{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T05:07:31Z","timestamp":1783832851625,"version":"3.55.0"},"reference-count":47,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T00:00:00Z","timestamp":1777680000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T00:00:00Z","timestamp":1777680000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2026,8]]},"abstract":"ABSTRACT<\/jats:title>\n \n Background<\/jats:title>\n MRI is essential for diagnosing and monitoring neurological diseases. Conventional protocols require multiple sequences to obtain complementary contrasts, increasing scan time, cost, and tolerability. Generating multiple contrasts from a single acquisition may streamline workflow while maintaining clinical utility.<\/jats:p>\n <\/jats:sec>\n \n Purpose<\/jats:title>\n To train attention\u2010based convolutional neural networks (ACNNs) to generate clinical\u2010quality Fluid\u2010Attenuated\u2010Inversion\u2010Recovery (FLAIR), Magnetization\u2010Prepared\u2010Rapid\u2010Gradient\u2010Echo (MPRAGE), R2* maps, and derived contrasts from a single Gradient Echo Plural Contrast Imaging (GEPCI) acquisition.<\/jats:p>\n <\/jats:sec>\n \n Study Type<\/jats:title>\n Retrospective.<\/jats:p>\n <\/jats:sec>\n \n Population<\/jats:title>\n 43 MRI scans from individuals with multiple sclerosis (25\/18\u2009F\/M, 49\u2009\u00b1\u200911\u2009years\u2010of\u2010age).<\/jats:p>\n <\/jats:sec>\n \n Field Strength\/Sequence<\/jats:title>\n 3\u2009T MRI, 3D GEPCI, MPRAGE, and FLAIR.<\/jats:p>\n <\/jats:sec>\n \n Assessment<\/jats:title>\n Technical quality of AI\u2010generated contrasts was evaluated against directly acquired MRI using structural similarity index (SSIM). Clinical image quality was assessed by physicians. Lesion volumes and counts were obtained using automated segmentation.<\/jats:p>\n <\/jats:sec>\n \n Statistical Tests<\/jats:title>\n One\u2010sample one\u2010sided Wilcoxon signed\u2010rank test was used to establish the clinical quality of images. Agreement between native\u2010 and AI\u2010derived lesion volume and lesion count measurements was assessed using intraclass correlation coefficients (ICC). Quantitative accuracy for R2* maps was evaluated using normalized root\u2010mean\u2010square error (NRMSE).<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n AI\u2010generated FLAIR and MPRAGE achieved mean SSIM values of 0.923\u2009\u00b1\u20090.028 and 0.935\u2009\u00b1\u20090.022, respectively. Generated R2* maps achieved a mean SSIM of 0.996\u2009\u00b1\u20090.006 and NRMSE of 0.031\u2009\u00b1\u20090.020. Physicians\u2010assigned mean clinical quality ratings of 4.2 for GEPCI\u2010FLAIR and 4.5 for GEPCI\u2010MPRAGE exceeded the 4.0 clinical standard on a 1\u2010to\u20105 scale. Lesion volume and count comparisons from automated segmentation showed strong agreement between AI\u2010generated and ground\u2010truth measurements: R\n 2<\/jats:sup>\n \u2009=\u20090.988 and R\n 2<\/jats:sup>\n \u2009=\u20090.933, ICC\u2009=\u20090.988 and ICC\u2009=\u20090.967, respectively.\n <\/jats:p>\n <\/jats:sec>\n \n Data Conclusion<\/jats:title>\n AI\u2010GEPCI generated multiple clinically relevant MRI contrasts from a single GEPCI acquisition with high similarity to corresponding acquired images, supporting high\u2010quality, intrinsically co\u2010registered multi\u2010contrast brain evaluation.<\/jats:p>\n <\/jats:sec>\n \n Evidence Level<\/jats:title>\n 2.<\/jats:p>\n <\/jats:sec>\n \n Technical Efficacy<\/jats:title>\n Stage 1.<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/jmri.70345","type":"journal-article","created":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T13:25:26Z","timestamp":1777728326000},"page":"346-358","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI<\/scp>\n \u2010Powered Gradient Echo Plural Contrast Imaging (\n AI<\/scp>\n \u2010\n GEPCI<\/scp>\n )\u2014A Comprehensive Neurological Protocol From a Single\n MRI<\/scp>\n Scan"],"prefix":"10.1002","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9442-9427","authenticated-orcid":false,"given":"Jeramy","family":"Lewis","sequence":"first","affiliation":[{"name":"Mallinckrodt Institute of Radiology, Washington University in St. Louis St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manu S.","family":"Goyal","sequence":"additional","affiliation":[{"name":"Mallinckrodt Institute of Radiology, Washington University in St. Louis St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gregory F.","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Neurology Washington University in St. Louis St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuyang","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Electrical & Systems Engineering Washington University in St. Louis St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander L.","family":"Sukstanskii","sequence":"additional","affiliation":[{"name":"Mallinckrodt Institute of Radiology, Washington University in St. Louis St. Louis Missouri USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Satya V. 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