{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T02:14:22Z","timestamp":1762308862049,"version":"build-2065373602"},"reference-count":19,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"name":"Changsha Natural Science Foundation","award":["kq2208211"],"award-info":[{"award-number":["kq2208211"]}]},{"DOI":"10.13039\/501100004735","name":"Hunan Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["2023JJ30477"],"award-info":[{"award-number":["2023JJ30477"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hunan Province Science and Technology Talent Support Project - Young  Outstanding Science and Technology Worker Training Program","award":["2022TJ-N05"],"award-info":[{"award-number":["2022TJ-N05"]}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","ejradiology.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["European Journal of Radiology"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1016\/j.ejrad.2025.112244","type":"journal-article","created":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T11:37:12Z","timestamp":1750073832000},"page":"112244","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["The value of a deep learning image reconstruction algorithm for assessing vertebral compression fractures using dual-energy computed tomography"],"prefix":"10.1016","volume":"190","author":[{"given":"Jiayi","family":"Tang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luyou","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Suping","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinling","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yewen","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.ejrad.2025.112244_b0005","doi-asserted-by":"crossref","first-page":"E203","DOI":"10.36076\/ppj.2023.26.E203","article-title":"Incidence and mortality of vertebral compression Fracture among all age groups: a Nationwide, population-based study in the Republic of Korea","volume":"26","author":"Kim","year":"2023","journal-title":"Pain Physician"},{"issue":"2","key":"10.1016\/j.ejrad.2025.112244_b0010","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1016\/j.acra.2023.07.011","article-title":"An MRI-based radiomics nomogram for differentiation of benign and malignant vertebral compression Fracture","volume":"31","author":"Feng","year":"2024","journal-title":"Acad Radiol."},{"issue":"2","key":"10.1016\/j.ejrad.2025.112244_b0015","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1002\/jmrs.546","article-title":"A narrative review of current and emerging MRI safety issues: what every MRI technologist (radiographer) needs to know","volume":"69","author":"Mittendorff","year":"2022","journal-title":"J Med Radiat Sci."},{"issue":"3","key":"10.1016\/j.ejrad.2025.112244_b0020","doi-asserted-by":"crossref","first-page":"604","DOI":"10.21037\/qims.2020.02.16","article-title":"Fast kilovoltage (KV)-switching dual-energy computed tomography hydroxyapatite (HAP)-water decomposition technique for identifying bone marrow edema in vertebral compression fractures","volume":"10","author":"Pan","year":"2020","journal-title":"Quant Imaging Med Surg."},{"issue":"1","key":"10.1016\/j.ejrad.2025.112244_b0025","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/s11657-021-00948-z","article-title":"Fast kilovoltage (KV)-switching dual-energy CT virtual noncalcium technique using different reconstruction kernels for identifying acute and chronic vertebral compression fractures","volume":"16","author":"Yan","year":"2021","journal-title":"Arch Osteoporos."},{"key":"10.1016\/j.ejrad.2025.112244_b0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2024.111857","article-title":"Diagnostic performance of spectral CT in detecting bone marrow edema for vertebral fracture: a multi-reader study","volume":"182","author":"Fran\u00e7ois","year":"2025","journal-title":"Eur J Radiol."},{"issue":"3","key":"10.1016\/j.ejrad.2025.112244_b0035","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1148\/radiol.2019191422","article-title":"State of the art in abdominal CT: the limits of iterative reconstruction algorithms","volume":"293","author":"Mileto","year":"2019","journal-title":"Radiology"},{"issue":"1","key":"10.1016\/j.ejrad.2025.112244_b0040","first-page":"125","article-title":"Comparison of image quality and radiation dose using different pre-ASiR-V and post-ASiR-V levels in coronary computed tomography angiography","volume":"29","author":"Zhao","year":"2021","journal-title":"J Xray Sci Technol."},{"issue":"1","key":"10.1016\/j.ejrad.2025.112244_b0045","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1148\/radiol.210551","article-title":"Deep Learning reconstruction shows better lung nodule detection for ultra-low-dose chest CT","volume":"303","author":"Jiang","year":"2022","journal-title":"Radiology"},{"issue":"4","key":"10.1016\/j.ejrad.2025.112244_b0050","doi-asserted-by":"crossref","first-page":"1960","DOI":"10.1007\/s10278-024-01016-x","article-title":"Deep Learning imaging reconstruction algorithm for Carotid dual energy CT angiography: opportunistic evaluation of Cervical intervertebral discs-a Preliminary study","volume":"37","author":"Jiang","year":"2024","journal-title":"J Imaging Inform Med."},{"issue":"1","key":"10.1016\/j.ejrad.2025.112244_b0055","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s00330-018-5568-y","article-title":"Single-source dual-energy computed tomography for the assessment of bone marrow oedema in vertebral compression fractures: a prospective diagnostic accuracy study","volume":"29","author":"Diekhoff","year":"2019","journal-title":"Eur Radiol."},{"issue":"3","key":"10.1016\/j.ejrad.2025.112244_b0060","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1016\/j.jaad.2020.04.104","article-title":"The validated investigator global assessment for atopic dermatitis (vIGA-AD): the development and reliability testing of a novel clinical outcome measurement instrument for the severity of atopic dermatitis","volume":"83","author":"Simpson","year":"2020","journal-title":"J Am Acad Dermatol."},{"issue":"2","key":"10.1016\/j.ejrad.2025.112244_b0065","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1159\/000514550","article-title":"Global epidemiology of end-stage kidney disease and Disparities in kidney replacement therapy","volume":"52","author":"Thurlow","year":"2021","journal-title":"Am J Nephrol."},{"issue":"3","key":"10.1016\/j.ejrad.2025.112244_b0070","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1007\/s00330-022-09146-y","article-title":"Improving spatial resolution and diagnostic confidence with thinner slice and deep learning image reconstruction in contrast-enhanced abdominal CT","volume":"33","author":"Cao","year":"2023","journal-title":"Eur Radiol."},{"issue":"1","key":"10.1016\/j.ejrad.2025.112244_b0075","doi-asserted-by":"crossref","first-page":"131","DOI":"10.3348\/kjr.2020.0116","article-title":"Validation of deep-Learning image reconstruction for low-dose chest computed tomography scan: emphasis on image quality and noise","volume":"22","author":"Kim","year":"2021","journal-title":"Korean J Radiol."},{"issue":"5","key":"10.1016\/j.ejrad.2025.112244_b0080","doi-asserted-by":"crossref","first-page":"2775","DOI":"10.1002\/mp.16300","article-title":"Spatial resolution, noise properties, and detectability index of a deep learning reconstruction algorithm for dual-energy CT of the abdomen","volume":"50","author":"Thor","year":"2023","journal-title":"Med Phys."},{"key":"10.1016\/j.ejrad.2025.112244_b0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2021.110070","article-title":"A deep-learning reconstruction algorithm that improves the image quality of low-tube-voltage coronary CT angiography","volume":"146","author":"Wang","year":"2022","journal-title":"Eur J Radiol."},{"issue":"1","key":"10.1016\/j.ejrad.2025.112244_b0090","doi-asserted-by":"crossref","first-page":"14580","DOI":"10.1038\/s41598-022-18741-4","article-title":"X-ray attenuation of bone, soft and adipose tissue in CT from 70 to 140kV and comparison with 3D printable additive manufacturing materials","volume":"12","author":"Ma","year":"2022","journal-title":"Sci Rep."},{"issue":"11","key":"10.1016\/j.ejrad.2025.112244_b0095","doi-asserted-by":"crossref","first-page":"6762","DOI":"10.1002\/mp.16711","article-title":"Dual-energy head cone-beam CT using a dual-layer flat-panel detector: hybrid material decomposition and a feasibility study","volume":"50","author":"Wang","year":"2023","journal-title":"Med Phys."}],"container-title":["European Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X25003304?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X25003304?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T02:10:59Z","timestamp":1762308659000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0720048X25003304"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":19,"alternative-id":["S0720048X25003304"],"URL":"https:\/\/doi.org\/10.1016\/j.ejrad.2025.112244","relation":{},"ISSN":["0720-048X"],"issn-type":[{"type":"print","value":"0720-048X"}],"subject":[],"published":{"date-parts":[[2025,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"The value of a deep learning image reconstruction algorithm for assessing vertebral compression fractures using dual-energy computed tomography","name":"articletitle","label":"Article Title"},{"value":"European Journal of Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ejrad.2025.112244","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"112244"}}