Self-supervised Contrastive Learning for Automatic Image Quality Assessment in Whole-body MRI: Preliminary results in UK Biobank

Motivation: MRI is vital for many medical decisions, yet susceptible to motion artifacts. Impairment by motion artifacts can reduce the reliability of diagnoses and a motion‐free reacquisition can become time-/cost‐intensive. Moreover, in large-scale cohorts, manual inspection is impractical. An automated quality assessment is desirable, but collection of motion-free references is challenging or even impractical. Goal(s): We aim for automatic image quality assessment without extensive labeled training data. Approach: We present a self-supervised quality classification framework based on SimCLR operating as zero-shot learning. Results: The framework achieves promising results for binary quality classification, while showcasing its potential for future work as continuous quality score. Impact: By automating MRI quality assessment, our approach helps in preventing artifact propagation into downstream tasks without additional efforts for manual inspection or data labeling.