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An automated DL-based algorithm (CS DL) was used to create reconstructions of the same <jats:italic>k<\/jats:italic>-space data as used for CS reconstructions. Images were analyzed by two radiologists and assessed for pathologies, image quality, and visibility of anatomical landmarks using a 4-point Likert scale.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Overall agreement for the detection of pathologies between the CS DL reconstructions and CS images was substantial to almost perfect (<jats:italic>\u03ba<\/jats:italic> 0.95 (95% confidence interval 0.82\u20131.00)). Image quality and the visibility of the rotator cuff, articular cartilage, and axillary recess were overall rated significantly higher for CS DL images compared to CS (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.03). Contrast-to-noise ratios were significantly higher for cartilage\/fluid (CS DL 198\u2009\u00b1\u200924.3, CS 130\u2009\u00b1\u200932.2, <jats:italic>p<\/jats:italic>\u2009=\u20090.02) and ligament\/fluid (CS DL 184\u2009\u00b1\u200917.3, CS 141\u2009\u00b1\u200923.5, <jats:italic>p<\/jats:italic>\u2009=\u20090.03) and SNR values were significantly higher for ligaments and muscle of the CS DL reconstructions (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.04).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Evaluation of shoulder pathologies was feasible using a DL-based algorithm for MRI reconstruction and denoising. In clinical routine, CS DL may be beneficial in particular for reducing image noise and may be useful for the detection and better discrimination of discrete pathologies.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Summary statement\n<\/jats:title>\n                <jats:p>Assessment of shoulder pathologies was feasible with improved image quality as well as higher SNR using a compressed sensing deep learning\u2013based framework for image reconstructions and denoising.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Key Points<\/jats:title>\n                <jats:p><jats:bold><jats:italic>\u2022<\/jats:italic><\/jats:bold><jats:italic> Automated deep learning\u2013based reconstructions showed a significant increase in signal-to-noise ratio and contrast-to-noise ratio (p\u2009&lt;\u20090.04) with only a slight increase of reconstruction time of 40\u00a0s compared to CS.<\/jats:italic><\/jats:p>\n                <jats:p><jats:bold><jats:italic>\u2022<\/jats:italic><\/jats:bold><jats:italic> All pathologies were accurately detected with no loss of diagnostic information or prolongation of the scan time.<\/jats:italic><\/jats:p>\n                <jats:p><jats:bold><jats:italic>\u2022<\/jats:italic><\/jats:bold><jats:italic> Significant improvements of the image quality as well as the visibility of the rotator cuff, articular cartilage, and axillary recess were detected.<\/jats:italic><\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s00330-023-09472-9","type":"journal-article","created":{"date-parts":[[2023,2,19]],"date-time":"2023-02-19T18:17:04Z","timestamp":1676830624000},"page":"4875-4884","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Evaluation of a deep learning-based reconstruction method for denoising and image enhancement of shoulder MRI in patients with shoulder pain"],"prefix":"10.1007","volume":"33","author":[{"given":"Georg C.","family":"Feuerriegel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kilian","family":"Weiss","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sophia","family":"Kronthaler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yannik","family":"Leonhardt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jan","family":"Neumann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Markus","family":"Wurm","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicolas S.","family":"Lenhart","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcus R.","family":"Makowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benedikt J.","family":"Schwaiger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Klaus","family":"Woertler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dimitrios C.","family":"Karampinos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexandra S.","family":"Gersing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,2,18]]},"reference":[{"issue":"2","key":"9472_CR1","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1080\/03009740310004667","volume":"33","author":"JJ Luime","year":"2004","unstructured":"Luime JJ, Koes BW, Hendriksen IJ et al (2004) Prevalence and incidence of shoulder pain in the general population; a systematic review. 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The rest of the authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No complex statistical methods were necessary for this paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was obtained from all subjects (patients) in this study.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional review board approval was obtained.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"\u2022 prospective\u2022 case\u2013control study\u2022 performed at one institution","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}