# DIMA: DIffusing Motion Artifacts for unsupervised correction in brain MRI images

arXiv: https://arxiv.org/abs/2504.06767 · HTML: https://mlsanity.com/paper/2504.06767/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 14 of its 44 references that are in the map.
- First posted on arXiv 9 Apr 2025.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 14 of 14)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Unsupervised Medical Image Translation Using Cycle-MedGAN](https://mlsanity.com/paper/1903.03374/) (2019)
- [Learning to Read Braille: Bridging the Tactile Reality Gap with Diffusion Models](https://mlsanity.com/paper/2304.01182/) (2023)
- [Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review](https://mlsanity.com/paper/2305.06739/) (2023)
- [Stacked U-Nets with Self-Assisted Priors Towards Robust Correction of Rigid Motion Artifact in Brain MRI](https://mlsanity.com/paper/2111.06401/) (2021)
- [Learning MRI Artifact Removal With Unpaired Data](https://mlsanity.com/paper/2110.04604/) (2021)
- [Accelerated Motion Correction with Deep Generative Diffusion Models](https://mlsanity.com/paper/2211.00199/) (2022)
- [Unsupervised Adversarial Correction of Rigid MR Motion Artifacts](https://mlsanity.com/paper/1910.05597/) (2019)
- [Annealed Score-Based Diffusion Model for MR Motion Artifact Reduction](https://mlsanity.com/paper/2301.03027/) (2023)
- [Sparse MRI for motion correction](https://mlsanity.com/paper/1302.0077/) (2013)
- [Autofocusing+: Noise-Resilient Motion Correction in Magnetic Resonance Imaging](https://mlsanity.com/paper/2203.05569/) (2022)
- [Assessing the use of Diffusion models for motion artifact correction in brain MRI](https://mlsanity.com/paper/2502.01418/) (2025)

## Built on by (most cited first, 3 of 3)

- [MotionDPS: Motion-Compensated 3D Brain MRI Reconstruction](https://mlsanity.com/paper/2605.22121/) (2026)
- [Motion Artifact-Aware Self-Supervised Representation Learning for 3D Brain MRI Motion Artifact Reduction](https://mlsanity.com/paper/2608.10170/) (2026)
- [Evaluating the Safety of Deep Learning-Based Brain MRI Reconstruction](https://mlsanity.com/paper/2608.28714/) (2026)

## Abstract (arXiv)

Motion artifacts remain a significant challenge in Magnetic Resonance Imaging (MRI), compromising diagnostic quality and potentially leading to misdiagnosis or repeated scans. Existing deep learning approaches for motion artifact correction typically require paired motion-free and motion-affected images for training, which are rarely available in clinical settings. To overcome this requirement, we present DIMA (DIffusing Motion Artifacts), a novel framework that leverages diffusion models to enable unsupervised motion artifact correction in brain MRI. Our two-phase approach first trains a diffusion model on unpaired motion-affected images to learn the distribution of motion artifacts. This model then generates realistic motion artifacts on clean images, creating paired datasets suitable for supervised training of correction networks. Unlike existing methods, DIMA operates without requiring k-space manipulation or detailed knowledge of MRI sequence parameters, making it adaptable across different scanning protocols and hardware. Comprehensive evaluations across multiple datasets and anatomical planes demonstrate that our method achieves comparable performance to state-of-the-art supervised approaches while offering superior generalizability to real clinical data. DIMA represents a significant advancement in making motion artifact correction more accessible for routine clinical use, potentially reducing the need for repeat scans and improving diagnostic accuracy.