{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:47:59Z","timestamp":1764175679647,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031721038"},{"type":"electronic","value":"9783031721045"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-72104-5_18","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T12:02:53Z","timestamp":1727870573000},"page":"181-190","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Contrast Representation Learning from\u00a0Imaging Parameters for\u00a0Magnetic Resonance Image Synthesis"],"prefix":"10.1007","author":[{"given":"Honglin","family":"Xiong","sequence":"first","affiliation":[]},{"given":"Yu","family":"Fang","sequence":"additional","affiliation":[]},{"given":"Kaicong","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Yulin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiaopeng","family":"Zong","sequence":"additional","affiliation":[]},{"given":"Weijun","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Qian","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"18_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1007\/978-3-319-10443-0_29","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2014","author":"DC Alexander","year":"2014","unstructured":"Alexander, D.C., Zikic, D., Zhang, J., Zhang, H., Criminisi, A.: Image quality transfer via random forest regression: applications in diffusion MRI. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8675, pp. 225\u2013232. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10443-0_29"},{"key":"18_CR2","doi-asserted-by":"crossref","unstructured":"Armanious, K., et al.: MedGAN: medical image translation using GANs. Comput. Med. Imaging Graph. 79, 101684 (2020)","DOI":"10.1016\/j.compmedimag.2019.101684"},{"issue":"3","key":"18_CR3","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1109\/TMI.2017.2764326","volume":"37","author":"A Chartsias","year":"2017","unstructured":"Chartsias, A., Joyce, T., Giuffrida, M.V., Tsaftaris, S.A.: Multimodal MR synthesis via modality-invariant latent representation. IEEE Trans. Med. Imaging 37(3), 803\u2013814 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"10","key":"18_CR4","doi-asserted-by":"publisher","first-page":"2598","DOI":"10.1109\/TMI.2022.3167808","volume":"41","author":"O Dalmaz","year":"2022","unstructured":"Dalmaz, O., Yurt, M., \u00c7ukur, T.: Resvit: residual vision transformers for multimodal medical image synthesis. IEEE Trans. Med. Imaging 41(10), 2598\u20132614 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"10","key":"18_CR5","doi-asserted-by":"publisher","first-page":"2375","DOI":"10.1109\/TMI.2019.2901750","volume":"38","author":"SU Dar","year":"2019","unstructured":"Dar, S.U., Yurt, M., Karacan, L., Erdem, A., Erdem, E., \u00c7ukur, T.: Image synthesis in multi-contrast mri with conditional generative adversarial networks. IEEE Trans. Med. Imaging 38(10), 2375\u20132388 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"18_CR6","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1016\/j.media.2016.08.009","volume":"35","author":"A Jog","year":"2017","unstructured":"Jog, A., Carass, A., Roy, S., Pham, D.L., Prince, J.L.: Random forest regression for magnetic resonance image synthesis. Med. Image Anal. 35, 475\u2013488 (2017)","journal-title":"Med. Image Anal."},{"key":"18_CR7","doi-asserted-by":"crossref","unstructured":"Lan, H., Initiative, A.D.N., Toga, A.W., Sepehrband, F.: SC-GAN: 3d self-attention conditional GAN with spectral normalization for multi-modal neuroimaging synthesis. BioRxiv pp. 2020\u201306 (2020)","DOI":"10.1101\/2020.06.09.143297"},{"key":"18_CR8","doi-asserted-by":"crossref","unstructured":"Lee, D., Kim, J., Moon, W.J., Ye, J.C.: Collagan: collaborative GAN for missing image data imputation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2487\u20132496 (2019)","DOI":"10.1109\/CVPR.2019.00259"},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Liu, J., Pasumarthi, S., Duffy, B., Gong, E., Datta, K., Zaharchuk, G.: One model to synthesize them all: multi-contrast multi-scale transformer for missing data imputation. IEEE Trans. Med. Imaging 42(9), 2577\u20132591 (2023)","DOI":"10.1109\/TMI.2023.3261707"},{"key":"18_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105928","volume":"148","author":"Z Qin","year":"2022","unstructured":"Qin, Z., Liu, Z., Zhu, P., Ling, W.: Style transfer in conditional GANs for cross-modality synthesis of brain magnetic resonance images. Comput. Biol. Med. 148, 105928 (2022)","journal-title":"Comput. Biol. Med."},{"key":"18_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1007\/978-3-319-02126-3_6","volume-title":"Multimodal Brain Image Analysis","author":"S Roy","year":"2013","unstructured":"Roy, S., Jog, A., Carass, A., Prince, J.L.: Atlas based intensity transformation of brain MR images. In: Shen, L., Liu, T., Yap, P.-T., Huang, H., Shen, D., Westin, C.-F. (eds.) MBIA 2013. LNCS, vol. 8159, pp. 51\u201362. Springer, Cham (2013). https:\/\/doi.org\/10.1007\/978-3-319-02126-3_6"},{"key":"18_CR12","doi-asserted-by":"crossref","unstructured":"Wang, G., et al.: Synthesize high-quality multi-contrast magnetic resonance imaging from multi-echo acquisition using multi-task deep generative model. IEEE Trans. Med. Imaging 39(10), 3089\u20133099 (2020)","DOI":"10.1109\/TMI.2020.2987026"},{"issue":"4","key":"18_CR13","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., Sheikh, H., Simoncelli, E.: 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":"18_CR14","doi-asserted-by":"crossref","unstructured":"Xin, B., Hu, Y., Zheng, Y., Liao, H.: Multi-modality generative adversarial networks with tumor consistency loss for brain MR image synthesis. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1803\u20131807. IEEE (2020)","DOI":"10.1109\/ISBI45749.2020.9098449"},{"key":"18_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1007\/978-3-030-87199-4_12","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"H Yang","year":"2021","unstructured":"Yang, H., Sun, J., Yang, L., Xu, Z.: A unified hyper-GAN model for unpaired multi-contrast MR image translation. In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) MICCAI 2021. LNCS, vol. 12903, pp. 127\u2013137. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87199-4_12"},{"key":"18_CR16","doi-asserted-by":"crossref","unstructured":"Yang, H., et al.: Synthesizing multi-contrast MR images via novel 3d conditional variational auto-encoding GAN. Mobile Netw. Appl. 26, 415\u2013424 (2021)","DOI":"10.1007\/s11036-020-01678-1"},{"issue":"7","key":"18_CR17","doi-asserted-by":"publisher","first-page":"1750","DOI":"10.1109\/TMI.2019.2895894","volume":"38","author":"B Yu","year":"2019","unstructured":"Yu, B., Zhou, L., Wang, L., Shi, Y., Fripp, J., Bourgeat, P.: Ea-gans: edge-aware generative adversarial networks for cross-modality mr image synthesis. IEEE Trans. Med. Imaging 38(7), 1750\u20131762 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"18_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, X., et al.: Ptnet3d: a 3d high-resolution longitudinal infant brain MRI synthesizer based on transformers. IEEE Trans. Med. Imaging 41(10), 2925\u20132940 (2022)","DOI":"10.1109\/TMI.2022.3174827"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72104-5_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T09:06:14Z","timestamp":1733562374000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72104-5_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031721038","9783031721045"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72104-5_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests."}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","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":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}