# Motion-compensated MR CINE reconstruction with reconstruction-driven motion estimation

arXiv: https://arxiv.org/abs/2302.02504 · HTML: https://mlsanity.com/paper/2302.02504/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 68 references that are in the map.
- First posted on arXiv 5 Feb 2023.

## Builds on (most cited first, 20 of 20)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [An Unsupervised Learning Model for Deformable Medical Image Registration](https://mlsanity.com/paper/1802.02604/) (2018)
- [Deep learning for cardiac image segmentation: A review](https://mlsanity.com/paper/1911.03723/) (2019)
- [Zero-Shot Self-Supervised Learning for MRI Reconstruction](https://mlsanity.com/paper/2102.07737/) (2021)
- [Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences](https://mlsanity.com/paper/1806.04066/) (2018)
- [Realistic Adversarial Data Augmentation for MR Image Segmentation](https://mlsanity.com/paper/2006.13322/) (2020)
- [OCMR (v1.0)--Open-Access Multi-Coil k-Space Dataset for Cardiovascular Magnetic Resonance Imaging](https://mlsanity.com/paper/2008.03410/) (2020)
- [Deep Low-rank plus Sparse Network for Dynamic MR Imaging](https://mlsanity.com/paper/2010.13677/) (2020)
- [Self-Supervised Learning for MRI Reconstruction with a Parallel Network Training Framework](https://mlsanity.com/paper/2109.12502/) (2021)
- [Improving the generalizability of convolutional neural network-based segmentation on CMR images](https://mlsanity.com/paper/1907.01268/) (2019)
- [Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation](https://mlsanity.com/paper/2006.15710/) (2020)
- [LAPNet: Non-rigid Registration derived in k-space for Magnetic Resonance Imaging](https://mlsanity.com/paper/2107.09060/) (2021)
- [Learning-based and unrolled motion-compensated reconstruction for cardiac MR CINE imaging](https://mlsanity.com/paper/2209.03671/) (2022)
- [Complementary Time-Frequency Domain Networks for Dynamic Parallel MR Image Reconstruction](https://mlsanity.com/paper/2012.11974/) (2020)
- [Compressed Sensing Plus Motion (CS+M): A New Perspective for Improving Undersampled MR Image Reconstruction](https://mlsanity.com/paper/1810.10828/) (2018)
- [Dynamic imaging using Motion-Compensated SmooThness Regularization on Manifolds (MoCo-SToRM)](https://mlsanity.com/paper/2112.03380/) (2021)
- [k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-temporal Correlations](https://mlsanity.com/paper/1907.09425/) (2019)
- [Learning Optical Flow for Fast MRI Reconstruction](https://mlsanity.com/paper/2004.10464/) (2020)

## Built on by (most cited first, 8 of 8)

- [Towards Cardiac MRI Foundation Models: Comprehensive Visual-Tabular Representations for Whole-Heart Assessment and Beyond](https://mlsanity.com/paper/2504.13037/) (2025)
- [Mamba? Catch The Hype Or Rethink What Really Helps for Image Registration](https://mlsanity.com/paper/2407.19274/) (2024)
- [Global k-Space Interpolation for Dynamic MRI Reconstruction using Masked Image Modeling](https://mlsanity.com/paper/2307.12672/) (2023)
- [Classification, Regression and Segmentation directly from k-Space in Cardiac MRI](https://mlsanity.com/paper/2407.20108/) (2024)
- [EAGLE: An Edge-Aware Gradient Localization Enhanced Loss for CT Image Reconstruction](https://mlsanity.com/paper/2403.10695/) (2024)
- [Non-rigid Motion Correction for MRI Reconstruction via Coarse-To-Fine Diffusion Models](https://mlsanity.com/paper/2505.15057/) (2025)
- [Flow-Guided Implicit Neural Representation for Motion-Aware Dynamic MRI Reconstruction](https://mlsanity.com/paper/2511.16948/) (2025)
- [Prospective Dynamic 3D MRI Reconstruction via Latent-Space Motion Tracking from Single Measurement](https://mlsanity.com/paper/2606.04249/) (2026)

## Abstract (arXiv)

In cardiac CINE, motion-compensated MR reconstruction (MCMR) is an effective approach to address highly undersampled acquisitions by incorporating motion information between frames. In this work, we propose a novel perspective for addressing the MCMR problem and a more integrated and efficient solution to the MCMR field. Contrary to state-of-the-art (SOTA) MCMR methods which break the original problem into two sub-optimization problems, i.e. motion estimation and reconstruction, we formulate this problem as a single entity with one single optimization. Our approach is unique in that the motion estimation is directly driven by the ultimate goal, reconstruction, but not by the canonical motion-warping loss (similarity measurement between motion-warped images and target images). We align the objectives of motion estimation and reconstruction, eliminating the drawbacks of artifacts-affected motion estimation and therefore error-propagated reconstruction. Further, we can deliver high-quality reconstruction and realistic motion without applying any regularization/smoothness loss terms, circumventing the non-trivial weighting factor tuning. We evaluate our method on two datasets: 1) an in-house acquired 2D CINE dataset for the retrospective study and 2) the public OCMR cardiac dataset for the prospective study. The conducted experiments indicate that the proposed MCMR framework can deliver artifact-free motion estimation and high-quality MR images even for imaging accelerations up to 20x, outperforming SOTA non-MCMR and MCMR methods in both qualitative and quantitative evaluation across all experiments. The code is available at https://github.com/JZPeterPan/MCMR-Recon-Driven-Motion.