Ultralow-Field-Strength MRI and Artificial Intelligence: How Low Can We Go and How High Can We Reach?

HomeRadiologyVol. 306, No. 3 PreviousNext Reviews and CommentaryEditorialUltralow-Field-Strength MRI and Artificial Intelligence: How Low Can We Go and How High Can We Reach?Birgit Ertl-Wagner , Matthias WagnerBirgit Ertl-Wagner , Matthias WagnerAuthor AffiliationsFrom the Department of Medical Imaging, University of Toronto, 555 University Ave, Toronto, ON, Canada M5S 1A1.Address correspondence to B.E.W. (email: [email protected]).Birgit Ertl-Wagner Matthias WagnerPublished Online:Nov 8 2022https://doi.org/10.1148/radiol.222302MoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In References1. Mazurek MH, Cahn BA, Yuen MM, et al. Portable, bedside, low-field magnetic resonance imaging for evaluation of intracerebral hemorrhage. Nat Commun 2021;12(1):5119. Crossref, Medline, Google Scholar2. Arnold TC, Tu D, Okar SV, et al. Sensitivity of portable low-field magnetic resonance imaging for multiple sclerosis lesions. Neuroimage Clin 2022;35:103101. Crossref, Medline, Google Scholar3. Iglesias JE, Schleicher R, Laguna S, et al. Quantitative brain morphometry of portable low-field-strength MRI using super-resolution machine learning. Radiology 2023;306(3):e220522. https://doi.org/10.1148/radiol.220522. Published online November 8, 2022. Google Scholar4. Iglesias JE, Billot B, Balbastre Y, et al. Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast. Neuroimage 2021;237:118206. Crossref, Medline, Google Scholar5. Fischl B, Salat DH, Busa E, et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 2002;33(3):341–355. Crossref, Medline, Google Scholar6. Patenaude B, Smith SM, Kennedy DN, Jenkinson M. A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage 2011;56(3):907–922. Crossref, Medline, Google Scholar7. Koonjoo N, Zhu B, Bagnall GC, Bhutto D, Rosen MS. Boosting the signal-to-noise of low-field MRI with deep learning image reconstruction. Sci Rep 2021;11(1):8248. Crossref, Medline, Google ScholarArticle HistoryReceived: Sept 11 2022Revision requested: Sept 21 2022Revision received: Sept 26 2022Accepted: Sept 28 2022Published online: Nov 08 2022 FiguresReferencesRelatedDetailsAccompanying This ArticleQuantitative Brain Morphometry of Portable Low-Field-Strength MRI Using Super-Resolution Machine LearningNov 8 2022RadiologyRecommended Articles Wearable Magnetoencephalography: Reality or Science Fiction?Radiology2022Volume: 304Issue: 2pp. 435-436A Multichannel Deep Neural Network Model Analyzing Multiscale Functional Brain Connectome Data for Attention Deficit Hyperactivity Disorder DetectionRadiology: Artificial Intelligence2019Volume: 2Issue: 1Quantitative Brain Morphometry of Portable Low-Field-Strength MRI Using Super-Resolution Machine LearningRadiology2022Volume: 306Issue: 3Point-of-Care Low-Field-Strength MRI Is Moving Beyond the HypeRadiology2022Volume: 305Issue: 3pp. 672-673Functional Network Dynamics on Functional MRI: A Primer on an Emerging Frontier in NeuroscienceRadiology2019Volume: 292Issue: 2pp. 460-463See More RSNA Education Exhibits Radiomics for Predicting Tumor Immune Profile of Pediatric GliomaDigital Posters2022Outsmarting AI: What Role Can The Radiologist Play In The Making And Deployment Of Artificial Intelligence ApplicationsDigital Posters2021Deep Learning Based Image Reconstruction (DL-R) for CT: Can It Replace the Existing Image Reconstruction Techniques? Digital Posters2020 RSNA Case Collection Brain Arteriovenous MalformationRSNA Case Collection2021Papillary Thyroid CarcinomaRSNA Case Collection2022 Grey matter HeterotopiaRSNA Case Collection2021 Vol. 306, No. 3 Metrics Altmetric Score PDF download