{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T09:50:37Z","timestamp":1758361837218,"version":"3.44.0"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032053244"},{"type":"electronic","value":"9783032053251"}],"license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"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\/978-3-032-05325-1_15","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T19:05:59Z","timestamp":1758308759000},"page":"151-160","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["D$$^3$$M: Deformation-Driven Diffusion Model for\u00a0Synthesis of\u00a0Contrast-Enhanced MRI with\u00a0Brain Tumors"],"prefix":"10.1007","author":[{"given":"Haowen","family":"Pang","sequence":"first","affiliation":[]},{"given":"Peng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xiaoming","family":"Hong","sequence":"additional","affiliation":[]},{"given":"Shannan","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Chuyang","family":"Ye","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"issue":"8","key":"15_CR1","doi-asserted-by":"publisher","first-page":"1788","DOI":"10.1109\/TMI.2019.2897538","volume":"38","author":"G Balakrishnan","year":"2019","unstructured":"Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: VoxelMorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imaging 38(8), 1788\u20131800 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"15_CR2","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)","journal-title":"Water Res."},{"key":"15_CR3","unstructured":"Chen, T.: On the importance of noise scheduling for diffusion models (2023). arXiv preprint arXiv:2301.10972"},{"issue":"10","key":"15_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"},{"key":"15_CR5","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1109\/RBME.2019.2946868","volume":"13","author":"M Ghaffari","year":"2019","unstructured":"Ghaffari, M., Sowmya, A., Oliver, R.: Automated brain tumor segmentation using multimodal brain scans: a survey based on models submitted to the BraTS 2012\u20132018 challenges. IEEE Rev. Biomed. Eng. 13, 156\u2013168 (2019)","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"15_CR6","doi-asserted-by":"crossref","unstructured":"Gui, L., Ye, C., Yan, T.: CAVM: Conditional autoregressive vision model for contrast-enhanced brain tumor MRI synthesis. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 161\u2013170. Springer (2024)","DOI":"10.1007\/978-3-031-72104-5_16"},{"key":"15_CR7","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":"15_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.marpolbul.2020.111148","volume":"154","author":"K Inoue","year":"2020","unstructured":"Inoue, K., et al.: Impact on gadolinium anomaly in river waters in Tokyo related to the increased number of MRI devices in use. Mar. Pollut. Bull. 154, 111148 (2020)","journal-title":"Mar. Pollut. Bull."},{"issue":"2","key":"15_CR9","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"key":"15_CR10","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":"15_CR11","unstructured":"Kazerooni, A.F., et\u00a0al.: The brain tumor segmentation in pediatrics (BraTS-PEDs) challenge: Focus on pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs) (2024). arXiv preprint arXiv:2404.15009"},{"key":"15_CR12","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization (2014). arXiv preprint arXiv:1412.6980"},{"key":"15_CR13","unstructured":"Li, H.B., et\u00a0al.: The brain tumor segmentation (BraTS) challenge 2023: Brain MR image synthesis for tumor segmentation (BraSyn). ArXiv (2023)"},{"key":"15_CR14","unstructured":"Liu, G.H., Vahdat, A., Huang, D.A., Theodorou, E.A., Nie, W., Anandkumar, A.: I$$^2$$SB: Image-to-image schr\u00f6dinger bridge. In: Proceedings of the 40th International Conference on Machine Learning, pp. 22042\u201322062 (2023)"},{"issue":"10","key":"15_CR15","doi-asserted-by":"publisher","first-page":"746","DOI":"10.1097\/RLI.0000000000000983","volume":"58","author":"CA Mallio","year":"2023","unstructured":"Mallio, C.A., et al.: Artificial intelligence to reduce or eliminate the need for gadolinium-based contrast agents in brain and cardiac MRI: A literature review. Invest. Radiol. 58(10), 746\u2013753 (2023)","journal-title":"Invest. Radiol."},{"key":"15_CR16","doi-asserted-by":"crossref","unstructured":"Meng, X., Sun, K., Xu, J., He, X., Shen, D.: Multi-modal modality-masked diffusion network for brain MRI synthesis with random modality missing. IEEE Trans. Med. Imaging (2024)","DOI":"10.1109\/TMI.2024.3368664"},{"issue":"12","key":"15_CR17","doi-asserted-by":"publisher","first-page":"e784","DOI":"10.1016\/S2589-7500(21)00205-3","volume":"3","author":"CJ Preetha","year":"2021","unstructured":"Preetha, C.J., et al.: Deep-learning-based synthesis of post-contrast T1-weighted MRI for Tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study. Lancet Digit. Health 3(12), e784\u2013e794 (2021)","journal-title":"Lancet Digit. Health"},{"key":"15_CR18","doi-asserted-by":"crossref","unstructured":"Saharia, C., et al.: Palette: Image-to-image diffusion models. In: ACM SIGGRAPH 2022 Conference Proceedings, pp. 1\u201310 (2022)","DOI":"10.1145\/3528233.3530757"},{"key":"15_CR19","unstructured":"Salimans, T., Ho, J.: Progressive distillation for fast sampling of diffusion models (2022). arXiv preprint arXiv:2202.00512"},{"key":"15_CR20","unstructured":"Salimans, T., Karpathy, A., Chen, X., Kingma, D.P.: PixelCNN++: Improving the PixelCNN with discretized logistic mixture likelihood and other modifications (2017). arXiv preprint arXiv:1701.05517"},{"key":"15_CR21","unstructured":"Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models (2020). arXiv preprint arXiv:2010.02502"},{"issue":"3","key":"15_CR22","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1093\/neuros\/nyx103","volume":"81","author":"JE Villanueva-Meyer","year":"2017","unstructured":"Villanueva-Meyer, J.E., Mabray, M.C., Cha, S.: Current clinical brain tumor imaging. Neurosurgery 81(3), 397 (2017)","journal-title":"Neurosurgery"},{"issue":"2","key":"15_CR23","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.230793","volume":"310","author":"IJ Wamelink","year":"2024","unstructured":"Wamelink, I.J., et al.: Brain tumor imaging without gadolinium-based contrast agents: feasible or fantasy? Radiology 310(2), e230793 (2024)","journal-title":"Radiology"},{"key":"15_CR24","doi-asserted-by":"crossref","unstructured":"Wang, Z., et al.: Mutual information guided diffusion for zero-shot cross-modality medical image translation. IEEE Trans. Med. Imaging (2024)","DOI":"10.1109\/TMI.2024.3382043"},{"issue":"2","key":"15_CR25","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1001\/jamainternmed.2019.5284","volume":"180","author":"SA Woolen","year":"2020","unstructured":"Woolen, S.A., Shankar, P.R., Gagnier, J.J., MacEachern, M.P., Singer, L., Davenport, M.S.: Risk of nephrogenic systemic fibrosis in patients with stage 4 or 5 chronic kidney disease receiving a group II gadolinium-based contrast agent: a systematic review and meta-analysis. JAMA Intern. Med. 180(2), 223\u2013230 (2020)","journal-title":"JAMA Intern. Med."},{"key":"15_CR26","doi-asserted-by":"crossref","unstructured":"Xu, C., et al.: Common-unique decomposition driven diffusion model for contrast-enhanced liver MR images multi-phase interconversion. IEEE J. Biomed. Health Inform. (2024)","DOI":"10.1109\/JBHI.2024.3421254"},{"key":"15_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.101976","volume":"69","author":"C Xu","year":"2021","unstructured":"Xu, C., Zhang, D., Chong, J., Chen, B., Li, S.: Synthesis of gadolinium-enhanced liver tumors on nonenhanced liver MR images using pixel-level graph reinforcement learning. Med. Image Anal. 69, 101976 (2021)","journal-title":"Med. Image Anal."},{"key":"15_CR28","unstructured":"Zagoruyko, S.: Wide residual networks (2016). arXiv preprint arXiv:1605.07146"},{"key":"15_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103300","volume":"98","author":"Y Zhou","year":"2024","unstructured":"Zhou, Y., Chen, T., Hou, J., Xie, H., Dvornek, N.C., Zhou, S.K., Wilson, D.L., Duncan, J.S., Liu, C., Zhou, B.: Cascaded multi-path shortcut diffusion model for medical image translation. Med. Image Anal. 98, 103300 (2024)","journal-title":"Med. Image Anal."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05325-1_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T19:06:06Z","timestamp":1758308766000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05325-1_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032053244","9783032053251"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05325-1_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"20 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","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":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","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":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}