# Simultaneous super-resolution and motion artifact removal in diffusion-weighted MRI using unsupervised deep learning

arXiv: https://arxiv.org/abs/2105.00240 · HTML: https://mlsanity.com/paper/2105.00240/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 1 May 2021.

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

- [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)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Unsupervised Medical Image Translation Using Cycle-MedGAN](https://mlsanity.com/paper/1903.03374/) (2019)
- [Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution](https://mlsanity.com/paper/1705.00664/) (2017)
- [Unpaired Deep Learning for Accelerated MRI using Optimal Transport Driven CycleGAN](https://mlsanity.com/paper/2008.12967/) (2020)
- [Unsupervised Adversarial Correction of Rigid MR Motion Artifacts](https://mlsanity.com/paper/1910.05597/) (2019)
- [Method for motion artifact reduction using a convolutional neural network for dynamic contrast enhanced MRI of the liver](https://mlsanity.com/paper/1807.06956/) (2018)
- [Two-Stage Deep Learning for Accelerated 3D Time-of-Flight MRA without Matched Training Data](https://mlsanity.com/paper/2008.01362/) (2020)
- [Sparse MRI for motion correction](https://mlsanity.com/paper/1302.0077/) (2013)
- [Geometric Approaches to Increase the Expressivity of Deep Neural Networks for MR Reconstruction](https://mlsanity.com/paper/2003.07740/) (2020)
- [Unsupervised MR Motion Artifact Deep Learning using Outlier-Rejecting Bootstrap Aggregation](https://mlsanity.com/paper/2011.06337/) (2020)

## Built on by (most cited first, 6 of 6)

- [MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion](https://mlsanity.com/paper/2203.12621/) (2022)
- [Stacked U-Nets with Self-Assisted Priors Towards Robust Correction of Rigid Motion Artifact in Brain MRI](https://mlsanity.com/paper/2111.06401/) (2021)
- [Performance of a GPU- and Time-Efficient Pseudo 3D Network for Magnetic Resonance Image Super-Resolution and Motion Artifact Reduction](https://mlsanity.com/paper/2111.14259/) (2021)
- [Motion Artifact Removal in Pixel-Frequency Domain via Alternate Masks and Diffusion Model](https://mlsanity.com/paper/2412.07590/) (2024)
- [DDO-IN: Dual Domains Optimization for Implicit Neural Network to Eliminate Motion Artifact in Magnetic Resonance Imaging](https://mlsanity.com/paper/2503.08056/) (2025)
- [Structure-Accurate Medical Image Translation via Dynamic Frequency Balance and Knowledge Guidance](https://mlsanity.com/paper/2504.09441/) (2025)

## Abstract (arXiv)

Diffusion-weighted MRI is nowadays performed routinely due to its prognostic ability, yet the quality of the scans are often unsatisfactory which can subsequently hamper the clinical utility. To overcome the limitations, here we propose a fully unsupervised quality enhancement scheme, which boosts the resolution and removes the motion artifact simultaneously. This process is done by first training the network using optimal transport driven cycleGAN with stochastic degradation block which learns to remove aliasing artifacts and enhance the resolution, then using the trained network in the test stage by utilizing bootstrap subsampling and aggregation for motion artifact suppression. We further show that we can control the trade-off between the amount of artifact correction and resolution by controlling the bootstrap subsampling ratio at the inference stage. To the best of our knowledge, the proposed method is the first to tackle super-resolution and motion artifact correction simultaneously in the context of MRI using unsupervised learning. We demonstrate the efficiency of our method by applying it to both quantitative evaluation using simulation study, and to in vivo diffusion-weighted MR scans, which shows that our method is superior to the current state-of-the-art methods. The proposed method is flexible in that it can be applied to various quality enhancement schemes in other types of MR scans, and also directly to the quality enhancement of apparent diffusion coefficient maps.