{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T09:45:50Z","timestamp":1758361550211,"version":"3.44.0"},"publisher-location":"Cham","reference-count":23,"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_22","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T19:05:09Z","timestamp":1758308709000},"page":"227-236","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FMM-Diff: A Feature Mapping and\u00a0Merging Diffusion Model for\u00a0MRI Generation with\u00a0Missing Modality"],"prefix":"10.1007","author":[{"given":"Wenjin","family":"Zhong","sequence":"first","affiliation":[]},{"given":"Cong","family":"Cong","sequence":"additional","affiliation":[]},{"given":"Zihan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zeya","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Antonio","family":"Di Ieva","sequence":"additional","affiliation":[]},{"given":"Sidong","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"22_CR1","unstructured":"Azad, R., Khosravi, N., Dehghanmanshadi, M., Cohen-Adad, J., Merhof, D.: Medical image segmentation on mri images with missing modalities: a review. arXiv preprint arXiv:2203.06217 (2022)"},{"key":"22_CR2","unstructured":"Baid, U., et\u00a0al.: The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv preprint arXiv:2107.02314 (2021)"},{"issue":"2","key":"22_CR3","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1148\/radiol.2021203786","volume":"299","author":"GM Conte","year":"2021","unstructured":"Conte, G.M., et al.: Generative adversarial networks to synthesize missing t1 and flair mri sequences for use in a multisequence brain tumor segmentation model. Radiology 299(2), 313\u2013323 (2021)","journal-title":"Radiology"},{"key":"22_CR4","unstructured":"Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. Adv. Neural Inf. Process. Syst. 34, 8780\u20138794 (2021)"},{"key":"22_CR5","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural Inf. Process. Syst. 33, 6840\u20136851 (2020)"},{"key":"22_CR6","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: Greenspan, H., et al. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023. MICCAI 2023. LNCS, vol. 14229, pp. 398\u2013408. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43999-5_38","DOI":"10.1007\/978-3-031-43999-5_38"},{"issue":"4","key":"22_CR7","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1002\/mrm.26509","volume":"78","author":"Y Jiang","year":"2017","unstructured":"Jiang, Y., Ma, D., Keenan, K.E., Stupic, K.F., Gulani, V., Griswold, M.A.: Repeatability of magnetic resonance fingerprinting t1 and t2 estimates assessed using the ismrm\/nist mri system phantom. Magn. Reson. Med. 78(4), 1452\u20131457 (2017)","journal-title":"Magn. Reson. Med."},{"key":"22_CR8","doi-asserted-by":"crossref","unstructured":"Lei, X., Yu, X., Bai, M., Wu, C.: Modalities guided latent diffusion model for brain mri synthesis. In: 2024 36th Chinese Control and Decision Conference (CCDC), pp. 4440\u20134444. IEEE (2024)","DOI":"10.1109\/CCDC62350.2024.10588343"},{"issue":"7440","key":"22_CR9","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1038\/nature11971","volume":"495","author":"D Ma","year":"2013","unstructured":"Ma, D., et al.: Magnetic resonance fingerprinting. Nature 495(7440), 187\u2013192 (2013)","journal-title":"Nature"},{"key":"22_CR10","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":"1","key":"22_CR11","doi-asserted-by":"publisher","first-page":"12098","DOI":"10.1038\/s41598-023-39278-0","volume":"13","author":"G M\u00fcller-Franzes","year":"2023","unstructured":"M\u00fcller-Franzes, G., et al.: A multimodal comparison of latent denoising diffusion probabilistic models and generative adversarial networks for medical image synthesis. Sci. Rep. 13(1), 12098 (2023)","journal-title":"Sci. Rep."},{"issue":"1","key":"22_CR12","first-page":"28","volume":"51","author":"MW Parsons","year":"2002","unstructured":"Parsons, M.W., et al.: Diffusion-and perfusion-weighted MRI response to thrombolysis in stroke. Ann. Neurol. Off. J. Am. Neurol. Assoc. Child Neurol. Soc. 51(1), 28\u201337 (2002)","journal-title":"Ann. Neurol. Off. J. Am. Neurol. Assoc. Child Neurol. Soc."},{"issue":"7","key":"22_CR13","doi-asserted-by":"publisher","first-page":"1581","DOI":"10.1161\/01.STR.32.7.1581","volume":"32","author":"MW Parsons","year":"2001","unstructured":"Parsons, M.W., et al.: Perfusion magnetic resonance imaging maps in hyperacute stroke: relative cerebral blood flow most accurately identifies tissue destined to infarct. Stroke 32(7), 1581\u20131587 (2001)","journal-title":"Stroke"},{"key":"22_CR14","unstructured":"Vaswani, A.: Attention is all you need. Adv. Neural Inf. Process. Syst. (2017)"},{"key":"22_CR15","doi-asserted-by":"crossref","unstructured":"Wang, H., Chen, Y., Ma, C., Avery, J., Hull, L., Carneiro, G.: Multi-modal learning with missing modality via shared-specific feature modelling. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15878\u201315887 (2023)","DOI":"10.1109\/CVPR52729.2023.01524"},{"key":"22_CR16","unstructured":"Wang, J., et al.: Hypergraph tversky-aware domain incremental learning for brain tumor segmentation with missing modalities. arXiv preprint arXiv:2505.16809 (2025)"},{"key":"22_CR17","doi-asserted-by":"crossref","unstructured":"Xiao, X., Hu, Q.V., Wang, G.: Fgc2f-udiff: frequency-guided and coarse-to-fine unified diffusion model for multi-modality missing mri synthesis. IEEE Trans. Comput. Imaging (2024)","DOI":"10.1109\/TCI.2024.3516574"},{"key":"22_CR18","doi-asserted-by":"publisher","unstructured":"Xie, L., et al.: SHISRCNet: super-resolution and classification network for low-resolution breast cancer histopathology image. In: Greenspan, H., et al. (eds.) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023. MICCAI 2023. LNCS, vol. 14224, pp. 23\u201332. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-43904-9_3","DOI":"10.1007\/978-3-031-43904-9_3"},{"key":"22_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102429","volume":"78","author":"M Yurt","year":"2022","unstructured":"Yurt, M., \u00d6zbey, M., Dar, S.U., Tinaz, B., Oguz, K.K., \u00c7ukur, T.: Progressively volumetrized deep generative models for data-efficient contextual learning of mr image recovery. Med. Image Anal. 78, 102429 (2022)","journal-title":"Med. Image Anal."},{"key":"22_CR20","doi-asserted-by":"publisher","unstructured":"Zhao, Z., Zhang, K.N., Wang, Q., et\u00a0al.: Chinese glioma genome atlas (CGGA): a comprehensive resource with functional genomic data from Chinese glioma patients. Genomics Proteomics Bioinformatics 19(1), 1\u201312 (2021). https:\/\/doi.org\/10.1016\/j.gpb.2020.10.005","DOI":"10.1016\/j.gpb.2020.10.005"},{"issue":"1","key":"22_CR21","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1109\/JBHI.2021.3088866","volume":"26","author":"B Zhan","year":"2021","unstructured":"Zhan, B., Li, D., Wu, X., Zhou, J., Wang, Y.: Multi-modal mri image synthesis via gan with multi-scale gate mergence. IEEE J. Biomed. Health Inform. 26(1), 17\u201326 (2021)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"22_CR22","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Peng, C., Wang, Q., Song, D., Li, K., Zhou, S.K.: Unified multi-modal image synthesis for missing modality imputation. IEEE Trans. Med. Imaging (2024)","DOI":"10.1109\/TMI.2024.3424785"},{"key":"22_CR23","doi-asserted-by":"crossref","unstructured":"Zhong, W., Cong, C., Azemi, G., Tabassum, M., Di\u00a0Ieva, A., Liu, S.: Multi-sequence mri to multi-tracer pet generation via diffusion model. In: 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI), pp.\u00a01\u20134. IEEE (2025)","DOI":"10.1109\/ISBI60581.2025.10981101"}],"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_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T19:05:15Z","timestamp":1758308715000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05325-1_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032053244","9783032053251"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05325-1_22","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"}}]}}