{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T16:14:13Z","timestamp":1790784853920,"version":"4.1.0"},"reference-count":31,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["19K17266"],"award-info":[{"award-number":["19K17266"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["21K15623"],"award-info":[{"award-number":["21K15623"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["21K15826"],"award-info":[{"award-number":["21K15826"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["JP18K07711"],"award-info":[{"award-number":["JP18K07711"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"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":[[2022,6]]},"DOI":"10.1016\/j.ejrad.2022.110294","type":"journal-article","created":{"date-parts":[[2022,4,9]],"date-time":"2022-04-09T11:01:17Z","timestamp":1649502077000},"page":"110294","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":13,"special_numbering":"C","title":["Evaluation of moyamoya disease in CT angiography using ultra-high-resolution computed tomography: Application of deep learning reconstruction"],"prefix":"10.1016","volume":"151","author":[{"given":"Yasuhiro","family":"Fukushima","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yasutaka","family":"Fushimi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takeshi","family":"Funaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akihiko","family":"Sakata","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takuya","family":"Hinoda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Satoshi","family":"Nakajima","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryo","family":"Sakamoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kazumichi","family":"Yoshida","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susumu","family":"Miyamoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuji","family":"Nakamoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ejrad.2022.110294_b0005","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1001\/archneur.1969.00480090076012","article-title":"Cerebrovascular \u201cmoyamoya\u201d disease. Disease showing abnormal net-like vessels in base of brain","volume":"20","author":"Suzuki","year":"1969","journal-title":"Arch. Neurol."},{"key":"10.1016\/j.ejrad.2022.110294_b0010","doi-asserted-by":"crossref","first-page":"189","DOI":"10.2176\/nmc.ra.2014-0307","article-title":"Diagnosis of moyamoya disease: International standard and regional differences","volume":"55","author":"Fujimura","year":"2015","journal-title":"Neurol. Med. Chir. (Tokyo)"},{"key":"10.1016\/j.ejrad.2022.110294_b0015","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1148\/radiol.2433060536","article-title":"Complications of diagnostic cerebral angiography: evaluation of 19 826 consecutive patients","volume":"243","author":"Kaufmann","year":"2007","journal-title":"Radiology"},{"key":"10.1016\/j.ejrad.2022.110294_b0020","doi-asserted-by":"crossref","unstructured":"Guidelines for diagnosis and treatment of moyamoya disease (spontaneous occlusion of the circle of Willis), Neurol. Med. Chir. (Tokyo). 52 (2012) 245\u2013266. https:\/\/doi.org\/10.2176\/nmc.52.245.","DOI":"10.2176\/nmc.52.245"},{"issue":"1","key":"10.1016\/j.ejrad.2022.110294_b0025","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1148\/radiol.2383042020","article-title":"Comparison of 3.0- and 1.5-T three-dimensional time-of-flight MR angiography in moyamoya disease: preliminary experience","volume":"239","author":"Fushimi","year":"2006","journal-title":"Radiology"},{"key":"10.1016\/j.ejrad.2022.110294_b0030","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1148\/radiol.2017162279","article-title":"Acceleration-selective arterial spin-labeling MR angiography used to visualize distal cerebral arteries and collateral vessels in moyamoya disease","volume":"286","author":"Togao","year":"2018","journal-title":"Radiology"},{"issue":"1","key":"10.1016\/j.ejrad.2022.110294_b0035","doi-asserted-by":"crossref","first-page":"e0189493","DOI":"10.1371\/journal.pone.0189493","article-title":"Magnetic resonance angiography with compressed sensing: An evaluation of moyamoya disease","volume":"13","author":"Yamamoto","year":"2018","journal-title":"PLoS One"},{"key":"10.1016\/j.ejrad.2022.110294_b0040","doi-asserted-by":"crossref","first-page":"408","DOI":"10.3171\/2018.10.JNS181139","article-title":"Effect of choroidal collateral vessels on de novo hemorrhage in moyamoya disease: Analysis of nonhemorrhagic hemispheres in the Japan Adult Moyamoya Trial","volume":"132","author":"Funaki","year":"2020","journal-title":"J. Neurosurg."},{"key":"10.1016\/j.ejrad.2022.110294_b0045","doi-asserted-by":"crossref","first-page":"777","DOI":"10.3171\/2016.11.JNS161650","article-title":"Angiographic features of hemorrhagic moyamoya disease with high recurrence risk: a supplementary analysis of the Japan Adult Moyamoya Trial","volume":"128","author":"Funaki","year":"2018","journal-title":"J. Neurosurg."},{"issue":"1","key":"10.1016\/j.ejrad.2022.110294_b0050","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s00234-017-1927-7","article-title":"Ultra-high-resolution CT angiography of the artery of Adamkiewicz: a feasibility study","volume":"60","author":"Yoshioka","year":"2018","journal-title":"Neuroradiology"},{"key":"10.1016\/j.ejrad.2022.110294_b0055","doi-asserted-by":"crossref","first-page":"219","DOI":"10.3174\/ajnr.A6377","article-title":"Visualization of lenticulostriate arteries on CT angiography using ultra-high-resolution CT compared with conventional-detector CT","volume":"41","author":"Murayama","year":"2020","journal-title":"Am. J. Neuroradiol."},{"key":"10.1016\/j.ejrad.2022.110294_b0060","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1016\/j.jstrokecerebrovasdis.2012.01.014","article-title":"Assessment of moyamoya disease using multidetector row computed tomography","volume":"22","author":"Sugino","year":"2013","journal-title":"J. Stroke Cerebrovasc. Dis."},{"key":"10.1016\/j.ejrad.2022.110294_b0065","doi-asserted-by":"crossref","first-page":"2185","DOI":"10.1007\/s00330-018-5810-7","article-title":"The evolution of image reconstruction for CT\u2014from filtered back projection to artificial intelligence","volume":"29","author":"Willemink","year":"2019","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.ejrad.2022.110294_b0070","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1148\/radiol.13122019","article-title":"Model-based iterative reconstruction for multi-detector row CT assessment of the Adamkiewicz artery","volume":"270","author":"Nishida","year":"2014","journal-title":"Radiology"},{"issue":"6","key":"10.1016\/j.ejrad.2022.110294_b0075","doi-asserted-by":"crossref","first-page":"e180011","DOI":"10.1148\/ryai.2019180011","article-title":"Deep Learning\u2013based CT Image Reconstruction: Initial Evaluation Targeting Hypovascular Hepatic Metastases","volume":"1","author":"Nakamura","year":"2019","journal-title":"Radiol. Artif. Intell."},{"key":"10.1016\/j.ejrad.2022.110294_b0080","doi-asserted-by":"crossref","first-page":"6163","DOI":"10.1007\/s00330-019-06170-3","article-title":"Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT","volume":"29","author":"Akagi","year":"2019","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.ejrad.2022.110294_b0085","doi-asserted-by":"crossref","first-page":"2698","DOI":"10.1007\/s00261-020-02508-4","article-title":"Deep learning reconstruction of drip-infusion cholangiography acquired with ultra-high-resolution computed tomography","volume":"45","author":"Narita","year":"2020","journal-title":"Abdom. Radiol."},{"key":"10.1016\/j.ejrad.2022.110294_b0090","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1097\/RLI.0000000000000601","article-title":"Recent and upcoming technological developments in computed tomography: High speed, low dose, deep learning, multienergy","volume":"55","author":"Lell","year":"2020","journal-title":"Invest. Radiol."},{"key":"10.1016\/j.ejrad.2022.110294_b0095","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1097\/RLI.0000000000000673","article-title":"Artificial intelligence and machine learning in radiology: current state and considerations for routine clinical implementation","volume":"55","author":"Wichmann","year":"2020","journal-title":"Invest. Radiol."},{"key":"10.1016\/j.ejrad.2022.110294_b0100","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1016\/j.acra.2017.11.017","article-title":"Effect of matrix size on the image quality of ultra-high-resolution CT of the lung: comparison of 512\u00d7 512, 1024\u00d7 1024, and 2048\u00d7 2048","volume":"25","author":"Hata","year":"2018","journal-title":"Acad. Radiol."},{"key":"10.1016\/j.ejrad.2022.110294_b0105","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v096.i06","article-title":"Wilcoxon rank-based tests for clustered data with R package clusrank","volume":"96","author":"Jiang","year":"2020","journal-title":"J. Stat. Softw."},{"key":"10.1016\/j.ejrad.2022.110294_b0110","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1348\/000711006X126600","article-title":"Computing inter-rater reliability and its variance in the presence of high agreement","volume":"61","author":"Gwet","year":"2008","journal-title":"Br. J. Math. Stat. Psychol."},{"key":"10.1016\/j.ejrad.2022.110294_b0115","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2288-13-61","article-title":"A comparison of Cohen\u2019s Kappa and Gwet\u2019s AC1 when calculating inter-rater reliability coefficients: a study conducted with personality disorder samples","volume":"13","author":"Wongpakaran","year":"2013","journal-title":"BMC Med. Res. Methodol."},{"key":"10.1016\/j.ejrad.2022.110294_b0120","unstructured":"Japan Network for Research and Information on Medical Exposures, National Diagnostic Reference Levels in Japan (2020) -Japan DRLs 2020-, 2020. http:\/\/www.radher.jp\/J-RIME\/report\/DRL2020_Engver.pdf."},{"key":"10.1016\/j.ejrad.2022.110294_b0125","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.acra.2019.09.008","article-title":"Deep learning reconstruction at CT: phantom study of the image characteristics","volume":"27","author":"Higaki","year":"2020","journal-title":"Acad. Radiol."},{"key":"10.1016\/j.ejrad.2022.110294_b0130","doi-asserted-by":"crossref","first-page":"4115","DOI":"10.1118\/1.4725171","article-title":"Towards task-based assessment of CT performance: system and object MTF across different reconstruction algorithms","volume":"39","author":"Richard","year":"2012","journal-title":"Med. Phys."},{"key":"10.1016\/j.ejrad.2022.110294_b0135","doi-asserted-by":"crossref","first-page":"2804","DOI":"10.1007\/s00586-020-06426-3","article-title":"Cervical spinal computed tomography utilizing model-based iterative reconstruction reduces radiation to an equivalent of three cervical X-rays","volume":"29","author":"Masamoto","year":"2020","journal-title":"Eur. Spine J."},{"key":"10.1016\/j.ejrad.2022.110294_b0140","doi-asserted-by":"crossref","first-page":"S214","DOI":"10.1016\/S1076-6332(96)80538-1","article-title":"Heat and pain sensations induced by arterial injection of low-osmolality contrast media: a comparison of patients' discomfort with ionic saline, nonionic glucose, and vasodilator nitrate","volume":"3","author":"Himi","year":"1996","journal-title":"Acad. Radiol."},{"key":"10.1016\/j.ejrad.2022.110294_b0145","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1007\/s003300000729","article-title":"Adverse reactions to iodinated contrast media","volume":"11","author":"Morcos","year":"2001","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.ejrad.2022.110294_b0150","doi-asserted-by":"crossref","first-page":"5322","DOI":"10.1007\/s00330-019-06183-y","article-title":"Deep learning\u2013based image restoration algorithm for coronary CT angiography","volume":"29","author":"Tatsugami","year":"2019","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.ejrad.2022.110294_b0155","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1097\/RLI.0000000000000283","article-title":"Iodine concentration and optimization in computed tomography angiography current issues","volume":"51","author":"Faggioni","year":"2016","journal-title":"Invest. Radiol."}],"container-title":["European Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X22001449?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X22001449?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T07:29:22Z","timestamp":1759994962000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0720048X22001449"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6]]},"references-count":31,"alternative-id":["S0720048X22001449"],"URL":"https:\/\/doi.org\/10.1016\/j.ejrad.2022.110294","relation":{},"ISSN":["0720-048X"],"issn-type":[{"value":"0720-048X","type":"print"}],"subject":[],"published":{"date-parts":[[2022,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Evaluation of moyamoya disease in CT angiography using ultra-high-resolution computed tomography: Application of deep learning reconstruction","name":"articletitle","label":"Article Title"},{"value":"European Journal of Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ejrad.2022.110294","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"110294"}}