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We aimed to compare the image quality and lung nodule detectability between chest CT using a quarter of the low dose (QLD) reconstructed with vendor-agnostic deep-learning image reconstruction (DLIR) and conventional low-dose (LD) CT reconstructed with iterative reconstruction (IR).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Materials and methods<\/jats:title>\n                <jats:p>We retrospectively collected 100 patients (median age, 61 years [IQR, 53\u201370 years]) who received LDCT using a dual-source scanner, where total radiation was split into a 1:3 ratio. QLD CT was generated using a quarter dose and reconstructed with DLIR (QLD-DLIR), while LDCT images were generated using a full dose and reconstructed with IR (LD-IR). Three thoracic radiologists reviewed subjective noise, spatial resolution, and overall image quality, and image noise was measured in five areas. The radiologists were also asked to detect all Lung-RADS category 3 or 4 nodules, and their performance was evaluated using area under the jackknife free-response receiver operating characteristic curve (AUFROC).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The median effective dose was 0.16 (IQR, 0.14\u20130.18) mSv for QLD CT and 0.65 (IQR, 0.57\u20130.71) mSv for LDCT. The radiologists\u2019 evaluations showed no significant differences in subjective noise (QLD-DLIR vs. LD-IR, lung-window setting; 3.23\u2009\u00b1\u20090.19 vs. 3.27\u2009\u00b1\u20090.22; <jats:italic>P<\/jats:italic>\u2009=\u2009.11), spatial resolution (3.14\u2009\u00b1\u20090.28 vs. 3.16\u2009\u00b1\u20090.27; <jats:italic>P<\/jats:italic>\u2009=\u2009.12), and overall image quality (3.14\u2009\u00b1\u20090.21 vs. 3.17\u2009\u00b1\u20090.17; <jats:italic>P<\/jats:italic>\u2009=\u2009.15). QLD-DLIR demonstrated lower measured noise than LD-IR in most areas (<jats:italic>P<\/jats:italic>\u2009&lt;\u2009.001 for all). No significant difference was found between QLD-DLIR and LD-IR for the sensitivity (76.4% vs. 72.2%; <jats:italic>P<\/jats:italic>\u2009=\u2009.35) or the AUFROCs (0.77 vs. 0.78; <jats:italic>P<\/jats:italic>\u2009=\u2009.68) in detecting Lung-RADS category 3 or 4 nodules. Under a noninferiority limit of -0.1, QLD-DLIR showed noninferior detection performance (95% CI for AUFROC difference, -0.04 to 0.06).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>QLD-DLIR images showed comparable image quality and noninferior nodule detectability relative to LD-IR images.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12880-023-01081-8","type":"journal-article","created":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T09:02:20Z","timestamp":1694422940000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["75% radiation dose reduction using deep learning reconstruction on low-dose chest CT"],"prefix":"10.1186","volume":"23","author":[{"given":"Gyeong Deok","family":"Jo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chulkyun","family":"Ahn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jung Hee","family":"Hong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Da Som","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jongsoo","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyungjin","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jong Hyo","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin Mo","family":"Goo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ju Gang","family":"Nam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,9,11]]},"reference":[{"key":"1081_CR1","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1056\/NEJMoa1102873","volume":"365","author":"DR Aberle","year":"2011","unstructured":"National Lung Screening Trial Research Team, Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, et al. 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This study was conducted in accordance with the Declaration of Helsinki. We followed the Strengthening the Reporting of Observational Studies in Epidemiology guideline []. None of the study patients have been analyzed in previous publications.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Activities related to the present article: J.H.K. is a co-CEO of ClariPi and C.A. is an employee of ClariPi. The role of these two authors (J.H.K. and C.A.) were confined to providing the deep-learning reconstruction software (ClariCT.AI), and they did not participate in data analysis or writing of original draft. Activities not related to the present article: J.G.N. received research grants from Vuno; J.M.G. received research grants from Lunit, INFINITT Healthcare, Dongkook Lifescience, and LG electronics; H.K. received consulting fees from RADISEN; holds stock and stock options in Medical IP. All other authors (G.D.J., J.H.H., D.S.K., and J.P.) declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"121"}}