{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T07:29:57Z","timestamp":1772868597860,"version":"3.50.1"},"reference-count":70,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100003399","name":"Science and Technology Commission of Shanghai Municipality","doi-asserted-by":"publisher","award":["24ZR1418900"],"award-info":[{"award-number":["24ZR1418900"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003399","name":"Science and Technology Commission of Shanghai Municipality","doi-asserted-by":"publisher","award":["22DZ2229004"],"award-info":[{"award-number":["22DZ2229004"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62475072"],"award-info":[{"award-number":["62475072"]}],"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":["62471182"],"award-info":[{"award-number":["62471182"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003395","name":"Shanghai Municipal Education Commission","doi-asserted-by":"publisher","award":["2024AI02012"],"award-info":[{"award-number":["2024AI02012"]}],"id":[{"id":"10.13039\/501100003395","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Circuits Syst. Video Technol."],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1109\/tcsvt.2025.3536807","type":"journal-article","created":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T14:25:01Z","timestamp":1738247101000},"page":"5617-5632","source":"Crossref","is-referenced-by-count":4,"title":["WFANet-DDCL: Wavelet-Based Frequency Attention Network and Dual Domain Consistency Learning for 7T MRI Synthesis From 3T MRI"],"prefix":"10.1109","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4724-4012","authenticated-orcid":false,"given":"Xiaolong","family":"Liu","sequence":"first","affiliation":[{"name":"Shanghai Key Laboratory of Multidimensional Information Processing, and MoE Engineering Research Center of Software\/Hardware Co-Design Technology and Application, East China Normal University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4555-3947","authenticated-orcid":false,"given":"Song","family":"Qiu","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of Multidimensional Information Processing, and MoE Engineering Research Center of Software\/Hardware Co-Design Technology and Application, East China Normal University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6756-3225","authenticated-orcid":false,"given":"Mei","family":"Zhou","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of Multidimensional Information Processing, and MoE Engineering Research Center of Software\/Hardware Co-Design Technology and Application, East China Normal University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weijie","family":"Le","sequence":"additional","affiliation":[{"name":"Radiology Department, Ninth People&#x2019;s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5063-8801","authenticated-orcid":false,"given":"Qingli","family":"Li","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of Multidimensional Information Processing, and MoE Engineering Research Center of Software\/Hardware Co-Design Technology and Application, East China Normal University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1592-9627","authenticated-orcid":false,"given":"Yan","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of Multidimensional Information Processing, and MoE Engineering Research Center of Software\/Hardware Co-Design Technology and Application, East China Normal University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"issue":"3","key":"ref1","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1016\/j.neuroimage.2011.05.010","article-title":"Clinical fMRI: Evidence for a 7T benefit over 3T","volume":"57","author":"Beisteiner","year":"2011","journal-title":"NeuroImage"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1002\/nbm.3275"},{"issue":"5","key":"ref3","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1016\/j.ejrad.2011.07.007","article-title":"Clinical applications of 7T MRI in the brain","volume":"82","author":"Van der Kolk","year":"2013","journal-title":"Eur. J. Radiol."},{"issue":"1","key":"ref4","doi-asserted-by":"crossref","DOI":"10.1038\/s41597-023-02400-y","article-title":"A paired dataset of T1- and T2-weighted MRI at 3 Tesla and 7 Tesla","volume":"10","author":"Chen","year":"2023","journal-title":"Sci. Data"},{"key":"ref5","volume-title":"Ultra-High Field MRI Scanners","author":"Huber","year":"2024"},{"issue":"11","key":"ref6","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1109\/TMI.2012.2202245","article-title":"Hierarchical patch-based sparse representation\u2014A new approach for resolution enhancement of 4D-CT lung data","volume":"31","author":"Zhang","year":"2012","journal-title":"IEEE Trans. Med. Imag."},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2900125"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24571-3_79"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00928-1_47"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12132"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10443-0_29"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46976-8_5"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66182-7_87"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2865304"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.mri.2019.05.023","article-title":"Dual-domain convolutional neural networks for improving structural information in 3T MRI","volume":"64","author":"Zhang","year":"2019","journal-title":"Magn. Reson. Imag."},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2018.2814538"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32251-9_86"},{"key":"ref19","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101663","article-title":"Synthesized 7T MRI from 3T MRI via deep learning in spatial and wavelet domains","volume":"62","author":"Qu","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref20","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1007\/978-3-030-87589-3_8","article-title":"Learning to synthesize 7TMRI from 3T MRI with few data by deformable augmentation","volume-title":"Machine Learning in Medical Imaging","author":"Wei","year":"2021"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2023.3288001"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2019.2957990"},{"issue":"1","key":"ref23","article-title":"Multifocus image fusion using wavelet-domain-based deep CNN","volume":"2019","author":"Li","year":"2019","journal-title":"Comput. Intell. Neurosci."},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2020.3010627"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2019.2910119"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3218735"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2549918"},{"key":"ref28","article-title":"PTNet: A high-resolution infant MRI synthesizer based on transformer","author":"Zhang","year":"2021","journal-title":"arXiv:2105.13993"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3167808"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72104-5_4"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-66535-6_1"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2769839"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"issue":"5","key":"ref34","doi-asserted-by":"crossref","first-page":"1620","DOI":"10.1002\/jmri.28944","article-title":"Synthesized 7T MPRAGE from 3T MPRAGE using generative adversarial network and validation in clinical brain imaging: A feasibility study","volume":"59","author":"Duan","year":"2024","journal-title":"J. Magn. Reson. Imag."},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3430968"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3222456"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3097713"},{"key":"ref39","article-title":"Wasserstein GAN","author":"Arjovsky","year":"2017","journal-title":"arXiv:1701.07875"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3286405"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3299324"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3283289"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3277462"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2102.04306"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/s11760-021-02083-1"},{"key":"ref46","first-page":"1","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Tarvainen"},{"key":"ref47","article-title":"Unsupervised data augmentation for consistency training","author":"Xie","year":"2019","journal-title":"arXiv:1904.12848"},{"key":"ref48","article-title":"Temporal ensembling for semi-supervised learning","author":"Laine","year":"2016","journal-title":"arXiv:1610.02242"},{"key":"ref49","article-title":"Improved regularization of convolutional neural networks with cutout","author":"DeVries","year":"2017","journal-title":"arXiv:1708.04552"},{"key":"ref50","first-page":"6022","article-title":"CutMix: Regularization strategy to train strong classifiers with localizable features","volume-title":"Proc. IEEE\/CVF Int. Conf. Comput. Vis. (ICCV)","author":"Yun"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.48550\/arxiv.1710.09412"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1603.08155"},{"key":"ref53","first-page":"586","article-title":"The unreasonable effectiveness of deep features as a perceptual metric","volume-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit.","author":"Zhang"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2010.11929"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01213"},{"issue":"2","key":"ref56","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1016\/j.neuroimage.2011.09.015","article-title":"FSL","volume":"62","author":"Jenkinson","year":"2012","journal-title":"NeuroImage"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43999-5_38"},{"issue":"4","key":"ref58","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1007\/s12021-011-9109-y","article-title":"An open source multivariate framework for n-tissue segmentation with evaluation on public data","volume":"9","author":"Avants","year":"2011","journal-title":"Neuroinformatics"},{"issue":"4","key":"ref59","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"},{"key":"ref60","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. NIPS","volume":"33","author":"Ho"},{"key":"ref61","first-page":"8780","article-title":"Diffusion models beat GANs on image synthesis","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Dhariwal"},{"key":"ref62","first-page":"8162","article-title":"Improved denoising diffusion probabilistic models","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","volume":"139","author":"Nichol"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2023.3290149"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-34341-2"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16847"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2016.2643009"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2020.2998696"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2023.3342808"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3070489"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2024.3371178"}],"container-title":["IEEE Transactions on Circuits and Systems for Video Technology"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/76\/11027896\/10858436.pdf?arnumber=10858436","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T18:42:00Z","timestamp":1767638520000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10858436\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":70,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tcsvt.2025.3536807","relation":{},"ISSN":["1051-8215","1558-2205"],"issn-type":[{"value":"1051-8215","type":"print"},{"value":"1558-2205","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6]]}}}