{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T01:48:41Z","timestamp":1789177721402,"version":"build-2803163510"},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"3-4","license":[{"start":{"date-parts":[[2021,3,16]],"date-time":"2021-03-16T00:00:00Z","timestamp":1615852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,10,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>This study\u2019s aim was to assess whether deep learning image reconstruction (DLIR) techniques are non-inferior to ASIR-V for the clinical task of pulmonary nodule detection in chest computed tomography. Up to 6 (range 3\u20136, mean 4.2) artificial lung nodules (diameter: 3, 5, 8\u00a0mm; density: \u2212800, \u2212630, +100 HU) were inserted at different locations in the Kyoto Kagaku Lungman phantom. In total, 16 configurations (10 abnormal, 6 normal) were scanned at 7.6, 3, 1.6 and 0.38\u00a0mGy CTDIvol (respectively 0, 60, 80 and 95% dose reduction). Images were reconstructed using 50% ASIR-V and a deep learning-based algorithm with low (DL-L), medium (DL-M) and high (DL-H) strength. Four chest radiologists evaluated 256 series by locating and scoring nodules on a five-point scale. No statistically significant difference was found among the reconstruction algorithms (p\u00a0=\u00a00.987, average across readers AUC: 0.555, 0.561, 0.557, 0.558 for ASIR-V, DL-L, DL-M, DL-H).<\/jats:p>","DOI":"10.1093\/rpd\/ncab025","type":"journal-article","created":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T13:15:37Z","timestamp":1612876537000},"page":"158-163","source":"Crossref","is-referenced-by-count":16,"title":["PULMONARY NODULE DETECTION IN CHEST CT USING A DEEP LEARNING-BASED RECONSTRUCTION ALGORITHM"],"prefix":"10.1093","volume":"195","author":[{"given":"C","family":"Franck","sequence":"first","affiliation":[{"name":"Department of Radiology, University Hospital Antwerp, Drie Eikenstraat 655, 2650 Edegem, Belgium"},{"name":"mVISION, Faculty of Medicine and Health Sciences, University of Antwerp, Drie Eikenstraat 655, 2650 Edegem, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A","family":"Snoeckx","sequence":"additional","affiliation":[{"name":"Department of Radiology, University Hospital Antwerp, Drie Eikenstraat 655, 2650 Edegem, Belgium"},{"name":"mVISION, Faculty of Medicine and Health Sciences, University of Antwerp, Drie Eikenstraat 655, 2650 Edegem, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M","family":"Spinhoven","sequence":"additional","affiliation":[{"name":"Department of Radiology, University Hospital Antwerp, Drie Eikenstraat 655, 2650 Edegem, Belgium"},{"name":"mVISION, Faculty of Medicine and Health Sciences, University of Antwerp, Drie Eikenstraat 655, 2650 Edegem, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H","family":"El Addouli","sequence":"additional","affiliation":[{"name":"Department of Radiology, University Hospital Antwerp, Drie Eikenstraat 655, 2650 Edegem, Belgium"},{"name":"mVISION, Faculty of Medicine and Health Sciences, University of Antwerp, Drie Eikenstraat 655, 2650 Edegem, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S","family":"Nicolay","sequence":"additional","affiliation":[{"name":"Department of Radiology, University Hospital Antwerp, Drie Eikenstraat 655, 2650 Edegem, Belgium"},{"name":"mVISION, Faculty of Medicine and Health Sciences, University of Antwerp, Drie Eikenstraat 655, 2650 Edegem, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A","family":"Van Hoyweghen","sequence":"additional","affiliation":[{"name":"Department of Radiology, University Hospital Antwerp, Drie Eikenstraat 655, 2650 Edegem, Belgium"},{"name":"mVISION, Faculty of Medicine and Health Sciences, University of Antwerp, Drie Eikenstraat 655, 2650 Edegem, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P","family":"Deak","sequence":"additional","affiliation":[{"name":"GE Healthcare, Glattbrugg, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"F","family":"Zanca","sequence":"additional","affiliation":[{"name":"Palindromo Consulting, Leuven, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,3,16]]},"reference":[{"issue":"3","key":"2021101213333064900_ref1","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1148\/radiol.2462070712","article-title":"Fleischner society: glossary of terms for thoracic imaging","volume":"246","author":"Hansell","year":"2008","journal-title":"Radiology"},{"issue":"6","key":"2021101213333064900_ref2","doi-asserted-by":"publisher","first-page":"394","DOI":"10.3322\/caac.21492","article-title":"Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"68","author":"Bray","year":"2018","journal-title":"CA Cancer J. 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