{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,2]],"date-time":"2026-11-02T13:31:11Z","timestamp":1793626271175,"version":"4.5.1"},"reference-count":54,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["2022YFC2402102"],"award-info":[{"award-number":["2022YFC2402102"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["2023YFA1607502"],"award-info":[{"award-number":["2023YFA1607502"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12375291"],"award-info":[{"award-number":["12375291"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62331021"],"award-info":[{"award-number":["62331021"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82071913"],"award-info":[{"award-number":["82071913"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Medical Image Analysis"],"published-print":{"date-parts":[[2027,1]]},"DOI":"10.1016\/j.media.2026.104348","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T15:12:15Z","timestamp":1791558735000},"page":"104348","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A unified and domain-robust framework enables controllable multimodal brain MRI synthesis"],"prefix":"10.1016","volume":"115","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8426-3335","authenticated-orcid":false,"given":"Haoye","family":"Zheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Xing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liuhong","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianfeng","family":"Bao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuebin","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zejun","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiakai","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hejie","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yudan","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feifei","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiefeng","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2767-9490","authenticated-orcid":false,"given":"Shuhui","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1473-2224","authenticated-orcid":false,"given":"Zhong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0600-8594","authenticated-orcid":false,"given":"Congbo","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.media.2026.104348_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102789","article-title":"SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining","volume":"86","author":"Billot","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.104348_b2","series-title":"Med3D: Transfer Learning for 3D Medical Image Analysis","author":"Chen","year":"2019"},{"key":"10.1016\/j.media.2026.104348_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103280","article-title":"TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers","volume":"97","author":"Chen","year":"2024","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.104348_b4","series-title":"2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"8789","article-title":"StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation","author":"Choi","year":"2018"},{"issue":"5","key":"10.1016\/j.media.2026.104348_b5","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.1038\/s41591-024-02959-y","article-title":"Vision\u2013language foundation model for echocardiogram interpretation","volume":"30","author":"Christensen","year":"2024","journal-title":"Nature Med."},{"issue":"12","key":"10.1016\/j.media.2026.104348_b6","doi-asserted-by":"crossref","first-page":"6343","DOI":"10.1002\/mp.14539","article-title":"Multimodal MRI synthesis using unified generative adversarial networks","volume":"47","author":"Dai","year":"2020","journal-title":"Med. Phys."},{"issue":"10","key":"10.1016\/j.media.2026.104348_b7","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.1109\/TMI.2019.2901750","article-title":"Image synthesis in multi-contrast MRI with conditional generative adversarial networks","volume":"38","author":"Dar","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.104348_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.103046","article-title":"Deep learning based synthesis of MRI, CT and PET: Review and analysis","volume":"92","author":"Dayarathna","year":"2024","journal-title":"Med. Image Anal."},{"issue":"23","key":"10.1016\/j.media.2026.104348_b9","doi-asserted-by":"crossref","first-page":"4483","DOI":"10.3390\/rs16234483","article-title":"Remote sensing for urban biodiversity: A review and meta-analysis","volume":"16","author":"Finizio","year":"2024","journal-title":"Remote. Sens."},{"issue":"2","key":"10.1016\/j.media.2026.104348_b10","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1148\/radiol.2019182627","article-title":"Artificial intelligence for mammography and digital breast tomosynthesis: Current concepts and future perspectives","volume":"293","author":"Geras","year":"2019","journal-title":"Radiology"},{"issue":"7615","key":"10.1016\/j.media.2026.104348_b11","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1038\/nature18933","article-title":"A multi-modal parcellation of human cerebral cortex","volume":"536","author":"Glasser","year":"2016","journal-title":"Nature"},{"issue":"2","key":"10.1016\/j.media.2026.104348_b12","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1016\/j.jmr.2011.09.021","article-title":"FLASH imaging: Rapid NMR imaging using low flip-angle pulses","volume":"213","author":"Haase","year":"2011","journal-title":"J. Magn. Reson."},{"issue":"6","key":"10.1016\/j.media.2026.104348_b13","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1097\/00004728-199211000-00001","article-title":"Use of fluid attenuated inversion recovery (FLAIR) pulse sequences in MRI of the brain","volume":"16","author":"Hajnal","year":"1992","journal-title":"J. Comput. Assist. Tomogr."},{"issue":"4","key":"10.1016\/j.media.2026.104348_b14","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1097\/00004728-199207000-00002","article-title":"High Signal Regions in normal white matter shown by heavily T2-weighted CSF nulled IR sequences","volume":"16","author":"Hajnal","year":"1992","journal-title":"J. Comput. Assist. Tomogr."},{"issue":"5","key":"10.1016\/j.media.2026.104348_b15","doi-asserted-by":"crossref","first-page":"eadd3607","DOI":"10.1126\/sciadv.add3607","article-title":"SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry","volume":"9","author":"Iglesias","year":"2023","journal-title":"Sci. Adv."},{"key":"10.1016\/j.media.2026.104348_b16","series-title":"IXI Dataset","author":"Imperial College London","year":"2025"},{"key":"10.1016\/j.media.2026.104348_b17","series-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"5967","article-title":"Image-to-image translation with conditional adversarial networks","author":"Isola","year":"2017"},{"issue":"2","key":"10.1016\/j.media.2026.104348_b18","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/S1361-8415(01)00036-6","article-title":"A global optimisation method for robust affine registration of brain images","volume":"5","author":"Jenkinson","year":"2001","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.104348_b19","series-title":"The 14th European Conference on Computer Vision (ECCV)","article-title":"Perceptual losses for real-time style transfer and super-resolution","volume":"vol. 9906","author":"Johnson","year":"2016"},{"issue":"6","key":"10.1016\/j.media.2026.104348_b20","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1093\/neuonc\/noaa030","article-title":"Consensus recommendations for a standardized brain tumor imaging protocol for clinical trials in brain metastases","volume":"22","author":"Kaufmann","year":"2020","journal-title":"Neuro-Oncol."},{"key":"10.1016\/j.media.2026.104348_b21","series-title":"International Conference on Learning Representations (ICLR)","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2015"},{"key":"10.1016\/j.media.2026.104348_b22","series-title":"Medical Image Computing and Computer Assisted Intervention (MICCAI)","first-page":"88","article-title":"Anatomically-controllable medical image generation with segmentation-guided diffusion models","volume":"vol. 15007","author":"Konz","year":"2024"},{"key":"10.1016\/j.media.2026.104348_b23","series-title":"The brain tumor segmentation (BraTS) challenge 2023: Brain MR image synthesis for tumor segmentation (BraSyn)","author":"Li","year":"2024"},{"issue":"3","key":"10.1016\/j.media.2026.104348_b24","doi-asserted-by":"crossref","first-page":"980","DOI":"10.1109\/TMI.2023.3325703","article-title":"Zero-shot medical image translation via frequency-guided diffusion models","volume":"43","author":"Li","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"11","key":"10.1016\/j.media.2026.104348_b25","doi-asserted-by":"crossref","first-page":"3395","DOI":"10.1109\/TMI.2023.3288001","article-title":"Multi-scale transformer network with edge-aware pre-training for cross-modality MR image synthesis","volume":"42","author":"Li","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"9","key":"10.1016\/j.media.2026.104348_b26","doi-asserted-by":"crossref","first-page":"2577","DOI":"10.1109\/TMI.2023.3261707","article-title":"One model to synthesize them all: Multi-contrast multi-scale transformer for missing data imputation","volume":"42","author":"Liu","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"10.1016\/j.media.2026.104348_b27","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1038\/s41591-024-02856-4","article-title":"A visual-language foundation model for computational pathology","volume":"30","author":"Lu","year":"2024","journal-title":"Nature Med."},{"issue":"14","key":"10.1016\/j.media.2026.104348_b28","doi-asserted-by":"crossref","first-page":"eads4593","DOI":"10.1126\/sciadv.ads4593","article-title":"FairDiffusion: Enhancing equity in latent diffusion models via fair Bayesian perturbation","volume":"11","author":"Luo","year":"2025","journal-title":"Sci. Adv."},{"issue":"1","key":"10.1016\/j.media.2026.104348_b29","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","article-title":"Segment anything in medical images","volume":"15","author":"Ma","year":"2024","journal-title":"Nat. Commun."},{"key":"10.1016\/j.media.2026.104348_b30","series-title":"2017 IEEE International Conference on Computer Vision (ICCV)","first-page":"2813","article-title":"Least squares generative adversarial networks","author":"Mao","year":"2017"},{"issue":"6","key":"10.1016\/j.media.2026.104348_b31","doi-asserted-by":"crossref","first-page":"1274","DOI":"10.1002\/jmri.23638","article-title":"Gradient echo imaging","volume":"35","author":"Markl","year":"2012","journal-title":"J. Magn. Reson. Imaging"},{"key":"10.1016\/j.media.2026.104348_b32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2015\/813696","article-title":"MRBrainS Challenge: Online evaluation framework for brain image segmentation in 3T MRI scans","volume":"2015","author":"Mendrik","year":"2015","journal-title":"Comput. Intell. Neurosci."},{"issue":"7956","key":"10.1016\/j.media.2026.104348_b33","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1038\/s41586-023-05881-4","article-title":"Foundation models for generalist medical artificial intelligence","volume":"616","author":"Moor","year":"2023","journal-title":"Nature"},{"issue":"12","key":"10.1016\/j.media.2026.104348_b34","doi-asserted-by":"crossref","first-page":"3524","DOI":"10.1109\/TMI.2023.3290149","article-title":"Unsupervised medical image translation with adversarial diffusion models","volume":"42","author":"\u00d6zbey","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/j.media.2026.104348_b35","doi-asserted-by":"crossref","DOI":"10.1038\/s41597-023-02386-7","article-title":"An open presurgery MRI dataset of people with epilepsy and focal cortical dysplasia type II","volume":"10","author":"Schuch","year":"2023","journal-title":"Sci. Data"},{"issue":"4","key":"10.1016\/j.media.2026.104348_b36","doi-asserted-by":"crossref","first-page":"1170","DOI":"10.1109\/TMI.2019.2945521","article-title":"Missing MRI pulse sequence synthesis using multi-modal generative adversarial network","volume":"39","author":"Sharma","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"10.1016\/j.media.2026.104348_b37","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1002\/hbm.10062","article-title":"Fast robust automated brain extraction","volume":"17","author":"Smith","year":"2002","journal-title":"Hum. Brain Mapp."},{"issue":"4","key":"10.1016\/j.media.2026.104348_b38","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1016\/j.neuroimage.2012.02.018","article-title":"The Human Connectome Project: A data acquisition perspective","volume":"62","author":"Van Essen","year":"2012","journal-title":"NeuroImage"},{"issue":"1","key":"10.1016\/j.media.2026.104348_b39","doi-asserted-by":"crossref","first-page":"5915","DOI":"10.1038\/s41467-021-26216-9","article-title":"Annotation-efficient deep learning for automatic medical image segmentation","volume":"12","author":"Wang","year":"2021","journal-title":"Nat. Commun."},{"issue":"2","key":"10.1016\/j.media.2026.104348_b40","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1038\/s41591-024-03359-y","article-title":"Self-improving generative foundation model for synthetic medical image generation and clinical applications","volume":"31","author":"Wang","year":"2025","journal-title":"Nature Med."},{"issue":"6","key":"10.1016\/j.media.2026.104348_b41","article-title":"Toward general text-guided multimodal brain MRI synthesis for diagnosis and medical image analysis","volume":"6","author":"Wang","year":"2025","journal-title":"Cell Rep. Med."},{"issue":"1","key":"10.1016\/j.media.2026.104348_b42","doi-asserted-by":"crossref","first-page":"6359","DOI":"10.1038\/s41467-023-41195-9","article-title":"Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images","volume":"14","author":"Wang","year":"2023","journal-title":"Nat. Commun."},{"key":"10.1016\/j.media.2026.104348_b43","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.34133\/research.1029","article-title":"Generative artificial intelligence in medical imaging: Foundations, progress, and clinical translation","volume":"8","author":"Wang","year":"2025","journal-title":"Research"},{"issue":"4","key":"10.1016\/j.media.2026.104348_b44","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1002\/(SICI)1522-2586(199904)9:4<531::AID-JMRI4>3.0.CO;2-L","article-title":"NMR relaxation times in the human brain at 3.0 tesla","volume":"9","author":"Wansapura","year":"1999","journal-title":"J. Magn. Reson. Imaging"},{"key":"10.1016\/j.media.2026.104348_b45","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1109\/RBME.2016.2515127","article-title":"Robust nucleus\/cell detection and segmentation in digital pathology and microscopy images: A comprehensive review","volume":"9","author":"Xing","year":"2016","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"10.1016\/j.media.2026.104348_b46","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2025.103635","article-title":"Learning contrast and content representations for synthesizing magnetic resonance image of arbitrary contrast","volume":"104","author":"Xiong","year":"2025","journal-title":"Med. Image Anal."},{"issue":"11","key":"10.1016\/j.media.2026.104348_b47","doi-asserted-by":"crossref","first-page":"3167","DOI":"10.1109\/TMI.2022.3179981","article-title":"Model-based synthetic data-driven learning (MOST-DL): Application in single-shot T2 mapping with severe head motion using overlapping-echo acquisition","volume":"41","author":"Yang","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"12","key":"10.1016\/j.media.2026.104348_b48","doi-asserted-by":"crossref","first-page":"3895","DOI":"10.1109\/TMI.2022.3199155","article-title":"Semi-supervised learning of MRI synthesis without fully-sampled ground truths","volume":"41","author":"Yurt","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.104348_b49","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102429","article-title":"Progressively volumetrized deep generative models for data-efficient contextual learning of MR image recovery","volume":"78","author":"Yurt","year":"2022","journal-title":"Med. Image Anal."},{"issue":"1","key":"10.1016\/j.media.2026.104348_b50","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/42.906424","article-title":"Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm","volume":"20","author":"Zhang","year":"2001","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"10","key":"10.1016\/j.media.2026.104348_b51","doi-asserted-by":"crossref","first-page":"2925","DOI":"10.1109\/TMI.2022.3174827","article-title":"PTNet3D: A 3D high-resolution longitudinal infant brain MRI synthesizer based on transformers","volume":"41","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/j.media.2026.104348_b52","doi-asserted-by":"crossref","first-page":"6486","DOI":"10.1038\/s41467-025-61754-6","article-title":"Generative AI enables medical image segmentation in ultra low-data regimes","volume":"16","author":"Zhang","year":"2025","journal-title":"Nat. Commun."},{"issue":"1","key":"10.1016\/j.media.2026.104348_b53","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TMI.2024.3424785","article-title":"Unified multi-modal image synthesis for missing modality imputation","volume":"44","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.104348_b54","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.mri.2021.02.001","article-title":"A 3D densely connected convolution neural network with connection-wise attention mechanism for Alzheimer\u2019s disease classification","volume":"78","author":"Zhang","year":"2021","journal-title":"Magn. Reson. Imaging"}],"container-title":["Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841526004172?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841526004172?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,11,2]],"date-time":"2026-11-02T13:04:00Z","timestamp":1793624640000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1361841526004172"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2027,1]]},"references-count":54,"alternative-id":["S1361841526004172"],"URL":"https:\/\/doi.org\/10.1016\/j.media.2026.104348","relation":{},"ISSN":["1361-8415"],"issn-type":[{"value":"1361-8415","type":"print"}],"subject":[],"published":{"date-parts":[[2027,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A unified and domain-robust framework enables controllable multimodal brain MRI synthesis","name":"articletitle","label":"Article Title"},{"value":"Medical Image Analysis","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.media.2026.104348","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104348"}}