{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T07:13:58Z","timestamp":1760080438793,"version":"3.37.3"},"reference-count":47,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61771279","61527807"],"award-info":[{"award-number":["61771279","61527807"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014219","name":"National Science Fund for Distinguished Young Scholars","doi-asserted-by":"publisher","award":["11525521"],"award-info":[{"award-number":["11525521"]}],"id":[{"id":"10.13039\/501100014219","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2020]]},"DOI":"10.1109\/access.2020.3020406","type":"journal-article","created":{"date-parts":[[2020,8,31]],"date-time":"2020-08-31T21:05:23Z","timestamp":1598907923000},"page":"159260-159273","source":"Crossref","is-referenced-by-count":9,"title":["A Model-Based Unsupervised Deep Learning Method for Low-Dose CT Reconstruction"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9232-850X","authenticated-orcid":false,"given":"Kaichao","family":"Liang","sequence":"first","affiliation":[]},{"given":"Li","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Hongkai","family":"Yang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9946-8049","authenticated-orcid":false,"given":"Zhiqiang","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9723-5655","authenticated-orcid":false,"given":"Yuxiang","family":"Xing","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/s41365-019-0581-7"},{"key":"ref38","article-title":"Comparison of projection domain, image domain, and comprehensive deep learning for sparse-view X-ray CT image reconstruction","author":"liang","year":"2018","journal-title":"arXiv 1804 04289"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1117\/12.2534848"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1117\/12.2548946"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2832217"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3002534"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/53\/12\/018"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/42.993128"},{"key":"ref35","doi-asserted-by":"crossref","first-page":"1228","DOI":"10.1109\/TIP.2009.2017139","article-title":"Electronic noise modeling in statistical iterative reconstruction","volume":"18","author":"xu","year":"2009","journal-title":"IEEE Trans Image Process"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1118\/1.4722751"},{"key":"ref10","article-title":"Adaptive nonlocal means-based regularization for statistical image reconstruction of low-dose X-ray CT","volume":"9412","author":"zhang","year":"2015","journal-title":"Proc SPIE"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2823768"},{"key":"ref11","first-page":"1","article-title":"Low-dose limited view 4D CT reconstruction using patch-based low-rank regularization","author":"sang kim","year":"2013","journal-title":"Proc IEEE Nucl Sci Symp Med Imag Conf (NSS\/MIC)"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/56\/18\/011"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2014.05.002"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2016.2582042"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2016.7493371"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"1682","DOI":"10.1109\/TMI.2012.2195669","article-title":"Low-dose X-ray CT reconstruction via dictionary learning","volume":"31","author":"xu","year":"2012","journal-title":"IEEE Trans Med Imag"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2715284"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2018.8513453"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2766438"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2832656"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1148\/radiology.175.3.2343122"},{"key":"ref27","article-title":"A cascaded convolutional neural network for X-ray low-dose CT image denoising","author":"wu","year":"2017","journal-title":"arXiv 1705 04267"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2373041643"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/NSSMIC.2009.5402090"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2865202"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcl.2008.10.006"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1117\/12.465552"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TENCON.2016.7848089"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMra072149"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2757035"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr\/01948454"},{"key":"ref46","first-page":"6970","article-title":"High-quality self-supervised deep image denoising","author":"laine","year":"2019","journal-title":"Proc Conf Neural Inf Process Syst"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.01.015"},{"key":"ref45","first-page":"2129","article-title":"Noise2 Void&#x2013;Learning denoising from single noisy images","author":"krull","year":"2019","journal-title":"Proc IEEE\/CVF Conf Comput Vis Pattern Recognit (CVPR)"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1109\/TRPMS.2018.2867611","article-title":"Deep-neural-network-based sinogram synthesis for sparse-view CT image reconstruction","volume":"3","author":"hoyeon","year":"2019","journal-title":"IEEE Trans Radiat Plasma Med Sci"},{"key":"ref47","first-page":"53","article-title":"Noise2self: Blind denoising by self-supervision","volume":"97","author":"batson","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1117\/12.2293903"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1137\/100802001"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2827462"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1118\/1.595715"},{"key":"ref23","article-title":"Wavelet domain residual network (WavResNet) for low-dose X-ray CT reconstruction","author":"kang","year":"2017","journal-title":"arXiv 1703 01383"},{"key":"ref44","first-page":"2965","article-title":"Noise2noise: Learning image restoration without clean data","author":"lehtinen","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2799231"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2708987"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8948470\/09180342.pdf?arnumber=9180342","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:55:34Z","timestamp":1639770934000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9180342\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"references-count":47,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3020406","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2020]]}}}