{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T19:41:08Z","timestamp":1792698068902,"version":"4.5.0"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T00:00:00Z","timestamp":1792627200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T00:00:00Z","timestamp":1792627200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Canon Inc.","award":["1818KA7007"],"award-info":[{"award-number":["1818KA7007"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Jpn J Radiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>The aim of this study was to evaluate whether Precise IQ Engine (PIQE), a super-resolution deep-learning reconstruction designed to enhance spatial resolution while reducing image noise using a deep convolutional neural network, improves the visibility of hepatocellular carcinoma (HCC) compared with hybrid iterative reconstruction (Adaptive Iterative Dose Reduction with three-dimensional processing [AIDR 3D]) and conventional deep-learning reconstruction (Advanced Intelligent Clear-IQ Engine [AiCE]) in hepatic dynamic CT.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and materials<\/jats:title>\n                    <jats:p>\n                      We evaluated 64 patients with HCC who had undergone hepatic dynamic CT. Hepatic arterial- and equilibrium phase (HAP and EP) scans were reconstructed with AIDR, AiCE, and PIQE. A radiologist measured the standard deviation of attenuation in the liver parenchyma as the image noise and calculated the contrast-to-noise ratio (CNR) = (ROI\n                      <jats:sub>T<\/jats:sub>\n                      - ROI\n                      <jats:sub>L<\/jats:sub>\n                      )\/N in HAP and (ROI\n                      <jats:sub>L<\/jats:sub>\n                      - ROI\n                      <jats:sub>T<\/jats:sub>\n                      )\/N in EP. ROI\n                      <jats:sub>L<\/jats:sub>\n                      is the mean attenuation of the liver parenchyma, ROI\n                      <jats:sub>T<\/jats:sub>\n                      the mean tumor attenuation, and N the noise. HCC visibility was assessed by 2 additional radiologists and graded with a focus on tumor margin delineation on a 5-point confidence scale.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      PIQE showed the lowest image noise, followed by AiCE and AIDR, in both HAP and EP (all\n                      <jats:italic>p<\/jats:italic>\n                      &lt; 0.01). The CNR of HCCs was highest with PIQE, followed by AiCE and AIDR, in both HAP and EP (all\n                      <jats:italic>p<\/jats:italic>\n                      &lt; 0.01). Qualitative visibility scores for HCCs were also highest with PIQE, followed by AiCE and AIDR, in both HAP and EP (all\n                      <jats:italic>p<\/jats:italic>\n                      &lt; 0.01).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>PIQE significantly improves HCC visibility compared with AIDR and AiCE in hepatic dynamic CT, along with reduced image noise and increased CNR.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11604-026-02102-6","type":"journal-article","created":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T06:16:32Z","timestamp":1792649792000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Utility of super-resolution deep-learning reconstruction for improving visibility of hepatocellular carcinoma at hepatic dynamic CT: comparison with hybrid iterative reconstruction and conventional deep-learning reconstruction"],"prefix":"10.1007","author":[{"given":"Shota","family":"Kondo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3128-4731","authenticated-orcid":false,"given":"Yuko","family":"Nakamura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dara","family":"Fonseca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toru","family":"Higaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shota","family":"Nakano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Megumi","family":"Nishigaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shogo","family":"Maeda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Motonori","family":"Akagi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keigo","family":"Narita","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hidenori","family":"Mitani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wataru","family":"Fukumoto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuminari","family":"Tatsugami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kazuo","family":"Awai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,22]]},"reference":[{"issue":"6","key":"2102_CR1","doi-asserted-by":"publisher","first-page":"1922","DOI":"10.1097\/HEP.0000000000000466","volume":"78","author":"AG Singal","year":"2023","unstructured":"Singal AG, Llovet JM, Yarchoan M, Mehta N, Heimbach JK, Dawson LA, et al. AASLD practice guidance on prevention, diagnosis, and treatment of hepatocellular carcinoma. Hepatology. 2023;78(6):1922\u201365.","journal-title":"Hepatology"},{"key":"2102_CR2","unstructured":"American College of Radiology. CT\/MRI LI-RADS\u00ae. v2018. 2018. https:\/\/www.acr.org\/Clinical-Resources\/Clinical-Tools-and-Reference\/Reporting-and-Data-Systems\/LI-RADS."},{"issue":"11","key":"2102_CR3","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, Narita K, Honda Y, Zhou J, et al. Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT. Eur Radiol. 2019;29(11):6163\u201371.","journal-title":"Eur Radiol"},{"issue":"6","key":"2102_CR4","doi-asserted-by":"publisher","first-page":"e180011","DOI":"10.1148\/ryai.2019180011","volume":"1","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;1(6):e180011.","journal-title":"Radiol Artif Intell"},{"issue":"4","key":"2102_CR5","doi-asserted-by":"publisher","first-page":"1280","DOI":"10.1007\/s00261-023-03834-z","volume":"48","author":"N Okimoto","year":"2023","unstructured":"Okimoto N, Yasaka K, Kaiume M, Kanemaru N, Suzuki Y, Abe O. Improving detection performance of hepatocellular carcinoma and interobserver agreement for liver imaging reporting and data system on CT using deep learning reconstruction. Abdom Radiol (NY). 2023;48(4):1280\u20139.","journal-title":"Abdom Radiol (NY)"},{"issue":"8","key":"2102_CR6","doi-asserted-by":"publisher","first-page":"2724","DOI":"10.1007\/s00261-023-03966-2","volume":"48","author":"MA Shehata","year":"2023","unstructured":"Shehata MA, Saad AM, Kamel S, Stanietzky N, Roman-Colon AM, Morani AC, et al. Deep-learning CT reconstruction in clinical scans of the abdomen: a systematic review and meta-analysis. Abdom Radiol (NY). 2023;48(8):2724\u201356.","journal-title":"Abdom Radiol (NY)"},{"key":"2102_CR7","doi-asserted-by":"crossref","unstructured":"Ariizumi S, Kitagawa K, Kotera Y, Takahashi Y, Katagiri S, Kuwatsuru R et al. A non-smooth tumor margin in the hepatobiliary phase of gadoxetic acid disodium (Gd-EOB-DTPA)-enhanced magnetic resonance imaging predicts microscopic portal vein invasion, intrahepatic metastasis, and early recurrence after hepatectomy in patients with hepatocellular carcinoma. J Hepatobiliary Pancreat Sci. 2011; 18(4):575\u201385.","DOI":"10.1007\/s00534-010-0369-y"},{"issue":"6","key":"2102_CR8","doi-asserted-by":"publisher","first-page":"975","DOI":"10.1016\/S0168-8278(00)80131-2","volume":"33","author":"AM Hui","year":"2000","unstructured":"Hui AM, Takayama T, Sano K, Kubota K, Akahane M, Ohtomo K, et al. Predictive value of gross classification of hepatocellular carcinoma on recurrence and survival after hepatectomy. J Hepatol. 2000;33(6):975\u20139.","journal-title":"J Hepatol"},{"issue":"2","key":"2102_CR9","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/S0002-9610(01)00682-1","volume":"182","author":"M Shimada","year":"2001","unstructured":"Shimada M, Rikimaru T, Hamatsu T, Yamashita Y, Terashi T, Taguchi K, et al. The role of macroscopic classification in nodular-type hepatocellular carcinoma. Am J Surg. 2001;182(2):177\u201382.","journal-title":"Am J Surg"},{"issue":"6","key":"2102_CR10","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1046\/j.1440-1746.2003.03021.x","volume":"18","author":"J Inayoshi","year":"2003","unstructured":"Inayoshi J, Ichida T, Sugitani S, Tsuboi Y, Genda T, Honma N, et al. Gross appearance of hepatocellular carcinoma reflects E-cadherin expression and risk of early recurrence after surgical treatment. J Gastroenterol Hepatol. 2003;18(6):673\u20137.","journal-title":"J Gastroenterol Hepatol"},{"issue":"5","key":"2102_CR11","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1007\/s00534-005-1009-9","volume":"12","author":"K Kondo","year":"2005","unstructured":"Kondo K, Chijiiwa K, Makino I, Kai M, Maehara N, Ohuchida J, et al. Risk factors for early death after liver resection in patients with solitary hepatocellular carcinoma. J Hepatobiliary Pancreat Surg. 2005;12(5):399\u2013404.","journal-title":"J Hepatobiliary Pancreat Surg"},{"issue":"3","key":"2102_CR12","doi-asserted-by":"publisher","first-page":"1517","DOI":"10.1016\/j.acra.2024.09.012","volume":"32","author":"Y Funama","year":"2025","unstructured":"Funama Y, Nagayama Y, Sakabe D, Ito Y, Chiba Y, Nakaura T, et al. Advances in spatial resolution and radiation dose reduction using super-resolution deep learning-based reconstruction for abdominal computed tomography: a phantom study. Acad Radiol. 2025;32(3):1517\u201324.","journal-title":"Acad Radiol"},{"issue":"1","key":"2102_CR13","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/s41747-025-00670-2","volume":"10","author":"J Greffier","year":"2026","unstructured":"Greffier J, Roy C, Dabli D, Beregi JP, Pastor M. Phantom-based performance comparison of two commercial deep learning CT reconstruction algorithms with super- and normal-resolution settings. Eur Radiol experimental. 2026;10(1):9.","journal-title":"Eur Radiol experimental"},{"issue":"11","key":"2102_CR14","doi-asserted-by":"publisher","first-page":"2497","DOI":"10.1016\/j.acra.2022.12.044","volume":"30","author":"F Tatsugami","year":"2023","unstructured":"Tatsugami F, Higaki T, Kawashita I, Fukumoto W, Nakamura Y, Matsuura M, et al. Improvement of spatial resolution on coronary CT angiography by using super-resolution deep learning reconstruction. Acad Radiol. 2023;30(11):2497\u2013504.","journal-title":"Acad Radiol"},{"issue":"3","key":"2102_CR15","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1007\/s11604-024-01685-2","volume":"43","author":"A Nakamoto","year":"2025","unstructured":"Nakamoto A, Onishi H, Ota T, Honda T, Tsuboyama T, Fukui H, et al. Contrast-enhanced thin-slice abdominal CT with super-resolution deep learning reconstruction technique: evaluation of image quality and visibility of anatomical structures. Jpn J Radiol. 2025;43(3):445\u201354.","journal-title":"Jpn J Radiol"},{"key":"2102_CR16","doi-asserted-by":"publisher","first-page":"111953","DOI":"10.1016\/j.ejrad.2025.111953","volume":"184","author":"Y Nagayama","year":"2025","unstructured":"Nagayama Y, Ishiuchi S, Inoue T, Funama Y, Shigematsu S, Emoto T, et al. Super-resolution deep-learning reconstruction with 1024 matrix improves CT image quality for pancreatic ductal adenocarcinoma assessment. Eur J Radiol. 2025;184:111953.","journal-title":"Eur J Radiol"},{"issue":"5","key":"2102_CR17","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1088\/0031-9155\/32\/5\/003","volume":"32","author":"MF Kijewski","year":"1987","unstructured":"Kijewski MF, Judy PF. The noise power spectrum of CT images. Phys Med Biol. 1987;32(5):565\u201375.","journal-title":"Phys Med Biol"},{"issue":"1","key":"2102_CR18","first-page":"1","volume":"12","author":"ICRU Report 87","year":"2012","unstructured":"ICRU Report 87. Radiation dose and image-quality assessment in computed tomography. J ICRU. 2012;12(1):1\u2013149.","journal-title":"J ICRU"},{"issue":"7","key":"2102_CR19","doi-asserted-by":"publisher","first-page":"4115","DOI":"10.1118\/1.4725171","volume":"39","author":"S Richard","year":"2012","unstructured":"Richard S, Husarik DB, Yadava G, Murphy SN, Samei E. Towards task-based assessment of CT performance: system and object MTF across different reconstruction algorithms. Med Phys. 2012;39(7):4115\u201322.","journal-title":"Med Phys"},{"issue":"11","key":"2102_CR20","doi-asserted-by":"publisher","first-page":"e735","DOI":"10.1002\/mp.13763","volume":"46","author":"E Samei","year":"2019","unstructured":"Samei E, Bakalyar D, Boedeker KL, Brady S, Fan J, Leng S, et al. Performance evaluation of computed tomography systems: Summary of AAPM Task Group 233. Med Phys. 2019;46(11):e735\u201356.","journal-title":"Med Phys"},{"issue":"3","key":"2102_CR21","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1097\/RCT.0000000000001139","volume":"45","author":"K Narita","year":"2021","unstructured":"Narita K, Nakamura Y, Higaki T, Nishihara T, Harada K, Takizawa M, et al. Utility of radial scanning for the identification of arterial hypervascularity of hepatocellular carcinoma on gadoxetic acid-enhanced magnetic resonance images. J Comput Assist Tomogr. 2021;45(3):359\u201366.","journal-title":"J Comput Assist Tomogr"},{"issue":"3","key":"2102_CR22","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1148\/radiol.2212010280","volume":"221","author":"M Gonen","year":"2001","unstructured":"Gonen M, Panageas KS, Larson SM. Statistical issues in analysis of diagnostic imaging experiments with multiple observations per patient. Radiology. 2001;221(3):763\u20137.","journal-title":"Radiology"},{"key":"2102_CR23","first-page":"55","volume":"140","author":"R Likert","year":"1932","unstructured":"Likert R. A technique for the measurement of attitudes. Arch Psychol. 1932;140:55.","journal-title":"Arch Psychol"},{"issue":"3","key":"2102_CR24","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1097\/RCT.0b013e3182ab6cc0","volume":"38","author":"BY Hur","year":"2014","unstructured":"Hur BY, Lee JM, Joo I, Yu MH, Yoon JH, Han JK, et al. Liver computed tomography with low tube voltage and model-based iterative reconstruction algorithm for hepatic vessel evaluation in living liver donor candidates. J Comput Assist Tomogr. 2014;38(3):367\u201375.","journal-title":"J Comput Assist Tomogr"},{"issue":"6","key":"2102_CR25","doi-asserted-by":"publisher","first-page":"937","DOI":"10.1097\/RCT.0000000000000752","volume":"42","author":"M Matsuda","year":"2018","unstructured":"Matsuda M, Tsuda T, Kido T, Tanaka H, Nishiyama H, Itoh T, et al. Dual-energy computed tomography in patients with small hepatocellular carcinoma: utility of noise-reduced monoenergetic images for the evaluation of washout and image quality in the equilibrium phase. J Comput Assist Tomogr. 2018;42(6):937\u201343.","journal-title":"J Comput Assist Tomogr"},{"issue":"6","key":"2102_CR26","doi-asserted-by":"publisher","first-page":"1205","DOI":"10.1007\/s00330-011-2372-3","volume":"22","author":"K Sugimoto","year":"2012","unstructured":"Sugimoto K, Moriyasu F, Shiraishi J, Saito K, Taira J, Saguchi T, et al. Assessment of arterial hypervascularity of hepatocellular carcinoma: comparison of contrast-enhanced US and gadoxetate disodium-enhanced MR imaging. Eur Radiol. 2012;22(6):1205\u201313.","journal-title":"Eur Radiol"},{"issue":"9","key":"2102_CR27","doi-asserted-by":"publisher","first-page":"2698","DOI":"10.1007\/s00261-020-02508-4","volume":"45","author":"K Narita","year":"2020","unstructured":"Narita K, Nakamura Y, Higaki T, Akagi M, Honda Y, Awai K. Deep learning reconstruction of drip-infusion cholangiography acquired with ultra-high-resolution computed tomography. Abdom Radiol (NY). 2020;45(9):2698\u2013704.","journal-title":"Abdom Radiol (NY)"},{"issue":"1","key":"2102_CR28","doi-asserted-by":"publisher","first-page":"3603","DOI":"10.1038\/s41598-023-30460-y","volume":"13","author":"K Narita","year":"2023","unstructured":"Narita K, Nakamura Y, Higaki T, Kondo S, Honda Y, Kawashita I, et al. Iodine maps derived from sparse-view kV-switching dual-energy CT equipped with a deep learning reconstruction for diagnosis of hepatocellular carcinoma. Sci Rep. 2023;13(1):3603.","journal-title":"Sci Rep"},{"issue":"8","key":"2102_CR29","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1111\/j.1699-0463.1989.tb00464.x","volume":"97","author":"H Svanholm","year":"1989","unstructured":"Svanholm H, Starklint H, Gundersen HJ, Fabricius J, Barlebo H, Olsen S. Reproducibility of histomorphologic diagnoses with special reference to the kappa statistic. APMIS. 1989;97(8):689\u201398.","journal-title":"APMIS"},{"issue":"4","key":"2102_CR30","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1148\/rg.344135128","volume":"34","author":"EC Ehman","year":"2014","unstructured":"Ehman EC, Yu L, Manduca A, Hara AK, Shiung MM, Jondal D, et al. Methods for clinical evaluation of noise reduction techniques in abdominopelvic CT. Radiographics. 2014;34(4):849\u201362.","journal-title":"Radiographics"},{"issue":"2","key":"2102_CR31","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1148\/radiol.2015132766","volume":"276","author":"LL Geyer","year":"2015","unstructured":"Geyer LL, Schoepf UJ, Meinel FG, Nance JW Jr., Bastarrika G, Leipsic JA, et al. State of the art: iterative CT reconstruction techniques. Radiology. 2015;276(2):339\u201357.","journal-title":"Radiology"}],"container-title":["Japanese Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11604-026-02102-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11604-026-02102-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11604-026-02102-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T19:02:11Z","timestamp":1792695731000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11604-026-02102-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,22]]},"references-count":31,"alternative-id":["2102"],"URL":"https:\/\/doi.org\/10.1007\/s11604-026-02102-6","relation":{},"ISSN":["1867-1071","1867-108X"],"issn-type":[{"value":"1867-1071","type":"print"},{"value":"1867-108X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,22]]},"assertion":[{"value":"18 July 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 October 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 October 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Yuko Nakamura received a research grant from Canon Inc. Megumi Nishigaki is an employee of Canon Inc. Shota Nakano is an employee of Canon Medical Systems. The other authors declare that they have no conflict of interest. Canon Medical Systems and Canon Inc. provided technical support but had no role in the study design, data acquisition, image interpretation, statistical analysis, manuscript preparation, or the decision to submit the manuscript for publication.","order":1,"name":"Ethics","label":"Conflict of interest and funding information","group":{"name":"EthicsHeading","label":"Declarations"}}]}}