mlsanityData to 14 Oct 2026

Learning MRI Artifact Removal With Unpaired Data

arXiv 2110.04604 ↗First posted 9 Oct 2021eess.IV cs.CV cs.LGcited by 13

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Abstract (arXiv)

Retrospective artifact correction (RAC) improves image quality post acquisition and enhances image usability. Recent machine learning driven techniques for RAC are predominantly based on supervised learning and therefore practical utility can be limited as data with paired artifact-free and artifact-corrupted images are typically insufficient or even non-existent. Here we show that unwanted image artifacts can be disentangled and removed from an image via an RAC neural network learned with unpaired data. This implies that our method does not require matching artifact-corrupted data to be either collected via acquisition or generated via simulation. Experimental results demonstrate that our method is remarkably effective in removing artifacts and retaining anatomical details in images with different contrasts.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 9 of its 35 references that are in the map. First posted on arXiv 9 Oct 2021.

Builds on

9 of 35 references.

References · 9

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  2. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  3. 2016Least Squares Generative Adversarial Networkscited by 1,001
  4. 2017Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkscited by 707
  5. 2017Unsupervised Image-to-Image Translation Networkscited by 603
  6. 2016Deep Convolutional Neural Network for Inverse Problems in Imagingcited by 433
  7. 2017Toward Multimodal Image-to-Image Translationcited by 296
  8. 2017Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesiscited by 215
  9. 2018Method for motion artifact reduction using a convolutional neural network for dynamic contrast enhanced MRI of the livercited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 2; 2023: 3; 2024: 0; 2025: 6; 2026: 1.

13 papers.

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  1. 2023Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Reviewcited by 18
  2. 2023Annealed Score-Based Diffusion Model for MR Motion Artifact Reductioncited by 7
  3. 2025A Physics-Informed Deep Learning Model for MRI Brain Motion Correctioncited by 4
  4. 2023Multimodal and multicontrast image fusion via deep generative modelscited by 3
  5. 2025DIMA: DIffusing Motion Artifacts for unsupervised correction in brain MRI imagescited by 3
  6. 2025Systematic Review and Meta-analysis of AI-driven MRI Motion Artifact Detection and Correctioncited by 3
  7. 2021Deep multi-modal aggregation network for MR image reconstruction with auxiliary modalitycited by 2
  8. 2022Wide Range MRI Artifact Removal with Transformerscited by 2
  9. 2025Assessing the use of Diffusion models for motion artifact correction in brain MRIcited by 2
  10. 2025Res-MoCoDiff: Residual-guided diffusion models for motion artifact correction in brain MRIcited by 1
  11. 2022Physics-informed Deep Diffusion MRI Reconstruction with Synthetic Data: Break Training Data Bottleneck in Artificial Intelligencecited by 0
  12. 2025MAISY: Motion-Aware Image SYnthesis for Medical Image Motion Correctioncited by 0
1 more
  1. 2026Motion Artifact-Aware Self-Supervised Representation Learning for 3D Brain MRI Motion Artifact Reductioncited by 0