{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T15:52:20Z","timestamp":1776441140525,"version":"3.51.2"},"reference-count":48,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"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":["62322119"],"award-info":[{"award-number":["62322119"]}],"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":["12226008"],"award-info":[{"award-number":["12226008"]}],"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":["52293425"],"award-info":[{"award-number":["52293425"]}],"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":["62125111"],"award-info":[{"award-number":["62125111"]}],"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":["62106252"],"award-info":[{"award-number":["62106252"]}],"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":["12026603"],"award-info":[{"award-number":["12026603"]}],"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":["62206273"],"award-info":[{"award-number":["62206273"]}],"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":["62106252"],"award-info":[{"award-number":["62106252"]}],"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":["62476268"],"award-info":[{"award-number":["62476268"]}],"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":["52293425"],"award-info":[{"award-number":["52293425"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFF0501402"],"award-info":[{"award-number":["2021YFF0501402"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFA1011403"],"award-info":[{"award-number":["2023YFA1011403"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012571","name":"Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province","doi-asserted-by":"publisher","award":["2023B1212060052"],"award-info":[{"award-number":["2023B1212060052"]}],"id":[{"id":"10.13039\/501100012571","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shenzhen Science and Technology Program","award":["RCYX20210609104444089"],"award-info":[{"award-number":["RCYX20210609104444089"]}]},{"name":"Shenzhen Science and Technology Program","award":["JCYJ20220818101205012"],"award-info":[{"award-number":["JCYJ20220818101205012"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Med. Imaging"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1109\/tmi.2024.3473009","type":"journal-article","created":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T17:22:24Z","timestamp":1727976144000},"page":"1019-1031","source":"Crossref","is-referenced-by-count":10,"title":["SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI"],"prefix":"10.1109","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9283-881X","authenticated-orcid":false,"given":"Zhuo-Xu","family":"Cui","sequence":"first","affiliation":[{"name":"Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3974-3413","authenticated-orcid":false,"given":"Chentao","family":"Cao","sequence":"additional","affiliation":[{"name":"Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3564-0961","authenticated-orcid":false,"given":"Yue","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China"}]},{"given":"Sen","family":"Jia","sequence":"additional","affiliation":[{"name":"Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9098-8048","authenticated-orcid":false,"given":"Jing","family":"Cheng","sequence":"additional","affiliation":[{"name":"Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9075-7207","authenticated-orcid":false,"given":"Xin","family":"Liu","sequence":"additional","affiliation":[{"name":"Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8558-5102","authenticated-orcid":false,"given":"Hairong","family":"Zheng","sequence":"additional","affiliation":[{"name":"Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6257-0875","authenticated-orcid":false,"given":"Dong","family":"Liang","sequence":"additional","affiliation":[{"name":"Lauterbur Research Center for Biomedical Imaging, the Research Center for Medical AI, and the Key Laboratory of Biomedical Imaging Science and Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9131-689X","authenticated-orcid":false,"given":"Yanjie","family":"Zhu","sequence":"additional","affiliation":[{"name":"Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2007.914728"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.mri.2014.08.031"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1186\/s42490-019-0006-z"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2010.2085084"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2012.2203921"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2018.2882089"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2883941"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2865356"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1038\/nature25988"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2019.2950557"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.29085"},{"key":"ref12","first-page":"1","article-title":"Generative modeling by estimating gradients of the data distribution","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Song"},{"key":"ref13","first-page":"1","article-title":"Score-based generative modeling through stochastic differential equations","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Song"},{"key":"ref14","first-page":"12438","article-title":"Improved techniques for training score-based generative models","volume-title":"Proc. NIPS","volume":"33","author":"Song"},{"key":"ref15","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. NIPS","volume":"33","author":"Ho"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA60987.2023.10302579"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102479"},{"key":"ref18","first-page":"14938","article-title":"Robust compressed sensing MRI with deep generative priors","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Jalal"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.24751"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.25685"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.26191"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.10171"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.22428"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102872"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16446-0_59"},{"key":"ref26","article-title":"Learning Fourier-constrained diffusion bridges for MRI reconstruction","author":"Mirza","year":"2023","journal-title":"arXiv:2308.01096"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2024.3351702"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43999-5_47"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/SIU59756.2023.10223786"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16446-0_62"},{"key":"ref31","article-title":"Physics-informed DeepMRI: Bridging the gap from heat diffusion to k-Space interpolation","author":"Cui","year":"2023","journal-title":"arXiv:2308.15918"},{"key":"ref32","first-page":"1","article-title":"Decomposed diffusion sampler for accelerating large-scale inverse problems","volume-title":"Proc. 12th Int. Conf. Learn. Represent.","author":"Chung"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.27420"},{"key":"ref34","article-title":"LORAKI: Autocalibrated recurrent neural networks for autoregressive MRI reconstruction in k-Space","author":"Hyung Kim","year":"2019","journal-title":"arXiv:1904.09390"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2927101"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3014581"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2015.7164018"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1017\/9781108186735"},{"key":"ref39","first-page":"1","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Paszke"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.24267"},{"key":"ref41","first-page":"1","article-title":"The bart toolbox for computational magnetic resonance imaging","volume-title":"Proc. Intl. Soc. Mag. Res. Med.","author":"Uecker"},{"key":"ref42","article-title":"Deep learning regularized spirit reconstruction accelerates joint intracranial and carotid vessel wall imaging into 3.5 minutes","author":"Jia"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00196"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"ref45","article-title":"FastMRI: An open dataset and benchmarks for accelerated MRI","author":"Zbontar","year":"2018","journal-title":"arXiv:1811.08839"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2020190007"},{"key":"ref47","article-title":"Analytic-DPM: An analytic estimate of the optimal reverse variance in diffusion probabilistic models","author":"Bao","year":"2022","journal-title":"arXiv:2201.06503"},{"key":"ref48","article-title":"DPM-solver: A fast ODE solver for diffusion probabilistic model sampling in around 10 steps","author":"Lu","year":"2022","journal-title":"arXiv:2206.00927"}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/42\/10870394\/10704728.pdf?arnumber=10704728","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T18:54:04Z","timestamp":1738781644000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10704728\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2]]},"references-count":48,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2024.3473009","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"value":"0278-0062","type":"print"},{"value":"1558-254X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2]]}}}