{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T03:05:17Z","timestamp":1792465517053,"version":"4.5.0"},"reference-count":30,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T00:00:00Z","timestamp":1721865600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62001380"],"award-info":[{"award-number":["62001380"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62073260"],"award-info":[{"award-number":["62073260"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2025,3]]},"abstract":"Background<\/jats:title>There is increasing interest in utilizing AI\u2010generated content for gadolinium\u2010free contrast\u2010enhanced breast MRI.<\/jats:p><\/jats:sec>Purpose<\/jats:title>To develop a generative model for gadolinium\u2010free contrast\u2010enhanced breast MRI and evaluate the diagnostic utility of the generated scans.<\/jats:p><\/jats:sec>Study Type<\/jats:title>Retrospective.<\/jats:p><\/jats:sec>Population<\/jats:title>Two hundred seventy\u2010six women with 304 breast MRI examinations (49\u2009\u00b1\u200913\u2009years, 243\/61 for training\/testing).<\/jats:p><\/jats:sec>Field Strength\/Sequence<\/jats:title>ZOOMit diffusion\u2010weighted imaging (DWI), T1\u2010weighted volumetric interpolated breath\u2010hold examination (T1W VIBE), and axial T2 3D SPACE at 3.0\u2009T.<\/jats:p><\/jats:sec>Assessment<\/jats:title>A generative model was developed to generate contrast\u2010enhanced scans using precontrast T1W VIBE and DWI images. The generated and real images were quantitatively compared using the structural similarity index (SSIM), mean absolute error (MAE), and Dice similarity coefficient. Three radiologists with 8, 5, and 5\u2009years of experience independently rated the image quality and lesion visibility on AI\u2010generated and real images within various subgroups using a five\u2010point scale. Four breast radiologists, with 8, 8, 5, and 5\u2009years of experience, independently and blindly interpreted four reading protocols: unenhanced MRI protocol alone and combined with AI\u2010generated scans, abbreviated MRI protocol, and full\u2010MRI protocol.<\/jats:p><\/jats:sec>Statistical Analysis<\/jats:title>Results were assessed using t<\/jats:italic>\u2010tests and McNemar tests. Using pathology diagnosis as reference standard, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each reading protocol. A P<\/jats:italic> value <0.05 was considered significant.<\/jats:p><\/jats:sec>Results<\/jats:title>In the test set, the generated images showed similarity to the real images (SSIM: 0.935\u2009\u00b1\u20090.047 [SD], MAE: 0.015\u2009\u00b1\u20090.012 [SD], and Dice coefficient: 0.726\u2009\u00b1\u20090.177 [SD]). No significant difference in lesion visibility was observed between real and AI\u2010generated scans of the mass, non\u2010mass, and benign lesion subgroups. Adding AI\u2010generated scans to the unenhanced MRI protocol slightly improved breast cancer detection (sensitivity: 92.86% vs. 85.71%, NPV: 76.92% vs. 70.00%); achieved non\u2010inferior diagnostic utility compared to the AB\u2010MRI protocol and full\u2010protocol (sensitivity: 92.86%, 95.24%; NPV: 75.00%, 81.82%).<\/jats:p><\/jats:sec>Data Conclusion<\/jats:title>AI\u2010generated gadolinium\u2010free contrast\u2010enhanced breast MRI has potential to improve the sensitivity of unenhanced MRI in detecting breast cancer.<\/jats:p><\/jats:sec>Evidence Level<\/jats:title>4<\/jats:p><\/jats:sec>Technical Efficacy<\/jats:title>Stage 3<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmri.29528","type":"journal-article","created":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T15:39:07Z","timestamp":1721921947000},"page":"1232-1243","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Enabling AI<\/scp>\u2010Generated Content for Gadolinium\u2010Free Contrast\u2010Enhanced Breast Magnetic Resonance Imaging"],"prefix":"10.1002","volume":"61","author":[{"given":"Pingping","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Xi'an International Medical Center Hospital Northwest University Xi'an China"},{"name":"Department of Information Science & Technology Northwest University Xi'an China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4556-9546","authenticated-orcid":false,"given":"Hongyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of School of Computer Science & Technology Xi'an University of Posts and Telecommunications Xi'an China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pin","family":"Nie","sequence":"additional","affiliation":[{"name":"Department of Xi'an International Medical Center Hospital Northwest University Xi'an 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