Retrospective Head Motion Estimation in Structural Brain MRI with 3D CNNs

Abstract

Head motion is one of the most important nuisance variables in neuroimaging, particularly in studies of clinical or special populations, such as children. However, the possibility of estimating motion in structural MRI is limited to a few specialized sites using advanced MRI acquisition techniques. Here we propose a supervised learning method to retrospectively estimate motion from plain MRI. Using sparsely labeled training data, we trained a 3D convolutional neural network to assess if voxels are corrupted by motion or not. The output of the network is a motion probability map, which we integrate across a region of interest (ROI) to obtain a scalar motion score. Using cross-validation on a dataset of n=48 healthy children scanned at our center, and the cerebral cortex as ROI, we show that the proposed measure of motion explains away 37% of the variation in cortical thickness. We also show that the motion score is highly correlated with the results from human quality control of the scans. The proposed technique can not only be applied to current studies, but also opens up the possibility of reanalyzing large amounts of legacy datasets with motion into consideration: we applied the classifier trained on data from our center to the ABIDE dataset (autism), and managed to recover group differences that were confounded by motion.