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A deep learning model was applied to generate sCECT from NCCT. We collected three separate data sets, the development set (n\u2009=\u200925) for model training and tuning, test set 1 (n\u2009=\u200925) for technical evaluation, and test set 2 (n\u2009=\u200912) for clinical utility evaluation. In test set 1, image similarity metrics were calculated. In test set 2, the lesion contrast-to-noise ratio of the mediastinal lymph nodes was measured, and an observer study was conducted to compare lesion conspicuity. Comparisons were performed using the paired t-test or Wilcoxon signed-rank test. In test set 1, sCECT showed a lower mean absolute error (41.72 vs 48.74;\n                    <jats:italic>P<\/jats:italic>\n                    \u2009&lt;\u2009.001), higher peak signal-to-noise ratio (17.44 vs 15.97;\n                    <jats:italic>P<\/jats:italic>\n                    \u2009&lt;\u2009.001), higher multiscale structural similarity index measurement (0.84 vs 0.81;\n                    <jats:italic>P<\/jats:italic>\n                    \u2009&lt;\u2009.001), and lower learned perceptual image patch similarity metric (0.14 vs 0.15;\n                    <jats:italic>P<\/jats:italic>\n                    \u2009&lt;\u2009.001) than NCCT. In test set 2, the contrast-to-noise ratio of the mediastinal lymph nodes was higher in the sCECT group than in the NCCT group (6.15\u2009\u00b1\u20095.18 vs 0.74\u2009\u00b1\u20090.69;\n                    <jats:italic>P<\/jats:italic>\n                    \u2009&lt;\u2009.001). The observer study showed for all reviewers higher lesion conspicuity in NCCT with sCECT than in NCCT alone (\n                    <jats:italic>P<\/jats:italic>\n                    \u2009\u2264\u2009.001). Synthetic CECT generated from NCCT improves the depiction of mediastinal lymph nodes.\n                  <\/jats:p>","DOI":"10.1038\/s41598-021-00058-3","type":"journal-article","created":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T06:09:16Z","timestamp":1634191756000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Generating synthetic contrast enhancement from non-contrast chest computed tomography using a generative adversarial network"],"prefix":"10.1038","volume":"11","author":[{"given":"Jae Won","family":"Choi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yeon Jin","family":"Cho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji Young","family":"Ha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seul Bi","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seunghyun","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Young Hun","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jung-Eun","family":"Cheon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Woo Sun","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,10,14]]},"reference":[{"key":"58_CR1","doi-asserted-by":"publisher","first-page":"1738","DOI":"10.1148\/rg.2015150033","volume":"35","author":"KR Beckett","year":"2015","unstructured":"Beckett, K. 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