mlsanityData to 13 Oct 2026

4D Myocardium Reconstruction with Decoupled Motion and Shape Model

arXiv 2308.14083 ↗First posted 27 Aug 2023cs.CVcited by 15

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Abstract (arXiv)

Estimating the shape and motion state of the myocardium is essential in diagnosing cardiovascular diseases.However, cine magnetic resonance (CMR) imaging is dominated by 2D slices, whose large slice spacing challenges inter-slice shape reconstruction and motion acquisition.To address this problem, we propose a 4D reconstruction method that decouples motion and shape, which can predict the inter-/intra- shape and motion estimation from a given sparse point cloud sequence obtained from limited slices. Our framework comprises a neural motion model and an end-diastolic (ED) shape model. The implicit ED shape model can learn a continuous boundary and encourage the motion model to predict without the supervision of ground truth deformation, and the motion model enables canonical input of the shape model by deforming any point from any phase to the ED phase. Additionally, the constructed ED-space enables pre-training of the shape model, thereby guiding the motion model and addressing the issue of data scarcity. We propose the first 4D myocardial dataset as we know and verify our method on the proposed, public, and cross-modal datasets, showing superior reconstruction performance and enabling various clinical applications.

Key facts. Cited by 15 papers in the map (data through 13 Oct 2026). Builds on 35 of its 50 references that are in the map. First posted on arXiv 27 Aug 2023.

Builds on

35 of 50 references.

References · 35

  1. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,686
  2. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,039
  3. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
  4. 2018VoxelMorph: A Learning Framework for Deformable Medical Image Registrationcited by 657
  5. 2021SMPLicit: Topology-aware Generative Model for Clothed Peoplecited by 154
  6. 2020Deformed Implicit Field: Modeling 3D Shapes with Learned Dense Correspondencecited by 119
  7. 2021LEAP: Learning Articulated Occupancy of Peoplecited by 107
  8. 2021Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothingcited by 102
  9. 2021NPMs: Neural Parametric Models for 3D Deformable Shapescited by 89
  10. 2020Deep Implicit Templates for 3D Shape Representationcited by 86
  11. 2020DualSDF: Semantic Shape Manipulation using a Two-Level Representationcited by 82
  12. 2019Voxel2Mesh: 3D Mesh Model Generation from Volumetric Datacited by 52
23 more
  1. 2018Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequencescited by 47
  2. 2021A Deep-Learning Approach For Direct Whole-Heart Mesh Reconstructioncited by 47
  3. 20183D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single Imagecited by 40
  4. 2022Topology-Preserving Shape Reconstruction and Registration via Neural Diffeomorphic Flowcited by 38
  5. 2019Automated Design of Deep Learning Methods for Biomedical Image Segmentationcited by 27
  6. 2021DeepTag: An Unsupervised Deep Learning Method for Motion Tracking on Cardiac Tagging Magnetic Resonance Imagescited by 26
  7. 2021Deep Implicit Statistical Shape Models for 3D Medical Image Delineationcited by 26
  8. 2020Biomechanics-informed Neural Networks for Myocardial Motion Tracking in MRIcited by 24
  9. 2020FOAL: Fast Online Adaptive Learning for Cardiac Motion Estimationcited by 21
  10. 2020Uncertain-DeepSSM: From Images to Probabilistic Shape Modelscited by 20
  11. 2022MulViMotion: Shape-aware 3D Myocardial Motion Tracking from Multi-View Cardiac MRIcited by 16
  12. 2019DeepOrganNet: On-the-Fly Reconstruction and Visualization of 3D / 4D Lung Models from Single-View Projections by Deep Deformation Networkcited by 15
  13. 2020Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimationcited by 15
  14. 2021Whole Heart Mesh Generation For Image-Based Computational Simulations By Learning Free-From Deformationscited by 12
  15. 2019One-stage Shape Instantiation from a Single 2D Image to 3D Point Cloudcited by 9
  16. 2021Unsupervised Landmark Detection Based Spatiotemporal Motion Estimation for 4D Dynamic Medical Imagescited by 9
  17. 2022Mesh-based 3D Motion Tracking in Cardiac MRI using Deep Learningcited by 9
  18. 20193D Cardiac Shape Prediction with Deep Neural Networks: Simultaneous Use of Images and Patient Metadatacited by 8
  19. 2022LoRD: Local 4D Implicit Representation for High-Fidelity Dynamic Human Modelingcited by 8
  20. 2022Learning correspondences of cardiac motion from images using biomechanics-informed modelingcited by 8
  21. 2019Probabilistic Motion Modeling from Medical Image Sequences: Application to Cardiac Cine-MRIcited by 7
  22. 2020PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Datacited by 7
  23. 2020Anatomy-Aware Cardiac Motion Estimationcited by 3

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 6; 2026: 7.

15 papers.

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  1. 2023SDF4CHD: Generative Modeling of Cardiac Anatomies with Congenital Heart Defectscited by 17
  2. 2025End-to-End 4D Heart Mesh Recovery Across Full-Stack and Sparse Cardiac MRIcited by 5
  3. 2025From Pixels to Polygons: A Survey of Deep Learning Approaches for Medical Image-to-Mesh Reconstructioncited by 4
  4. 2025Temporal Differential Fields for 4D Motion Modeling via Image-to-Video Synthesiscited by 3
  5. 2025Personalized 3D Myocardial Infarct Geometry Reconstruction from Cine MRI with Explicit Cardiac Motion Modelingcited by 3
  6. 2024MorphiNet: A Graph Subdivision Network for Adaptive Bi-ventricle Surface Reconstructioncited by 1
  7. 2025Dyna3DGR: 4D Cardiac Motion Tracking with Dynamic 3D Gaussian Representationcited by 1
  8. 2026VecHeart: Holistic Four-Chamber Cardiac Anatomy Modeling via Hybrid VecSetscited by 1
  9. 2026Personalized 3D Myocardial Infarct Geometry Reconstruction from Cine MRI for Cardiac Digital Twinscited by 1
  10. 2026Personalized 4D Whole-Heart Mesh Reconstruction from Cine MRI via Multi-Scale Temporal Modeling and Differentiable Contour Renderingcited by 1
  11. 2025Personalized 4D Whole Heart Geometry Reconstruction from Cine MRI for Cardiac Digital Twinscited by 0
  12. 2026Uncertainty Quantification for Cardiac Shape Reconstruction with Deep Signed Distance Functions via MCMC methodscited by 0
3 more
  1. 2026Echo4DIR: 4D Implicit Heart Reconstruction from 2D Echocardiography Videoscited by 0
  2. 2026Learning to Beat: Phenotype-Guided Latent Flow with Regional Motion Priors for Biventricular Motion Synthesiscited by 0
  3. 2026Anatomy-Aligned Surface Field Learning for Myocardial Reconstruction from Sparse Short-Axis Cine MRIcited by 0