# 4D Myocardium Reconstruction with Decoupled Motion and Shape Model

arXiv: https://arxiv.org/abs/2308.14083 · HTML: https://mlsanity.com/paper/2308.14083/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 50 references that are in the map.
- First posted on arXiv 27 Aug 2023.

## Builds on (most cited first, 30 of 35)

- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [Learning Implicit Fields for Generative Shape Modeling](https://mlsanity.com/paper/1812.02822/) (2018)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [SMPLicit: Topology-aware Generative Model for Clothed People](https://mlsanity.com/paper/2103.06871/) (2021)
- [Deformed Implicit Field: Modeling 3D Shapes with Learned Dense Correspondence](https://mlsanity.com/paper/2011.13650/) (2020)
- [LEAP: Learning Articulated Occupancy of People](https://mlsanity.com/paper/2104.06849/) (2021)
- [Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothing](https://mlsanity.com/paper/2108.08807/) (2021)
- [NPMs: Neural Parametric Models for 3D Deformable Shapes](https://mlsanity.com/paper/2104.00702/) (2021)
- [Deep Implicit Templates for 3D Shape Representation](https://mlsanity.com/paper/2011.14565/) (2020)
- [DualSDF: Semantic Shape Manipulation using a Two-Level Representation](https://mlsanity.com/paper/2004.02869/) (2020)
- [Voxel2Mesh: 3D Mesh Model Generation from Volumetric Data](https://mlsanity.com/paper/1912.03681/) (2019)
- [Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences](https://mlsanity.com/paper/1806.04066/) (2018)
- [A Deep-Learning Approach For Direct Whole-Heart Mesh Reconstruction](https://mlsanity.com/paper/2102.07899/) (2021)
- [3D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single Image](https://mlsanity.com/paper/1807.07796/) (2018)
- [Topology-Preserving Shape Reconstruction and Registration via Neural Diffeomorphic Flow](https://mlsanity.com/paper/2203.08652/) (2022)
- [Automated Design of Deep Learning Methods for Biomedical Image Segmentation](https://mlsanity.com/paper/1904.08128/) (2019)
- [DeepTag: An Unsupervised Deep Learning Method for Motion Tracking on Cardiac Tagging Magnetic Resonance Images](https://mlsanity.com/paper/2103.02772/) (2021)
- [Deep Implicit Statistical Shape Models for 3D Medical Image Delineation](https://mlsanity.com/paper/2104.02847/) (2021)
- [Biomechanics-informed Neural Networks for Myocardial Motion Tracking in MRI](https://mlsanity.com/paper/2006.04725/) (2020)
- [FOAL: Fast Online Adaptive Learning for Cardiac Motion Estimation](https://mlsanity.com/paper/2003.04492/) (2020)
- [Uncertain-DeepSSM: From Images to Probabilistic Shape Models](https://mlsanity.com/paper/2007.06516/) (2020)
- [MulViMotion: Shape-aware 3D Myocardial Motion Tracking from Multi-View Cardiac MRI](https://mlsanity.com/paper/2208.00034/) (2022)
- [DeepOrganNet: On-the-Fly Reconstruction and Visualization of 3D / 4D Lung Models from Single-View Projections by Deep Deformation Network](https://mlsanity.com/paper/1907.09375/) (2019)
- [Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation](https://mlsanity.com/paper/2006.15710/) (2020)
- [Whole Heart Mesh Generation For Image-Based Computational Simulations By Learning Free-From Deformations](https://mlsanity.com/paper/2107.10839/) (2021)
- [One-stage Shape Instantiation from a Single 2D Image to 3D Point Cloud](https://mlsanity.com/paper/1907.10763/) (2019)
- [Unsupervised Landmark Detection Based Spatiotemporal Motion Estimation for 4D Dynamic Medical Images](https://mlsanity.com/paper/2109.14805/) (2021)
- [Mesh-based 3D Motion Tracking in Cardiac MRI using Deep Learning](https://mlsanity.com/paper/2209.02004/) (2022)
- [3D Cardiac Shape Prediction with Deep Neural Networks: Simultaneous Use of Images and Patient Metadata](https://mlsanity.com/paper/1907.01913/) (2019)

## Built on by (most cited first, 15 of 15)

- [SDF4CHD: Generative Modeling of Cardiac Anatomies with Congenital Heart Defects](https://mlsanity.com/paper/2311.00332/) (2023)
- [End-to-End 4D Heart Mesh Recovery Across Full-Stack and Sparse Cardiac MRI](https://mlsanity.com/paper/2509.12090/) (2025)
- [From Pixels to Polygons: A Survey of Deep Learning Approaches for Medical Image-to-Mesh Reconstruction](https://mlsanity.com/paper/2505.03599/) (2025)
- [Temporal Differential Fields for 4D Motion Modeling via Image-to-Video Synthesis](https://mlsanity.com/paper/2505.17333/) (2025)
- [Personalized 3D Myocardial Infarct Geometry Reconstruction from Cine MRI with Explicit Cardiac Motion Modeling](https://mlsanity.com/paper/2507.15194/) (2025)
- [MorphiNet: A Graph Subdivision Network for Adaptive Bi-ventricle Surface Reconstruction](https://mlsanity.com/paper/2412.10985/) (2024)
- [Dyna3DGR: 4D Cardiac Motion Tracking with Dynamic 3D Gaussian Representation](https://mlsanity.com/paper/2507.16608/) (2025)
- [VecHeart: Holistic Four-Chamber Cardiac Anatomy Modeling via Hybrid VecSets](https://mlsanity.com/paper/2604.19403/) (2026)
- [Personalized 3D Myocardial Infarct Geometry Reconstruction from Cine MRI for Cardiac Digital Twins](https://mlsanity.com/paper/2606.01808/) (2026)
- [Personalized 4D Whole-Heart Mesh Reconstruction from Cine MRI via Multi-Scale Temporal Modeling and Differentiable Contour Rendering](https://mlsanity.com/paper/2607.01952/) (2026)
- [Personalized 4D Whole Heart Geometry Reconstruction from Cine MRI for Cardiac Digital Twins](https://mlsanity.com/paper/2507.15203/) (2025)
- [Uncertainty Quantification for Cardiac Shape Reconstruction with Deep Signed Distance Functions via MCMC methods](https://mlsanity.com/paper/2605.07987/) (2026)
- [Echo4DIR: 4D Implicit Heart Reconstruction from 2D Echocardiography Videos](https://mlsanity.com/paper/2605.22066/) (2026)
- [Learning to Beat: Phenotype-Guided Latent Flow with Regional Motion Priors for Biventricular Motion Synthesis](https://mlsanity.com/paper/2608.19738/) (2026)
- [Anatomy-Aligned Surface Field Learning for Myocardial Reconstruction from Sparse Short-Axis Cine MRI](https://mlsanity.com/paper/2609.29825/) (2026)

## 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.