{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T10:22:51Z","timestamp":1772187771984,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T00:00:00Z","timestamp":1768953600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T00:00:00Z","timestamp":1768953600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Shanghai General Hospital Clinical Research Project","award":["CCTR-2022ZD01"],"award-info":[{"award-number":["CCTR-2022ZD01"]}]},{"name":"Shanghai General Hospital Clinical Research Project","award":["CCTR-2023C02"],"award-info":[{"award-number":["CCTR-2023C02"]}]},{"name":"Shanghai General Hospital Academic Training Program","award":["SHLJxxq"],"award-info":[{"award-number":["SHLJxxq"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Jpn J Radiol"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1007\/s11604-025-01909-z","type":"journal-article","created":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T04:31:42Z","timestamp":1768969902000},"page":"520-530","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparison of conventional 512-matrix CT images with Swin2SR-based 2048-matrix images in the visualization and diagnosis of lung nodules"],"prefix":"10.1007","volume":"44","author":[{"given":"Yaping","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingyao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingyun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhijie","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanfei","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6669-0097","authenticated-orcid":false,"given":"Xueqian","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,21]]},"reference":[{"issue":"4","key":"1909_CR1","doi-asserted-by":"publisher","first-page":"340","DOI":"10.6004\/jnccn.2023.0020","volume":"21","author":"DS Ettinger","year":"2023","unstructured":"Ettinger DS, Wood DE, Aisner DL, et al. NCCN Guidelines\u00ae Insights: Non-Small Cell Lung Cancer, Version 2. 2023. J Natl Compr Canc Netw. 2023;21(4):340\u201350.","journal-title":"J Natl Compr Canc Netw"},{"issue":"6","key":"1909_CR2","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1056\/NEJMoa1911793","volume":"382","author":"HJ de Koning","year":"2020","unstructured":"de Koning HJ, van der Aalst CM, de Jong PA, et al. Reduced lung-cancer mortality with volume CT screening in a randomized trial. N Engl J Med. 2020;382(6):503\u201313.","journal-title":"N Engl J Med"},{"key":"1909_CR3","unstructured":"Radiology ACo. the ACR Lung CT Screening Reporting and Data System (Lung-RADS) v2022. https:\/\/www.acr.org\/Practice-Management-Quality-Informatics\/ACR-Bulletin\/Articles\/November-2022\/Lung-RADS-2022. 2022."},{"issue":"7","key":"1909_CR4","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1097\/RLI.0000000000000968","volume":"58","author":"M Remy-Jardin","year":"2023","unstructured":"Remy-Jardin M, Hutt A, Flohr T, et al. Ultra-high-resolution photon-counting CT imaging of the chest: a new era for morphology and function. Invest Radiol. 2023;58(7):482\u20137.","journal-title":"Invest Radiol"},{"issue":"2","key":"1909_CR5","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1097\/RLI.0000000000000814","volume":"57","author":"L Jungblut","year":"2022","unstructured":"Jungblut L, Bl\u00fcthgen C, Polacin M, et al. First performance evaluation of an artificial intelligence-based computer-aided detection system for pulmonary nodule evaluation in dual-source photon-counting detector CT at different low-dose levels. Invest Radiol. 2022;57(2):108\u201314.","journal-title":"Invest Radiol"},{"issue":"2","key":"1909_CR6","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1148\/radiol.2020201911","volume":"297","author":"M Yanagawa","year":"2020","unstructured":"Yanagawa M, Tsubamoto M, Satoh 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;297(2):462\u201371.","journal-title":"Radiology"},{"key":"1909_CR7","doi-asserted-by":"crossref","unstructured":"Conde MV, Choi U-J, Burchi M, Timofte R. Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration. 2022; arXiv:2209.11345v1.","DOI":"10.1007\/978-3-031-25063-7_42"},{"key":"1909_CR8","doi-asserted-by":"publisher","first-page":"109433","DOI":"10.1016\/j.mehy.2019.109433","volume":"134","author":"F Ozyurt","year":"2020","unstructured":"Ozyurt F, Sert E, Avci D. An expert system for brain tumor detection: fuzzy C-means with super resolution and convolutional neural network with extreme learning machine. Med Hypotheses. 2020;134:109433.","journal-title":"Med Hypotheses"},{"issue":"6","key":"1909_CR9","doi-asserted-by":"publisher","first-page":"1524","DOI":"10.1109\/TMI.2018.2883517","volume":"38","author":"J Hatvani","year":"2019","unstructured":"Hatvani J, Basarab A, Tourneret JY, et al. A tensor factorization method for 3-D super resolution with application to dental CT. IEEE Trans Med Imaging. 2019;38(6):1524\u201331.","journal-title":"IEEE Trans Med Imaging"},{"key":"1909_CR10","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.compmedimag.2018.10.005","volume":"71","author":"D Mahapatra","year":"2019","unstructured":"Mahapatra D, Bozorgtabar B, Garnavi R. Image super-resolution using progressive generative adversarial networks for medical image analysis. Comput Med Imaging Graph. 2019;71:30\u20139.","journal-title":"Comput Med Imaging Graph"},{"issue":"10","key":"1909_CR11","doi-asserted-by":"publisher","first-page":"3365","DOI":"10.1109\/TPAMI.2020.2982166","volume":"43","author":"Z Wang","year":"2021","unstructured":"Wang Z, Chen J, Hoi SCH. Deep learning for image super-resolution: a survey. IEEE Trans Pattern Anal Mach Intell. 2021;43(10):3365\u201387.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1909_CR12","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.media.2019.03.009","volume":"54","author":"V Cheplygina","year":"2019","unstructured":"Cheplygina V, de Bruijne M, Pluim JPW. Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis. Med Image Anal. 2019;54:280\u201396.","journal-title":"Med Image Anal"},{"key":"1909_CR13","doi-asserted-by":"publisher","first-page":"102762","DOI":"10.1016\/j.media.2023.102762","volume":"85","author":"J Li","year":"2023","unstructured":"Li J, Chen J, Tang Y, et al. Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives. Med Image Anal. 2023;85:102762.","journal-title":"Med Image Anal"},{"issue":"2","key":"1909_CR14","doi-asserted-by":"publisher","first-page":"e2255113","DOI":"10.1001\/jamanetworkopen.2022.55113","volume":"6","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Liu M, Zhang L, et al. Comparison of chest radiograph captions based on natural language processing vs completed by radiologists. JAMA Netw Open. 2023;6(2):e2255113.","journal-title":"JAMA Netw Open"},{"issue":"6","key":"1909_CR15","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1038\/s41551-023-01010-8","volume":"7","author":"D Kiyasseh","year":"2023","unstructured":"Kiyasseh D, Ma R, Haque TF, et al. A vision transformer for decoding surgeon activity from surgical videos. Nat Biomed Eng. 2023;7(6):780\u201396.","journal-title":"Nat Biomed Eng"},{"key":"1909_CR16","doi-asserted-by":"publisher","first-page":"180202","DOI":"10.1038\/sdata.2018.202","volume":"5","author":"S Bakr","year":"2018","unstructured":"Bakr S, Gevaert O, Echegaray S, et al. A radiogenomic dataset of non-small cell lung cancer. Sci Data. 2018;5:180202.","journal-title":"Sci Data"},{"issue":"10","key":"1909_CR17","doi-asserted-by":"publisher","first-page":"7303","DOI":"10.1007\/s00330-021-07901-1","volume":"31","author":"B Jiang","year":"2021","unstructured":"Jiang B, Zhang Y, Zhang L, et al. Human-recognizable CT image features of subsolid lung nodules associated with diagnosis and classification by convolutional neural networks. Eur Radiol. 2021;31(10):7303\u201315.","journal-title":"Eur Radiol"},{"issue":"6","key":"1909_CR18","doi-asserted-by":"publisher","first-page":"1411","DOI":"10.2214\/AJR.20.24807","volume":"216","author":"L Chelala","year":"2021","unstructured":"Chelala L, Hossain R, Kazerooni EA, et al. Lung-RADS version 1.1: challenges and a look ahead, from the AJR special series on radiology reporting and data systems. AJR Am J Roentgenol. 2021;216(6):1411\u201322.","journal-title":"AJR Am J Roentgenol"},{"key":"1909_CR19","doi-asserted-by":"crossref","unstructured":"Lyu J, Li G, Wang C, et al. Multicontrast MRI Super-Resolution via Transformer-Empowered Multiscale Contextual Matching and Aggregation. IEEE Trans Neural Netw Learn Syst. 2023.","DOI":"10.1109\/TNNLS.2023.3250491"},{"issue":"1","key":"1909_CR20","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1148\/radiol.211593","volume":"303","author":"NA Obuchowski","year":"2022","unstructured":"Obuchowski NA, Bullen J. Multireader diagnostic accuracy imaging studies: fundamentals of design and analysis. Radiology. 2022;303(1):26\u201334.","journal-title":"Radiology"},{"issue":"11","key":"1909_CR21","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1097\/RLI.0000000000000888","volume":"57","author":"A Inoue","year":"2022","unstructured":"Inoue A, Johnson TF, White D, et al. Estimating the clinical impact of photon-counting-detector CT in diagnosing usual interstitial pneumonia. Invest Radiol. 2022;57(11):734\u201341.","journal-title":"Invest Radiol"},{"issue":"3","key":"1909_CR22","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1177\/0284185113495834","volume":"55","author":"B Jiang","year":"2014","unstructured":"Jiang B, Takashima S, Miyake C, et al. Thin-section CT findings in peripheral lung cancer of 3 cm or smaller: are there any characteristic features for predicting tumor histology or do they depend only on tumor size? Acta Radiol. 2014;55(3):302\u20138.","journal-title":"Acta Radiol"},{"issue":"6","key":"1909_CR23","doi-asserted-by":"publisher","first-page":"1801359","DOI":"10.1183\/13993003.01359-2018","volume":"52","author":"A Nair","year":"2018","unstructured":"Nair A, Bartlett EC, Walsh SLF, et al. Variable radiological lung nodule evaluation leads to divergent management recommendations. Eur Respir J. 2018;52(6):1801359.","journal-title":"Eur Respir J"},{"key":"1909_CR24","doi-asserted-by":"publisher","DOI":"10.1109\/JTEHM.2014.2303806","author":"H Ashikaga","year":"2014","unstructured":"Ashikaga H, Estner HL, Herzka DA, et al. Quantitative assessment of single-image super-resolution in myocardial scar imaging. IEEE J Transl Eng Health Med. 2014. https:\/\/doi.org\/10.1109\/JTEHM.2014.2303806.","journal-title":"IEEE J Transl Eng Health Med"},{"issue":"3","key":"1909_CR25","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1002\/jmri.1183","volume":"14","author":"MA Bernstein","year":"2001","unstructured":"Bernstein MA, Fain SB, Riederer SJ. Effect of windowing and zero-filled reconstruction of MRI data on spatial resolution and acquisition strategy. J Magn Reson Imaging. 2001;14(3):270\u201380.","journal-title":"J Magn Reson Imaging"},{"issue":"10","key":"1909_CR26","doi-asserted-by":"publisher","first-page":"e1024","DOI":"10.1002\/mp.15936","volume":"49","author":"C Li","year":"2022","unstructured":"Li C, Li W, Liu C, et al. Artificial intelligence in multiparametric magnetic resonance imaging: a review. Med Phys. 2022;49(10):e1024\u201354.","journal-title":"Med Phys"},{"issue":"3","key":"1909_CR27","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1148\/radiol.2020192173","volume":"295","author":"EM Masutani","year":"2020","unstructured":"Masutani EM, Bahrami N, Hsiao A. Deep learning single-frame and multiframe super-resolution for cardiac MRI. Radiology. 2020;295(3):552\u201361.","journal-title":"Radiology"},{"issue":"3","key":"1909_CR28","doi-asserted-by":"publisher","first-page":"e220522","DOI":"10.1148\/radiol.220522","volume":"306","author":"JE Iglesias","year":"2023","unstructured":"Iglesias JE, Schleicher R, Laguna S, et al. Quantitative brain morphometry of portable low-field-strength MRI using super-resolution machine learning. Radiology. 2023;306(3):e220522.","journal-title":"Radiology"},{"key":"1909_CR29","doi-asserted-by":"publisher","first-page":"4840","DOI":"10.1109\/TIP.2021.3076285","volume":"30","author":"Y Li","year":"2021","unstructured":"Li Y, Iwamoto Y, Lin L, et al. Volumenet: a lightweight parallel network for super-resolution of MR and CT volumetric data. IEEE Trans Image Process. 2021;30:4840\u201354.","journal-title":"IEEE Trans Image Process"},{"key":"1909_CR30","doi-asserted-by":"publisher","first-page":"120104","DOI":"10.1016\/j.eswa.2023.120104","volume":"225","author":"E Baccarelli","year":"2023","unstructured":"Baccarelli E, Scarpiniti M, Momenzadeh A. Twinned residual auto-encoder (TRAE)-a new DL architecture for denoising super-resolution and task-aware feature learning from COVID-19 CT images. Expert Syst Appl. 2023;225:120104.","journal-title":"Expert Syst Appl"},{"issue":"2","key":"1909_CR31","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2016","unstructured":"Dong C, Loy CC, He K, Tang X. Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell. 2016;38(2):295\u2013307.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"8","key":"1909_CR32","doi-asserted-by":"publisher","first-page":"830","DOI":"10.1038\/s42256-023-00689-3","volume":"5","author":"C Bouchard","year":"2023","unstructured":"Bouchard C, Wiesner T, Desch\u00eanes A, et al. Resolution enhancement with a task-assisted GAN to guide optical nanoscopy image analysis and acquisition. Nat Mach Intell. 2023;5(8):830\u201344.","journal-title":"Nat Mach Intell"},{"issue":"1","key":"1909_CR33","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1109\/TMI.2019.2922960","volume":"39","author":"C You","year":"2020","unstructured":"You C, Li G, Zhang Y, et al. CT super-resolution GAN constrained by the identical, residual, and cycle learning ensemble (GAN-CIRCLE). IEEE Trans Med Imaging. 2020;39(1):188\u2013203.","journal-title":"IEEE Trans Med Imaging"},{"key":"1909_CR34","doi-asserted-by":"publisher","first-page":"102615","DOI":"10.1016\/j.media.2022.102615","volume":"82","author":"J Chen","year":"2022","unstructured":"Chen J, Frey EC, He Y, et al. Transmorph: transformer for unsupervised medical image registration. Med Image Anal. 2022;82:102615.","journal-title":"Med Image Anal"},{"key":"1909_CR35","doi-asserted-by":"publisher","DOI":"10.1088\/2057-1976\/ac9da7","author":"S Zhou","year":"2022","unstructured":"Zhou S, Yu L, Jin M. Texture transformer super-resolution for low-dose computed tomography. Biomed Phys Eng Express. 2022. https:\/\/doi.org\/10.1088\/2057-1976\/ac9da7.","journal-title":"Biomed Phys Eng Express"},{"key":"1909_CR36","doi-asserted-by":"publisher","first-page":"102802","DOI":"10.1016\/j.media.2023.102802","volume":"88","author":"F Shamshad","year":"2023","unstructured":"Shamshad F, Khan S, Zamir SW, et al. Transformers in medical imaging: a survey. Med Image Anal. 2023;88:102802.","journal-title":"Med Image Anal"},{"issue":"5","key":"1909_CR37","doi-asserted-by":"publisher","first-page":"921","DOI":"10.2214\/ajr.156.5.2017952","volume":"156","author":"K Kuriyama","year":"1991","unstructured":"Kuriyama K, Tateishi R, Doi O, et al. Prevalence of air bronchograms in small peripheral carcinomas of the lung on thin-section CT: comparison with benign tumors. AJR Am J Roentgenol. 1991;156(5):921\u20134.","journal-title":"AJR Am J Roentgenol"},{"issue":"6","key":"1909_CR38","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1097\/00004728-199611000-00021","volume":"20","author":"M Kui","year":"1996","unstructured":"Kui M, Templeton PA, White CS, et al. Evaluation of the air bronchogram sign on CT in solitary pulmonary lesions. J Comput Assist Tomogr. 1996;20(6):983\u20136.","journal-title":"J Comput Assist Tomogr"},{"issue":"1","key":"1909_CR39","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1186\/s12967-019-2058-1","volume":"17","author":"S Xu","year":"2019","unstructured":"Xu S, Xu H, Wang W, et al. The role of collagen in cancer: from bench to bedside. J Transl Med. 2019;17(1):309.","journal-title":"J Transl Med"},{"issue":"7","key":"1909_CR40","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1016\/j.acra.2017.11.017","volume":"25","author":"A Hata","year":"2018","unstructured":"Hata A, Yanagawa M, Honda O, et al. 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. Acad Radiol. 2018;25(7):869\u201376.","journal-title":"Acad Radiol"}],"container-title":["Japanese Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11604-025-01909-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11604-025-01909-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11604-025-01909-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T09:27:19Z","timestamp":1772184439000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11604-025-01909-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,21]]},"references-count":40,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["1909"],"URL":"https:\/\/doi.org\/10.1007\/s11604-025-01909-z","relation":{},"ISSN":["1867-1071","1867-108X"],"issn-type":[{"value":"1867-1071","type":"print"},{"value":"1867-108X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,21]]},"assertion":[{"value":"19 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing financial interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The local institutional review board approved the study and waived informed consent.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}