{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:38:41Z","timestamp":1787027921944,"version":"build-2736575974"},"reference-count":36,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,7,5]],"date-time":"2022-07-05T00:00:00Z","timestamp":1656979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Deep-learning (DL) noise reduction techniques in computed tomography (CT) are expected to reduce the image noise while maintaining the clinically relevant information in reduced dose acquisitions. This study aimed to assess the size, attenuation, and objective image quality of reno-ureteric stones denoised using DL-software in comparison to traditionally reconstructed low-dose abdominal CT-images and evaluated its clinical impact. In this institutional review-board-approved retrospective study, 45 patients with renal and\/or ureteral stones were included. All patients had undergone abdominal CT between August 2019 and October 2019. CT-images were reconstructed using the following three methods: filtered back-projection, iterative reconstruction, and PixelShine (DL-software) with both sharp and soft kernels. Stone size, CT attenuation, and objective image quality (signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR)) were evaluated and compared using Bonferroni-corrected Friedman tests. Objective image quality was measured in six regions-of-interest. Stone size ranged between 4.4 \u00d7 3.1\u20134.4 \u00d7 3.2 mm (sharp kernel) and 5.1 \u00d7 3.8\u20135.6 \u00d7 4.2 mm (soft kernel). Mean attenuation ranged between 704\u2013717 Hounsfield Units (HU) (soft kernel) and 915\u20131047 HU (sharp kernel). Differences in measured stone sizes were \u22641.3 mm. DL-processed images resulted in significantly higher CNR and SNR values (p &lt; 0.001) by decreasing image noise significantly (p &lt; 0.001). DL-software significantly improved objective image quality while maintaining both correct stone size and CT-attenuation values. Therefore, the clinical impact of stone assessment in denoised image data sets remains unchanged. Through the relevant noise suppression, the software additionally offers the potential to further reduce radiation exposure.<\/jats:p>","DOI":"10.3390\/diagnostics12071627","type":"journal-article","created":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T23:38:55Z","timestamp":1656977935000},"page":"1627","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Influence of a Deep Learning Noise Reduction on the CT Values, Image Noise and Characterization of Kidney and Ureter Stones"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6259-5765","authenticated-orcid":false,"given":"Andrea","family":"Steuwe","sequence":"first","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, D-40225 Dusseldorf, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6487-0801","authenticated-orcid":false,"given":"Birte","family":"Valentin","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, D-40225 Dusseldorf, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oliver T.","family":"Bethge","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, D-40225 Dusseldorf, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9748-9927","authenticated-orcid":false,"given":"Alexandra","family":"Ljimani","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, D-40225 Dusseldorf, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6929-8691","authenticated-orcid":false,"given":"G\u00fcnter","family":"Niegisch","sequence":"additional","affiliation":[{"name":"Department of Urology, Medical Faculty, University Dusseldorf, D-40225 Dusseldorf, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gerald","family":"Antoch","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, D-40225 Dusseldorf, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9624-758X","authenticated-orcid":false,"given":"Joel","family":"Aissa","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, Medical Faculty, University Dusseldorf, D-40225 Dusseldorf, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1016\/j.eururo.2017.12.031","article-title":"EAU Guidelines on Assessment and Nonsurgical Management of Urinary Incontinence","volume":"73","author":"Nambiar","year":"2018","journal-title":"Eur. 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