{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T08:38:42Z","timestamp":1792485522380,"version":"4.5.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T00:00:00Z","timestamp":1741305600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T00:00:00Z","timestamp":1741305600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100015650","name":"Canon Medical Systems Corporation","doi-asserted-by":"publisher","award":["C2023-255"],"award-info":[{"award-number":["C2023-255"]}],"id":[{"id":"10.13039\/100015650","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["22K07708"],"award-info":[{"award-number":["22K07708"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Jpn J Radiol"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Purpose<\/jats:title>\n            <jats:p>This is a preliminary analysis of one of the secondary endpoints in the prospective study cohort. The aim of this study is to assess the image quality and diagnostic confidence for lung cancer of CT images generated by using cadmium\u2013zinc\u2013telluride (CZT)-based photon-counting-detector-CT (PCD-CT) and comparing these super-high-resolution (SHR) images with conventional normal-resolution (NR) CT images.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Materials and methods<\/jats:title>\n            <jats:p>Twenty-five patients (median age 75\u00a0years, interquartile range 66\u201378\u00a0years, 18 men and 7 women) with 29 lung nodules overall (including two patients with 4 and 2 nodules, respectively) were enrolled to undergo PCD-CT. Three types of images were reconstructed: a 512\u2009\u00d7\u2009512 matrix with adaptive iterative dose reduction 3D (AIDR 3D) as the NR<jats:sub>AIDR3D<\/jats:sub> image, a 1024\u2009\u00d7\u20091024 matrix with AIDR 3D as the SHR<jats:sub>AIDR3D<\/jats:sub> image, and a 1024\u2009\u00d7\u20091024 matrix with deep-learning reconstruction (DLR) as the SHR<jats:sub>DLR<\/jats:sub> image. For qualitative analysis, two radiologists evaluated the matched reconstructed series twice (NR<jats:sub>AIDR3D<\/jats:sub> vs. SHR<jats:sub>AIDR3D<\/jats:sub> and SHR<jats:sub>AIDR3D<\/jats:sub> vs. SHR<jats:sub>DLR<\/jats:sub>) and scored the presence of imaging findings, such as spiculation, lobulation, appearance of ground-glass opacity or air bronchiologram, image quality, and diagnostic confidence, using a 5-point Likert scale. For quantitative analysis, contrast-to-noise ratios (CNRs) of the three images were compared.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>In the qualitative analysis, compared to NR<jats:sub>AIDR3D<\/jats:sub>, SHR<jats:sub>AIDR3D<\/jats:sub> yielded higher image quality and diagnostic confidence, except for image noise (all <jats:italic>P<\/jats:italic>\u2009&lt;\u20090.01). In comparison with SHR<jats:sub>AIDR3D<\/jats:sub>, SHR<jats:sub>DLR<\/jats:sub> yielded higher image quality and diagnostic confidence (all <jats:italic>P<\/jats:italic>\u2009&lt;\u20090.01). In the quantitative analysis, CNRs in the modified NR<jats:sub>AIDR3D<\/jats:sub> and SHR<jats:sub>DLR<\/jats:sub> groups were higher than those in the SHR<jats:sub>AIDR3D<\/jats:sub> group (<jats:italic>P<\/jats:italic>\u2009=\u20090.003, &lt;0.001, respectively).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>In PCD-CT, SHR<jats:sub>DLR<\/jats:sub> images provided the highest image quality and diagnostic confidence for lung tumor evaluation, followed by SHR<jats:sub>AIDR3D<\/jats:sub> and NR<jats:sub>AIDR3D<\/jats:sub> images. DLR demonstrated superior noise reduction compared to other reconstruction methods.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s11604-025-01759-9","type":"journal-article","created":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T07:11:48Z","timestamp":1741331508000},"page":"1132-1144","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["CZT-based photon-counting-detector CT with deep-learning reconstruction: image quality and diagnostic confidence for lung tumor assessment"],"prefix":"10.1007","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6811-2381","authenticated-orcid":false,"given":"Tomoaki","family":"Sasaki","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4337-2782","authenticated-orcid":false,"given":"Hirofumi","family":"Kuno","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9832-7524","authenticated-orcid":false,"given":"Keiichi","family":"Nomura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3485-9978","authenticated-orcid":false,"given":"Yoshihisa","family":"Muramatsu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keiju","family":"Aokage","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9615-7883","authenticated-orcid":false,"given":"Joji","family":"Samejima","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tetsuro","family":"Taki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eisuke","family":"Goto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masashi","family":"Wakabayashi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8879-945X","authenticated-orcid":false,"given":"Hideki","family":"Furuya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroki","family":"Taguchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7310-2097","authenticated-orcid":false,"given":"Tatsushi","family":"Kobayashi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,7]]},"reference":[{"key":"1759_CR1","doi-asserted-by":"publisher","DOI":"10.1148\/rg.220158","author":"AK Nehra","year":"2023","unstructured":"Nehra AK, Rajendran K, Baffour FI, Mileto A, Rajiah PS, Horst KK, et al. Seeing more with less: clinical benefits of photon-counting detector CT. Radiographics. 2023. https:\/\/doi.org\/10.1148\/rg.220158.","journal-title":"Radiographics"},{"key":"1759_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/s11604-022-01350-6","author":"Y Nakamura","year":"2023","unstructured":"Nakamura Y, Higaki T, Kondo S, Kawashita I, Takahashi I, Awai K. An introduction to photon-counting detector CT (PCD CT) for radiologists. Jpn J Radiol. 2023. https:\/\/doi.org\/10.1007\/s11604-022-01350-6.","journal-title":"Jpn J Radiol"},{"key":"1759_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-023-09545-9","author":"CH McCollough","year":"2023","unstructured":"McCollough CH, Rajendran K, Leng S, Yu L, Fletcher JG, Stierstorfer K, et al. The technical development of photon-counting detector CT. Eur Radiol. 2023. https:\/\/doi.org\/10.1007\/s00330-023-09545-9.","journal-title":"Eur Radiol"},{"key":"1759_CR4","doi-asserted-by":"publisher","DOI":"10.1097\/rli.0000000000000858","author":"I Molwitz","year":"2022","unstructured":"Molwitz I, Campbell GM, Yamamura J, Knopp T, Toedter K, Fischer R, et al. Fat quantification in dual-layer detector spectral computed tomography: experimental development and first in-patient validation. Invest Radiol. 2022. https:\/\/doi.org\/10.1097\/rli.0000000000000858.","journal-title":"Invest Radiol"},{"key":"1759_CR5","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2018172656","author":"MJ Willemink","year":"2018","unstructured":"Willemink MJ, Persson M, Pourmorteza A, Pelc NJ, Fleischmann D. Photon-counting CT: technical principles and clinical prospects. Radiology. 2018. https:\/\/doi.org\/10.1148\/radiol.2018172656.","journal-title":"Radiology"},{"key":"1759_CR6","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/acebb3","author":"X Zhan","year":"2023","unstructured":"Zhan X, Zhang R, Niu X, Hein I, Budden B, Wu S, et al. Comprehensive evaluations of a prototype full field-of-view photon counting CT system through phantom studies. Phys Med Biol. 2023. https:\/\/doi.org\/10.1088\/1361-6560\/acebb3.","journal-title":"Phys Med Biol"},{"key":"1759_CR7","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.22.28515","author":"RI Milos","year":"2023","unstructured":"Milos RI, R\u00f6hrich S, Prayer F, Strassl A, Beer L, Heidinger BH, et al. Ultrahigh-resolution photon-counting detector CT of the lungs: association of reconstruction kernel and slice thickness with image quality. AJR Am J Roentgenol. 2023. https:\/\/doi.org\/10.2214\/ajr.22.28515.","journal-title":"AJR Am J Roentgenol"},{"key":"1759_CR8","doi-asserted-by":"publisher","DOI":"10.1148\/rg.2019180115","author":"S Leng","year":"2019","unstructured":"Leng S, Bruesewitz M, Tao S, Rajendran K, Halaweish AF, Campeau NG, et al. Photon-counting detector CT: system design and clinical applications of an emerging technology. Radiographics. 2019. https:\/\/doi.org\/10.1148\/rg.2019180115.","journal-title":"Radiographics"},{"key":"1759_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-023-09596-y","author":"CH McCollough","year":"2023","unstructured":"McCollough CH, Rajendran K, Baffour FI, Diehn FE, Ferrero A, Glazebrook KN, et al. Clinical applications of photon counting detector CT. Eur Radiol. 2023. https:\/\/doi.org\/10.1007\/s00330-023-09596-y.","journal-title":"Eur Radiol"},{"key":"1759_CR10","doi-asserted-by":"publisher","DOI":"10.1097\/rli.0000000000000980","author":"T Flohr","year":"2023","unstructured":"Flohr T, Schmidt B. Technical basics and clinical benefits of photon-counting CT. Invest Radiol. 2023. https:\/\/doi.org\/10.1097\/rli.0000000000000980.","journal-title":"Invest Radiol"},{"key":"1759_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-023-10174-5","author":"V Van Ballaer","year":"2024","unstructured":"Van Ballaer V, Dubbeldam A, Muscogiuri E, Cockmartin L, Bosmans H, Coudyzer W, et al. Impact of ultra-high-resolution imaging of the lungs on perceived diagnostic image quality using photon-counting CT. Eur Radiol. 2024. https:\/\/doi.org\/10.1007\/s00330-023-10174-5.","journal-title":"Eur Radiol"},{"key":"1759_CR12","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.23.29077","author":"MJ Siegel","year":"2023","unstructured":"Siegel MJ, Bugenhagen SM, Sanchez A, Kim S, Abadia A, Ramirez-Giraldo JC. Comparison of radiation dose and image quality of pediatric high-resolution chest CT between photon-counting detector CT and energy-integrated detector CT: a matched study. Am J Roentgenol. 2023. https:\/\/doi.org\/10.2214\/ajr.23.29077.","journal-title":"Am J Roentgenol"},{"key":"1759_CR13","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.230318","author":"SC Scharm","year":"2023","unstructured":"Scharm SC, Schaefer-Prokop C, Winther HB, Huisinga C, Werncke T, Vogel-Claussen J, et al. Regional pulmonary morphology and function: photon-counting CT assessment. Radiology. 2023. https:\/\/doi.org\/10.1148\/radiol.230318.","journal-title":"Radiology"},{"key":"1759_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-023-09616-x","author":"Y Gaillandre","year":"2023","unstructured":"Gaillandre Y, Duhamel A, Flohr T, Faivre JB, Khung S, Hutt A, et al. Ultra-high resolution CT imaging of interstitial lung disease: impact of photon-counting CT in 112 patients. Eur Radiol. 2023. https:\/\/doi.org\/10.1007\/s00330-023-09616-x.","journal-title":"Eur Radiol"},{"key":"1759_CR15","doi-asserted-by":"publisher","DOI":"10.1097\/rli.0000000000000888","author":"A Inoue","year":"2022","unstructured":"Inoue A, Johnson TF, White D, Cox CW, Hartman TE, Thorne JE, et al. Estimating the clinical impact of photon-counting-detector CT in diagnosing usual interstitial pneumonia. Invest Radiol. 2022. https:\/\/doi.org\/10.1097\/rli.0000000000000888.","journal-title":"Invest Radiol"},{"key":"1759_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-024-10724-5","author":"M Remy-Jardin","year":"2024","unstructured":"Remy-Jardin M, Oufriche I, Guiffault L, Duhamel A, Flohr T, Schmidt B, et al. Diagnosis of acute pulmonary embolism: when photon-counting-detector CT replaces energy-integrating-detector CT in daily routine. Eur Radiol. 2024. https:\/\/doi.org\/10.1007\/s00330-024-10724-5.","journal-title":"Eur Radiol"},{"key":"1759_CR17","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-023-09777-9","author":"P Pannenbecker","year":"2023","unstructured":"Pannenbecker P, Huflage H, Grunz JP, Gruschwitz P, Patzer TS, Weng AM, et al. Photon-counting CT for diagnosis of acute pulmonary embolism: potential for contrast medium and radiation dose reduction. Eur Radiol. 2023. https:\/\/doi.org\/10.1007\/s00330-023-09777-9.","journal-title":"Eur Radiol"},{"key":"1759_CR18","doi-asserted-by":"publisher","DOI":"10.1097\/rct.0000000000001419","author":"A Inoue","year":"2023","unstructured":"Inoue A, Johnson TF, Walkoff LA, Levin DL, Hartman TE, Burke KA, et al. Lung cancer screening using clinical photon-counting detector computed tomography and energy-integrating-detector computed tomography: a prospective patient study. J Comput Assist Tomogr. 2023. https:\/\/doi.org\/10.1097\/rct.0000000000001419.","journal-title":"J Comput Assist Tomogr"},{"key":"1759_CR19","unstructured":"Borczuk AC, Chan JKC, Cooper WA, Dacic S, Kerr KM, Lantuejoul S, et al. Tumours of the lung. In: WHO Classification of Tumours, Thoracic Tumours Lyon: International Agency for Research on Cancer. 2021. p.\u00a019\u2013192"},{"key":"1759_CR20","doi-asserted-by":"publisher","DOI":"10.1148\/rg.230136","author":"T Sasaki","year":"2024","unstructured":"Sasaki T, Kuno H, Hiyama T, Oda S, Masuoka S, Miyasaka Y, et al. 2021 WHO classification of lung cancer: molecular biology research and radiologic-pathologic correlation. Radiographics. 2024. https:\/\/doi.org\/10.1148\/rg.230136.","journal-title":"Radiographics"},{"key":"1759_CR21","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020201911","author":"M Yanagawa","year":"2020","unstructured":"Yanagawa M, Tsubamoto M, Satoh Y, Hata A, Miyata T, Yoshida Y, et al. Lung adenocarcinoma at CT with 0.25-mm section thickness and a 2048 matrix: high-spatial-resolution imaging for predicting invasiveness. Radiology. 2020. https:\/\/doi.org\/10.1148\/radiol.2020201911.","journal-title":"Radiology"},{"key":"1759_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.athoracsur.2020.05.041","author":"K Suda","year":"2021","unstructured":"Suda K, Mitsudomi T, Shintani Y, Okami J, Ito H, Ohtsuka T, et al. Clinical impacts of EGFR mutation status: analysis of 5780 surgically resected lung cancer cases. Ann Thorac Surg. 2021. https:\/\/doi.org\/10.1016\/j.athoracsur.2020.05.041.","journal-title":"Ann Thorac Surg"},{"key":"1759_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.athoracsur.2019.01.079","author":"T Miyoshi","year":"2019","unstructured":"Miyoshi T, Aokage K, Katsumata S, Tane K, Ishii G, Tsuboi M. Ground-glass opacity is a strong prognosticator for pathologic stage IA lung adenocarcinoma. Ann Thorac Surg. 2019. https:\/\/doi.org\/10.1016\/j.athoracsur.2019.01.079.","journal-title":"Ann Thorac Surg"},{"key":"1759_CR24","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.18.20519","author":"JH Woo","year":"2019","unstructured":"Woo JH, Kim MY, Lee KS, Jeong DY, Chung MJ, Han J, et al. Resected pure small cell lung carcinomas and combined small cell lung carcinomas: histopathology features, imaging features, and prognoses. AJR Am J Roentgenol. 2019. https:\/\/doi.org\/10.2214\/ajr.18.20519.","journal-title":"AJR Am J Roentgenol"},{"key":"1759_CR25","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-020-07537-7","author":"JG Nam","year":"2021","unstructured":"Nam JG, Ahn C, Choi H, Hong W, Park J, Kim JH, et al. Image quality of ultralow-dose chest CT using deep learning techniques: potential superiority of vendor-agnostic post-processing over vendor-specific techniques. Eur Radiol. 2021. https:\/\/doi.org\/10.1007\/s00330-020-07537-7.","journal-title":"Eur Radiol"},{"key":"1759_CR26","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14319","author":"J Solomon","year":"2020","unstructured":"Solomon J, Lyu P, Marin D, Samei E. Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm. Med Phys. 2020. https:\/\/doi.org\/10.1002\/mp.14319.","journal-title":"Med Phys"},{"key":"1759_CR27","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020202317","author":"SL Brady","year":"2021","unstructured":"Brady SL, Trout AT, Somasundaram E, Anton CG, Li Y, Dillman JR. Improving image quality and reducing radiation dose for pediatric CT by using deep learning reconstruction. Radiology. 2021. https:\/\/doi.org\/10.1148\/radiol.2020202317.","journal-title":"Radiology"},{"key":"1759_CR28","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.221257","author":"LR Koetzier","year":"2023","unstructured":"Koetzier LR, Mastrodicasa D, Szczykutowicz TP, van der Werf NR, Wang AS, Sandfort V, et al. Deep learning image reconstruction for CT: technical principles and clinical prospects. Radiology. 2023. https:\/\/doi.org\/10.1148\/radiol.221257.","journal-title":"Radiology"},{"key":"1759_CR29","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.210551","author":"B Jiang","year":"2022","unstructured":"Jiang B, Li N, Shi X, Zhang S, Li J, de Bock GH, et al. Deep learning reconstruction shows better lung nodule detection for ultra-low-dose chest CT. Radiology. 2022. https:\/\/doi.org\/10.1148\/radiol.210551.","journal-title":"Radiology"},{"key":"1759_CR30","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.12111719","author":"H Kuno","year":"2012","unstructured":"Kuno H, Onaya H, Iwata R, Kobayashi T, Fujii S, Hayashi R, et al. Evaluation of cartilage invasion by laryngeal and hypopharyngeal squamous cell carcinoma with dual-energy CT. Radiology. 2012. https:\/\/doi.org\/10.1148\/radiol.12111719.","journal-title":"Radiology"},{"key":"1759_CR31","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.21.26208","author":"H Nagano","year":"2022","unstructured":"Nagano H, Takumi K, Nakajo M, Fukukura Y, Kumagae Y, Jinguji M, et al. Dual-energy CT-derived electron density for diagnosing metastatic mediastinal lymph nodes in non-small cell lung cancer: comparison with conventional CT and FDG PET\/CT findings. AJR Am J Roentgenol. 2022. https:\/\/doi.org\/10.2214\/ajr.21.26208.","journal-title":"AJR Am J Roentgenol"},{"key":"1759_CR32","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-019-06183-y","author":"F Tatsugami","year":"2019","unstructured":"Tatsugami F, Higaki T, Nakamura Y, Yu Z, Zhou J, Lu Y, et al. Deep learning-based image restoration algorithm for coronary CT angiography. Eur Radiol. 2019. https:\/\/doi.org\/10.1007\/s00330-019-06183-y.","journal-title":"Eur Radiol"},{"key":"1759_CR33","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2019180011","author":"Y Nakamura","year":"2019","unstructured":"Nakamura Y, Higaki T, Tatsugami F, Zhou J, Yu Z, Akino N, et al. Deep learning-based CT image reconstruction: initial evaluation targeting hypovascular hepatic metastases. Radiol Artif Intell. 2019. https:\/\/doi.org\/10.1148\/ryai.2019180011.","journal-title":"Radiol Artif Intell"},{"key":"1759_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-019-06170-3","author":"M Akagi","year":"2019","unstructured":"Akagi M, Nakamura Y, Higaki T, Narita K, Honda Y, Zhou J, et al. Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT. Eur Radiol. 2019. https:\/\/doi.org\/10.1007\/s00330-019-06170-3.","journal-title":"Eur Radiol"},{"key":"1759_CR35","doi-asserted-by":"publisher","DOI":"10.1007\/s11604-023-01470-7","author":"N Hamabuchi","year":"2023","unstructured":"Hamabuchi N, Ohno Y, Kimata H, Ito Y, Fujii K, Akino N, et al. Effectiveness of deep learning reconstruction on standard to ultra-low-dose high-definition chest CT images. Jpn J Radiol. 2023. https:\/\/doi.org\/10.1007\/s11604-023-01470-7.","journal-title":"Jpn J Radiol"},{"key":"1759_CR36","doi-asserted-by":"publisher","DOI":"10.1007\/s11604-018-0796-2","author":"T Higaki","year":"2019","unstructured":"Higaki T, Nakamura Y, Tatsugami F, Nakaura T, Awai K. Improvement of image quality at CT and MRI using deep learning. Jpn J Radiol. 2019. https:\/\/doi.org\/10.1007\/s11604-018-0796-2.","journal-title":"Jpn J Radiol"},{"key":"1759_CR37","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.211780","author":"SA Si-Mohamed","year":"2022","unstructured":"Si-Mohamed SA, Boccalini S, Lacombe H, Diaw A, Varasteh M, Rodesch PA, et al. Coronary CT angiography with photon-counting CT: first-in-human results. Radiology. 2022. https:\/\/doi.org\/10.1148\/radiol.211780.","journal-title":"Radiology"},{"key":"1759_CR38","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.220311","author":"FI Baffour","year":"2023","unstructured":"Baffour FI, Huber NR, Ferrero A, Rajendran K, Glazebrook KN, Larson NB, et al. Photon-counting detector CT with deep learning noise reduction to detect multiple myeloma. Radiology. 2023. https:\/\/doi.org\/10.1148\/radiol.220311.","journal-title":"Radiology"},{"key":"1759_CR39","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.14.13126","author":"W Huda","year":"2015","unstructured":"Huda W, Abrahams RB. X-ray-based medical imaging and resolution. Am J Roentgenol. 2015. https:\/\/doi.org\/10.2214\/ajr.14.13126.","journal-title":"Am J Roentgenol"}],"container-title":["Japanese Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11604-025-01759-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11604-025-01759-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11604-025-01759-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T05:34:07Z","timestamp":1751002447000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11604-025-01759-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,7]]},"references-count":39,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["1759"],"URL":"https:\/\/doi.org\/10.1007\/s11604-025-01759-9","relation":{},"ISSN":["1867-1071","1867-108X"],"issn-type":[{"value":"1867-1071","type":"print"},{"value":"1867-108X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,7]]},"assertion":[{"value":"13 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This present study was supported by Canon Medical Systems (TK) and was partially supported by Grant-in-Aid for Scientific Research C (Grant No. 22K07708, TS). One of the authors (HT) is an employee of Canon Medical Systems. Authors who are not employees of Canon Medical Systems monitored and had control of the included data and information submitted for publication.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Written informed consent was obtained from all participants.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"This prospective study was approved by the National Cancer Center Hospital East Certified Review Board (K2022003).","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}}]}}