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Due to the growing number of MRI data, automated quality control (QC) hasbecome essential, especially for larger scale analysis. Several attempts havebeen made in order to develop reliable and scalable QC pipelines. However, thegeneralization of these methods on new data independent of those used forlearning is a difficult problem because of the biases inherent in MRI data.This work aimed at evaluating the performances of the MRIQC pipeline on variouslarge-scale datasets (ABIDE, N = 1102 and CATI derived datasets, N = 9037) usedfor both training and evaluation purposes. We focused our analysis on the MRIQCpreprocessing steps and tested the pipeline with and without them. We furtheranalyzed the site-wise and study-wise predicted classification probabilitydistributions of the models without preprocessing trained on ABIDE and CATIdata. Our main results were that a model using features extracted from MRIQCwithout preprocessing yielded the best results when trained and evaluated onlarge multi-center datasets with a heterogeneous population (an improvement ofthe ROC-AUC score on unseen data of 0.10 for the model trained on a subset ofthe CATI dataset). We concluded that a model trained with data from aheterogeneous population, such as the CATI dataset, provides the best scores onunseen data. In spite of the performance improvement, the generalizationabilities of the models remain questionable when looking at thesite-wise/study-wise probability predictions and the optimal classificationthreshold derived from them.

Learning brain MRI quality control: a multi-factorial generalization problem