Leveraging noise and contrast simulation for the automatic quality control of routine clinical T1-weighted brain MRI

The recent advent of clinical data warehouses (CDWs) has facilitated the sharing of very large volumes of medical data for research purposes. MRIs can be affected by various artefacts such as motion, noise or poor contrast that can severely degrade the overall quality of an image. In CDWs, a large amount of MRIs are unusable because corrupted by these diverse artefacts. Given the huge number of MRIs present in CDWs, manually detecting these artefacts becomes an impractical task. Therefore, it is necessary to develop an automated tool that can efficiently identify and exclude corrupted images. We previously proposed an approach for the detection of motion artefacts in 3D T1-weighted brain MRIs. In this paper, we propose to extend our work to two other types of artefacts: poor contrast and noise. We rely on a transfer learning approach, which leverages synthetic artefact generation, and comprises two steps: model pre-training on research data using synthetic artefacts, followed by a fine-tuning step, where we generalise the pre-trained models to clinical routine data relying on the manual labelling of 5000 images. The main objectives of our study were two-fold: to be able to exclude images with severe artefacts and to detect moderate artefacts. Our approach excelled in meeting the first objective, achieving a balanced accuracy of over 84% for the detection of severe noise and very poor contrast, which closely matched the performance of human annotators. Nevertheless, performance in the pursuit of the second objective was less satisfactory and inferior to that of the human annotators. Overall, our framework will be useful for taking full advantage of MRIs present in CDWs.