Estimation of Head Motion in Structural MRI and its Impact on Cortical Thickness Measurements in Retrospective Data
This provides a scalable tool for researchers to objectively assess motion-related biases in neuroimaging studies, particularly for populations like children or individuals with ADHD, though it is incremental as it builds on existing automated approaches.
The researchers tackled the problem of motion artifacts in structural MRI biasing cortical thickness measurements by training a 3D CNN to estimate motion from retrospective scans, achieving an R^2 of 0.65 against manual labels and significant correlations in 12 out of 15 datasets.
Motion-related artifacts are inevitable in Magnetic Resonance Imaging (MRI) and can bias automated neuroanatomical metrics such as cortical thickness. These biases can interfere with statistical analysis which is a major concern as motion has been shown to be more prominent in certain populations such as children or individuals with ADHD. Manual review cannot objectively quantify motion in anatomical scans, and existing quantitative automated approaches often require specialized hardware or custom acquisition protocols. Here, we train a 3D convolutional neural network to estimate a summary motion metric in retrospective routine research scans by leveraging a large training dataset of synthetically motion-corrupted volumes. We validate our method with one held-out site from our training cohort and with 14 fully independent datasets, including one with manual ratings, achieving a representative $R^2 = 0.65$ versus manual labels and significant thickness-motion correlations in 12/15 datasets. Furthermore, our predicted motion correlates with subject age in line with prior studies. Our approach generalizes across scanner brands and protocols, enabling objective, scalable motion assessment in structural MRI studies without prospective motion correction. By providing reliable motion estimates, our method offers researchers a tool to assess and account for potential biases in cortical thickness analyses.