{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T23:23:32Z","timestamp":1776122612546,"version":"3.50.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T00:00:00Z","timestamp":1723507200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T00:00:00Z","timestamp":1723507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-024-01214-7","type":"journal-article","created":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T15:05:38Z","timestamp":1723561538000},"page":"1236-1244","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["The Usefulness of Low-Kiloelectron Volt Virtual Monochromatic Contrast-Enhanced Computed Tomography with Deep Learning Image Reconstruction Technique in Improving the Delineation of Pancreatic Ductal Adenocarcinoma"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6383-207X","authenticated-orcid":false,"given":"Yasutaka","family":"Ichikawa","sequence":"first","affiliation":[]},{"given":"Yoshinori","family":"Kanii","sequence":"additional","affiliation":[]},{"given":"Akio","family":"Yamazaki","sequence":"additional","affiliation":[]},{"given":"Mai","family":"Kobayashi","sequence":"additional","affiliation":[]},{"given":"Kensuke","family":"Domae","sequence":"additional","affiliation":[]},{"given":"Motonori","family":"Nagata","sequence":"additional","affiliation":[]},{"given":"Hajime","family":"Sakuma","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,13]]},"reference":[{"key":"1214_CR1","doi-asserted-by":"publisher","first-page":"17","DOI":"10.3322\/caac.21763","volume":"73","author":"RL Siegel","year":"2023","unstructured":"Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin 73:17-48, 2023.","journal-title":"CA Cancer J Clin"},{"key":"1214_CR2","doi-asserted-by":"publisher","first-page":"5827","DOI":"10.3748\/wjg.15.5827","volume":"15","author":"M Klauss","year":"2009","unstructured":"Klauss M, Sch\u00f6binger M, Wolf I et al. Value of three-dimensional reconstructions in pancreatic carcinoma using multidetector CT: initial results. World J Gastroenterol 15:5827-32, 2009.","journal-title":"World J Gastroenterol"},{"key":"1214_CR3","doi-asserted-by":"publisher","first-page":"568","DOI":"10.1007\/s00261-018-1754-2","volume":"44","author":"S Aslan","year":"2019","unstructured":"Aslan S, Camlidag I, Nural MS. Lower energy levels and iodine-based material decomposition images increase pancreatic ductal adenocarcinoma conspicuity on rapid kV-switching dual-energy CT. Abdom Radiol (NY) 44:568-575, 2019.","journal-title":"Abdom Radiol (NY)"},{"key":"1214_CR4","doi-asserted-by":"publisher","first-page":"888","DOI":"10.2214\/AJR.20.25430","volume":"217","author":"Y Fukukura","year":"2021","unstructured":"Fukukura Y, Kumagae Y, Fujisaki Y et al. Adding Delayed Phase Images to Dual-Phase Contrast-Enhanced CT Increases Sensitivity for Small Pancreatic Ductal Adenocarcinoma. AJR American journal of roentgenology 217:888-897, 2021.","journal-title":"AJR Am J Roentgenol"},{"key":"1214_CR5","doi-asserted-by":"publisher","first-page":"320.e17","DOI":"10.1016\/j.crad.2019.11.012","volume":"75","author":"Y Noda","year":"2020","unstructured":"Noda Y, Goshima S, Kaga T et al. Virtual monochromatic image at lower energy level for assessing pancreatic ductal adenocarcinoma in fast kV-switching dual-energy CT. Clin Radiol 75:320.e17-320.e23, 2020.","journal-title":"Clin Radiol"},{"key":"1214_CR6","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.1007\/s00261-016-0689-8","volume":"41","author":"S Gupta","year":"2016","unstructured":"Gupta S, Wagner-Bartak N, Jensen CT et al. Dual-energy CT of pancreatic adenocarcinoma: reproducibility of primary tumor measurements and assessment of tumor conspicuity and margin sharpness. Abdom Radiol (NY) 41:1317-24, 2016.","journal-title":"Abdom Radiol (NY)"},{"key":"1214_CR7","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1007\/s00330-019-06337-y","volume":"30","author":"Y Nagayama","year":"2020","unstructured":"Nagayama Y, Tanoue S, Inoue T et al. Dual-layer spectral CT improves image quality of multiphasic pancreas CT in patients with pancreatic ductal adenocarcinoma. Eur Radiol 30:394-403, 2020.","journal-title":"Eur Radiol"},{"key":"1214_CR8","doi-asserted-by":"publisher","first-page":"3617","DOI":"10.1007\/s00330-019-06116-9","volume":"29","author":"L Beer","year":"2019","unstructured":"Beer L, Toepker M, Ba-Ssalamah A et al. Objective and subjective comparison of virtual monoenergetic vs. polychromatic images in patients with pancreatic ductal adenocarcinoma. Eur Radiol 29:3617-3625, 2019.","journal-title":"Eur Radiol"},{"key":"1214_CR9","doi-asserted-by":"publisher","first-page":"2610","DOI":"10.1007\/s00261-020-02921-9","volume":"46","author":"Y Noda","year":"2021","unstructured":"Noda Y, Tochigi T, Parakh A, Kambadakone A. Simulated twin-phase pancreatic CT generated using single portal venous phase dual-energy CT acquisition in pancreatic ductal adenocarcinoma. Abdom Radiol (NY), 2021.","journal-title":"Abdom Radiol (NY)"},{"key":"1214_CR10","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1186\/s13244-022-01297-2","volume":"13","author":"H Liang","year":"2022","unstructured":"Liang H, Zhou Y, Zheng Q et al. Dual-energy CT with virtual monoenergetic images and iodine maps improves tumor conspicuity in patients with pancreatic ductal adenocarcinoma. Insights Imaging 13:153, 2022.","journal-title":"Insights Imaging"},{"key":"1214_CR11","doi-asserted-by":"publisher","first-page":"6163","DOI":"10.1007\/s00330-019-06170-3","volume":"29","author":"M Akagi","year":"2019","unstructured":"Akagi M, Nakamura Y, Higaki T et al. Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT. Eur Radiol 29:6163-6171, 2019.","journal-title":"Eur Radiol"},{"key":"1214_CR12","unstructured":"Jiang Hsieh EL, Brian Nett, Jie Tang, Jean-Baptiste Thibault, Sonia Sahney. A new era of image reconstruction: TrueFidelity\u2122 - Technical white paper on deep learning image reconstruction. https:\/\/www.gehealthcarecom\/-\/jssmedia\/040dd213fa89463287155151fdb01922pdf, 2019."},{"key":"1214_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2214\/AJR.19.22332","volume":"215","author":"CT Jensen","year":"2020","unstructured":"Jensen CT, Liu X, Tamm EP et al. Image Quality Assessment of Abdominal CT by Use of New Deep Learning Image Reconstruction: Initial Experience. AJR American journal of roentgenology 215:1-8, 2020.","journal-title":"AJR American Journal of Roentgenology"},{"key":"1214_CR14","doi-asserted-by":"publisher","first-page":"598","DOI":"10.1007\/s11604-021-01089-6","volume":"39","author":"Y Ichikawa","year":"2021","unstructured":"Ichikawa Y, Kanii Y, Yamazaki A et al. Deep learning image reconstruction for improvement of image quality of abdominal computed tomography: comparison with hybrid iterative reconstruction. Jpn J Radiol 39:598-604, 2021.","journal-title":"Jpn J Radiol"},{"key":"1214_CR15","doi-asserted-by":"publisher","first-page":"5499","DOI":"10.1007\/s00330-022-08647-0","volume":"32","author":"M Sato","year":"2022","unstructured":"Sato M, Ichikawa Y, Domae K et al. Deep learning image reconstruction for improving image quality of contrast-enhanced dual-energy CT in abdomen. Eur Radiol 32:5499-5507, 2022.","journal-title":"Eur Radiol"},{"key":"1214_CR16","unstructured":"American Association of Physicists in Medicine. The measurement, reporting, and management of radiation dose in CT: report of AAPM Task Group 23 of the Diagnostic Imaging Council CT Committee. AAPM report no. 96. College Park (MD): American Association of Physicists in Medicine. https:\/\/www.aapm.org\/pubs\/reports\/RPT_96.pdf, 2008."},{"key":"1214_CR17","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.jcm.2016.02.012","volume":"15","author":"TK Koo","year":"2016","unstructured":"Koo TK, Li MY. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J Chiropr Med 15:155-63, 2016.","journal-title":"J Chiropr Med"},{"key":"1214_CR18","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/s12149-022-01815-8","volume":"37","author":"N Li","year":"2023","unstructured":"Li N, Zhou X, Zhu H et al. (99m)Tc-Rituximab sentinel lymph node mapping and biopsy, the effective technique avoids axillary dissection and predicts prognosis in 533 cutaneous melanoma. Annals of nuclear medicine 37:189-197, 2023.","journal-title":"Annals of nuclear medicine"},{"key":"1214_CR19","doi-asserted-by":"publisher","first-page":"897","DOI":"10.2214\/ajr.182.4.1820897","volume":"182","author":"S Gangi","year":"2004","unstructured":"Gangi S, Fletcher JG, Nathan MA et al. Time interval between abnormalities seen on CT and the clinical diagnosis of pancreatic cancer: retrospective review of CT scans obtained before diagnosis. AJR American journal of roentgenology 182:897-903, 2004.","journal-title":"AJR American Journal of Roentgenology"},{"key":"1214_CR20","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1111\/j.1440-1746.2007.05117.x","volume":"23","author":"DV Sahani","year":"2008","unstructured":"Sahani DV, Shah ZK, Catalano OA, Boland GW, Brugge WR. Radiology of pancreatic adenocarcinoma: current status of imaging. J Gastroenterol Hepatol 23:23-33, 2008.","journal-title":"J Gastroenterol Hepatol"},{"key":"1214_CR21","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1148\/radiol.10100015","volume":"257","author":"JH Kim","year":"2010","unstructured":"Kim JH, Park SH, Yu ES et al. Visually isoattenuating pancreatic adenocarcinoma at dynamic-enhanced CT: frequency, clinical and pathologic characteristics, and diagnosis at imaging examinations. Radiology 257:87-96, 2010.","journal-title":"Radiology"},{"key":"1214_CR22","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.ejrad.2012.09.012","volume":"82","author":"M Klauss","year":"2013","unstructured":"Klauss M, Stiller W, Pahn G et al. Dual-energy perfusion-CT of pancreatic adenocarcinoma. Eur J Radiol 82:208-14, 2013.","journal-title":"Eur J Radiol"},{"key":"1214_CR23","doi-asserted-by":"publisher","first-page":"764","DOI":"10.1148\/radiol.2243011284","volume":"224","author":"RW Prokesch","year":"2002","unstructured":"Prokesch RW, Chow LC, Beaulieu CF, Bammer R, Jeffrey RB, Jr. Isoattenuating pancreatic adenocarcinoma at multi-detector row CT: secondary signs. Radiology 224:764-8, 2002.","journal-title":"Radiology"},{"key":"1214_CR24","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1148\/radiol.2015140857","volume":"276","author":"S Leng","year":"2015","unstructured":"Leng S, Yu L, Fletcher JG, McCollough CH. Maximizing Iodine Contrast-to-Noise Ratios in Abdominal CT Imaging through Use of Energy Domain Noise Reduction and Virtual Monoenergetic Dual-Energy CT. Radiology 276:562-70, 2015.","journal-title":"Radiology"},{"key":"1214_CR25","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1097\/RLI.0000000000000272","volume":"51","author":"MH Albrecht","year":"2016","unstructured":"Albrecht MH, Trommer J, Wichmann JL et al. Comprehensive Comparison of Virtual Monoenergetic and Linearly Blended Reconstruction Techniques in Third-Generation Dual-Source Dual-Energy Computed Tomography Angiography of the Thorax and Abdomen. Invest Radiol 51:582-90, 2016.","journal-title":"Invest Radiol"},{"key":"1214_CR26","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1016\/j.ejmp.2020.07.024","volume":"77","author":"J Greffier","year":"2020","unstructured":"Greffier J, Frandon J, Hamard A et al. Impact of iterative reconstructions on image quality and detectability of focal liver lesions in low-energy monochromatic images. Phys Med 77:36-42, 2020.","journal-title":"Phys Med"},{"key":"1214_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2023.111128","volume":"168","author":"S Li","year":"2023","unstructured":"Li S, Yuan L, Lu T et al. Deep learning imaging reconstruction of reduced-dose 40 keV virtual monoenergetic imaging for early detection of colorectal cancer liver metastases. Eur J Radiol 168:111128, 2023.","journal-title":"Eur J Radiol"},{"key":"1214_CR28","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1007\/s00330-023-10033-3","volume":"34","author":"P Lyu","year":"2024","unstructured":"Lyu P, Li Z, Chen Y et al. Deep learning reconstruction CT for liver metastases: low-dose dual-energy vs standard-dose single-energy. Eur Radiol 34:28-38, 2024.","journal-title":"Eur Radiol"},{"key":"1214_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2023.111121","volume":"168","author":"CT Jensen","year":"2023","unstructured":"Jensen CT, Wong VK, Wagner-Bartak NA et al. Accuracy of liver metastasis detection and characterization: Dual-energy CT versus single-energy CT with deep learning reconstruction. Eur J Radiol 168:111121, 2023.","journal-title":"Eur J Radiol"},{"key":"1214_CR30","doi-asserted-by":"publisher","first-page":"2347","DOI":"10.1007\/s10278-023-00893-y","volume":"36","author":"B Chu","year":"2023","unstructured":"Chu B, Gan L, Shen Y et al. A Deep Learning Image Reconstruction Algorithm for Improving Image Quality and Hepatic Lesion Detectability in Abdominal Dual-Energy Computed Tomography: Preliminary Results. J Digit Imaging 36:2347-2355, 2023.","journal-title":"J Digit Imaging"},{"key":"1214_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2022.110685","volume":"159","author":"Y Noda","year":"2023","unstructured":"Noda Y, Takai Y, Asano M et al. Comparison of image quality and pancreatic ductal adenocarcinoma conspicuity between the low-kVp and dual-energy CT reconstructed with deep-learning image reconstruction algorithm. Eur J Radiol 159:110685, 2023.","journal-title":"Eur J Radiol"},{"key":"1214_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2021.109825","volume":"141","author":"P Lyu","year":"2021","unstructured":"Lyu P, Neely B, Solomon J et al. Effect of deep learning image reconstruction in the prediction of resectability of pancreatic cancer: Diagnostic performance and reader confidence. Eur J Radiol 141:109825, 2021.","journal-title":"Eur J Radiol"},{"key":"1214_CR33","doi-asserted-by":"publisher","first-page":"698","DOI":"10.1097\/RCT.0000000000001485","volume":"47","author":"A Nakamoto","year":"2023","unstructured":"Nakamoto A, Onishi H, Tsuboyama T et al. Image Quality and Lesion Detectability of Pancreatic Phase Thin-Slice Computed Tomography Images With a Deep Learning-Based Reconstruction Algorithm. J Comput Assist Tomogr 47:698-703, 2023.","journal-title":"J Comput Assist Tomogr"},{"key":"1214_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2023.110960","volume":"165","author":"Y Takai","year":"2023","unstructured":"Takai Y, Noda Y, Asano M et al. Deep-learning image reconstruction for 80-kVp pancreatic CT protocol: Comparison of image quality and pancreatic ductal adenocarcinoma visibility with hybrid-iterative reconstruction. Eur J Radiol 165:110960, 2023.","journal-title":"Eur J Radiol"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01214-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-024-01214-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01214-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T14:15:23Z","timestamp":1743344123000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-024-01214-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,13]]},"references-count":34,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["1214"],"URL":"https:\/\/doi.org\/10.1007\/s10278-024-01214-7","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,13]]},"assertion":[{"value":"27 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 July 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 August 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Mie University Hospital (approval number: H2019-207).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Written informed consent was waived because this study used existing clinical CT imaging data. The opportunity to withdraw from this study was provided via a notice on the hospital website. No patient expressed an intention to withdraw from this study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}}]}}