{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,13]],"date-time":"2026-09-13T03:29:13Z","timestamp":1789270153389,"version":"build-2803163510"},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"1182","license":[{"start":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T00:00:00Z","timestamp":1772668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/100016275","name":"SNUH","doi-asserted-by":"publisher","award":["0620221870"],"award-info":[{"award-number":["0620221870"]}],"id":[{"id":"10.13039\/100016275","id-type":"DOI","asserted-by":"publisher"}]},{"name":"ClariPi., Ltd."}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To evaluate the accuracy of CT-derived fat fraction (CDFF) software for quantifying hepatic steatosis at various radiation doses, using MRI-derived proton density fat fraction (MRI-PDFF) as the reference standard, and examines the impact of deep learning (DL)-based noise reduction on CDFF accuracy in low-dose CT (LDCT) scans.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a retrospective analysis of 125 living liver donor candidates who underwent non-contrast CT and MRI between July 2016 and December 2017. CDFF was measured on full-dose and simulated LDCT scans at 50%, 25%, and 10% radiation doses. DL-based denoising reconstruction (DLDR) was applied to LDCT scans for CDFF recalculation. The accuracy of CDFF was compared with MRI-PDFF using Pearson correlation coefficients and receiver operating characteristic (ROC) curve analysis, focusing on the effects of radiation dose and DLDR.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of the 125 participants (mean age 38\u2009\u00b1\u200910\u2009years; 77 males), 29 (23%) had hepatic steatosis (MRI-PDFF \u22655%). Full-dose CDFF showed moderate correlation with MRI-PDFF (r\u2009=\u20090.728; P &amp;lt; .001). Correlation decreased with lower doses (r\u2009=\u20090.684-0.725) but improved with DLDR (r\u2009=\u20090.725-0.736). ROC AUC for diagnosing hepatic steatosis was 0.82 for full-dose CDFF, with similar performance across other doses except 10%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>CDFF accuracy declines at lower radiation doses, but DLDR enhances accuracy, improving alignment with MRI-PDFF, especially at reduced doses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Advances in knowledge<\/jats:title>\n                    <jats:p>DLDR significantly enhances the accuracy of CDFF accuracy at lower radiation doses, enabling high diagnostic performance for hepatic steatosis while potentially reducing patient radiation exposure.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bjr\/tqag054","type":"journal-article","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T08:28:55Z","timestamp":1772785735000},"page":"1114-1124","source":"Crossref","is-referenced-by-count":1,"title":["Automatic hepatic steatosis quantification using low-dose CT with deep learning-based noise reduction and CT fat fraction analysis software"],"prefix":"10.1093","volume":"99","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5401-2787","authenticated-orcid":false,"given":"Sung Joon","family":"Youn","sequence":"first","affiliation":[{"name":"Seoul National University College of Medicine , Seoul 03080,","place":["Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8991-3986","authenticated-orcid":false,"given":"Sun Kyung","family":"Jeon","sequence":"additional","affiliation":[{"name":"Department of Radiology, Seoul National University Hospital , Seoul 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