Programmatic Theme: Translational Bioinformatics

Abstract: 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.

Learning Objective: Upon attending this session, the attendee should be able to understand the structure of a resting-state functional magnetic resonance image and how it is affected by patient motion, recognize the limitations of traditional volume registration, and perceive the value of motion correction and its impacts on data retention.

Authors:

Jenna Schabdach (Presenter)
University of Pittsburgh

Rafael Ceschin, University of Pittsburgh
Vince Lee, UPMC Children's Hospital of Pittsburgh
Vincent Schmithorst, UPMC Children's Hospital of Pittsburgh
Ashok Panigrahy, UPMC Children's Hospital of Pittsburgh