathway for personalized real-time cardiac assessment. The code will be publicly released once the manuscript is accepted.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 20 of its 27 references that are in the map. First posted on arXiv 13 May 2026.

Builds on

20 of 27 references.

References · 20

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  12. 2021A Deep-Learning Approach For Direct Whole-Heart Mesh Reconstructioncited by 47
8 more
  1. 2019DARNet: Deep Active Ray Network for Building Segmentationcited by 30
  2. 2022Multi-Modality Cardiac Image Computing: A Surveycited by 19
  3. 2023Towards Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inferencecited by 18
  4. 2019End to End Trainable Active Contours via Differentiable Renderingcited by 15
  5. 2023DeepMesh: Mesh-based Cardiac Motion Tracking using Deep Learningcited by 14
  6. 2023Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRIcited by 12
  7. 2024Improved 3D Whole Heart Geometry from Sparse CMR Slicescited by 8
  8. 2022Generative Modelling of the Ageing Heart with Cross-Sectional Imaging and Clinical Datacited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.