# Retrospective Motion Correction of MR Images using Prior-Assisted Deep Learning

arXiv: https://arxiv.org/abs/2011.14134 · HTML: https://mlsanity.com/paper/2011.14134/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2020.

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

- [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)
- [SISMIK for brain MRI: Deep-learning-based motion estimation and model-based motion correction in k-space](https://mlsanity.com/paper/2312.13220/) (2023)
- [DDoS-UNet: Incorporating temporal information using Dynamic Dual-channel UNet for enhancing super-resolution of dynamic MRI](https://mlsanity.com/paper/2202.05355/) (2022)
- [Enhancing and Accelerating Brain MRI through Deep Learning Reconstruction Using Prior Subject-Specific Imaging](https://mlsanity.com/paper/2507.21349/) (2025)
- [Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images](https://mlsanity.com/paper/2206.06725/) (2022)
- [A Unified Deep Learning Framework for Motion Correction in Medical Imaging](https://mlsanity.com/paper/2409.14204/) (2024)
- [Retrospective motion correction in MRI using disentangled embeddings](https://mlsanity.com/paper/2511.08365/) (2025)

## Abstract (arXiv)

In MRI, motion artefacts are among the most common types of artefacts. They can degrade images and render them unusable for accurate diagnosis. Traditional methods, such as prospective or retrospective motion correction, have been proposed to avoid or alleviate motion artefacts. Recently, several other methods based on deep learning approaches have been proposed to solve this problem. This work proposes to enhance the performance of existing deep learning models by the inclusion of additional information present as image priors. The proposed approach has shown promising results and will be further investigated for clinical validity.