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Images were analyzed by two radiologists and assessed for qualitative image quality using a 4-point Likert scale, quantitative signal-to-noise ratio (SNR) of anatomic landmarks, and detection of common pathologies. Paired-sample <jats:italic>t<\/jats:italic>, Wilcoxon, and McNemar tests, unweighted\/linearly weighted Cohen <jats:italic>\u03ba<\/jats:italic> statistics, and intraclass correlation coefficients were used.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Scan time for TSE-DL and TSE-SD protocols was 2:55 and 5:17\u00a0min:s, respectively. The overall image quality was either significantly higher for TSE-DL or not significantly different between TSE-SD and TSE-DL. TSE-DL demonstrated higher SNR and subject noise scores than TSE-SD. For pathology detection, the interreader agreement was substantial to almost perfect for TSE-DL, with <jats:italic>\u03ba<\/jats:italic> values ranging from 0.61 to 1.00; the interprotocol agreement was almost perfect for both readers, with <jats:italic>\u03ba<\/jats:italic> values ranging from 0.84 to 1.00. There was no significant difference in the diagnostic confidence or detection rate of common pathologies between the two sequences (<jats:italic>p<\/jats:italic>\u2009\u2265\u20090.081).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>TSE-DL allowed for a 45% reduction in scan time over TSE-SD in lumbar spine MRI without compromising the overall image quality and showed comparable detection performance of common pathologies in the evaluation of degenerative lumbar spine changes.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Relevance statement<\/jats:title>\n                <jats:p>Deep learning-reconstructed lumbar spine MRI protocol enabled a 45% reduction in scan time compared with conventional reconstruction, with comparable image quality and detection performance of common degenerative pathologies.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Key points<\/jats:title>\n                <jats:p>\u2022 Lumbar spine MRI with deep learning reconstruction has broad application prospects.<\/jats:p>\n                <jats:p>\u2022 Deep learning reconstruction of lumbar spine MRI saved 45% scan time without compromising overall image quality.<\/jats:p>\n                <jats:p>\u2022 When compared with standard sequences, deep learning reconstruction showed similar detection performance of common degenerative lumbar spine pathologies.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Graphical Abstract<\/jats:title>\n                \n              <\/jats:sec>","DOI":"10.1186\/s41747-024-00470-0","type":"journal-article","created":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T00:02:11Z","timestamp":1718928131000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Deep learning reconstruction for lumbar spine MRI acceleration: a prospective study"],"prefix":"10.1186","volume":"8","author":[{"given":"Hui","family":"Tang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Hong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengqiu","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Xiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2528-2836","authenticated-orcid":false,"given":"Shiteng","family":"Suo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,21]]},"reference":[{"key":"470_CR1","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1016\/B978-0-444-62627-1.00020-2","volume":"131","author":"FS Violante","year":"2015","unstructured":"Violante FS, Mattioli S, Bonfiglioli R (2015) Low-back pain. 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