# MulViMotion: Shape-aware 3D Myocardial Motion Tracking from Multi-View Cardiac MRI

arXiv: https://arxiv.org/abs/2208.00034 · HTML: https://mlsanity.com/paper/2208.00034/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 59 references that are in the map.
- First posted on arXiv 29 Jul 2022.

## Builds on (most cited first, 21 of 21)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration](https://mlsanity.com/paper/1809.06130/) (2018)
- [Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation](https://mlsanity.com/paper/1611.05250/) (2016)
- [SegFlow: Joint Learning for Video Object Segmentation and Optical Flow](https://mlsanity.com/paper/1709.06750/) (2017)
- [Learning a Probabilistic Model for Diffeomorphic Registration](https://mlsanity.com/paper/1812.07460/) (2018)
- [Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences](https://mlsanity.com/paper/1806.04066/) (2018)
- [Deep learning cardiac motion analysis for human survival prediction](https://mlsanity.com/paper/1810.03382/) (2018)
- [Global and Local Interpretability for Cardiac MRI Classification](https://mlsanity.com/paper/1906.06188/) (2019)
- [DeepTag: An Unsupervised Deep Learning Method for Motion Tracking on Cardiac Tagging Magnetic Resonance Images](https://mlsanity.com/paper/2103.02772/) (2021)
- [Adversarial Uni- and Multi-modal Stream Networks for Multimodal Image Registration](https://mlsanity.com/paper/2007.02790/) (2020)
- [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)
- [Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flow](https://mlsanity.com/paper/1811.03433/) (2018)
- [Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images](https://mlsanity.com/paper/1907.09983/) (2019)
- [3D Cardiac Shape Prediction with Deep Neural Networks: Simultaneous Use of Images and Patient Metadata](https://mlsanity.com/paper/1907.01913/) (2019)
- [3D High-Resolution Cardiac Segmentation Reconstruction from 2D Views using Conditional Variational Autoencoders](https://mlsanity.com/paper/1902.11000/) (2019)
- [Fully Automated Myocardial Strain Estimation from CMR Tagged Images using a Deep Learning Framework in the UK Biobank](https://mlsanity.com/paper/2004.07064/) (2020)
- [Enhanced 3D Myocardial Strain Estimation from Multi-View 2D CMR Imaging](https://mlsanity.com/paper/2009.12466/) (2020)

## Built on by (most cited first, 16 of 16)

- [A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond](https://mlsanity.com/paper/2307.15615/) (2023)
- [4D Myocardium Reconstruction with Decoupled Motion and Shape Model](https://mlsanity.com/paper/2308.14083/) (2023)
- [DeepMesh: Mesh-based Cardiac Motion Tracking using Deep Learning](https://mlsanity.com/paper/2309.14306/) (2023)
- [Towards Cardiac MRI Foundation Models: Comprehensive Visual-Tabular Representations for Whole-Heart Assessment and Beyond](https://mlsanity.com/paper/2504.13037/) (2025)
- [Whole Heart 3D+T Representation Learning Through Sparse 2D Cardiac MR Images](https://mlsanity.com/paper/2406.00329/) (2024)
- [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)
- [Latent Interpolation Learning Using Diffusion Models for Cardiac Volume Reconstruction](https://mlsanity.com/paper/2508.13826/) (2025)
- [Assessment of Left Atrium Motion Deformation Through Full Cardiac Cycle](https://mlsanity.com/paper/2405.17518/) (2024)
- [Deep Learning for Automatic Strain Quantification in Arrhythmogenic Right Ventricular Cardiomyopathy](https://mlsanity.com/paper/2311.14448/) (2023)
- [CardioSpectrum: Comprehensive Myocardium Motion Analysis with 3D Deep Learning and Geometric Insights](https://mlsanity.com/paper/2407.03794/) (2024)
- [CardioMorphNet: Cardiac Motion Prediction Using a Shape-Guided Bayesian Recurrent Deep Network](https://mlsanity.com/paper/2508.20734/) (2025)
- [Systole-Conditioned Generative Cardiac Motion](https://mlsanity.com/paper/2507.15894/) (2025)
- [MCM: Mamba-based Cardiac Motion Tracking using Sequential Images in MRI](https://mlsanity.com/paper/2507.17678/) (2025)
- [Deformable Image Registration for Self-supervised Cardiac Phase Detection in Multi-View Multi-Disease Cardiac Magnetic Resonance Images](https://mlsanity.com/paper/2510.05819/) (2025)
- [Local2Mesh: Spatially Localized Contour-to-Mesh for Left Ventricular Reconstruction from Sparse 2D Cardiac MRI](https://mlsanity.com/paper/2610.06052/) (2026)

## Abstract (arXiv)

Recovering the 3D motion of the heart from cine cardiac magnetic resonance (CMR) imaging enables the assessment of regional myocardial function and is important for understanding and analyzing cardiovascular disease. However, 3D cardiac motion estimation is challenging because the acquired cine CMR images are usually 2D slices which limit the accurate estimation of through-plane motion. To address this problem, we propose a novel multi-view motion estimation network (MulViMotion), which integrates 2D cine CMR images acquired in short-axis and long-axis planes to learn a consistent 3D motion field of the heart. In the proposed method, a hybrid 2D/3D network is built to generate dense 3D motion fields by learning fused representations from multi-view images. To ensure that the motion estimation is consistent in 3D, a shape regularization module is introduced during training, where shape information from multi-view images is exploited to provide weak supervision to 3D motion estimation. We extensively evaluate the proposed method on 2D cine CMR images from 580 subjects of the UK Biobank study for 3D motion tracking of the left ventricular myocardium. Experimental results show that the proposed method quantitatively and qualitatively outperforms competing methods.