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A reader study by three musculoskeletal radiologists assessed cartilage image quality, overall image sharpness, and osteophytes incidence in all three sets of scans. A referenceless blurring metric evaluated blurring in all three image dimensions for the three sets of scans.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>Mann\u2013Whitney <jats:italic>U<\/jats:italic>\u2010tests compared Dice coefficients (DC) of segmentation accuracy for the DESS, super\u2010resolution, and TCI images, along with the image quality readings and blurring metrics. Sensitivity, specificity, and diagnostic odds ratio (DOR) with 95% confidence intervals compared osteophyte detection for the super\u2010resolution and TCI images, with the original\u2010resolution as a reference.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>DC for the original\u2010resolution (90.2\u2009\u00b1\u20091.7%) and super\u2010resolution (89.6\u2009\u00b1\u20092.0%) were significantly higher (<jats:italic>P<\/jats:italic>\u2009&lt;\u20090.001) than TCI (86.3\u2009\u00b1\u20095.6%). Segmentation overlap of super\u2010resolution with the original\u2010resolution (DC = 97.6\u2009\u00b1\u20090.7%) was significantly higher (<jats:italic>P<\/jats:italic>\u2009&lt;\u20090.0001) than TCI overlap (DC = 95.0\u2009\u00b1\u20091.1%). Cartilage image quality for sharpness and contrast levels, and the through\u2010plane quantitative blur factor for super\u2010resolution images, was significantly (<jats:italic>P<\/jats:italic>\u2009&lt;\u20090.001) better than TCI. Super\u2010resolution osteophyte detection sensitivity of 80% (76\u201382%), specificity of 93% (92\u201394%), and DOR of 32 (22\u201346) was significantly higher (<jats:italic>P<\/jats:italic>\u2009&lt;\u20090.001) than TCI sensitivity of 73% (69\u201376%), specificity of 90% (89\u201391%), and DOR of 17 (13\u201322).<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Conclusion<\/jats:title><jats:p>Super\u2010resolution appears to consistently outperform na\u00efve interpolation and may improve image quality without biasing quantitative biomarkers.<\/jats:p><jats:p><jats:bold>Level of Evidence:<\/jats:bold> 2<\/jats:p><jats:p><jats:bold>Technical Efficacy:<\/jats:bold> Stage 2<\/jats:p><jats:p>J. Magn. Reson. Imaging 2020;51:768\u2013779.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmri.26872","type":"journal-article","created":{"date-parts":[[2019,7,17]],"date-time":"2019-07-17T06:44:09Z","timestamp":1563345849000},"page":"768-779","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["Utility of deep learning super\u2010resolution in the context of osteoarthritis MRI biomarkers"],"prefix":"10.1002","volume":"51","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3667-6796","authenticated-orcid":false,"given":"Akshay S.","family":"Chaudhari","sequence":"first","affiliation":[{"name":"Department of Radiology Stanford University Stanford  California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kathryn J.","family":"Stevens","sequence":"additional","affiliation":[{"name":"Department of Radiology Stanford University Stanford  California USA"},{"name":"Department of Orthopaedic Surgery Stanford University, Stanford  California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeff P.","family":"Wood","sequence":"additional","affiliation":[{"name":"Austin Radiological Association  Austin Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amit K.","family":"Chakraborty","sequence":"additional","affiliation":[{"name":"Department of Radiology Stanford University Stanford  California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric K.","family":"Gibbons","sequence":"additional","affiliation":[{"name":"Department of Radiology and Imaging Sciences University of Utah  Salt Lake City Utah USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongnan","family":"Fang","sequence":"additional","affiliation":[{"name":"LVIS Corp.  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