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MICCAI paper on retrospective head motion estimation

posted May 16, 2017, 7:26 AM by Juan Eugenio Iglesias   [ updated May 16, 2017, 2:29 PM ]
Our paper "Retrospective head motion estimation in structural brain MRI with 3D CNNs" has been accepted at MICCAI 2017. 

In this study, we propose a supervised method to retrospectively detect whether voxels of a brain MRI scan show signs of being corrupted by motion or not. We can average the probability of motion across the voxels in a region of interest (e.g., the cerebral cortex) and analyze its impact on morphometric features (e.g., cortical thickness). We show that, when we factor in our estimate of motion in group studies, the conclusions of the analyses can be very different, particularly when one group is more prone to moving in the scanner than the other - in the paper, we illustrate this with a public, autism MRI dataset (ABIDE).

You can find a preprint of the manuscript under Publications.

Thanks Gari, Luis, Sara and Kepa for the hard work!