{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T02:30:46Z","timestamp":1787797846686,"version":"build-2784847793"},"reference-count":26,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T00:00:00Z","timestamp":1656028800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Tomography"],"abstract":"<jats:p>(1) This study evaluates the impact of an AI denoising algorithm on image quality, diagnostic accuracy, and radiological workflows in pediatric chest ultra-low-dose CT (ULDCT). (2) Methods: 100 consecutive pediatric thorax ULDCT were included and reconstructed using weighted filtered back projection (wFBP), iterative reconstruction (ADMIRE 2), and AI denoising (PixelShine). Place-consistent noise measurements were used to compare objective image quality. Eight blinded readers independently rated the subjective image quality on a Likert scale (1 = worst to 5 = best). Each reader wrote a semiquantitative report to evaluate disease severity using a severity score with six common pathologies. The time to diagnosis was measured for each reader to compare the possible workflow benefits. Properly corrected mixed-effects analysis with post-hoc subgroup tests were used. Spearman\u2019s correlation coefficient measured inter-reader agreement for the subjective image quality analysis and the severity score sheets. (3) Results: The highest noise was measured for wFBP, followed by ADMIRE 2, and PixelShine (76.9 \u00b1 9.62 vs. 43.4 \u00b1 4.45 vs. 34.8 \u00b1 3.27 HU; each p &lt; 0.001). The highest subjective image quality was measured for PixelShine, followed by ADMIRE 2, and wFBP (4 (4\u20135) vs. 3 (4\u20135) vs. 3 (2\u20134), each p &lt; 0.001) with good inter-rater agreement (r \u2265 0.790; p \u2264 0.001). In diagnostic accuracy analysis, there was a good inter-rater agreement between the severity scores (r \u2265 0.764; p &lt; 0.001) without significant differences between severity score items per reconstruction mode (F (5.71; 566) = 0.792; p = 0.570). The shortest time to diagnosis was measured for the PixelShine datasets, followed by ADMIRE 2, and wFBP (2.28 \u00b1 1.56 vs. 2.45 \u00b1 1.90 vs. 2.66 \u00b1 2.31 min; F (1.000; 99.00) = 268.1; p &lt; 0.001). (4) Conclusions: AI denoising significantly improves image quality in pediatric thorax ULDCT without compromising the diagnostic confidence and reduces the time to diagnosis substantially.<\/jats:p>","DOI":"10.3390\/tomography8040140","type":"journal-article","created":{"date-parts":[[2022,6,26]],"date-time":"2022-06-26T22:50:23Z","timestamp":1656283823000},"page":"1678-1689","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["AI Denoising Improves Image Quality and Radiological Workflows in Pediatric Ultra-Low-Dose Thorax Computed Tomography Scans"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5961-9727","authenticated-orcid":false,"given":"Andreas S.","family":"Brendlin","sequence":"first","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, D-72076 Tuebingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ulrich","family":"Schmid","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, D-72076 Tuebingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Plajer","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, D-72076 Tuebingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6582-5105","authenticated-orcid":false,"given":"Maryanna","family":"Chaika","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, D-72076 Tuebingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7733-6965","authenticated-orcid":false,"given":"Markus","family":"Mader","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, D-72076 Tuebingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robin","family":"Wrazidlo","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, D-72076 Tuebingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon","family":"M\u00e4nnlin","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, D-72076 Tuebingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jakob","family":"Spogis","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, D-72076 Tuebingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1930-7497","authenticated-orcid":false,"given":"Arne","family":"Estler","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, D-72076 Tuebingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Esser","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, D-72076 Tuebingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J\u00fcrgen","family":"Sch\u00e4fer","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, D-72076 Tuebingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6861-6245","authenticated-orcid":false,"given":"Saif","family":"Afat","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, D-72076 Tuebingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7166-4689","authenticated-orcid":false,"given":"Ilias","family":"Tsiflikas","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, Eberhard-Karls University, D-72076 Tuebingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104377","DOI":"10.1016\/j.jcv.2020.104377","article-title":"Clinical features of pediatric patients with coronavirus disease (COVID-19)","volume":"127","author":"Song","year":"2020","journal-title":"J. Clin. Virol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1111\/ped.13755","article-title":"Pediatric oncologic emergencies: Clinical and imaging review for pediatricians","volume":"61","author":"Handa","year":"2019","journal-title":"Pediatrics Int."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"S40","DOI":"10.1002\/ppul.24931","article-title":"Novel imaging techniques for cystic fibrosis lung disease","volume":"56","author":"Goralski","year":"2021","journal-title":"Pediatric Pulmonol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e017548","DOI":"10.1136\/bmjopen-2017-017548","article-title":"Radiation dose from common radiological investigations and cumulative exposure in children with cystic fibrosis: An observational study from a single UK centre","volume":"7","author":"Ward","year":"2017","journal-title":"BMJ Open"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.jacr.2010.12.025","article-title":"Frequent body CT scanning of young adults: Indications, outcomes, and risk for radiation-induced cancer","volume":"8","author":"Zondervan","year":"2011","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1016\/S0140-6736(12)60815-0","article-title":"Radiation exposure from CT scans in childhood and subsequent risk of leukaemia and brain tumours: A retrospective cohort study","volume":"380","author":"Pearce","year":"2012","journal-title":"Lancet"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1097\/RLI.0000000000000037","article-title":"Ultralow-Dose Chest Computed Tomography for Pulmonary Nodule Detection: First Performance Evaluation of Single Energy Scanning With Spectral Shaping","volume":"49","author":"Gordic","year":"2014","journal-title":"Investig. Radiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1608","DOI":"10.1016\/j.ejrad.2015.04.018","article-title":"Unenhanced third-generation dual-source chest CT using a tin filter for spectral shaping at 100 kVp","volume":"84","author":"Haubenreisser","year":"2015","journal-title":"Eur. J. Radiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1007\/s00247-017-3813-1","article-title":"Single-energy pediatric chest computed tomography with spectral filtration at 100 kVp: Effects on radiation parameters and image quality","volume":"47","author":"Bodelle","year":"2017","journal-title":"Pediatric Radiol."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","unstructured":"Brendlin, A.S., Plajer, D., Chaika, M., Wrazidlo, R., Estler, A., Tsiflikas, I., Artzner, C.P., Afat, S., and Bongers, M.N. (2022). AI Denoising Significantly Improves Image Quality in Whole-Body Low-Dose Computed Tomography Staging. Diagnostics, 12.","DOI":"10.3390\/diagnostics12010225"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.bspc.2018.01.010","article-title":"A review on CT image noise and its denoising","volume":"42","author":"Diwakar","year":"2018","journal-title":"Biomed. Signal Processing Control"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.1148\/rg.2021200196","article-title":"CT Noise-Reduction Methods for Lower-Dose Scanning: Strengths and Weaknesses of Iterative Reconstruction Algorithms and New Techniques","volume":"41","author":"Mohammadinejad","year":"2021","journal-title":"RadioGraphics"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.3758\/BRM.41.4.1149","article-title":"Statistical power analyses using G* Power 3.1: Tests for correlation and regression analyses","volume":"41","author":"Faul","year":"2009","journal-title":"Behav. Res. Methods"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Boone, J., Strauss, K., Cody, D., McCollough, C., McNitt-Gray, M., Toth, T., Goske, M., and Frush, D. (2011). AAPM Report n. 204: Size-Specific Dose Estimates (SSDE) in Pediatric and Adult Body CT Examinations, The Report of AAPM Task Group.","DOI":"10.37206\/143"},{"key":"ref_16","first-page":"6","article-title":"Use of water equivalent diameter for calculating patient size and size-specific dose estimates (SSDE) in CT: The report of AAPM task group 220","volume":"2014","author":"McCollough","year":"2014","journal-title":"AAPM Rep."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1038\/nmeth.2019","article-title":"Fiji: An open-source platform for biological-image analysis","volume":"9","author":"Schindelin","year":"2012","journal-title":"Nat. Methods"},{"key":"ref_18","unstructured":"Bongartz, G., Golding, S.J., Jurik, A.G., Leonardi, M., van Meerten, E.v.P., Geleijns, J., Jessen, K.A., Panzer, W., Shrimpton, P.C., and Tosi, G. (2000). European Guidelines on Quality Criteria for Computed Tomography, Publications Office of the European Union."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2273","DOI":"10.1007\/s00330-017-5243-8","article-title":"Whole-body ultra-low dose CT using spectral shaping for detection of osteolytic lesion in multiple myeloma","volume":"28","author":"Suntharalingam","year":"2018","journal-title":"Eur. Radiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1240","DOI":"10.1007\/s00247-020-04714-0","article-title":"Dual-source computed tomography of the lung with spectral shaping and advanced iterative reconstruction: Potential for maximum radiation dose reduction","volume":"50","author":"Wetzl","year":"2020","journal-title":"Pediatric Radiol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.ejrad.2019.04.026","article-title":"Effect of a novel denoising technique on image quality and diagnostic accuracy in low-dose CT in patients with suspected appendicitis","volume":"116","author":"Kolb","year":"2019","journal-title":"Eur. J. Radiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1177\/0146645320940827","article-title":"Use of artificial intelligence in computed tomography dose optimisation","volume":"49","author":"McCollough","year":"2020","journal-title":"Ann. ICRP"},{"key":"ref_23","unstructured":"Yang, Q., Yan, P., Kalra, M.K., and Wang, G. (2017). CT image denoising with perceptive deep neural networks. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1038\/s41568-018-0016-5","article-title":"Artificial intelligence in radiology","volume":"18","author":"Hosny","year":"2018","journal-title":"Nat. Rev. Cancer"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1016\/j.jacr.2020.08.016","article-title":"Workflow Applications of Artificial Intelligence in Radiology and an Overview of Available Tools","volume":"17","author":"Kapoor","year":"2020","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"016502","DOI":"10.1117\/1.JMI.7.1.016502","article-title":"Integrating AI into radiology workflow: Levels of research, production, and feedback maturity","volume":"7","author":"Dikici","year":"2020","journal-title":"J. Med. 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