{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T09:14:06Z","timestamp":1790932446653,"version":"4.1.0"},"reference-count":20,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2022,10,9]],"date-time":"2022-10-09T00:00:00Z","timestamp":1665273600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objectives<\/jats:title><jats:p>To investigate the clinical utility of deep learning image reconstruction (DLIR) for improving image quality in low\u2010dose chest CT in comparison with 40% adaptive statistical iterative reconstruction\u2010Veo (ASiR\u2010V40%) algorithm.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This retrospective study included 86 patients who underwent low\u2010dose CT for lung cancer screening. Images were reconstructed with ASiR\u2010V40% and DLIR at low (DLIR\u2010L), medium (DLIR\u2010M), and high (DLIR\u2010H) levels. CT value and standard deviation of lung tissue, erector spinae muscles, aorta, and fat were measured and compared across the four reconstructions. Subjective image quality was evaluated by two blind readers from three aspects: image noise, artifact, and visualization of small structures.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The effective dose was 1.03\u00a0\u00b1\u00a00.36\u00a0mSv. There was no significant difference in CT values of erector spinae muscles and aorta, whereas the maximum difference for lung tissue and fat was less than 5\u00a0HU among the four reconstructions. Compared with ASiR\u2010V40%, the DLIR\u2010L, DLIR\u2010M, and DLIR\u2010H reconstructions reduced the noise in aorta by 11.44%, 33.03%, and 56.1%, respectively, and had significantly higher subjective quality scores in image artifacts (all <jats:italic>p<\/jats:italic> &lt;\u00a00.001). ASiR\u2010V40%, DLIR\u2010L, and DLIR\u2010M had equivalent score in visualizing small structures (all <jats:italic>p<\/jats:italic> &gt;\u00a00.05), whereas DLIR\u2010H had slightly lower score.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Compared with ASiR\u2010V40%, DLIR significantly reduces image noise in low\u2010dose chest CT. DLIR strength is important and should be adjusted for different diagnostic needs in clinical application.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acm2.13796","type":"journal-article","created":{"date-parts":[[2022,10,9]],"date-time":"2022-10-09T23:53:10Z","timestamp":1665359590000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Image quality improvement in low\u2010dose chest CT with deep learning image reconstruction"],"prefix":"10.1002","volume":"23","author":[{"given":"Qian","family":"Tian","sequence":"first","affiliation":[{"name":"Department of Radiology The First Affiliated Hospital of Xi'an Jiaotong University  Xi'an Shaanxi P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiology The First Affiliated Hospital of Xi'an Jiaotong University  Xi'an Shaanxi P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianying","family":"Li","sequence":"additional","affiliation":[{"name":"GE Healthcare, Computed Tomography Research Center  Beijing P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yannan","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Radiology The First Affiliated Hospital of Xi'an Jiaotong University  Xi'an Shaanxi P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyi","family":"Niu","sequence":"additional","affiliation":[{"name":"Department of Radiology The First Affiliated Hospital of Xi'an Jiaotong University  Xi'an Shaanxi P. R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shumeng","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Radiology The First Affiliated Hospital of Xi'an Jiaotong University  Xi'an Shaanxi P. R. 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