{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T10:05:53Z","timestamp":1775383553838,"version":"3.50.1"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T00:00:00Z","timestamp":1715731200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T00:00:00Z","timestamp":1715731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100005760","name":"University of Gothenburg","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005760","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Cardiovasc Imaging"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Image noise and vascular attenuation are important factors affecting image quality and diagnostic accuracy of coronary computed tomography angiography (CCTA). The aim of this study was to develop an algorithm that automatically performs noise and attenuation measurements in CCTA and to evaluate the ability of the algorithm to identify non-diagnostic examinations. The algorithm, \u201cNoiseNet\u201d, was trained and tested on 244 CCTA studies from the Swedish CArdioPulmonary BioImage Study. The model is a 3D U-Net that automatically segments the aortic root and measures attenuation (Hounsfield Units, HU), noise (standard deviation of HU, HUsd) and signal-to-noise ratio (SNR, HU\/HUsd) in the aortic lumen, close to the left coronary ostium. NoiseNet was then applied to 529 CCTA studies previously categorized into three subgroups: fully diagnostic, diagnostic with excluded parts and non-diagnostic. There was excellent correlation between NoiseNet and manual measurements of noise (<jats:italic>r<\/jats:italic>\u2009=\u20090.948; <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001) and SNR (<jats:italic>r<\/jats:italic>\u2009=\u20090.948; &lt;0.001). There was a significant difference in noise levels between the image quality subgroups: fully diagnostic 33.1 (29.8\u201337.9); diagnostic with excluded parts 36.1 (31.5\u201340.3) and non-diagnostic 42.1 (35.2\u201347.7; <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001). Corresponding values for SNR were 16.1 (14.0\u201318.0); 14.0 (12.4\u201316.2) and 11.1 (9.6\u201314.0; <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001). ROC analysis for prediction of a non-diagnostic study showed an AUC for noise of 0.73 (CI 0.64\u20130.83) and for SNR of 0.80 (CI 0.71\u20130.89). In conclusion, NoiseNet can perform noise and SNR measurements with high accuracy. Noise and SNR impact image quality and automatic measurements may be used to identify CCTA studies with low image quality.<\/jats:p>","DOI":"10.1007\/s10554-024-03130-x","type":"journal-article","created":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T08:02:05Z","timestamp":1715760125000},"page":"1493-1500","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["NoiseNet, a fully automatic noise assessment tool that can identify non-diagnostic CCTA examinations"],"prefix":"10.1007","volume":"40","author":[{"given":"Emma","family":"Palmquist","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jennifer","family":"Alv\u00e9n","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Kercsik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M\u00e5ns","family":"Larsson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Niklas","family":"Lundqvist","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ola","family":"Hjelmgren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0983-0313","authenticated-orcid":false,"given":"Erika","family":"Fagman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,15]]},"reference":[{"key":"3130_CR1","unstructured":"Knuuti J, Wijns W, Saraste A, Capodanno D, Barbato E, Funck-Brentano C et al (2020) 2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes"},{"key":"3130_CR2","doi-asserted-by":"crossref","unstructured":"van Diemen PA, Driessen RS, Stuijfzand WJ, Raijmakers PG, Schumacher SP, Bom MJ et al (2020) Impact of scan quality on the diagnostic performance of CCTA, SPECT, and PET for diagnosing myocardial ischemia defined by fractional flow reserve","DOI":"10.1016\/j.dib.2019.104584"},{"key":"3130_CR3","doi-asserted-by":"crossref","unstructured":"Ghekiere O, Salgado R, Buls N, Leiner T, Mancini I, Vanhoenacker P et al (2017) Image quality in coronary CT angiography: challenges and technical solutions","DOI":"10.1259\/bjr.20160567"},{"key":"3130_CR4","doi-asserted-by":"crossref","unstructured":"Kim H, Park CH, Han KH, Kim TH (2015) Predicting the image noise level of prospective ECG-triggered coronary computed tomography angiography: quantitative measurement of thoracic component versus body mass index","DOI":"10.1007\/s10554-015-0796-6"},{"key":"3130_CR5","doi-asserted-by":"crossref","unstructured":"Law WY, Huang GL, Yang CC (2022) Effect of body Mass Index in Coronary CT Angiography Performed on a 256-Slice multi-detector CT scanner. 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The participants gave written informed consent. The current sub-study was approved by the ethical review board in Gothenburg, (#570\u2009\u2212\u200918).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"M\u00e5ns Larsson is an employee at Eigenvision, a consulting company working with medical image analysis. The other authors have no relevant financial or non-financial interests to disclose.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}