# Stacked U-Nets with Self-Assisted Priors Towards Robust Correction of Rigid Motion Artifact in Brain MRI

arXiv: https://arxiv.org/abs/2111.06401 · HTML: https://mlsanity.com/paper/2111.06401/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 48 references that are in the map.
- First posted on arXiv 11 Nov 2021.

## Builds on (most cited first, 13 of 13)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [MoDL: Model Based Deep Learning Architecture for Inverse Problems](https://mlsanity.com/paper/1712.02862/) (2017)
- [A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction](https://mlsanity.com/paper/1704.02422/) (2017)
- [DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation](https://mlsanity.com/paper/2006.04868/) (2020)
- [Deep learning for undersampled MRI reconstruction](https://mlsanity.com/paper/1709.02576/) (2017)
- [Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning](https://mlsanity.com/paper/1810.12185/) (2018)
- [Unsupervised Adversarial Correction of Rigid MR Motion Artifacts](https://mlsanity.com/paper/1910.05597/) (2019)
- [Stacked U-Nets: A No-Frills Approach to Natural Image Segmentation](https://mlsanity.com/paper/1804.10343/) (2018)
- [Retrospective Motion Correction of MR Images using Prior-Assisted Deep Learning](https://mlsanity.com/paper/2011.14134/) (2020)
- [Simultaneous super-resolution and motion artifact removal in diffusion-weighted MRI using unsupervised deep learning](https://mlsanity.com/paper/2105.00240/) (2021)
- [Generative adversarial network for segmentation of motion affected neonatal brain MRI](https://mlsanity.com/paper/1906.04704/) (2019)

## Built on by (most cited first, 14 of 14)

- [Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review](https://mlsanity.com/paper/2305.06739/) (2023)
- [Annealed Score-Based Diffusion Model for MR Motion Artifact Reduction](https://mlsanity.com/paper/2301.03027/) (2023)
- [Autofocusing+: Noise-Resilient Motion Correction in Magnetic Resonance Imaging](https://mlsanity.com/paper/2203.05569/) (2022)
- [IM-MoCo: Self-supervised MRI Motion Correction using Motion-Guided Implicit Neural Representations](https://mlsanity.com/paper/2407.02974/) (2024)
- [DIMA: DIffusing Motion Artifacts for unsupervised correction in brain MRI images](https://mlsanity.com/paper/2504.06767/) (2025)
- [Systematic Review and Meta-analysis of AI-driven MRI Motion Artifact Detection and Correction](https://mlsanity.com/paper/2509.05071/) (2025)
- [MotionTTT: 2D Test-Time-Training Motion Estimation for 3D Motion Corrected MRI](https://mlsanity.com/paper/2409.09370/) (2024)
- [Assessing the use of Diffusion models for motion artifact correction in brain MRI](https://mlsanity.com/paper/2502.01418/) (2025)
- [Iterative Learning for Joint Image Denoising and Motion Artifact Correction of 3D Brain MRI](https://mlsanity.com/paper/2403.08162/) (2024)
- [MotionDPS: Motion-Compensated 3D Brain MRI Reconstruction](https://mlsanity.com/paper/2605.22121/) (2026)
- [Retrospective motion correction in MRI using disentangled embeddings](https://mlsanity.com/paper/2511.08365/) (2025)
- [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)
- [SliceBridge: Context-Consistent Repair of Corrupted Slice Intervals in T1-Weighted MRI](https://mlsanity.com/paper/2609.01827/) (2026)

## Abstract (arXiv)

In this paper, we develop an efficient retrospective deep learning method called stacked U-Nets with self-assisted priors to address the problem of rigid motion artifacts in MRI. The proposed work exploits the usage of additional knowledge priors from the corrupted images themselves without the need for additional contrast data. The proposed network learns missed structural details through sharing auxiliary information from the contiguous slices of the same distorted subject. We further design a refinement stacked U-Nets that facilitates preserving of the image spatial details and hence improves the pixel-to-pixel dependency. To perform network training, simulation of MRI motion artifacts is inevitable. We present an intensive analysis using various types of image priors: the proposed self-assisted priors and priors from other image contrast of the same subject. The experimental analysis proves the effectiveness and feasibility of our self-assisted priors since it does not require any further data scans.