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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Auteurs principaux:	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
Langue:	EN
Publié:	Frontiers Media S.A. 2021
Sujets:	health data MRI sequence naming standardization data share and exchange machine learning metadata learning AI-assisted data management Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Accès en ligne:	https://doaj.org/article/b987f8f10e054d3e9bc11ba9f80a144f
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Magnetic Resonance Imaging Sequence Identification Using a Metadata Learning Approach

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