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

TL;DR

This paper introduces an unsupervised deep learning framework, UdAD-AC, for automated detection of artifacts in diffusion MRI data, improving reliability and efficiency without requiring manual inspection.

Contribution

The novel UdAD-AC framework combines angular resolution enhancement and cycle consistency learning for effective unsupervised artifact detection in dMRI.

Findings

UdAD-AC outperforms existing methods in artifact detection accuracy.

The framework effectively identifies various artifacts like bias field and susceptibility distortion.

Experimental results validate the robustness of UdAD-AC across different artifact types.

Abstract

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 $\textbf{U}$nsupervised $\textbf{d}$MRI $\textbf{A}$rtifact $\textbf{D}$etection via $\textbf{A}$ngular Resolution Enhancement and $\textbf{C}$ycle 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.