Estimating Head Motion from MR-Images
This addresses the issue of undetected head motion affecting MRI accuracy for researchers and clinicians, though it is incremental as it builds on existing motion estimation methods.
The paper tackles the problem of estimating subtle head motion in MRI analyses, which is a confounder for morphometric measurements, by introducing a deep learning method that predicts motion from T1w, T2w, and FLAIR images using depth camera data as ground truth. The result is improved performance compared to state-of-the-art methods, with the ability to quantify drift and respiration independently and preserve correlations with age on unseen data.
Head motion is an omnipresent confounder of magnetic resonance image (MRI) analyses as it systematically affects morphometric measurements, even when visual quality control is performed. In order to estimate subtle head motion, that remains undetected by experts, we introduce a deep learning method to predict in-scanner head motion directly from T1-weighted (T1w), T2-weighted (T2w) and fluid-attenuated inversion recovery (FLAIR) images using motion estimates from an in-scanner depth camera as ground truth. Since we work with data from compliant healthy participants of the Rhineland Study, head motion and resulting imaging artifacts are less prevalent than in most clinical cohorts and more difficult to detect. Our method demonstrates improved performance compared to state-of-the-art motion estimation methods and can quantify drift and respiration movement independently. Finally, on unseen data, our predictions preserve the known, significant correlation with age.