{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T05:02:18Z","timestamp":1789621338215,"version":"build-2803163510"},"reference-count":32,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002865","name":"Chongqing Science and Technology Bureau","doi-asserted-by":"publisher","award":["CSTC2021jscxksbN0030"],"award-info":[{"award-number":["CSTC2021jscxksbN0030"]}],"id":[{"id":"10.13039\/501100002865","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Chongqing Health Commission and Science and Technology Bureau","award":["2022ZDXM006"],"award-info":[{"award-number":["2022ZDXM006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Ultra-low-dose (ULD) chest CT can reduce radiation exposure but may compromise image quality. We evaluated image-domain generative adversarial network (GAN)-based denoising at low-dose (LD) and ULD levels, focusing on ULD-AiR versus LD-ADMIRE S3. Methods: In this single-center retrospective paired study, 262 participants underwent LD and ULD chest CT on the same scanner. Images reconstructed with Advanced Modeled Iterative Reconstruction at strength 3 (ADMIRE S3) were post-processed with AiR Denoising v1.0, yielding four series. Objective metrics, including signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), and 5-point subjective ratings were compared within dose levels and between ULD-AiR and LD-ADMIRE S3. Exploratory regression assessed associations of ULD-to-LD SNR log-ratios with the volume CT dose index (CTDIvol) log-ratio and anthropometric variables. Results: The median paired reduction in estimated effective dose from LD to ULD CT was 46.5%. Within each dose level, AiR reduced image noise and increased SNR, CNR, and subjective scores. Compared with LD-ADMIRE S3, ULD-AiR showed lower image noise and higher SNR\/CNR in the lung, aorta, and muscle; liver findings were less consistent, whereas vertebral metrics were less favorable. Lung-parenchyma scores did not differ significantly, mediastinal soft-tissue scores favored ULD-AiR, and overall image-noise scores favored LD-ADMIRE S3. Exploratory regression identified CTDIvol log-ratio associations with aortic and muscle SNR log-ratios; some anthropometric associations were sensitive to an influential observation. Conclusions: Image-domain GAN denoising improved several objective and subjective image-quality metrics at both dose levels. With a median paired reduction of 46.5% in estimated effective dose, ULD-AiR showed tissue- and endpoint-specific image-quality differences relative to LD-ADMIRE S3. These findings do not establish diagnostic or screening equivalence.<\/jats:p>","DOI":"10.3390\/diagnostics16182988","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T13:33:42Z","timestamp":1789479222000},"page":"2988","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Image-Domain GAN Denoising for Sn100 kVp Ultra-Low-Dose Chest CT: A Retrospective Paired Image-Quality Study"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7681-2002","authenticated-orcid":false,"given":"Kaiqing","family":"Yao","sequence":"first","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, 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, Chongqing 400044, 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, Chongqing 400016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Physical Algorithm, Sinovision Technologies (Beijing) Co., Ltd., Beijing 100176, 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., Chongqing 401122, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8633-0101","authenticated-orcid":false,"given":"Wangjia","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yineng","family":"Zheng","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, 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, Chongqing 400016, 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, Chongqing 400016, 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, Chongqing 400016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6484-9738","authenticated-orcid":false,"given":"Fajin","family":"Lv","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"National Lung Screening Trial Research Team, Aberle, D.R., Adams, A.M., Berg, C.D., Black, W.C., Clapp, J.D., Fagerstrom, R.M., Gareen, I.F., Gatsonis, C., and Marcus, P.M. (2011). Reduced lung-cancer mortality with low-dose computed tomographic screening. N. Engl. J. Med., 365, 395\u2013409.","DOI":"10.1056\/NEJMoa1102873"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1056\/NEJMoa1911793","article-title":"Reduced lung-cancer mortality with volume CT screening in a randomized trial","volume":"382","author":"Scholten","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"j347","DOI":"10.1136\/bmj.j347","article-title":"Exposure to low dose computed tomography for lung cancer screening and risk of cancer: Secondary analysis of trial data and risk-benefit analysis","volume":"356","author":"Rampinelli","year":"2017","journal-title":"BMJ"},{"key":"ref_4","doi-asserted-by":"crossref","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":"ref_5","doi-asserted-by":"crossref","first-page":"3290","DOI":"10.1007\/s00330-017-4739-6","article-title":"Ultralow dose CT for pulmonary nodule detection with chest x-ray equivalent dose\u2014A prospective intra-individual comparative study","volume":"27","author":"Messerli","year":"2017","journal-title":"Eur. Radiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1598","DOI":"10.1007\/s00330-014-3559-1","article-title":"Chest CT using spectral filtration: Radiation dose, image quality, and spectrum of clinical utility","volume":"25","author":"Braun","year":"2015","journal-title":"Eur. Radiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1148\/radiol.2019191422","article-title":"State of the art in abdominal CT: The limits of iterative reconstruction algorithms","volume":"293","author":"Mileto","year":"2019","journal-title":"Radiology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1148\/radiol.15142005","article-title":"Diagnostic performance of an advanced modeled iterative reconstruction algorithm for low-contrast detectability with a third-generation dual-source multidetector CT scanner: Potential for radiation dose reduction in a multireader study","volume":"275","author":"Solomon","year":"2015","journal-title":"Radiology"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"155.e15","DOI":"10.1016\/j.crad.2020.10.011","article-title":"The image quality of deep-learning image reconstruction of chest CT images on a mediastinal window setting","volume":"76","author":"Hata","year":"2021","journal-title":"Clin. Radiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"131","DOI":"10.3348\/kjr.2020.0116","article-title":"Validation of deep-learning image reconstruction for low-dose chest computed tomography scan: Emphasis on image quality and noise","volume":"22","author":"Kim","year":"2021","journal-title":"Korean J. Radiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"566","DOI":"10.2214\/AJR.19.21809","article-title":"Image quality and lesion detection on deep learning reconstruction and iterative reconstruction of submillisievert chest and abdominal CT","volume":"214","author":"Singh","year":"2020","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1148\/radiol.210551","article-title":"Deep learning reconstruction shows better lung nodule detection for ultra-low-dose chest CT","volume":"303","author":"Jiang","year":"2022","journal-title":"Radiology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e0806","DOI":"10.1097\/RTI.0000000000000806","article-title":"Detection of pulmonary nodules on ultra-low-dose chest computed tomography with deep-learning image reconstruction algorithm","volume":"40","author":"Bocquet","year":"2025","journal-title":"J. Thorac. Imaging"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3739","DOI":"10.1007\/s00330-024-11317-y","article-title":"Deep learning-based image domain reconstruction enhances image quality and pulmonary nodule detection in ultralow-dose CT with adaptive statistical iterative reconstruction-V","volume":"35","author":"Ye","year":"2025","journal-title":"Eur. Radiol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"20220915","DOI":"10.1259\/bjr.20220915","article-title":"Implementation of AI image reconstruction in CT\u2014How is it validated and what dose reductions can be achieved?","volume":"96","author":"Brady","year":"2023","journal-title":"Br. J. Radiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5139","DOI":"10.1007\/s00330-020-07537-7","article-title":"Image quality of ultralow-dose chest CT using deep learning techniques: Potential superiority of vendor-agnostic post-processing over vendor-specific techniques","volume":"31","author":"Nam","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jo, G.D., Ahn, C., Hong, J.H., Kim, D.S., Park, J., Kim, H., Kim, J.H., Goo, J.M., and Nam, J.G. (2023). 75% radiation dose reduction using deep learning reconstruction on low-dose chest CT. BMC Med. Imaging, 23.","DOI":"10.1186\/s12880-023-01081-8"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.2214\/AJR.11.6533","article-title":"Estimated radiation dose associated with low-dose chest CT of average-size participants in the National Lung Screening Trial","volume":"197","author":"Larke","year":"2011","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Jiang, J.M., Miao, L., Liang, X., Liu, Z.H., Zhang, L., and Li, M. (2022). The value of deep learning image reconstruction in improving the quality of low-dose chest CT images. Diagnostics, 12.","DOI":"10.3390\/diagnostics12102560"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1016\/j.ejmp.2014.01.006","article-title":"How to measure CT image quality: Variations in CT-numbers, uniformity and low contrast resolution for a CT quality assurance phantom","volume":"30","author":"Gulliksrud","year":"2014","journal-title":"Phys. Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"110659","DOI":"10.1016\/j.ejrad.2022.110659","article-title":"Ultra-low-dose hepatic multiphase CT using deep learning-based image reconstruction algorithm focused on arterial phase in chronic liver disease: A non-inferiority study","volume":"159","author":"Lee","year":"2023","journal-title":"Eur. J. Radiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.jcm.2016.02.012","article-title":"A guideline of selecting and reporting intraclass correlation coefficients for reliability research","volume":"15","author":"Koo","year":"2016","journal-title":"J. Chiropr. Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3109","DOI":"10.1002\/1097-0258(20001130)19:22<3109::AID-SIM558>3.0.CO;2-F","article-title":"Sympercents: Symmetric percentage differences on the 100 log(e) scale simplify the presentation of log transformed data","volume":"19","author":"Cole","year":"2000","journal-title":"Stat. Med."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1936","DOI":"10.1148\/rg.2021210105","article-title":"Deep learning-based reconstruction for lower-dose pediatric CT: Technical principles, image characteristics, and clinical implementations","volume":"41","author":"Nagayama","year":"2021","journal-title":"Radiographics"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"110914","DOI":"10.1016\/j.ejrad.2023.110914","article-title":"Noise power spectrum properties of deep learning-based reconstruction and iterative reconstruction algorithms: Phantom and clinical study","volume":"165","author":"Funama","year":"2023","journal-title":"Eur. J. Radiol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yao, K., Jiang, X., Lv, L., Li, Y., Zhang, G., Zhang, Z., Zhang, Z., Li, X., and Lv, F. (2026). Image-domain deep learning denoising for low-dose chest CT on a single 128-slice CT platform: A retrospective image-quality assessment. Front. Med., 13.","DOI":"10.3389\/fmed.2026.1888006"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1007\/s00330-023-10087-3","article-title":"Value of deep learning reconstruction of chest low-dose CT for image quality improvement and lung parenchyma assessment on lung window","volume":"34","author":"Wang","year":"2024","journal-title":"Eur. Radiol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e0472","DOI":"10.3348\/kjr.2024.0472","article-title":"Deep learning-based reconstruction algorithm with lung enhancement filter for chest CT: Effect on image quality and ground glass nodule sharpness","volume":"25","author":"Hwang","year":"2024","journal-title":"Korean J. Radiol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"20210380","DOI":"10.1259\/bjr.20210380","article-title":"Application of deep learning image reconstruction in low-dose chest CT scan","volume":"95","author":"Wang","year":"2022","journal-title":"Br. J. Radiol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"8140","DOI":"10.1007\/s00330-022-08870-9","article-title":"Can deep learning improve image quality of low-dose CT: A prospective study in interstitial lung disease","volume":"32","author":"Zhao","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.forsciint.2012.03.004","article-title":"How reliable are Hounsfield-unit measurements in forensic radiology?","volume":"220","author":"Ruder","year":"2012","journal-title":"Forensic Sci. Int."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1037\/1082-989X.1.1.30","article-title":"Forming inferences about some intraclass correlation coefficients","volume":"1","author":"McGraw","year":"1996","journal-title":"Psychol. Methods"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/18\/2988\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T04:12:28Z","timestamp":1789618348000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/18\/2988"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,15]]},"references-count":32,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["diagnostics16182988"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16182988","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,15]]}}}