Interpretable Motion Artificat Detection in structural Brain MRI

TL;DR

This paper introduces a lightweight, interpretable framework that effectively detects motion artifacts in structural brain MRI by combining 2D and 3D features, achieving high accuracy and robustness across different datasets.

Contribution

The work extends the Discriminative Histogram of Gradient Magnitude (DHoGM) to 3D and integrates it with 2D features for improved artifact detection in MRI, using a simple classifier with few parameters.

Findings

Achieved up to 94.34% accuracy in domain and 89% on unseen sites.

Demonstrated strong generalization and low false acceptance of poor-quality scans.

Confirmed the benefits of combining 2D and 3D features through ablation studies.

Abstract

Automated quality assessment of structural brain MRI is an important prerequisite for reliable neuroimaging analysis, but yet remains challenging due to motion artifacts and poor generalization across acquisition sites. Existing approaches based on image quality metrics (IQMs) or deep learning either requires extensive preprocessing, which incurs high computational cost, or poor generalization to unseen data. In this work, we propose a lightweight and interpretable framework for detecting motion related artifacts in T1 weighted brain MRI by extending the Discriminative Histogram of Gradient Magnitude (DHoGM) to a three dimensional space. The proposed method integrates complementary slice-level (2D) and volume-level (3D) DHoGM features through a parallel decision strategy, capturing both localized and global motion-induced degradation. Volumetric analysis is performed using overlapping 3D cuboids to achieve comprehensive spatial coverage while maintaining computational efficiency. A simple threshold-based classifier and a low parameter multilayer perceptron are used, which results in a model with only 209 trainable parameters. Our method was evaluated on the MR-ART and ABIDE datasets under both seen-site and unseen-site conditions. Experimental results demonstrate strong performance, achieving up to 94.34\% accuracy the in domain evaluation and 89\% accuracy on unseen sites, while almost completely avoiding false acceptance of poor-quality scans. Ablation studies confirms the complementary benefits of combining 2D and 3D features. Overall, the proposed approach offers an effective, efficient, and robust solution for automated MRI quality check, with strong potential for integration into large scale clinical and research workflows.