Automatic quality control of brain 3D FLAIR MRIs for a clinical data warehouse.

Clinical data warehouses, which have arisen over the last decade, bring together the medical data of millions of patients and offer the potential to train and validate machine learning models in real-world scenarios. The quality of MRIs collected in clinical data warehouses differs significantly from that generally observed in research datasets, reflecting the variability inherent to clinical practice. Consequently, the use of clinical data requires the implementation of robust quality control tools. By using a substantial number of pre-existing manually labelled T1-weighted MR images (5,500) alongside a smaller set of newly labelled FLAIR images (926), we present a novel semi-supervised adversarial domain adaptation architecture designed to exploit shared representations between MRI sequences thanks to a shared feature extractor, while taking into account the specificities of the FLAIR thanks to a specific classification head for each sequence. This architecture thus consists of a common invariant feature extractor, a domain classifier and two classification heads specific to the source and target, all designed to effectively deal with potential class distribution shifts between the source and target data classes. The primary objectives of this paper were: (1) to identify images which are not proper 3D FLAIR brain MRIs; (2) to rate the overall image quality. For the first objective, our approach demonstrated excellent results, with a balanced accuracy of 89%, comparable to that of human raters. For the second objective, our approach achieved good performance, although lower than that of human raters. Nevertheless, the automatic approach accurately identified bad quality images (balanced accuracy >79%). In conclusion, our proposed approach overcomes the initial barrier of heterogeneous image quality in clinical data warehouses, thereby facilitating the development of new research using clinical routine 3D FLAIR brain images.