Spinning Gold out of Straw: Using Modern AI Methods to Create Sequences for Cortical and Juxtacortical Multiple Sclerosis Lesion Detection When Only Conventional Imaging Is Available

HomeRadiologyRecently Published PreviousNext Reviews and CommentaryEditorialSpinning Gold out of Straw: Using Modern AI Methods to Create Sequences for Cortical and Juxtacortical Multiple Sclerosis Lesion Detection When Only Conventional Imaging Is AvailableRobert Zivadinov , Michael G. DwyerRobert Zivadinov , Michael G. DwyerAuthor AffiliationsFrom the Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New York, 100 High St, Buffalo, NY 14203; and Center for Biomedical Imaging at the Clinical Translational Science Institute, University at Buffalo, State University of New York, Buffalo, NY.Address correspondence to R.Z. (email: [email protected]).Robert Zivadinov Michael G. DwyerPublished Online:Feb 7 2023https://doi.org/10.1148/radiol.223244MoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In References1. Bouman PM, Noteboom S, Nobrega Santos FA, et al. Multicenter evaluation of AI-generated DIR and PSIR for cortical and juxtacortical multiple sclerosis lesion detection. Radiology 2023. https://doi.org/10.1148/radiol.221425. Published online February 7, 2023. Google Scholar2. Calabrese M, Filippi M, Gallo P. Cortical lesions in multiple sclerosis. Nat Rev Neurol 2010;6(8):438–444. Crossref, Medline, Google Scholar3. Zivadinov R, Jakimovski D, Gandhi S, et al. Clinical relevance of brain atrophy assessment in multiple sclerosis. Implications for its use in a clinical routine. Expert Rev Neurother 2016;16(7):777–793. Crossref, Medline, Google Scholar4. Seewann A, Kooi EJ, Roosendaal SD, et al. Postmortem verification of MS cortical lesion detection with 3D DIR. Neurology 2012;78(5):302–308. Crossref, Medline, Google Scholar5. Wattjes MP, Ciccarelli O, Reich DS, et al. 2021 MAGNIMS-CMSC-NAIMS consensus recommendations on the use of MRI in patients with multiple sclerosis. Lancet Neurol 2021;20(8):653–670. Crossref, Medline, Google Scholar6. Bonacchi R, Filippi M, Rocca MA. Role of artificial intelligence in MS clinical practice. Neuroimage Clin 2022;35:103065. Crossref, Medline, Google Scholar7. Bouman PM, Strijbis VI, Jonkman LE, Hulst HE, Geurts JJ, Steenwijk MD. Artificial double inversion recovery images for (juxta)cortical lesion visualization in multiple sclerosis. Mult Scler 2022;28(4):541–549. Crossref, Medline, Google Scholar8. Finck T, Li H, Grundl L, et al. Deep-learning generated synthetic double inversion recovery images improve multiple sclerosis lesion detection. Invest Radiol 2020;55(5):318–323. Crossref, Medline, Google Scholar9. Bouman PM, Steenwijk MD, Geurts JJG, Jonkman LE. Artificial double inversion recovery images can substitute conventionally acquired images: an MRI-histology study. Sci Rep 2022;12(1):2620. Crossref, Medline, Google Scholar10. Fujita S, Yokoyama K, Hagiwara A, et al. 3D quantitative synthetic MRI in the evaluation of multiple sclerosis lesions. AJNR Am J Neuroradiol 2021;42(3):471–478. Crossref, Medline, Google ScholarArticle HistoryReceived: Dec 16 2022Revision requested: Dec 20 2022Revision received: Dec 20 2022Accepted: Dec 22 2022Published online: Feb 07 2023 FiguresReferencesRelatedDetailsAccompanying This ArticleMulticenter Evaluation of AI-generated DIR and PSIR for Cortical and Juxtacortical Multiple Sclerosis Lesion DetectionFeb 7 2023RadiologyRecommended Articles RSNA Education Exhibits RSNA Case Collection Recently Published Metrics Altmetric Score PDF download