Synthetic Images Are Here to Stay.

HomeRadiologyVol. 308, No. 1 PreviousNext Reviews and CommentaryEditorialSynthetic Images Are Here to StayMark L. Schiebler , Carri Glide-HurstMark L. Schiebler , Carri Glide-HurstAuthor AffiliationsFrom the Department of Radiology (M.L.S.) and Department of Human Oncology and Medical Physics (C.G.H.), University of Wisconsin, 600 Highland Ave, E3/378 Clinical Science Center, Madison, WI 53792.Address correspondence to M.L.S. (email: [email protected]).Mark L. Schiebler Carri Glide-HurstPublished Online:Jul 5 2023https://doi.org/10.1148/radiol.231098MoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In References1. Hsu SH, Cao Y, Huang K, Feng M, Balter JM. Investigation of a method for generating synthetic CT models from MRI scans of the head and neck for radiation therapy. Phys Med Biol 2013;58(23):8419–8435. Crossref, Medline, Google Scholar2. Emami H, Dong M, Nejad-Davarani SP, Glide-Hurst CK. Generating synthetic CTs from magnetic resonance images using generative adversarial networks. Med Phys 2018;45(8):3627–3636. Crossref, Google Scholar3. Longuefosse A, Raoult J, Benlala I, et al. Generating high-resolution synthetic CT from lung MRI with ultrashort echo times: initial evaluation in cystic fibrosis. Radiology 2023;308(1); e230052. Google Scholar4. Johnson KM, Fain SB, Schiebler ML, Nagle S. Optimized 3D ultrashort echo time pulmonary MRI. Magn Reson Med 2013;70(5):1241–1250. Crossref, Medline, Google Scholar5. Bluemke DA, Moy L, Bredella MA, et al. Assessing radiology research on artificial intelligence: a brief guide for authors, reviewers, and readers—from the Radiology Editorial Board. Radiology 2020;294(3):487–489. Link, Google Scholar6. Liu X, Emami H, Nejad-Davarani SP, et al. Performance of deep learning synthetic CTs for MR-only brain radiation therapy. J Appl Clin Med Phys 2021;22(1):308–317. Crossref, Medline, Google Scholar7. Bluemke DA, Lima JAC. Cardiac Imaging 2040. Radiology 2023;307(3):e230160. Link, Google Scholar8. Chung M, Calabrese E, Mongan J, et al. Deep learning to simulate contrast-enhanced breast MRI of invasive breast cancer. Radiology 2023;306(3):e213199. [Published correction appears in Radiology 2023;306(3):e239004.] Link, Google Scholar9. Kijowski R, Fritz J. Emerging technology in musculoskeletal MRI and CT. Radiology 2023;306(1):6–19. Link, Google Scholar10. 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. Link, Google ScholarArticle HistoryReceived: Apr 28 2023Revision requested: May 15 2023Revision received: May 16 2023Accepted: May 17 2023Published online: July 05 2023 FiguresReferencesRelatedDetailsAccompanying This ArticleGenerating High-Resolution Synthetic CT from Lung MRI with Ultrashort Echo Times: Initial Evaluation in Cystic FibrosisJul 5 2023RadiologyRecommended Articles RSNA Education Exhibits RSNA Case Collection Vol. 308, No. 1 Metrics Altmetric Score PDF download