AUTHOR=Liang Shuai , Beaton Derek , Arnott Stephen R. , Gee Tom , Zamyadi Mojdeh , Bartha Robert , Symons Sean , MacQueen Glenda M. , Hassel Stefanie , Lerch Jason P. , Anagnostou Evdokia , Lam Raymond W. , Frey Benicio N. , Milev Roumen , Müller Daniel J. , Kennedy Sidney H. , Scott Christopher J. M. , The ONDRI Investigators , Strother Stephen C. , Troyer Angela , Lang Anthony E. , Greenberg Barry , Hudson Chris , Corbett Dale , Grimes David A. , Munoz David G. , Munoz Douglas P. , Finger Elizabeth , Orange J. B. , Zinman Lorne , Montero-Odasso Manuel , Tartaglia Maria Carmela , Masellis Mario , Borrie Michael , Strong Michael J. , Freedman Morris , McLaughlin Paula M. , Swartz Richard H. , Hegele Robert A. , Bartha Robert , Black Sandra E. , Symons Sean , Strother Stephen C. , McIlroy William E. TITLE=Magnetic Resonance Imaging Sequence Identification Using a Metadata Learning Approach JOURNAL=Frontiers in Neuroinformatics VOLUME=15 YEAR=2021 URL=https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2021.622951 DOI=10.3389/fninf.2021.622951 ISSN=1662-5196 ABSTRACT=

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 knowledge of the type of the MRI sequences to be processed. This issue becomes increasingly critical with the current efforts toward open-sharing of MRI data in the neuroscience community. This manuscript reports an MRI sequence detection method using imaging metadata and a supervised machine learning technique. Three datasets from the Brain Center for Ontario Data Exploration (Brain-CODE) data platform, each involving MRI data from multiple research institutes, are used to build and test our model. The preliminary results show that a random forest model can be trained to accurately identify MRI sequence types, and to recognize MRI scans that do not belong to any of the known sequence types. Therefore the proposed approach can be used to automate processing of MRI data that involves a large number of variations in sequence names, and to help standardize sequence naming in ongoing data collections. This study highlights the potential of the machine learning approaches in helping manage health data.