{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T14:01:58Z","timestamp":1790258518035,"version":"4.1.0"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032381880","type":"print"},{"value":"9783032381897","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"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":[[2027]]},"DOI":"10.1007\/978-3-032-38189-7_3","type":"book-chapter","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T13:25:13Z","timestamp":1790256313000},"page":"25-35","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AsynDiff: Asynchronous-Timestep Diffusion with\u00a0Noise-Aware Attention for\u00a0Multi-sequence MRI Synthesis"],"prefix":"10.1007","author":[{"given":"Luyi","family":"Han","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengxiang","family":"Min","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinghe","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianyu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunyao","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinglong","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaofei","family":"Duan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Portaluri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nika","family":"Rasoolzadeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jarek","family":"van Dijk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muzhen","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chan-Tong","family":"Lam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ritse","family":"Mann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,25]]},"reference":[{"key":"3_CR1","unstructured":"Albergo, M.S., Vanden-Eijnden, E.: Building normalizing flows with stochastic interpolants. arXiv preprint arXiv:2209.15571 (2022)"},{"key":"3_CR2","unstructured":"Baid, U., et al.: The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv preprint arXiv:2107.02314 (2021)"},{"issue":"1","key":"3_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2017.117","volume":"4","author":"S Bakas","year":"2017","unstructured":"Bakas, S., et al.: Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features. Sci. Data 4(1), 1\u201313 (2017)","journal-title":"Sci. Data"},{"issue":"3","key":"3_CR4","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"},{"key":"3_CR5","doi-asserted-by":"crossref","unstructured":"Chen, J.H., Su, M.Y.: Clinical application of magnetic resonance imaging in management of breast cancer patients receiving neoadjuvant chemotherapy. Biomed. Res. Int. 2013 (2013)","DOI":"10.1155\/2013\/348167"},{"issue":"10","key":"3_CR6","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":"3_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.103046","volume":"92","author":"S Dayarathna","year":"2024","unstructured":"Dayarathna, S., Islam, K.T., Uribe, S., Yang, G., Hayat, M., Chen, Z.: Deep learning based synthesis of mri, ct and pet: review and analysis. Med. Image Anal. 92, 103046 (2024)","journal-title":"Med. Image Anal."},{"key":"3_CR8","doi-asserted-by":"crossref","unstructured":"Dayarathna, S., et al.: Mu-diff: a mutual learning diffusion model for synthetic mri with application for brain lesions. npj Artif. Intell. 1(1), 11 (2025)","DOI":"10.1038\/s44387-025-00016-8"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Dohmen, M., Klemens, M., Baltruschat, I., Truong, T., Lenga, M.: Similarity metrics for mr image-to-image translation. arXiv e-prints pp. arXiv\u20132405 (2024)","DOI":"10.1038\/s41598-025-87358-0"},{"key":"3_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.103044","volume":"92","author":"L Han","year":"2024","unstructured":"Han, L., et al.: Synthesis-based imaging-differentiation representation learning for multi-sequence 3d\/4d mri. Med. Image Anal. 92, 103044 (2024)","journal-title":"Med. Image Anal."},{"key":"3_CR11","doi-asserted-by":"publisher","unstructured":"Han, L., et al.: An explainable deep framework: towards task-specific fusion for multi-to-one mri synthesis. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 45\u201355. Springer, Heidelberg (2023). https:\/\/doi.org\/10.1007\/978-3-031-43999-5_5","DOI":"10.1007\/978-3-031-43999-5_5"},{"key":"3_CR12","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":"3_CR13","doi-asserted-by":"publisher","unstructured":"Jiang, L., Mao, Y., Wang, X., Chen, X., Li, C.: Cola-diff: conditional latent diffusion model for multi-modal mri synthesis. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 398\u2013408. Springer, Heidelberg (2023). https:\/\/doi.org\/10.1007\/978-3-031-43999-5_38","DOI":"10.1007\/978-3-031-43999-5_38"},{"key":"3_CR14","doi-asserted-by":"publisher","first-page":"26565","DOI":"10.52202\/068431-1926","volume":"35","author":"T Karras","year":"2022","unstructured":"Karras, T., Aittala, M., Aila, T., Laine, S.: Elucidating the design space of diffusion-based generative models. Adv. Neural. Inf. Process. Syst. 35, 26565\u201326577 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"3_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1007\/978-3-030-32251-9_87","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"H Li","year":"2019","unstructured":"Li, H., et al.: DiamondGAN: unified multi-modal generative adversarial networks for MRI sequences synthesis. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 795\u2013803. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32251-9_87"},{"key":"3_CR16","unstructured":"Li, T., He, K.: Back to basics: let denoising generative models denoise. arXiv preprint arXiv:2511.13720 (2025)"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Li, W., et\u00a0al.: Virtual contrast-enhanced magnetic resonance images synthesis for patients with nasopharyngeal carcinoma using multimodality-guided synergistic neural network. Int. J. Radiat. Oncol.* Biol.* Phys. 112(4), 1033\u20131044 (2022)","DOI":"10.1016\/j.ijrobp.2021.11.007"},{"key":"3_CR18","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":"3_CR19","unstructured":"Liu, X., Gong, C., Liu, Q.: Flow straight and fast: learning to generate and transfer data with rectified flow. arXiv preprint arXiv:2209.03003 (2022)"},{"issue":"3","key":"3_CR20","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)","journal-title":"Radiology"},{"issue":"10","key":"3_CR21","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"3_CR22","doi-asserted-by":"crossref","unstructured":"Peebles, W., Xie, S.: Scalable diffusion models with transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4195\u20134205 (2023)","DOI":"10.1109\/ICCV51070.2023.00387"},{"key":"3_CR23","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"issue":"4","key":"3_CR24","doi-asserted-by":"publisher","first-page":"1170","DOI":"10.1109\/TMI.2019.2945521","volume":"39","author":"A Sharma","year":"2019","unstructured":"Sharma, A., Hamarneh, G.: Missing mri pulse sequence synthesis using multi-modal generative adversarial network. IEEE Trans. Med. Imaging 39(4), 1170\u20131183 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"3_CR25","unstructured":"de\u00a0Verdier, M.C., et\u00a0al.: The 2024 brain tumor segmentation (brats) challenge: glioma segmentation on post-treatment mri. arXiv preprint arXiv:2405.18368 (2024)"},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"Zhan, C., Lin, Y., Wang, G., Wang, H., Wu, J.: Medm2g: unifying medical multi-modal generation via cross-guided diffusion with visual invariant. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11502\u201311512 (2024)","DOI":"10.1109\/CVPR52733.2024.01093"},{"key":"3_CR27","doi-asserted-by":"publisher","unstructured":"Zhong, W., Cong, C., Wang, Z., Yan, Z., Di Ieva, A., Liu, S.: Fmm-diff: a feature mapping and merging diffusion model for mri generation with missing modality. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 227\u2013236. Springer, Heidelberg (2025). https:\/\/doi.org\/10.1007\/978-3-032-05325-1_22","DOI":"10.1007\/978-3-032-05325-1_22"},{"issue":"9","key":"3_CR28","doi-asserted-by":"publisher","first-page":"2772","DOI":"10.1109\/TMI.2020.2975344","volume":"39","author":"T Zhou","year":"2020","unstructured":"Zhou, T., Fu, H., Chen, G., Shen, J., Shao, L.: Hi-net: hybrid-fusion network for multi-modal mr image synthesis. IEEE Trans. Med. Imaging 39(9), 2772\u20132781 (2020)","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2026"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-38189-7_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T13:25:21Z","timestamp":1790256321000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-38189-7_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,25]]},"ISBN":["9783032381880","9783032381897"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-38189-7_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,25]]},"assertion":[{"value":"25 September 2026","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","label":"Disclosure of Interests","group":{"name":"EthicsHeading","label":"Ethics"}},{"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":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2026\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}