mlsanityData to 7 Oct 2026

Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review

arXiv 2305.06739 ↗First posted 11 May 2023eess.IV cs.CV cs.LGcited by 18

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Abstract (arXiv)

Motion represents one of the major challenges in magnetic resonance imaging (MRI). Since the MR signal is acquired in frequency space, any motion of the imaged object leads to complex artefacts in the reconstructed image in addition to other MR imaging artefacts. Deep learning has been frequently proposed for motion correction at several stages of the reconstruction process. The wide range of MR acquisition sequences, anatomies and pathologies of interest, and motion patterns (rigid vs. deformable and random vs. regular) makes a comprehensive solution unlikely. To facilitate the transfer of ideas between different applications, this review provides a detailed overview of proposed methods for learning-based motion correction in MRI together with their common challenges and potentials. This review identifies differences and synergies in underlying data usage, architectures, training and evaluation strategies. We critically discuss general trends and outline future directions, with the aim to enhance interaction between different application areas and research fields.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 37 of its 117 references that are in the map. First posted on arXiv 11 May 2023.

Builds on

37 of 117 references.

References · 37

  1. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
  2. 2022Diffusion Models in Vision: A Surveycited by 1,008
  3. 2018Noise2Noise: Learning Image Restoration without Clean Datacited by 526
  4. 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 464
  5. 2022Transformers in Medical Imaging: A Surveycited by 245
  6. 2017A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstructioncited by 231
  7. 2019Deep Learning in Medical Image Registration: A Surveycited by 124
  8. 2017Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstructioncited by 105
  9. 2019Time-Dependent Deep Image Prior for Dynamic MRIcited by 70
  10. 2022Physics-Driven Deep Learning for Computational Magnetic Resonance Imagingcited by 49
  11. 2020OCMR (v1.0)--Open-Access Multi-Coil k-Space Dataset for Cardiovascular Magnetic Resonance Imagingcited by 36
  12. 2022Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstructioncited by 36
25 more
  1. 2022Neural Implicit k-Space for Binning-free Non-Cartesian Cardiac MR Imagingcited by 35
  2. 2019Deep Learning Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentationcited by 17
  3. 2019Motion Corrected Multishot MRI Reconstruction Using Generative Networks with Sensitivity Encodingcited by 14
  4. 2021Stacked U-Nets with Self-Assisted Priors Towards Robust Correction of Rigid Motion Artifact in Brain MRIcited by 14
  5. 2021Learning MRI Artifact Removal With Unpaired Datacited by 13
  6. 2020Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Networkcited by 12
  7. 2022Accelerated Motion Correction with Deep Generative Diffusion Modelscited by 12
  8. 2019Unsupervised Adversarial Correction of Rigid MR Motion Artifactscited by 11
  9. 2022Learning-based and unrolled motion-compensated reconstruction for cardiac MR CINE imagingcited by 11
  10. 2018A Machine-learning framework for automatic reference-free quality assessment in MRIcited by 10
  11. 2020Joint Frequency and Image Space Learning for MRI Reconstruction and Analysiscited by 10
  12. 2023Data Consistent Deep Rigid MRI Motion Correctioncited by 10
  13. 2021Deformation-Compensated Learning for Image Reconstruction without Ground Truthcited by 9
  14. 2020Retrospective Motion Correction of MR Images using Prior-Assisted Deep Learningcited by 8
  15. 2022Accelerated respiratory-resolved 4D-MRI with separable spatio-temporal neural networkscited by 8
  16. 2018Method for motion artifact reduction using a convolutional neural network for dynamic contrast enhanced MRI of the livercited by 7
  17. 2021Dynamic imaging using Motion-Compensated SmooThness Regularization on Manifolds (MoCo-SToRM)cited by 7
  18. 2022FSE Compensated Motion Correction for MRI Using Data Driven Methodscited by 7
  19. 2022Autofocusing+: Noise-Resilient Motion Correction in Magnetic Resonance Imagingcited by 5
  20. 2022AFFIRM: Affinity Fusion-based Framework for Iteratively Random Motion correction of multi-slice fetal brain MRIcited by 5
  21. 2023Physics-Aware Motion Simulation for T2*-Weighted Brain MRIcited by 4
  22. 2020Localized Motion Artifact Reduction on Brain MRI Using Deep Learning with Effective Data Augmentation Techniquescited by 2
  23. 2022Wide Range MRI Artifact Removal with Transformerscited by 2
  24. 2019Detection and Correction of Cardiac MR Motion Artefacts during Reconstruction from K-spacecited by 1
  25. 2023Retrospective Motion Correction in Gradient Echo MRI by Explicit Motion Estimation Using Deep CNNscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 5; 2025: 8; 2026: 4.

18 papers.

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  1. 2023ICoNIK: Generating Respiratory-Resolved Abdominal MR Reconstructions Using Neural Implicit Representations in k-Spacecited by 10
  2. 2024Moner: Motion Correction in Undersampled Radial MRI with Unsupervised Neural Representationcited by 4
  3. 2025PISCO: Self-Supervised k-Space Regularization for Improved Neural Implicit k-Space Representations of Dynamic MRIcited by 4
  4. 2025DIMA: DIffusing Motion Artifacts for unsupervised correction in brain MRI imagescited by 3
  5. 2025Systematic Review and Meta-analysis of AI-driven MRI Motion Artifact Detection and Correctioncited by 3
  6. 2024Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representationcited by 2
  7. 2024MotionTTT: 2D Test-Time-Training Motion Estimation for 3D Motion Corrected MRIcited by 2
  8. 2025Res-MoCoDiff: Residual-guided diffusion models for motion artifact correction in brain MRIcited by 1
  9. 2025From Coarse to Continuous: Progressive Refinement Implicit Neural Representation for Motion-Robust Anisotropic MRI Reconstructioncited by 1
  10. 2026MotionDPS: Motion-Compensated 3D Brain MRI Reconstructioncited by 1
  11. 2024MAMOC: MRI Motion Correction via Masked Autoencodingcited by 0
  12. 2024A Unified Deep Learning Framework for Motion Correction in Medical Imagingcited by 0
6 more
  1. 2025Motion-Robust T2* Quantification from Gradient Echo MRI with Physics-Informed Deep Learningcited by 0
  2. 2025Non-rigid Motion Correction for MRI Reconstruction via Coarse-To-Fine Diffusion Modelscited by 0
  3. 2025Retrospective motion correction in MRI using disentangled embeddingscited by 0
  4. 2026SANA I2I: A Text Free Flow Matching Framework for Paired Image to Image Translation with a Case Study in Fetal MRI Artifact Reductioncited by 0
  5. 2026Motion Artifact-Aware Self-Supervised Representation Learning for 3D Brain MRI Motion Artifact Reductioncited by 0
  6. 2026Primitive Representation Learning for Unsupervised Dynamic Contrast Enhanced MRI Reconstructioncited by 0