{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T15:58:07Z","timestamp":1778255887043,"version":"3.51.4"},"reference-count":45,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2018,9,1]],"date-time":"2018-09-01T00:00:00Z","timestamp":1535760000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"}],"funder":[{"DOI":"10.13039\/501100004084","name":"Korea Science and Engineering Foundation","doi-asserted-by":"publisher","award":["NRF-2016R1A2B3008104"],"award-info":[{"award-number":["NRF-2016R1A2B3008104"]}],"id":[{"id":"10.13039\/501100004084","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004084","name":"Korea Science and Engineering Foundation","doi-asserted-by":"publisher","award":["NRF-2013M3A9B2076548"],"award-info":[{"award-number":["NRF-2013M3A9B2076548"]}],"id":[{"id":"10.13039\/501100004084","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Human Connectome Project, MGH-USC Consortium"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Biomed. Eng."],"published-print":{"date-parts":[[2018,9]]},"DOI":"10.1109\/tbme.2018.2821699","type":"journal-article","created":{"date-parts":[[2018,4,2]],"date-time":"2018-04-02T18:03:50Z","timestamp":1522692230000},"page":"1985-1995","source":"Crossref","is-referenced-by-count":283,"title":["Deep Residual Learning for Accelerated MRI Using Magnitude and Phase Networks"],"prefix":"10.1109","volume":"65","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8657-5785","authenticated-orcid":false,"given":"Dongwook","family":"Lee","sequence":"first","affiliation":[]},{"given":"Jaejun","family":"Yoo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3836-0082","authenticated-orcid":false,"given":"Sungho","family":"Tak","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9763-9609","authenticated-orcid":false,"given":"Jong Chul","family":"Ye","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2016.2644865"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"ref33","doi-asserted-by":"crossref","DOI":"10.1137\/18M1174027","article-title":"A mathematical framework for deep learning in elastic source\n imaging","author":"yoo","year":"2018"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1137\/17M1141771"},{"key":"ref31","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4419-9467-7","author":"bauschke","year":"2011","journal-title":"Convex Analysis and Monotone Operator Theory in Hilbert Spaces"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.22428"},{"key":"ref37","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","volume":"9","author":"glorot","year":"0","journal-title":"Proc 13th Int Conf Artif Intell Statist"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/2733373.2807412"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/5.58337"},{"key":"ref34","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"0","journal-title":"Proc Int Conf Machine Learning"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.26077"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.278"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2016.2629078"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref13","first-page":"234","article-title":"U-net: Convolutional networks for biomedical\n image segmentation","author":"ronneberger","year":"0","journal-title":"Proc Int Conf Med Image Comput Comput -Assisted Int"},{"key":"ref14","first-page":"2802","article-title":"Image restoration using very deep convolutional\n encoder-decoder networks with symmetric skip connections","author":"mao","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2662206"},{"key":"ref16","first-page":"184","article-title":"Learning a deep convolutional network for\n image super-resolution","author":"dong","year":"0","journal-title":"Proc Eur Conf Comput Vision"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.182"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2016.7493320"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/s10444-008-9084-5"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2007.914728"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.acha.2007.10.002"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2006.871582"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.21757"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2823768"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/52\/11\/018"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2016.2601296"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.24997"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.10171"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.26081"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1522-2594(199911)42:5<952::AID-MRM16>3.0.CO;2-S"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12600"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2708987"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12344"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.26977"},{"key":"ref42","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2713099"},{"key":"ref41","first-page":"649","article-title":"Colorful image colorization","author":"zhang","year":"0","journal-title":"Proc Eur Conf Comput Vision"},{"key":"ref23","article-title":"Deep residual learning for compressed sensing CT\n reconstruction via persistent homology analysis","author":"han","year":"2016"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2827462"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref43","article-title":"Deep generative adversarial networks for\n compressed sensing automates MRI","author":"mardani","year":"2017"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.27106"}],"container-title":["IEEE Transactions on Biomedical Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10\/8440576\/08329428.pdf?arnumber=8329428","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T15:20:31Z","timestamp":1643210431000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8329428\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9]]},"references-count":45,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tbme.2018.2821699","relation":{},"ISSN":["0018-9294","1558-2531"],"issn-type":[{"value":"0018-9294","type":"print"},{"value":"1558-2531","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,9]]}}}