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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Frontiers Media S.A.
2021
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oai:doaj.org-article:b987f8f10e054d3e9bc11ba9f80a144f2021-11-17T06:33:29ZMagnetic Resonance Imaging Sequence Identification Using a Metadata Learning Approach1662-519610.3389/fninf.2021.622951https://doaj.org/article/b987f8f10e054d3e9bc11ba9f80a144f2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fninf.2021.622951/fullhttps://doaj.org/toc/1662-5196Despite 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.Shuai LiangShuai LiangDerek BeatonStephen R. ArnottTom GeeMojdeh ZamyadiRobert BarthaSean SymonsGlenda M. MacQueenStefanie HasselJason P. LerchJason P. LerchEvdokia AnagnostouEvdokia AnagnostouRaymond W. LamBenicio N. FreyBenicio N. FreyRoumen MilevDaniel J. MüllerDaniel J. MüllerSidney H. KennedySidney H. KennedySidney H. KennedySidney H. KennedyChristopher J. M. ScottChristopher J. M. ScottChristopher J. M. ScottThe ONDRI InvestigatorsStephen C. StrotherStephen C. StrotherAngela TroyerAnthony E. LangBarry GreenbergChris HudsonDale CorbettDavid A. GrimesDavid G. MunozDouglas P. MunozElizabeth FingerJ. B. OrangeLorne ZinmanManuel Montero-OdassoMaria Carmela TartagliaMario MasellisMichael BorrieMichael J. StrongMorris FreedmanPaula M. McLaughlinRichard H. SwartzRobert A. HegeleRobert BarthaSandra E. BlackSean SymonsStephen C. StrotherWilliam E. McIlroyFrontiers Media S.A.articlehealth dataMRI sequence naming standardizationdata share and exchangemachine learningmetadata learningAI-assisted data managementNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neuroinformatics, Vol 15 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
health data MRI sequence naming standardization data share and exchange machine learning metadata learning AI-assisted data management Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 |
| spellingShingle |
health data MRI sequence naming standardization data share and exchange machine learning metadata learning AI-assisted data management Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Shuai Liang Shuai Liang Derek Beaton Stephen R. Arnott Tom Gee Mojdeh Zamyadi Robert Bartha Sean Symons Glenda M. MacQueen Stefanie Hassel Jason P. Lerch Jason P. Lerch Evdokia Anagnostou Evdokia Anagnostou Raymond W. Lam Benicio N. Frey Benicio N. Frey Roumen Milev Daniel J. Müller Daniel J. Müller Sidney H. Kennedy Sidney H. Kennedy Sidney H. Kennedy Sidney H. Kennedy Christopher J. M. Scott Christopher J. M. Scott Christopher J. M. Scott The ONDRI Investigators Stephen C. Strother 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 Robert Bartha Sandra E. Black Sean Symons Stephen C. Strother William E. McIlroy Magnetic Resonance Imaging Sequence Identification Using a Metadata Learning Approach |
| description |
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. |
| format |
article |
| author |
Shuai Liang Shuai Liang Derek Beaton Stephen R. Arnott Tom Gee Mojdeh Zamyadi Robert Bartha Sean Symons Glenda M. MacQueen Stefanie Hassel Jason P. Lerch Jason P. Lerch Evdokia Anagnostou Evdokia Anagnostou Raymond W. Lam Benicio N. Frey Benicio N. Frey Roumen Milev Daniel J. Müller Daniel J. Müller Sidney H. Kennedy Sidney H. Kennedy Sidney H. Kennedy Sidney H. Kennedy Christopher J. M. Scott Christopher J. M. Scott Christopher J. M. Scott The ONDRI Investigators Stephen C. Strother 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 Robert Bartha Sandra E. Black Sean Symons Stephen C. Strother William E. McIlroy |
| author_facet |
Shuai Liang Shuai Liang Derek Beaton Stephen R. Arnott Tom Gee Mojdeh Zamyadi Robert Bartha Sean Symons Glenda M. MacQueen Stefanie Hassel Jason P. Lerch Jason P. Lerch Evdokia Anagnostou Evdokia Anagnostou Raymond W. Lam Benicio N. Frey Benicio N. Frey Roumen Milev Daniel J. Müller Daniel J. Müller Sidney H. Kennedy Sidney H. Kennedy Sidney H. Kennedy Sidney H. Kennedy Christopher J. M. Scott Christopher J. M. Scott Christopher J. M. Scott The ONDRI Investigators Stephen C. Strother 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 Robert Bartha Sandra E. Black Sean Symons Stephen C. Strother William E. McIlroy |
| author_sort |
Shuai Liang |
| title |
Magnetic Resonance Imaging Sequence Identification Using a Metadata Learning Approach |
| title_short |
Magnetic Resonance Imaging Sequence Identification Using a Metadata Learning Approach |
| title_full |
Magnetic Resonance Imaging Sequence Identification Using a Metadata Learning Approach |
| title_fullStr |
Magnetic Resonance Imaging Sequence Identification Using a Metadata Learning Approach |
| title_full_unstemmed |
Magnetic Resonance Imaging Sequence Identification Using a Metadata Learning Approach |
| title_sort |
magnetic resonance imaging sequence identification using a metadata learning approach |
| publisher |
Frontiers Media S.A. |
| publishDate |
2021 |
| url |
https://doaj.org/article/b987f8f10e054d3e9bc11ba9f80a144f |
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