  The emergence of clinical data warehouses (CDWs), which contain the medical
data of millions of patients, has paved the way for vast data sharing for
research. The quality of MRIs gathered in CDWs differs greatly from what is
observed in research settings and reflects a certain clinical reality.
Consequently, a significant proportion of these images turns out to be unusable
due to their poor quality. Given the massive volume of MRIs contained in CDWs,
the manual rating of image quality is impossible. Thus, it is necessary to
develop an automated solution capable of effectively identifying corrupted
images in CDWs. This study presents an innovative transfer learning method for
automated quality control of 3D gradient echo T1-weighted brain MRIs within a
CDW, leveraging artefact simulation. We first intentionally corrupt images from
research datasets by inducing poorer contrast, adding noise and introducing
motion artefacts. Subsequently, three artefact-specific models are pre-trained
using these corrupted images to detect distinct types of artefacts. Finally,
the models are generalised to routine clinical data through a transfer learning
technique, utilising 3660 manually annotated images. The overall image quality
is inferred from the results of the three models, each designed to detect a
specific type of artefact. Our method was validated on an independent test set
of 385 3D gradient echo T1-weighted MRIs. Our proposed approach achieved
excellent results for the detection of bad quality MRIs, with a balanced
accuracy of over 87%, surpassing our previous approach by 3.5 percent points.
Additionally, we achieved a satisfactory balanced accuracy of 79% for the
detection of moderate quality MRIs, outperforming our previous performance by 5
percent points. Our framework provides a valuable tool for exploiting the
potential of MRIs in CDWs.
