# Unsupervised dMRI Artifact Detection via Angular Resolution Enhancement and Cycle Consistency Learning

arXiv: https://arxiv.org/abs/2409.15883 · HTML: https://mlsanity.com/paper/2409.15883/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 24 Sep 2024.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training](https://mlsanity.com/paper/1805.06725/) (2018)
- [QC-Automator: Deep Learning-based Automated Quality Control for Diffusion MR Images](https://mlsanity.com/paper/1911.06816/) (2019)
- [3D-QCNet -- A Pipeline for Automated Artifact Detection in Diffusion MRI images](https://mlsanity.com/paper/2103.05285/) (2021)
- [TW-BAG: Tensor-wise Brain-aware Gate Network for Inpainting Disrupted Diffusion Tensor Imaging](https://mlsanity.com/paper/2210.17076/) (2022)
- [Enhancing Angular Resolution via Directionality Encoding and Geometric Constraints in Brain Diffusion Tensor Imaging](https://mlsanity.com/paper/2409.07186/) (2024)

## Abstract (arXiv)

Diffusion magnetic resonance imaging (dMRI) is a crucial technique in neuroimaging studies, allowing for the non-invasive probing of the underlying structures of brain tissues. Clinical dMRI data is susceptible to various artifacts during acquisition, which can lead to unreliable subsequent analyses. Therefore, dMRI preprocessing is essential for improving image quality, and manual inspection is often required to ensure that the preprocessed data is sufficiently corrected. However, manual inspection requires expertise and is time-consuming, especially with large-scale dMRI datasets. Given these challenges, an automated dMRI artifact detection tool is necessary to increase the productivity and reliability of dMRI data analysis. To this end, we propose a novel unsupervised deep learning framework called Unsupervised dMRI Artifact Detection via Angular Resolution Enhancement and Cycle Consistency Learning (UdAD-AC). UdAD-AC leverages dMRI angular resolution enhancement and cycle consistency learning to capture the effective representation of artifact-free dMRI data during training, and it identifies data containing artifacts using designed confidence score during inference. To assess the capability of UdAD-AC, several commonly reported dMRI artifacts, including bias field, susceptibility distortion, and corrupted volume, were added to the testing data. Experimental results demonstrate that UdAD-AC achieves the best performance compared to competitive methods in unsupervised dMRI artifact detection.