{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,13]],"date-time":"2026-11-13T02:59:50Z","timestamp":1794538790625,"version":"4.9.1"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,9,15]],"date-time":"2024-09-15T00:00:00Z","timestamp":1726358400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,9,15]],"date-time":"2024-09-15T00:00:00Z","timestamp":1726358400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cancer Imaging"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Objective<\/jats:title>\n                <jats:p>To explore the effects of tube voltage, radiation dose and adaptive statistical iterative reconstruction (ASiR-V) strength level on the detection and characterization of pulmonary nodules by an artificial intelligence (AI) software in ultra-low-dose chest CT (ULDCT).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Materials and methods<\/jats:title>\n                <jats:p>An anthropomorphic thorax phantom containing 12 spherical simulated nodules (Diameter: 12\u00a0mm, 10\u00a0mm, 8\u00a0mm, 5\u00a0mm; CT value: -800HU, -630HU, 100HU) was scanned with three ULDCT protocols: Dose-1 (70kVp:0.11mSv, 100kVp:0.10mSv), Dose-2 (70kVp:0.34mSv, 100kVp:0.32mSv), Dose-3 (70kVp:0.53mSv, 100kVp:0.51mSv). All scanning protocols were repeated five times. CT images were reconstructed using four different strength levels of ASiR-V (0%=FBP, 30%, 50%, 70%ASiR-V) with a slice thickness of 1.25\u00a0mm. The characteristics of the physical nodules were used as reference standards. All images were analyzed using a commercially available AI software to identify nodules for calculating nodule detection rate (DR) and to obtain their long diameter and short diameter, which were used to calculate the deformation coefficient (DC) and size measurement deviation percentage (SP) of nodules. DR, DC and SP of different imaging groups were statistically compared.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Image noise decreased with the increase of ASiR-V strength level, and the 70\u00a0kV images had lower noise under the same strength level (mean-value 70\u00a0kV: 40.14\u2009\u00b1\u20097.05 (dose 1), 27.55\u2009\u00b1\u20097.38 (dose 2), 23.88\u2009\u00b1\u20096.98 (dose 3); 100\u00a0kV: 42.36\u2009\u00b1\u20097.62 (dose 1); 30.78\u2009\u00b1\u20096.87 (dose 2); 26.49\u2009\u00b1\u20096.61 (dose 3)). Under the same dose level, there were no differences in DR between 70\u00a0kV and 100\u00a0kV (dose 1: 58.76% vs. 58.33%; dose 2: 73.33% vs. 70.83%; dose 3: 75.42% vs. 75.42%, all <jats:italic>p<\/jats:italic>\u2009&gt;\u20090.05). The DR of GGNs increased significantly at dose 2 and higher (70\u00a0kV: 38.12% (dose 1), 60.63% (dose 2), 64.38% (dose 3); 100\u00a0kV: 37.50% (dose 1), 59.38% (dose 2), 66.25% (dose 3)). In general, the use of ASiR-V at higher strength levels (&gt;\u200950%) and 100\u00a0kV provided better (lower) DC and SP.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Detection rates are similar between 70\u00a0kV and 100\u00a0kV scans. The 70\u00a0kV images have better noise performance under the same ASiR-V level, while images of 100\u00a0kV and higher ASiR-V levels are better in preserving the nodule morphology (lower DC and SP); the dose levels above 0.33mSv provide high sensitivity for nodules detection, especially the simulated ground glass nodules.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s40644-024-00770-z","type":"journal-article","created":{"date-parts":[[2024,9,15]],"date-time":"2024-09-15T11:01:44Z","timestamp":1726398104000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Effects of tube voltage, radiation dose and adaptive statistical iterative reconstruction strength level on the detection and characterization of pulmonary nodules in ultra-low-dose chest CT"],"prefix":"10.1186","volume":"24","author":[{"given":"Yue","family":"Yao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"JingBin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zengmiao","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianying","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohui","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,9,15]]},"reference":[{"key":"770_CR1","doi-asserted-by":"publisher","first-page":"412","DOI":"10.6004\/jnccn.2018.0020","volume":"16","author":"DE Wood","year":"2018","unstructured":"Wood DE, Kazerooni EA, Baum SL, et al. 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