{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,8,29]],"date-time":"2023-08-29T04:50:30Z","timestamp":1693284630920},"reference-count":39,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["WK5290000003"],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/access.2023.3300789","type":"journal-article","created":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T18:18:27Z","timestamp":1690913907000},"page":"83209-83220","source":"Crossref","is-referenced-by-count":0,"title":["GLFormer: An Efficient Transformer Network for Fast Magnetic Resonance Imaging Reconstruction"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"http:\/\/orcid.org\/0009-0004-2219-8465","authenticated-orcid":false,"given":"Rongqing","family":"Wang","sequence":"first","affiliation":[{"name":"Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, China"}]},{"ORCID":"http:\/\/orcid.org\/0009-0000-9793-4130","authenticated-orcid":false,"given":"Mengdie","family":"Song","sequence":"additional","affiliation":[{"name":"Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, China"}]},{"ORCID":"http:\/\/orcid.org\/0009-0001-4740-5692","authenticated-orcid":false,"given":"Jiantai","family":"Zhou","sequence":"additional","affiliation":[{"name":"Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-2987-7378","authenticated-orcid":false,"given":"Bensheng","family":"Qiu","sequence":"additional","affiliation":[{"name":"Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, China"}]}],"member":"263","reference":[{"key":"ref13","article-title":"Frequency principle: Fourier analysis sheds light on deep neural networks","author":"john xu","year":"2019","journal-title":"arXiv 1901 06523"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2011.2173206"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3180228"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/QoMEX.2012.6263880"},{"key":"ref15","first-page":"1","article-title":"Understanding the effective receptive field in deep convolutional neural networks","volume":"29","author":"luo","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1522-2586(199911)10:5<590::AID-JMRI2>3.3.CO;2-J"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00871"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.27201"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2016.2644865"},{"key":"ref30","article-title":"fastMRI: An open dataset and benchmarks for accelerated MRI","author":"zbontar","year":"2018","journal-title":"arXiv 1811 08839"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106513"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"ref10","article-title":"Reference-based MRI reconstruction using texture transformer","author":"guo","year":"2023","journal-title":"Medical Imaging with Deep Learning (MIDL)"},{"key":"ref32","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv 1412 6980"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1522-2594(199911)42:5<952::AID-MRM16>3.3.CO;2-J"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/S0720-048X(98)00172-7"},{"key":"ref17","article-title":"Deformable DETR: Deformable transformers for end-to-end object detection","author":"zhu","year":"2020","journal-title":"arXiv 2010 04159"},{"key":"ref39","first-page":"956","article-title":"Preoperative evaluation of neurovascular compression in patients with trigeminal neuralgia by use of three-dimensional reconstruction from two types of high-resolution magnetic resonance imaging","volume":"51","author":"akimoto","year":"2002","journal-title":"Neurosurgery"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.25749"},{"key":"ref19","article-title":"JPEG image compression using discrete cosine transform—A survey","author":"raid","year":"2014","journal-title":"arXiv 1405 6147"},{"key":"ref18","author":"baxes","year":"1994","journal-title":"Digital Image Processing Principles and Applications"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2020.3001737"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20053-3_1"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2019.2936913"},{"key":"ref25","first-page":"1","article-title":"Attention is all you need","volume":"30","author":"vaswani","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref20","article-title":"Adversarial defense by suppressing high-frequency components","author":"zhang","year":"2019","journal-title":"arXiv 1908 06566"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58610-2_6"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i2.16193"},{"key":"ref28","first-page":"3","article-title":"CBAM: Convolutional block attention module","author":"woo","year":"2018","journal-title":"Proc Eur Conf Comput Vis (ECCV)"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"1664","DOI":"10.1109\/PROC.1967.5957","article-title":"what is the fast fourier transform?","volume":"55","author":"cochran","year":"1967","journal-title":"Proceedings of the IEEE"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00460"},{"key":"ref8","first-page":"774","article-title":"Vision transformers enable fast and robust accelerated MRI","author":"lin","year":"2022","journal-title":"Proc Int Conf Med Imag Deep Learn"},{"key":"ref7","article-title":"An image is worth 16 × 16 words: Transformers for image recognition at scale","author":"dosovitskiy","year":"2020","journal-title":"arXiv 2010 11929"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2022.04.051"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2016.7493320"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2007.914728"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.1109\/TMI.2017.2785879","article-title":"DAGAN: Deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction","volume":"37","author":"yang","year":"2018","journal-title":"IEEE Trans Med Imag"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI45749.2020.9098491"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10005208\/10198417.pdf?arnumber=10198417","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T18:06:04Z","timestamp":1693245964000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10198417\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":39,"URL":"https:\/\/doi.org\/10.1109\/access.2023.3300789","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}