{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T00:30:43Z","timestamp":1793752243114,"version":"4.6.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T00:00:00Z","timestamp":1738108800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T00:00:00Z","timestamp":1738108800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Insights Imaging"],"abstract":"Abstract<\/jats:title>\n \n Objectives<\/jats:title>\n To determine whether deep learning-based reconstructions of zero-echo-time (ZTE-DL) sequences enhance image quality and bone visualization in cervical spine MRI compared to traditional zero-echo-time (ZTE) techniques, and to assess the added value of ZTE-DL sequences alongside standard cervical spine MRI for comprehensive pathology evaluation.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n In this retrospective study, 52 patients underwent cervical spine MRI using ZTE, ZTE-DL, and T2-weighted 3D sequences on a 1.5-Tesla scanner. ZTE-DL sequences were reconstructed from raw data using the AirReconDL algorithm. Three blinded readers independently evaluated image quality, artifacts, and bone delineation on a 5-point Likert scale. Cervical structures and pathologies, including soft tissue and bone components in spinal canal and neural foraminal stenosis, were analyzed. Image quality was quantitatively assessed by signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR).<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Mean image quality scores were 2.0\u2009\u00b1\u20090.7 for ZTE and 3.2\u2009\u00b1\u20090.6 for ZTE-DL, with ZTE-DL exhibiting fewer artifacts and superior bone delineation. Significant differences were observed between T2-weighted and ZTE-DL sequences for evaluating intervertebral space, anterior osteophytes, spinal canal, and neural foraminal stenosis (\n p<\/jats:italic>\n \u2009<\u20090.05), with ZTE-DL providing more accurate assessments. ZTE-DL also showed improved evaluation of the osseous components of neural foraminal stenosis compared to ZTE (\n p<\/jats:italic>\n \u2009<\u20090.05).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n ZTE-DL sequences offer superior image quality and bone visualization compared to ZTE sequences and enhance standard cervical spine MRI in assessing bone involvement in spinal canal and neural foraminal stenosis.<\/jats:p>\n <\/jats:sec>\n \n Critical relevance statement<\/jats:title>\n Deep learning-based reconstructions improve zero-echo-time sequences in cervical spine MRI by enhancing image quality and bone visualization. This advancement offers additional insights for assessing bone involvement in spinal canal and neural foraminal stenosis, advancing clinical radiology practice.<\/jats:p>\n <\/jats:sec>\n \n Key Points<\/jats:title>\n \n \n \n Conventional MRI encounters challenges with osseous structures due to low signal-to-noise ratio.<\/jats:p>\n <\/jats:list-item>\n \n Zero-echo-time (ZET) sequences offer CT-like images of the C-spine but with lower quality.<\/jats:p>\n <\/jats:list-item>\n \n Deep learning reconstructions improve image quality of zero-echo-time sequences.<\/jats:p>\n <\/jats:list-item>\n \n ZTE sequences with deep learning reconstructions refine cervical spine osseous pathology assessment.<\/jats:p>\n <\/jats:list-item>\n \n These sequences aid assessment of bone involvement in spinal and foraminal stenosis.<\/jats:p>\n <\/jats:list-item>\n <\/jats:list>\n <\/jats:p>\n <\/jats:sec>\n \n Graphical Abstract<\/jats:title>\n <\/jats:sec>","DOI":"10.1186\/s13244-025-01902-0","type":"journal-article","created":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T13:13:08Z","timestamp":1738156388000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Deep learning reconstruction of zero-echo time sequences to improve visualization of osseous structures and associated pathologies in MRI of cervical spine"],"prefix":"10.1186","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7840-8415","authenticated-orcid":false,"given":"Malwina","family":"Kaniewska","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabio","family":"Zecca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carina","family":"Oberm\u00fcller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Falko","family":"Ensle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eva","family":"Deininger-Czermak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maelene","family":"Lohezic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roman","family":"Guggenberger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,29]]},"reference":[{"key":"1902_CR1","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1016\/j.mayocp.2014.09.008","volume":"90","author":"SP Cohen","year":"2015","unstructured":"Cohen SP (2015) Epidemiology, diagnosis, and treatment of neck pain. 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