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This study aims to evaluate whether a 2D-U-Net neural network can generate virtual T2w-FS (VirtuT2w) images from routine multiparametric breast MRI images.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>This IRB-approved, retrospective study included 914 breast MRI examinations from January 2017 to June 2020. The dataset was divided into training (<jats:italic>n<\/jats:italic>\u2009=\u2009665), validation (<jats:italic>n<\/jats:italic>\u2009=\u200974), and test sets (<jats:italic>n<\/jats:italic>\u2009=\u2009175). The U-Net was trained using different input protocols consisting of T1-weighted, diffusion-weighted, and dynamic contrast-enhanced sequences to generate VirtuT2. Quantitative metrics were used to evaluate the different input protocols. A qualitative assessment by two radiologists was used to evaluate the VirtuT2w images of the best input protocol.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>VirtuT2w images demonstrated the best quantitative metrics compared to original T2w-FS images for an input protocol using all of the available data. A high level of high-frequency error norm (0.87) indicated a strong blurring presence in the VirtuT2 images, which was also confirmed by qualitative reading. Radiologists correctly identified VirtuT2 images with at least 96% accuracy. Significant difference in diagnostic image quality was noted for both readers (<jats:italic>p<\/jats:italic>\u2009\u2264\u20090.015). Moderate inter-reader agreement was observed for edema detection on both T2w-FS images (\u03ba\u2009=\u20090.49) and VirtuT2 images (\u03ba\u2009=\u20090.44).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>The 2D-U-Net generated virtual T2w-FS images similar to real T2w-FS images, though blurring remains a limitation. Investigation of other architectures and using larger datasets is necessary to improve\u00a0potential future clinical applicability.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Relevance statement<\/jats:title>\n            <jats:p>Generating VirtuT2 images could potentially decrease the examination time of multiparametric breast MRI, but its quality needs to improve before introduction into a clinical setting.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Key Points<\/jats:title>\n            <jats:p>\n              <jats:list list-type=\"bullet\">\n                <jats:list-item>\n                  <jats:p>Breast MRI T2w-fat-saturated (FS) images can be virtually generated using convolutional neural networks.<\/jats:p>\n                <\/jats:list-item>\n                <jats:list-item>\n                  <jats:p>Image blurring in virtual T2w-FS images currently limits their clinical applicability.<\/jats:p>\n                <\/jats:list-item>\n                <jats:list-item>\n                  <jats:p>Best quantitative performance could be achieved when using full dynamic-contrast-enhanced acquisition and DWI as input of the neural network.<\/jats:p>\n                <\/jats:list-item>\n              <\/jats:list>\n            <\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Graphical Abstract<\/jats:title>\n          <\/jats:sec>","DOI":"10.1186\/s41747-025-00580-3","type":"journal-article","created":{"date-parts":[[2025,5,2]],"date-time":"2025-05-02T13:27:41Z","timestamp":1746192461000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Feasibility of virtual T2-weighted fat-saturated breast MRI images by convolutional neural networks"],"prefix":"10.1186","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8450-3021","authenticated-orcid":false,"given":"Andrzej","family":"Liebert","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dominique","family":"Hadler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chris","family":"Ehring","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hannes","family":"Schreiter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luise","family":"Brock","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lorenz A.","family":"Kapsner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jessica","family":"Eberle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ramona","family":"Erber","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julius","family":"Emons","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frederik B.","family":"Laun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Uder","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evelyn","family":"Wenkel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sabine","family":"Ohlmeyer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian","family":"Bickelhaupt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,2]]},"reference":[{"key":"580_CR1","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1148\/radiol.2019182947","volume":"292","author":"RM Mann","year":"2019","unstructured":"Mann RM, Cho N, Moy L (2019) Breast MRI: state of the art. 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