{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T05:02:11Z","timestamp":1784264531729,"version":"3.55.0"},"reference-count":64,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["6230012077"],"award-info":[{"award-number":["6230012077"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Municipal Central Guided Local Science and Technology Development Fund","award":["YDZX20233100001001"],"award-info":[{"award-number":["YDZX20233100001001"]}]},{"DOI":"10.13039\/501100001809","name":"High Performance Computing (HPC) Platform of ShanghaiTech University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Med. Imaging"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1109\/tmi.2025.3585372","type":"journal-article","created":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T13:44:57Z","timestamp":1751463897000},"page":"4960-4972","source":"Crossref","is-referenced-by-count":28,"title":["3D MedDiffusion: A 3D Medical Latent Diffusion Model for Controllable and High-Quality Medical Image Generation"],"prefix":"10.1109","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6987-5872","authenticated-orcid":false,"given":"Haoshen","family":"Wang","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering and the State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech Univerisity, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2288-8319","authenticated-orcid":false,"given":"Zhentao","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering and the State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech Univerisity, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9999-2542","authenticated-orcid":false,"given":"Kaicong","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering and the State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech Univerisity, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodong","family":"Wang","sequence":"additional","affiliation":[{"name":"United Imaging Healthcare Company Ltd., Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7934-5698","authenticated-orcid":false,"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering and the State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech Univerisity, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3798-4504","authenticated-orcid":false,"given":"Zhiming","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering and the State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech Univerisity, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2023.3290149"},{"key":"ref2","article-title":"SegDiff: Image segmentation with diffusion probabilistic models","author":"Amit","year":"2021","journal-title":"arXiv:2112.00390"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00963"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2024.3351702"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1312.6114"},{"key":"ref6","first-page":"1530","article-title":"Variational inference with normalizing flows","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","volume":"37","author":"Rezende"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"ref8","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Ho"},{"key":"ref9","article-title":"Mode regularized generative adversarial networks","author":"Che","year":"2016","journal-title":"arXiv:1612.02136"},{"key":"ref10","first-page":"2256","article-title":"Deep unsupervised learning using nonequilibrium thermodynamics","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Sohl-Dickstein"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3528233.3530757"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-024-02168-7"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72744-3_2"},{"key":"ref15","article-title":"Medical diffusion: Denoising diffusion probabilistic models for 3D medical image generation","author":"Khader","year":"2022","journal-title":"arXiv:2211.03364"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43999-5_56"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59728-3_64"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43907-0_72"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00355"},{"key":"ref21","article-title":"Applying artificial intelligence to glioma imaging: Advances and challenges","author":"Jin","year":"2019","journal-title":"arXiv:1911.12886"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-72084-1_25"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2022.3172976"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-39278-0"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00204"},{"key":"ref26","article-title":"MAISI: Medical AI for synthetic imaging","author":"Guo","year":"2024","journal-title":"arXiv:2409.11169"},{"key":"ref27","article-title":"Megatron-LM: Training multi-billion parameter language models using model parallelism","author":"Shoeybi","year":"2019","journal-title":"arXiv:1909.08053"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-80965-1_13"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101545"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2018.2883958"},{"key":"ref31","article-title":"Decomposed diffusion sampler for accelerating large-scale inverse problems","author":"Chung","year":"2023","journal-title":"arXiv:2303.05754"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00743"},{"key":"ref33","first-page":"1","article-title":"Neural discrete representation learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Van Den Oord"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01268"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1603.08155"},{"key":"ref36","first-page":"1558","article-title":"Autoencoding beyond pixels using a learned similarity metric","volume-title":"Proc. 33rd Int. Conf. Mach. Learn.","author":"Larsen"},{"key":"ref37","article-title":"Discriminative regularization for generative models","author":"Lamb","year":"2016","journal-title":"arXiv:1602.03220"},{"key":"ref38","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014","journal-title":"arXiv:1409.1556"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00387"},{"key":"ref41","article-title":"Development and validation of deep learning algorithms for detection of critical findings in head CT scans","author":"Chilamkurthy","year":"2018","journal-title":"arXiv:1803.05854"},{"key":"ref42","first-page":"109","volume-title":"Proc. 24th Int. Conf. Med. Image Comput. Comput. Assist. Intervent.","volume":"12901","author":"Wang"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3078828"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI52829.2022.9761697"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32226-7_24"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1118\/1.3528204"},{"key":"ref47","article-title":"CTSpine1K: A large-scale dataset for spinal vertebrae segmentation in computed tomography","author":"Deng","year":"2021","journal-title":"arXiv:2105.14711"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3100536"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1001779"},{"key":"ref50","article-title":"FastMRI: An open dataset and benchmarks for accelerated MRI","author":"Zbontar","year":"2018","journal-title":"arXiv:1811.08839"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2024.3415032"},{"key":"ref52","first-page":"8162","article-title":"Improved denoising diffusion probabilistic models","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Nichol"},{"key":"ref53","first-page":"6629","article-title":"GANs trained by a two time-scale update rule converge to a local Nash equilibrium","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Heusel"},{"issue":"1","key":"ref54","first-page":"723","article-title":"A kernel two-sample test","volume":"13","author":"Gretton","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref55","article-title":"Med3D: Transfer learning for 3D medical image analysis","author":"Chen","year":"2019","journal-title":"arXiv:1904.00625"},{"issue":"11","key":"ref56","first-page":"1","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"ref58","article-title":"The KiTS19 challenge data: 300 kidney tumor cases with clinical context, CT semantic segmentations, and surgical outcomes","author":"Heller","year":"2019","journal-title":"arXiv:1904.00445"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1364\/OE.24.025129"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1137\/1.9780898719277"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2713099"},{"key":"ref62","article-title":"NnU-net: Self-adapting framework for U-net-based medical image segmentation","author":"Isensee","year":"2018","journal-title":"arXiv:1809.10486"},{"key":"ref63","article-title":"MosMedData: Chest CT scans with COVID-19 related findings dataset","author":"Morozov","year":"2020","journal-title":"arXiv:2005.06465"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00879"}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/42\/11279972\/11063450.pdf?arnumber=11063450","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T18:33:04Z","timestamp":1765305184000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11063450\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":64,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2025.3585372","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"value":"0278-0062","type":"print"},{"value":"1558-254X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12]]}}}