mlsanityData to 14 Oct 2026

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

arXiv 2208.00034 ↗First posted 29 Jul 2022eess.IV cs.CV cs.LGcited by 16

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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.

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

21 of 59 references.

References · 21

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  11. 2019Global and Local Interpretability for Cardiac MRI Classificationcited by 29
  12. 2021DeepTag: An Unsupervised Deep Learning Method for Motion Tracking on Cardiac Tagging Magnetic Resonance Imagescited by 26
9 more
  1. 2020Adversarial Uni- and Multi-modal Stream Networks for Multimodal Image Registrationcited by 25
  2. 2020Biomechanics-informed Neural Networks for Myocardial Motion Tracking in MRIcited by 24
  3. 2020FOAL: Fast Online Adaptive Learning for Cardiac Motion Estimationcited by 21
  4. 2018Explainable cardiac pathology classification on cine MRI with motion characterization by semi-supervised learning of apparent flowcited by 16
  5. 2019Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Imagescited by 16
  6. 20193D Cardiac Shape Prediction with Deep Neural Networks: Simultaneous Use of Images and Patient Metadatacited by 8
  7. 20193D High-Resolution Cardiac Segmentation Reconstruction from 2D Views using Conditional Variational Autoencoderscited by 6
  8. 2020Fully Automated Myocardial Strain Estimation from CMR Tagged Images using a Deep Learning Framework in the UK Biobankcited by 6
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 3; 2025: 8; 2026: 1.

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  1. 2023A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyondcited by 55
  2. 20234D Myocardium Reconstruction with Decoupled Motion and Shape Modelcited by 15
  3. 2023DeepMesh: Mesh-based Cardiac Motion Tracking using Deep Learningcited by 14
  4. 2025Towards Cardiac MRI Foundation Models: Comprehensive Visual-Tabular Representations for Whole-Heart Assessment and Beyondcited by 14
  5. 2024Whole Heart 3D+T Representation Learning Through Sparse 2D Cardiac MR Imagescited by 9
  6. 2025End-to-End 4D Heart Mesh Recovery Across Full-Stack and Sparse Cardiac MRIcited by 6
  7. 2025From Pixels to Polygons: A Survey of Deep Learning Approaches for Medical Image-to-Mesh Reconstructioncited by 4
  8. 2025Latent Interpolation Learning Using Diffusion Models for Cardiac Volume Reconstructioncited by 3
  9. 2024Assessment of Left Atrium Motion Deformation Through Full Cardiac Cyclecited by 2
  10. 2023Deep Learning for Automatic Strain Quantification in Arrhythmogenic Right Ventricular Cardiomyopathycited by 1
  11. 2024CardioSpectrum: Comprehensive Myocardium Motion Analysis with 3D Deep Learning and Geometric Insightscited by 1
  12. 2025CardioMorphNet: Cardiac Motion Prediction Using a Shape-Guided Bayesian Recurrent Deep Networkcited by 1
4 more
  1. 2025Systole-Conditioned Generative Cardiac Motioncited by 0
  2. 2025MCM: Mamba-based Cardiac Motion Tracking using Sequential Images in MRIcited by 0
  3. 2025Deformable Image Registration for Self-supervised Cardiac Phase Detection in Multi-View Multi-Disease Cardiac Magnetic Resonance Imagescited by 0
  4. 2026Local2Mesh: Spatially Localized Contour-to-Mesh for Left Ventricular Reconstruction from Sparse 2D Cardiac MRIcited by 0