{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T03:16:52Z","timestamp":1769915812969,"version":"3.49.0"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031777882","type":"print"},{"value":"9783031777899","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-77789-9_9","type":"book-chapter","created":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T22:36:00Z","timestamp":1739313360000},"page":"85-95","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Virtual Dynamic Contrast Enhanced Breast MRI Using 2D U-Net Architectures"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4373-6996","authenticated-orcid":false,"given":"Hannes","family":"Schreiter","sequence":"first","affiliation":[]},{"given":"Jessica","family":"Eberle","sequence":"additional","affiliation":[]},{"given":"Lorenz A.","family":"Kapsner","sequence":"additional","affiliation":[]},{"given":"Dominique","family":"Hadler","sequence":"additional","affiliation":[]},{"given":"Sabine","family":"Ohlmeyer","sequence":"additional","affiliation":[]},{"given":"Ramona","family":"Erber","sequence":"additional","affiliation":[]},{"given":"Julius","family":"Emons","sequence":"additional","affiliation":[]},{"given":"Frederik B.","family":"Laun","sequence":"additional","affiliation":[]},{"given":"Michael","family":"Uder","sequence":"additional","affiliation":[]},{"given":"Evelyn","family":"Wenkel","sequence":"additional","affiliation":[]},{"given":"Sebastian","family":"Bickelhaupt","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8450-3021","authenticated-orcid":false,"given":"Andrzej","family":"Liebert","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,12]]},"reference":[{"issue":"3","key":"9_CR1","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1148\/radiol.2019182947","volume":"292","author":"RM Mann","year":"2019","unstructured":"Mann, R.M., Cho, N., Moy, L.: Breast MRI: state of the art. Radiology 292(3), 520\u2013536 (2019). https:\/\/doi.org\/10.1148\/radiol.2019182947","journal-title":"Radiology"},{"key":"9_CR2","doi-asserted-by":"crossref","unstructured":"Tollens, F., Baltzer, P.A., Dietzel, M., R\u00fcbenthaler, J., Froelich, M.F., Kaiser, C.G.: Cost-effectiveness of digital breast tomosynthesis vs. abbreviated breast MRI for screening women with intermediate risk of breast cancer\u2014how low-cost must MRI be? Cancers 13(6), 1241 (2021)","DOI":"10.3390\/cancers13061241"},{"key":"9_CR3","first-page":"1097","volume":"10","author":"L Ko\u010do","year":"2023","unstructured":"Ko\u010do, L., Balkenende, L., Appelman, L., Moman, M.R., Sponsel, A., Schimanski, M., et al.: Optimized, person-centered workflow design for a high-throughput breast mri screening facility\u2014a simulation study. Invest. Radiol. 10, 1097 (2023)","journal-title":"Invest. Radiol."},{"issue":"8","key":"9_CR4","doi-asserted-by":"publisher","first-page":"1046","DOI":"10.1016\/j.jacr.2019.01.004","volume":"16","author":"A Borthakur","year":"2019","unstructured":"Borthakur, A., Weinstein, S.P., Schnall, M.D., Conant, E.F.: Comparison of study activity times for \u201cFull\u201d versus \u201cFast MRI\u201d for breast cancer screening. J. Am. Coll. Radiol. 16(8), 1046\u20131051 (2019). https:\/\/doi.org\/10.1016\/j.jacr.2019.01.004","journal-title":"J. Am. Coll. Radiol."},{"issue":"2","key":"9_CR5","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1148\/radiol.2021211349","volume":"302","author":"RJ McDonald","year":"2022","unstructured":"McDonald, R.J., Weinreb, J.C., Davenport, M.S.: Symptoms associated with gadolinium exposure (SAGE): a suggested term. Radiology 302(2), 270\u2013273 (2022). https:\/\/doi.org\/10.1148\/radiol.2021211349","journal-title":"Radiology"},{"key":"9_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.watres.2020.115966","volume":"182","author":"R Br\u00fcnjes","year":"2020","unstructured":"Br\u00fcnjes, R., Hofmann, T.: Anthropogenic gadolinium in freshwater and drinking water systems. Water Res. 182, 115966 (2020). https:\/\/doi.org\/10.1016\/j.watres.2020.115966","journal-title":"Water Res."},{"issue":"1","key":"9_CR7","doi-asserted-by":"publisher","first-page":"8015","DOI":"10.1038\/s41598-019-44539-y","volume":"9","author":"S Le Goff","year":"2019","unstructured":"Le Goff, S., Barrat, J.-A., Chauvaud, L., Paulet, Y.-M., Gueguen, B., Ben Salem, D.: Compound-specific recording of gadolinium pollution in coastal waters by great scallops. Sci. Rep. 9(1), 8015 (2019)","journal-title":"Sci. Rep."},{"issue":"6","key":"9_CR8","doi-asserted-by":"publisher","first-page":"1523","DOI":"10.1002\/etc.4116","volume":"37","author":"J Rogowska","year":"2018","unstructured":"Rogowska, J., Olkowska, E., Ratajczyk, W., Wolska, L.: Gadolinium as a new emerging contaminant of aquatic environments. Environ. Toxicol. Chem. 37(6), 1523\u20131534 (2018). https:\/\/doi.org\/10.1002\/etc.4116","journal-title":"Environ. Toxicol. Chem."},{"issue":"3","key":"9_CR9","doi-asserted-by":"publisher","first-page":"e213199","DOI":"10.1148\/radiol.213199","volume":"306","author":"M Chung","year":"2022","unstructured":"Chung, M., et al.: Deep learning to simulate contrast-enhanced breast MRI of invasive breast cancer. Radiology 306(3), e213199 (2022). https:\/\/doi.org\/10.1148\/radiol.213199","journal-title":"Radiology"},{"key":"9_CR10","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1109\/JTEHM.2022.3221918","volume":"11","author":"E Kim","year":"2022","unstructured":"Kim, E., Cho, H.-H., Kwon, J., Oh, Y.-T., Ko, E.S., Park, H.: Tumor-attentive segmentation-guided GAN for synthesizing breast contrast-enhanced MRI without contrast agents. IEEE J. Transl. Eng. Health Med. 11, 32\u201343 (2022)","journal-title":"IEEE J. Transl. Eng. Health Med."},{"issue":"3","key":"9_CR11","doi-asserted-by":"publisher","first-page":"e222211","DOI":"10.1148\/radiol.222211","volume":"307","author":"G M\u00fcller-Franzes","year":"2023","unstructured":"M\u00fcller-Franzes, G., et al.: Using machine learning to reduce the need for contrast agents in breast MRI through synthetic images. Radiology 307(3), e222211 (2023). https:\/\/doi.org\/10.1148\/radiol.222211","journal-title":"Radiology"},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"Zhang, T., et al.: Synthesis of contrast-enhanced breast MRI using T1- and multi-b-value DWI-based hierarchical fusion network with attention mechanism. In: Greenspan, H., et al. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023, pp. 79\u201388. Springer, Cham (2023)","DOI":"10.1007\/978-3-031-43990-2_8"},{"key":"9_CR13","doi-asserted-by":"publisher","first-page":"102381","DOI":"10.1016\/j.inffus.2024.102381","volume":"108","author":"TY Zhang","year":"2024","unstructured":"Zhang, T.Y., Tan, T., Han, L.Y., Wang, X., Gao, Y., van Dijk, J., et al.: IMPORTANT-Net: integrated MRI multi-parametric increment fusion generator with attention network for synthesizing absent data. Inf. Fusion 108, 102381 (2024). https:\/\/doi.org\/10.1016\/j.inffus.2024.102381","journal-title":"Inf. Fusion"},{"key":"9_CR14","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.jneumeth.2016.03.001","volume":"264","author":"X Li","year":"2016","unstructured":"Li, X., Morgan, P.S., Ashburner, J., Smith, J., Rorden, C.: The first step for neuroimaging data analysis: DICOM to NIfTI conversion. J. Neurosci. Methods 264, 47\u201356 (2016). https:\/\/doi.org\/10.1016\/j.jneumeth.2016.03.001","journal-title":"J. Neurosci. Methods"},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"Liebert, A., et al.: Feasibility to virtually generate T2 fat-saturated breast MRI by convolutional neural networks (2024)","DOI":"10.1101\/2024.06.25.24309404"},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"Chen, C., Raymond, C., Speier, W., Jin, X., Cloughesy, T.F., Enzmann, D., et al.: Synthesizing MR image contrast enhancement using 3D high-resolution ConvNets. IEEE Trans. Biomed. Eng. (2022)","DOI":"10.1109\/TBME.2022.3192309"},{"issue":"4","key":"9_CR17","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"9_CR18","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.792516","volume":"11","author":"P Wang","year":"2021","unstructured":"Wang, P., Nie, P., Dang, Y., Wang, L., Zhu, K., Wang, H., et al.: Synthesizing the first phase of dynamic sequences of breast MRI for enhanced lesion identification. Front. Oncol. 11, 792516 (2021). https:\/\/doi.org\/10.3389\/fonc.2021.792516","journal-title":"Front. Oncol."},{"issue":"1","key":"9_CR19","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1002\/2017sw001669","volume":"16","author":"SK Morley","year":"2018","unstructured":"Morley, S.K., Brito, T.V., Welling, D.T.: Measures of model performance based on the log accuracy ratio. Space Weather- Int. J. Res. Appl. 16(1), 69\u201388 (2018). https:\/\/doi.org\/10.1002\/2017sw001669","journal-title":"Space Weather- Int. J. Res. Appl."},{"issue":"9","key":"9_CR20","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1016\/j.mri.2012.05.001","volume":"30","author":"A Fedorov","year":"2012","unstructured":"Fedorov, A., Beichel, R., Kalpathy-Cramer, J., Finet, J., Fillion-Robin, J.C., Pujol, S., et al.: 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn. Reson. Imaging 30(9), 1323\u20131341 (2012). https:\/\/doi.org\/10.1016\/j.mri.2012.05.001","journal-title":"Magn. Reson. Imaging"},{"key":"9_CR21","doi-asserted-by":"publisher","unstructured":"Liebert, A., et al.: Impact of non-contrast enhanced imaging input sequences on the generation of virtual contrast-enhanced breast MRI scans using neural networks. medRxiv 2024.2005.2003.24306067 (2024). https:\/\/doi.org\/10.1101\/2024.05.03.24306067","DOI":"10.1101\/2024.05.03.24306067"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-77789-9_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,11]],"date-time":"2025-02-11T22:36:06Z","timestamp":1739313366000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-77789-9_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031777882","9783031777899"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-77789-9_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"12 February 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Deep-Breath","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"deep breath2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/deep-breath-miccai.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}