BrainQCNet: a Deep Learning attention-based model for multi-scale detection of artifacts in brain structural MRI scans

Analyses of structural MRI (sMRI) data depend on robust upstream data quality control (QC). It is also crucial that researchers retain the maximum amount of usable data to ensure reproducible, generalisable models. The time-consuming task of manual QC evaluation has prompted the development of tools for the automatic assessment of brain sMRI scans. Such tools are particularly valuable in this age of big data. One limitation of the most commonly used tools is that execution time is long, which poses a challenge in terms of duration and resource usage, particularly when processing large datasets. Further, evaluation is global (pass/fail) rather than localized. Having a tool that localizes areas of low quality could prevent unnecessary data loss. To address these issues, we trained a Deep Learning model, ProtoPNet, to classify minimally preprocessed 2D slices of scans that were manually annotated with a refined quality assessment (ABIDE 1 n = 980 scans). To validate the best model, we assessed 2141 ABCD scans for which gold-standard manual QC annotations were available. We obtained excellent accuracy: 82.4% for good quality scans (Pass), 91.4% for medium to low quality scans (Fail). Further validation using 799 scans from ABIDE 2 and 751 scans from ADHD-200 confirmed the reliability of our model. Accuracy was comparable to or exceeded that of another commonly used tool (MRIQC), but with dramatically reduced processing and prediction time (1 min per scan, GPU machine, CUDA-compatible). To facilitate faster and more accurate QC prediction for the neuroimaging community, we have shared the model that returned the most reliable global quality scores, local predictions of quality, and maps and prototypes of local artifacts as a BIDS-app (https://github.com/garciaml/BrainQCNet).