🤖 AI Summary
This study addresses the challenge of automated quality control in multi-site, multi-vendor structural MRI data corrupted by motion artifacts. The authors propose an end-to-end model that integrates a hierarchical 2D CNN with a multi-head cross-attention mechanism—a novel application of cross-attention in MRI quality assessment. By jointly capturing local features and modeling global dependencies, the model dynamically focuses on motion-related artifacts such as ringing and blurring while suppressing site-specific confounding variations. Cross-domain experiments on the MR-ART and ABIDE datasets demonstrate strong generalization: the model achieves 0.9920 accuracy on seen sites and maintains a robust 0.755 accuracy across 17 unseen, heterogeneous sites without fine-tuning, significantly advancing out-of-distribution generalization in MRI quality evaluation.
📝 Abstract
Motion artifacts present a significant challenge in structural MRI (sMRI), often compromising clinical diagnostics and large-scale automated analysis. While manual quality control (QC) remains the gold standard, it is increasingly unscalable for massive longitudinal studies. To address this, we propose a hybrid CNN-Attention framework designed for robust, site-invariant MRI quality assessment. Our architecture integrates a hierarchical 2D CNN encoder for local spatial feature extraction with a multi-head cross-attention mechanism to model global dependencies. This synergy enables the model to prioritize motion relevant artifact signatures, such as ringing and blurring, while dynamically filtering out site-specific intensity variations and background noise. The framework was trained end-to-end on the MR-ART dataset using a balanced cohort of 200 subjects. Performance was evaluated across two tiers: Seen Site Evaluation on a held-out MR-ART partition and Unseen Site Evaluation using 200 subjects from 17 heterogeneous sites in the ABIDE archive. On seen sites, the model achieved a scan-level accuracy of 0.9920 and an F1-score of 0.9919. Crucially, it maintained strong generalization across unseen ABIDE sites (Acc = 0.755) without any retraining or fine-tuning, demonstrating high resilience to domain shift. These results indicate that attention-based feature re-weighting successfully captures universal artifact descriptors, bridging the performance gap between diverse imaging environments and scanner manufacturers.