A Series Registration Framework to Recover Resting-State Functional Magnetic Resonance Data Degraded By Motion.

Data retention is a significant problem in the medical imaging domain. For example, resting-state functional magnetic resonance images (rs-fMRIs) are invaluable for studying neurodevelopment but are highly susceptible to corruption due to patient motion. The effects of patient motion can be reduced through post-acquisition techniques such as volume registration. Traditional volume registration minimizes the global differences between all volumes in the rs-fMRI sequence and a designated reference volume. We suggest using the spatiotemporal relationships between subsequent image volumes to inform the registration: they are used initialize each volume registration to reduce local differences between volumes while minimizing global differences. We apply both the traditional and novel registration methods to a set of healthy human neonatal rs-fMRIs with significant motion artifacts (N=17). Both methods impacted the mean and standard deviation of the image sequences' correlation ratio matrices similarly; however, the novel framework was more effective in meeting gold standard motion thresholds. ©2020 AMIA - All rights reserved.