A Series Registration Framework to Recover Resting-State Functional Magnetic Resonance Data Degraded By Motion.
Journal
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
ISSN: 2153-4063
Titre abrégé: AMIA Jt Summits Transl Sci Proc
Pays: United States
ID NLM: 101539486
Informations de publication
Date de publication:
2020
2020
Historique:
entrez:
2
6
2020
pubmed:
2
6
2020
medline:
2
6
2020
Statut:
epublish
Résumé
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.
Types de publication
Journal Article
Langues
eng
Pagination
569-578Subventions
Organisme : NLM NIH HHS
ID : T15 LM007059
Pays : United States
Informations de copyright
©2020 AMIA - All rights reserved.
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