Magnetic Resonance Imaging Sequence Identification Using a Metadata Learning Approach

Magnetic Resonance Imaging Sequence Identification Using a Metadata Learning Approach

Despite the wide application of the magnetic resonance imaging (MRI) technique, there are no widely used standards on naming and describing MRI sequences. The absence of consistent naming conventions presents a major challenge in automating image processing since most MRI software require a priori k...

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Bibliographic Details
Main Authors: Shuai Liang, Derek Beaton, Stephen R. Arnott, Tom Gee, Mojdeh Zamyadi, Robert Bartha, Sean Symons, Glenda M. MacQueen, Stefanie Hassel, Jason P. Lerch, Evdokia Anagnostou, Raymond W. Lam, Benicio N. Frey, Roumen Milev, Daniel J. Müller, Sidney H. Kennedy, Christopher J. M. Scott, The ONDRI Investigators, Stephen C. Strother, Angela Troyer, Anthony E. Lang, Barry Greenberg, Chris Hudson, Dale Corbett, David A. Grimes, David G. Munoz, Douglas P. Munoz, Elizabeth Finger, J. B. Orange, Lorne Zinman, Manuel Montero-Odasso, Maria Carmela Tartaglia, Mario Masellis, Michael Borrie, Michael J. Strong, Morris Freedman, Paula M. McLaughlin, Richard H. Swartz, Robert A. Hegele, Sandra E. Black, William E. McIlroy
Format: article
Language:EN
Published: Frontiers Media S.A. 2021
Subjects:
health data
MRI sequence naming standardization
data share and exchange
machine learning
metadata learning
AI-assisted data management
Neurosciences. Biological psychiatry. Neuropsychiatry
RC321-571
Online Access:https://doaj.org/article/b987f8f10e054d3e9bc11ba9f80a144f
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https://doaj.org/article/b987f8f10e054d3e9bc11ba9f80a144f

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