{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T16:14:23Z","timestamp":1789661663233,"version":"4.0.1"},"reference-count":28,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Image-domain deep learning denoising may provide a practical post-processing approach for improving low-dose chest CT image quality on scanners without native deep learning reconstruction. This retrospective study evaluated a vendor-independent image-domain denoising algorithm applied to low-dose chest CT on a single 128-slice CT platform and descriptively compared the resulting image-quality metrics with those obtained using a standard-dose iterative reconstruction protocol.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This retrospective study included 198 patients who underwent unenhanced chest CT and were assigned to a low-dose CT group (LDCT,\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f99) or a standard-dose CT group (SDCT,\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f99) according to the clinical acquisition protocol. All images were reconstructed using sinogram-affirmed iterative reconstruction (SAFIRE). LDCT images were additionally processed with a vendor-independent image-domain deep learning denoising algorithm (AiR Denoising), generating low-dose AiR-denoised images (LD-AiR). Objective image quality was assessed using attenuation, image noise, signal-to-noise ratio, and contrast-to-noise ratio. Subjective image quality of the lung parenchyma and mediastinal soft tissue was independently evaluated by readers using a 5-point scale.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Compared with low-dose SAFIRE (LD-SAFIRE), LD-AiR significantly reduced image noise and increased signal-to-noise ratio and contrast-to-noise ratio across all evaluated regions (all\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.05). Subjective image quality scores for both lung parenchyma and mediastinal soft tissue were also significantly higher with LD-AiR than with LD-SAFIRE (all\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.05). In the descriptive between-group comparison, the LDCT protocol was associated with an approximately 76% lower effective dose than the SDCT protocol. Most objective and subjective image-quality metrics of LD-AiR did not differ significantly from those of SD-SAFIRE; however, this comparison was based on different patient groups and should not be interpreted as evidence of equivalence or non-inferiority.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>On the evaluated CT platform, vendor-independent image-domain deep learning denoising improved objective and subjective image quality of low-dose chest CT compared with LD-SAFIRE. These findings suggest that image-domain denoising may have potential utility for image-quality improvement on CT systems without native deep learning reconstruction. Further prospective, within-subject, multicentre studies incorporating lesion-detectability and diagnostic-performance assessment are needed to clarify its clinical applicability.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1888006","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T04:21:44Z","timestamp":1784002904000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Image-domain deep learning denoising for low-dose chest CT on a single 128-slice CT platform: a retrospective image-quality assessment"],"prefix":"10.3389","volume":"13","author":[{"given":"Kaiqing","family":"Yao","sequence":"first","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Chongqing Medical University","place":["Chongqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xue","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Chongqing Medical University","place":["Chongqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Lv","sequence":"additional","affiliation":[{"name":"Department of Radiology, Chongqing Academy of Medical Sciences, Chongqing General Hospital, Chongqing University","place":["Chongqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Physical Algorithm, Sinovision Technologies (Beijing) Co., Ltd.","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangpeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Safety Assessment, Chongqing Changming Safety Technology Consulting Co., Ltd.","place":["Chongqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Chongqing Medical University","place":["Chongqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Chongqing Medical University","place":["Chongqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyou","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Chongqing Medical University","place":["Chongqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fajin","family":"Lv","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Chongqing Medical University","place":["Chongqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1001\/jama.2021.0377","article-title":"Screening for lung cancer with low-dose computed tomography: updated evidence report and systematic review for the US preventive services task force","volume":"325","author":"Jonas","year":"2021","journal-title":"JAMA"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.ejro.2016.03.002","article-title":"Standard-dose vs. low-dose CT protocols in the evaluation of localized lung lesions: capability for lesion characterization\u2014iLEAD study","volume":"3","author":"Kubo","year":"2016","journal-title":"Eur J Radiol Open"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.ejrad.2018.10.025","article-title":"Basics of iterative 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