{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T16:36:05Z","timestamp":1784392565867,"version":"3.55.0"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032113160","type":"print"},{"value":"9783032113177","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-11317-7_36","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:58:22Z","timestamp":1767322702000},"page":"429-440","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Benchmarking GANs, Diffusion Models, and\u00a0Flow Matching for\u00a0T1w-to-T2w MRI Translation"],"prefix":"10.1007","author":[{"given":"Andrea","family":"Moschetto","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5661-9873","authenticated-orcid":false,"given":"Lemuel","family":"Puglisi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alec","family":"Sargood","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pierluigi","family":"Dell\u2019Acqua","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7703-3367","authenticated-orcid":false,"given":"Francesco","family":"Guarnera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6127-2470","authenticated-orcid":false,"given":"Sebastiano","family":"Battiato","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0372-2677","authenticated-orcid":false,"given":"Daniele","family":"Rav\u00ec","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"issue":"1","key":"36_CR1","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/ad9a3a","volume":"6","author":"MU Akbar","year":"2025","unstructured":"Akbar, M.U., Wang, W., Eklund, A.: Beware of diffusion models for synthesizing medical images\u2013a comparison with GANS in terms of memorizing brain MRI and chest x-ray images. Mach. Learn. Sci. Tech. 6(1), 015022 (2025)","journal-title":"Mach. Learn. Sci. Tech."},{"key":"36_CR2","unstructured":"Alkan, C., Cocjin, J., Weitz, A.: Magnetic resonance contrast prediction using deep learning. Google Scholar (2016)"},{"issue":"1","key":"36_CR3","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.media.2007.06.004","volume":"12","author":"BB Avants","year":"2008","unstructured":"Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26\u201341 (2008)","journal-title":"Med. Image Anal."},{"key":"36_CR4","unstructured":"Bertrand, Q., Gagneux, A., Massias, M., Emonet, R.: On the closed-form of flow matching: generalization does not arise from target stochasticity. arXiv preprint arXiv:2506.03719 (2025)"},{"key":"36_CR5","unstructured":"Cardoso, M.J., et\u00a0al.: Monai: an open-source framework for deep learning in healthcare. arXiv preprint arXiv:2211.02701 (2022)"},{"key":"36_CR6","unstructured":"Chadebec, C., Tasar, O., Sreetharan, S., Aubin, B.: LBM: latent bridge matching for fast image-to-image translation. arXiv preprint arXiv:2503.07535 (2025)"},{"key":"36_CR7","doi-asserted-by":"crossref","unstructured":"Choo, K., Jun, Y., Yun, M., Hwang, S.J.: Slice-consistent 3d volumetric brain CT-to-MRI translation with 2d brownian bridge diffusion model. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 657\u2013667. Springer (2024)","DOI":"10.1007\/978-3-031-72104-5_63"},{"key":"36_CR8","unstructured":"Dey, A., Ebrahimi, M.: MTSR-MRI: Combined modality translation and super-resolution of magnetic resonance images. In: Medical Imaging with Deep Learning, pp. 743\u2013757. PMLR (2024)"},{"key":"36_CR9","first-page":"8780","volume":"34","author":"P Dhariwal","year":"2021","unstructured":"Dhariwal, P., Nichol, A.: Diffusion models beat GANS on image synthesis. Adv. Neural. Inf. Process. Syst. 34, 8780\u20138794 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"36_CR10","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840\u20136851 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"36_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2022.119474","volume":"260","author":"A Hoopes","year":"2022","unstructured":"Hoopes, A., Mora, J.S., Dalca, A.V., Fischl, B., Hoffmann, M.: Synthstrip: skull-stripping for any brain image. Neuroimage 260, 119474 (2022)","journal-title":"Neuroimage"},{"key":"36_CR12","doi-asserted-by":"crossref","unstructured":"Huang, Y., Zheng, F., Sun, X., Li, Y., Shao, L., Zheng, Y.: Generalized brain image synthesis with transferable convolutional sparse coding networks. In: ECCV, pp. 183\u2013199. Springer (2022)","DOI":"10.1007\/978-3-031-19830-4_11"},{"key":"36_CR13","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1125\u20131134 (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"36_CR14","doi-asserted-by":"crossref","unstructured":"Kawahara, D., Nagata, Y.: T1-weighted and t2-weighted MRI image synthesis with convolutional generative adversarial networks. Rep. Pract. Oncol. Radiother. 26(1), 35\u201342 (2021)","DOI":"10.5603\/RPOR.a2021.0005"},{"key":"36_CR15","unstructured":"Lipman, Y., Chen, R.T., Ben-Hamu, H., Nickel, M., Le, M.: Flow matching for generative modeling. arXiv preprint arXiv:2210.02747 (2022)"},{"key":"36_CR16","unstructured":"Martin, S., Gagneux, A., Hagemann, P., Steidl, G.: PNP-flow: plug-and-play image restoration with flow matching. arXiv preprint arXiv:2410.02423 (2024)"},{"issue":"4","key":"36_CR17","doi-asserted-by":"publisher","first-page":"2538","DOI":"10.1002\/mp.16847","volume":"51","author":"S Pan","year":"2024","unstructured":"Pan, S., et al.: Synthetic CT generation from MRI using 3d transformer-based denoising diffusion model. Med. Phys. 51(4), 2538\u20132548 (2024)","journal-title":"Med. Phys."},{"key":"36_CR18","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"36_CR19","doi-asserted-by":"crossref","unstructured":"Shinohara, R.T., et\u00a0al.: Statistical normalization techniques for magnetic resonance imaging. NeuroImage: Clinical 6, 9\u201319 (2014)","DOI":"10.1016\/j.nicl.2014.08.008"},{"issue":"6","key":"36_CR20","doi-asserted-by":"publisher","first-page":"1310","DOI":"10.1109\/TMI.2010.2046908","volume":"29","author":"NJ Tustison","year":"2010","unstructured":"Tustison, N.J., et al.: N4itk: improved n3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310\u20131320 (2010)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"36_CR21","doi-asserted-by":"crossref","unstructured":"Vaidya, A., Stough, J.V., Patel, A.A.: Perceptually improved t1-t2 MRI translations using conditional generative adversarial networks. In: Medical Imaging 2022: Image Processing. vol. 12032, pp. 505\u2013511. SPIE (2022)","DOI":"10.1117\/12.2608428"},{"key":"36_CR22","doi-asserted-by":"crossref","unstructured":"Yazdani, M., Medghalchi, Y., Ashrafian, P., Hacihaliloglu, I., Shahriari, D.: Flow matching for medical image synthesis: Bridging the gap between speed and quality. arXiv preprint arXiv:2503.00266 (2025)","DOI":"10.1007\/978-3-032-05325-1_21"},{"key":"36_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp. 3836\u20133847 (2023)","DOI":"10.1109\/ICCV51070.2023.00355"}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Processing - ICIAP 2025 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-11317-7_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:58:27Z","timestamp":1767322707000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-11317-7_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032113160","9783032113177"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-11317-7_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rome","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciap2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iciap.org\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}