Assessing the efficacy of artefact synthesis and transfer learning for quality control in clinical 3D FLAIR brain MRI

The recent advent of clinical data warehouses (CDWs) has greatly simplified the sharing of large medical datasets, including imaging, for research purposes. MRI scans can be affected by various artefacts such as motion, noise and low grey/white matter contrast, which can degrade image quality. Given the abundance of MRI scans available in CDWs, it is imperative to develop tools to automatically detect images corrupted by these artefacts, in order to guarantee the reliability of the data used for research. We have previously developed a method for detecting moderate artefacts in 3D T1-weighted brain MRI. We propose to test and extend our approach to the FLAIR sequence. Our approach involves pretraining models on research data augmented with synthetic artefacts, followed by a fine-tuning phase designed to adapt these models to routine clinical data, either keeping the clinical data unchanged, or balancing the classes by simulating artefacts on some of the artefact-free MRIs. This adaptation relies on the manual annotation of 637 FLAIR images. We evaluate whether this strategy can generalize to new MRI sequences and clinical settings where manual labels are limited. While leveraging synthetic artefacts and research datasets shows some benefit, the performance gains remain modest compared to models trained from scratch on clinical data, and the results fall short of human-level accuracy. A key limitation remains the scarcity of representative, annotated clinical images, which constrains overall performance.