{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T15:53:00Z","timestamp":1787500380173,"version":"build-2736575974"},"reference-count":49,"publisher":"IOP Publishing","issue":"4","license":[{"start":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T00:00:00Z","timestamp":1672272000000},"content-version":"vor","delay-in-days":28,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T00:00:00Z","timestamp":1672272000000},"content-version":"tdm","delay-in-days":28,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100019266","name":"Korea Medical Device Development Fund","doi-asserted-by":"crossref","award":["Project Number: 1711174276"],"award-info":[{"award-number":["Project Number: 1711174276"]}],"id":[{"id":"10.13039\/100019266","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","award":["2020R1A4A1016619"],"award-info":[{"award-number":["2020R1A4A1016619"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Electronics and Telecommunications Research Institute (ETRI) grant","award":["21YR2400"],"award-info":[{"award-number":["21YR2400"]}]},{"name":"Technology development Program of MSS","award":["S3146559"],"award-info":[{"award-number":["S3146559"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2022,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Accurate image quality assessment (IQA) is crucial to optimize computed tomography (CT) image protocols while keeping the radiation dose as low as reasonably achievable. In the medical domain, IQA is based on how well an image provides a useful and efficient presentation necessary for physicians to make a diagnosis. Moreover, IQA results should be consistent with radiologists\u2019 opinions on image quality, which is accepted as the gold standard for medical IQA. As such, the goals of medical IQA are greatly different from those of natural IQA. In addition, the lack of pristine reference images or radiologists\u2019 opinions in a real-time clinical environment makes IQA challenging. Thus, no-reference IQA (NR-IQA) is more desirable in clinical settings than full-reference IQA (FR-IQA). Leveraging an innovative self-supervised training strategy for object detection models by detecting virtually inserted objects with geometrically simple forms, we propose a novel NR-IQA method, named deep detector IQA (D2IQA), that can automatically calculate the quantitative quality of CT images. Extensive experimental evaluations on clinical and anthropomorphic phantom CT images demonstrate that our D2IQA is capable of robustly computing perceptual image quality as it varies according to relative dose levels. Moreover, when considering the correlation between the evaluation results of IQA metrics and radiologists\u2019 quality scores, our D2IQA is marginally superior to other NR-IQA metrics and even shows performance competitive with FR-IQA metrics.<\/jats:p>","DOI":"10.1088\/2632-2153\/aca87d","type":"journal-article","created":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T06:28:38Z","timestamp":1672295318000},"page":"045033","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["No-reference perceptual CT image quality assessment based on a self-supervised learning framework"],"prefix":"10.1088","volume":"3","author":[{"given":"Wonkyeong","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eunbyeol","family":"Cho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wonjin","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyebin","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyongmin Sarah","family":"Beck","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyun Jung","family":"Yoon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jongduk","family":"Baek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9273-034X","authenticated-orcid":true,"given":"Jang-Hwan","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2022,12,29]]},"reference":[{"key":"mlstaca87dbib1","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1148\/radiol.2511081296","volume":"251","author":"Sodickson","year":"2009","journal-title":"Radiology"},{"key":"mlstaca87dbib2","first-page":"pp 677","article-title":"Ntire 2021 Challenge on perceptual image quality assessment","author":"Gu","year":"2021"},{"key":"mlstaca87dbib3","doi-asserted-by":"publisher","first-page":"3440","DOI":"10.1109\/TIP.2006.881959","volume":"15","author":"Sheikh","year":"2006","journal-title":"IEEE Trans. Image Process."},{"key":"mlstaca87dbib4","doi-asserted-by":"publisher","DOI":"10.1117\/1.3267105","volume":"19","author":"Larson","year":"2010","journal-title":"J. Electron. Imaging"},{"key":"mlstaca87dbib5","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.image.2014.10.009","volume":"30","author":"Ponomarenko","year":"2015","journal-title":"Signal Process., Image Commun."},{"key":"mlstaca87dbib6","first-page":"pp 633","article-title":"Pipal: a large-scale image quality assessment dataset for perceptual image restoration","author":"Jinjin","year":"2020"},{"key":"mlstaca87dbib7","first-page":"pp 277","article-title":"Diagnostic quality assessment of medical images: challenges and trends","author":"Cavaro-M\u00e9nard","year":"2010"},{"key":"mlstaca87dbib8","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20170448","volume":"91","author":"Fang","year":"2018","journal-title":"Brit. J. Radiol."},{"key":"mlstaca87dbib9","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1007\/s10140-019-01732-w","volume":"27","author":"Speelman","year":"2020","journal-title":"Emerg. Radiol."},{"key":"mlstaca87dbib10","doi-asserted-by":"publisher","first-page":"9758","DOI":"10.1073\/pnas.90.21.9758","volume":"90","author":"Barrett","year":"1993","journal-title":"Proc. Natl Acad. Sci."},{"key":"mlstaca87dbib11","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1002\/mp.15362","volume":"49","author":"Gong","year":"2022","journal-title":"Med. Phys."},{"key":"mlstaca87dbib12","doi-asserted-by":"publisher","DOI":"10.1117\/12.2294962","article-title":"Realistic lesion simulation: application of hyperelastic deformation to lesion-local environment in lung CT","volume":"10573","author":"Sauer","year":"2018","journal-title":"Proc. SPIE"},{"key":"mlstaca87dbib13","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0194408","volume":"13","author":"Han","year":"2018","journal-title":"PLoS One"},{"key":"mlstaca87dbib14","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"mlstaca87dbib15","first-page":"pp 1808","article-title":"Pieapp: perceptual image-error assessment through pairwise preference","author":"Prashnani","year":"2018"},{"key":"mlstaca87dbib16","first-page":"pp 586","article-title":"The unreasonable effectiveness of deep features as a perceptual metric","author":"Zhang","year":"2018"},{"key":"mlstaca87dbib17","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1109\/LSP.2012.2227726","volume":"20","author":"Mittal","year":"2013","journal-title":"IEEE Signal Process. Lett."},{"key":"mlstaca87dbib18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cviu.2016.12.009","volume":"158","author":"Ma","year":"2017","journal-title":"Comput. Vis. Image Underst."},{"key":"mlstaca87dbib19","doi-asserted-by":"publisher","first-page":"4695","DOI":"10.1109\/TIP.2012.2214050","volume":"21","author":"Mittal","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"mlstaca87dbib20","first-page":"pp 1","article-title":"Blind image quality evaluation using perception based features","author":"Venkatanath","year":"2015"},{"key":"mlstaca87dbib21","first-page":"pp 6228","article-title":"The perception-distortion tradeoff","author":"Blau","year":"2018"},{"key":"mlstaca87dbib22","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s10278-002-0001-z","volume":"15","author":"Erickson","year":"2002","journal-title":"J. Digit. Imaging"},{"key":"mlstaca87dbib23","author":"Barrett","year":"2013"},{"key":"mlstaca87dbib24","doi-asserted-by":"publisher","first-page":"4027","DOI":"10.1088\/0031-9155\/52\/14\/002","volume":"52","author":"Boedeker","year":"2007","journal-title":"Phys. Med. Biol."},{"key":"mlstaca87dbib25","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1109\/42.363108","volume":"13","author":"Hudson","year":"1994","journal-title":"IEEE Trans. Med. Imaging"},{"key":"mlstaca87dbib26","doi-asserted-by":"publisher","first-page":"764","DOI":"10.2214\/AJR.09.2397","volume":"193","author":"Hara","year":"2009","journal-title":"Am. J. Roentgenol."},{"key":"mlstaca87dbib27","first-page":"pp 733","article-title":"Saliency filters: contrast based filtering for salient region detection","author":"Perazzi","year":"2012"},{"key":"mlstaca87dbib28","first-page":"pp 6154","article-title":"Cascade R-CNN: delving into high quality object detection","author":"Cai","year":"2018"},{"key":"mlstaca87dbib29","first-page":"pp 770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"mlstaca87dbib30","first-page":"pp 740","article-title":"Microsoft COCO: common objects in context","author":"Lin","year":"2014"},{"key":"mlstaca87dbib31","first-page":"1","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"mlstaca87dbib32","first-page":"21002","volume":"33","author":"Li","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"mlstaca87dbib33","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1080\/01621459.1952.10483441","volume":"47","author":"Kruskal","year":"1952","journal-title":"J. Am. Stat. Assoc."},{"key":"mlstaca87dbib34","article-title":"Low dose CT grand challenge","author":"","year":"2017"},{"key":"mlstaca87dbib35","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1007\/s10278-019-00274-4","volume":"33","author":"Gholizadeh-Ansari","year":"2020","journal-title":"J. Digit. imaging"},{"key":"mlstaca87dbib36","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102065","volume":"71","author":"Kim","year":"2021","journal-title":"Med. Image Anal."},{"key":"mlstaca87dbib37","author":"Macovski","year":"1983"},{"key":"mlstaca87dbib38","doi-asserted-by":"publisher","first-page":"873","DOI":"10.1177\/0284185114539319","volume":"56","author":"Beenen","year":"2015","journal-title":"Acta Radiol."},{"key":"mlstaca87dbib39","doi-asserted-by":"publisher","first-page":"566","DOI":"10.2214\/AJR.19.21809","volume":"214","author":"Singh","year":"2020","journal-title":"Am. J. Roentgenol."},{"key":"mlstaca87dbib40","first-page":"pp 1398","article-title":"Multiscale structural similarity for image quality assessment","volume":"vol 2","author":"Wang","year":"2003"},{"key":"mlstaca87dbib41","doi-asserted-by":"publisher","first-page":"684","DOI":"10.1109\/TIP.2013.2293423","volume":"23","author":"Xue","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"mlstaca87dbib42","doi-asserted-by":"publisher","first-page":"2378","DOI":"10.1109\/TIP.2011.2109730","volume":"20","author":"Zhang","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"mlstaca87dbib43","doi-asserted-by":"publisher","first-page":"636","DOI":"10.1109\/83.841940","volume":"9","author":"Damera-Venkata","year":"2000","journal-title":"IEEE Trans. Image Process."},{"key":"mlstaca87dbib44","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1109\/TIP.2005.859378","volume":"15","author":"Sheikh","year":"2006","journal-title":"IEEE Trans. Image Process."},{"key":"mlstaca87dbib45","doi-asserted-by":"publisher","first-page":"1237","DOI":"10.1364\/JOSAA.11.001237","volume":"11","author":"Burgess","year":"1994","journal-title":"J. Opt. Soc. Am. A"},{"key":"mlstaca87dbib46","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.1109\/TMI.2019.2930338","volume":"39","author":"Mason","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"mlstaca87dbib47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00138-021-01240-3","volume":"32","author":"Choi","year":"2021","journal-title":"Mach. Vis. Appl."},{"key":"mlstaca87dbib48","doi-asserted-by":"publisher","first-page":"2063","DOI":"10.1088\/0031-9155\/57\/7\/2063","volume":"57","author":"Yan","year":"2012","journal-title":"Phys. Med. Biol."},{"key":"mlstaca87dbib49","author":"Chilamkurthy","year":"2018"}],"container-title":["Machine Learning: Science and Technology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/aca87d","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/aca87d\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/aca87d\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/aca87d\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T06:28:47Z","timestamp":1672295327000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/aca87d"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,1]]},"references-count":49,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,12,29]]},"published-print":{"date-parts":[[2022,12,1]]}},"URL":"https:\/\/doi.org\/10.1088\/2632-2153\/aca87d","relation":{},"ISSN":["2632-2153"],"issn-type":[{"value":"2632-2153","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,1]]},"assertion":[{"value":"No-reference perceptual CT image quality assessment based on a self-supervised learning framework","name":"article_title","label":"Article Title"},{"value":"Machine Learning: Science and Technology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2022 The Author(s). Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2022-09-06","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2022-12-02","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2022-12-29","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}