{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,10]],"date-time":"2024-08-10T07:52:31Z","timestamp":1723276351241},"reference-count":45,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61902338"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3086839","type":"journal-article","created":{"date-parts":[[2021,6,8]],"date-time":"2021-06-08T12:47:50Z","timestamp":1623156470000},"page":"83185-83198","source":"Crossref","is-referenced-by-count":17,"title":["A Modified Generative Adversarial Network Using Spatial and Channel-Wise Attention for CS-MRI Reconstruction"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-2938-527X","authenticated-orcid":false,"given":"Guangyuan","family":"Li","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-1989-1360","authenticated-orcid":false,"given":"Jun","family":"Lv","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8890-4973","authenticated-orcid":false,"given":"Chengyan","family":"Wang","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","article-title":"Creation of fully sampled MR data repository for compressed sensing of the knee","author":"epperson","year":"2013","journal-title":"Proc 22nd Annu Meeting Sect Magn Reson Technol"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.08.021"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2020.3012928"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1642\/1\/012001"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2020.611666"},{"key":"ref30","article-title":"DTGAN: Dual attention generative adversarial networks for text-to-image generation","author":"zhang","year":"2020","journal-title":"arXiv 2011 02709"},{"key":"ref37","article-title":"ADMM-Net: A deep learning approach for compressive sensing MRI","author":"yang","year":"2017","journal-title":"arXiv 1705 06869"},{"key":"ref36","first-page":"10","article-title":"Deep ADMM-net for compressive sensing MRI","volume":"29","author":"sun","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref35","year":"2017","journal-title":"TensorFlow™"},{"key":"ref34","year":"2016","journal-title":"Tensorpack"},{"key":"ref10","first-page":"559","article-title":"Dictionary learning with segmentation for compressed-sensing magnetic resonance imaging","volume":"33","author":"yang","year":"2016","journal-title":"Chinese Journal of Magnetic Resonance"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014099"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20150487"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/60\/21\/R297"},{"key":"ref13","first-page":"2672","article-title":"Generative adversarial nets","volume":"27","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2858752"},{"key":"ref15","article-title":"Compressed sensing MRI reconstruction with cyclic loss in generative adversarial networks","author":"quan","year":"2017","journal-title":"arXiv 1709 00753"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.mri.2020.12.011"},{"key":"ref17","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"radford","year":"2015","journal-title":"arXiv 1511 06434"},{"key":"ref18","article-title":"Wasserstein GAN","author":"arjovsky","year":"2017","journal-title":"arXiv 1701 07875"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-2074"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2019.2950640"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2019.8759423"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.25210"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2894694"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59713-9_17"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2006.871582"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.22736"},{"key":"ref7","first-page":"486","article-title":"Compressive sensing low-field mri reconstruction with dual-tree wavelet transform and wavelet tree sparsity","volume":"35","author":"qing-huan","year":"2018","journal-title":"Chinese Journal of Magnetic Resonance"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.27201"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2013.2277798"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2007.914728"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2785879"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00679"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2820120"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2877932"},{"key":"ref42","doi-asserted-by":"crossref","first-page":"4509","DOI":"10.1109\/TIP.2017.2713099","article-title":"Deep convolutional neural network for inverse problems in imaging","volume":"26","author":"jin","year":"2016","journal-title":"IEEE Trans Image Process"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TRPMS.2018.2867611"},{"key":"ref23","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"Proc Int Conf Med Image Comput Comput -Assist Intervent"},{"key":"ref44","first-page":"451","article-title":"Ask, attend and answer: Exploring question-guided spatial attention for visual question answering","author":"xu","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.667"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14509"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2947606"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09447721.pdf?arnumber=9447721","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T16:52:38Z","timestamp":1643215958000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9447721\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":45,"URL":"http:\/\/dx.doi.org\/10.1109\/access.2021.3086839","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}
