{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T17:06:15Z","timestamp":1792083975539,"version":"4.4.0"},"reference-count":34,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T00:00:00Z","timestamp":1704758400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Neuroimaging"],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background and Purpose<\/jats:title><jats:p>This study explores the use of deep learning (DL) techniques in MRI of the orbit to enhance imaging. Standard protocols, although detailed, have lengthy acquisition times. We investigate DL\u2010based methods for T2\u2010weighted and T1\u2010weighted, fat\u2010saturated, contrast\u2010enhanced turbo spin echo (TSE) sequences, aiming to improve image quality, reduce acquisition time, minimize artifacts, and enhance diagnostic confidence in orbital imaging.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In a 3\u2010Tesla MRI study of 50 patients evaluating orbital diseases from March to July 2023, conventional (TSE<jats:sub>S<\/jats:sub>) and DL TSE sequences (TSE<jats:sub>DL<\/jats:sub>) were used. Two neuroradiologists independently assessed the image datasets for image quality, diagnostic confidence, noise levels, artifacts, and image sharpness using a randomized and blinded 4\u2010point Likert scale.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>TSE<jats:sub>DL<\/jats:sub> significantly reduced image noise and artifacts, enhanced image sharpness, and decreased scan time, outperforming TSE<jats:sub>S<\/jats:sub> (<jats:italic>p<\/jats:italic>\u00a0&lt;\u00a0.05). TSE<jats:sub>DL<\/jats:sub> showed superior overall image quality and diagnostic confidence, with relevant findings effectively detected in both DL\u2010based and conventional images. In 94% of cases, readers preferred accelerated imaging.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The study proved that using DL for MRI image reconstruction in orbital scans significantly cut acquisition time by 69%. This approach also enhanced image quality, reduced image noise, sharpened images, and boosted diagnostic confidence.<\/jats:p><\/jats:sec>","DOI":"10.1111\/jon.13187","type":"journal-article","created":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T04:09:07Z","timestamp":1704859747000},"page":"232-240","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Deep learning\u2010accelerated image reconstruction in MRI of the orbit to shorten acquisition time and enhance image quality"],"prefix":"10.1111","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1930-7497","authenticated-orcid":false,"given":"Arne","family":"Estler","sequence":"first","affiliation":[{"name":"Diagnostic and Interventional Neuroradiology Department of Radiology University Hospital Tuebingen  T\u00fcbingen 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