{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T17:56:01Z","timestamp":1789149361024,"version":"build-2803163510"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82472073"],"award-info":[{"award-number":["82472073"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai General Hospital Academic Leaders Training Program","award":["SHLJxxq"],"award-info":[{"award-number":["SHLJxxq"]}]},{"name":"Shanghai General Hospital Clinical Research Project","award":["CCTR-2022ZD01"],"award-info":[{"award-number":["CCTR-2022ZD01"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"DOI":"10.1007\/s00330-025-12055-5","type":"journal-article","created":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T04:14:38Z","timestamp":1761970478000},"page":"2435-2445","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Fully automated multi-sequence detection and alignment of focal liver lesions in dynamic contrast-enhanced MRI"],"prefix":"10.1007","volume":"36","author":[{"given":"Lu","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingyun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaping","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yimin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6669-0097","authenticated-orcid":false,"given":"Xueqian","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,1]]},"reference":[{"key":"12055_CR1","doi-asserted-by":"crossref","unstructured":"Frenette C, Mendiratta-Lala M, Salgia R, Wong RJ, Sauer BG, Pillai A (2024) ACG clinical guideline: focal liver lesions. Am J Gastroenterol 119:1235\u20131271 (2024)","DOI":"10.14309\/ajg.0000000000002857"},{"key":"12055_CR2","doi-asserted-by":"publisher","first-page":"1414","DOI":"10.1016\/j.cgh.2013.09.017","volume":"12","author":"SK Venkatesh","year":"2014","unstructured":"Venkatesh SK, Chandan V, Roberts LR (2014) Liver masses: a clinical, radiologic, and pathologic perspective. Clin Gastroenterol Hepatol 12:1414\u20131429","journal-title":"Clin Gastroenterol Hepatol"},{"key":"12055_CR3","doi-asserted-by":"publisher","first-page":"923","DOI":"10.4254\/wjh.v14.i5.923","volume":"14","author":"M Gatti","year":"2022","unstructured":"Gatti M, Maino C, Tore D et al (2022) Benign focal liver lesions: the role of magnetic resonance imaging. World J Hepatol 14:923\u2013943","journal-title":"World J Hepatol"},{"key":"12055_CR4","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-45325-9","volume":"15","author":"H Ying","year":"2024","unstructured":"Ying H, Liu X, Zhang M et al (2024) A multicenter clinical AI system study for detection and diagnosis of focal liver lesions. Nat Commun 15:1131","journal-title":"Nat Commun"},{"key":"12055_CR5","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1148\/radiol.2019192252","volume":"294","author":"FL Jacobson","year":"2020","unstructured":"Jacobson FL (2020) Medical image perception research in the emerging age of artificial intelligence. Radiology 294:210\u2013211","journal-title":"Radiology"},{"key":"12055_CR6","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/s00330-022-08971-5","volume":"33","author":"L Zhang","year":"2023","unstructured":"Zhang L, Jiang B, Chen Q et al (2023) Motion artifact removal in coronary CT angiography based on generative adversarial networks. Eur Radiol 33:43\u201353","journal-title":"Eur Radiol"},{"key":"12055_CR7","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 (2023) Comparison of chest radiograph captions based on natural language processing vs completed by radiologists. JAMA Netw Open 6:e2255113","journal-title":"JAMA Netw Open"},{"key":"12055_CR8","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.240885","volume":"312","author":"L Zhang","year":"2024","unstructured":"Zhang L, Liu M, Wang L et al (2024) Constructing a large language model to generate impressions from findings in radiology reports. Radiology 312:e240885","journal-title":"Radiology"},{"key":"12055_CR9","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-65875-4","volume":"10","author":"J Kim","year":"2020","unstructured":"Kim J, Min JH, Kim SK, Shin SY, Lee MW (2020) Detection of hepatocellular carcinoma in contrast-enhanced magnetic resonance imaging using deep learning classifier: a multi-center retrospective study. Sci Rep 10:9458","journal-title":"Sci Rep"},{"key":"12055_CR10","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1007\/s00261-020-02604-5","volume":"46","author":"K Bousabarah","year":"2021","unstructured":"Bousabarah K, Letzen B, Tefera J et al (2021) Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning. Abdom Radiol 46:216\u2013225","journal-title":"Abdom Radiol"},{"key":"12055_CR11","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1007\/s11548-021-02416-y","volume":"16","author":"T Takenaga","year":"2021","unstructured":"Takenaga T, Hanaoka S, Nomura Y et al (2021) Multichannel three-dimensional fully convolutional residual network-based focal liver lesion detection and classification in Gd-EOB-DTPA-enhanced MRI. Int J Comput Assist Radiol Surg 16:1527\u20131536","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"12055_CR12","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1186\/s13244-022-01163-1","volume":"13","author":"X Han","year":"2022","unstructured":"Han X, Wu X, Wang S et al (2022) Automated segmentation of liver segment on portal venous phase MR images using a 3D convolutional neural network. Insights Imaging 13:26","journal-title":"Insights Imaging"},{"key":"12055_CR13","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1007\/s00330-024-10941-y","volume":"35","author":"H Wei","year":"2024","unstructured":"Wei H, Zheng T, Zhang X et al (2024) Deep learning-based 3D quantitative total tumor burden predicts early recurrence of BCLC A and B HCC after resection. Eur Radiol 35:127\u2013139","journal-title":"Eur Radiol"},{"key":"12055_CR14","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick R (2017) Mask R-CNN. IEEE transactions on pattern analysis & machine intelligence. IEEE, pp 386\u2013397","DOI":"10.1109\/TPAMI.2018.2844175"},{"key":"12055_CR15","doi-asserted-by":"crossref","unstructured":"Lin TY, Dollar P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. IEEE Computer Society, Honolulu","DOI":"10.1109\/CVPR.2017.106"},{"key":"12055_CR16","doi-asserted-by":"publisher","first-page":"3078","DOI":"10.1007\/s00261-024-04277-w","volume":"49","author":"Y Wang","year":"2024","unstructured":"Wang Y, Yang D, Xu L et al (2024) Deep learning-based arterial subtraction images improve the detection of LR-TR algorithm for viable HCC on extracellular agents-enhanced MRI. Abdom Radiol 49:3078\u20133087","journal-title":"Abdom Radiol"},{"key":"12055_CR17","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1002\/jmri.29404","volume":"61","author":"H Dai","year":"2024","unstructured":"Dai H, Xiao Y, Fu C et al (2024) Deep learning-based approach for identifying and measuring focal liver lesions on contrast-enhanced MRI. J Magn Reson Imaging 61:111\u2013120","journal-title":"J Magn Reson Imaging"},{"key":"12055_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102154","volume":"73","author":"J Zhao","year":"2021","unstructured":"Zhao J, Li D, Xiao X et al (2021) United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast MRI. Med Image Anal 73:102154","journal-title":"Med Image Anal"},{"key":"12055_CR19","doi-asserted-by":"publisher","first-page":"7047","DOI":"10.1007\/s00330-021-07803-2","volume":"31","author":"DW Kim","year":"2021","unstructured":"Kim DW, Lee G, Kim SY et al (2021) Deep learning-based algorithm to detect primary hepatic malignancy in multiphase CT of patients at high risk for HCC. Eur Radiol 31:7047\u20137057","journal-title":"Eur Radiol"},{"key":"12055_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107032","volume":"225","author":"Y Cho","year":"2022","unstructured":"Cho Y, Han YE, Kim MJ et al (2022) Computer-aided hepatocellular carcinoma detection on the hepatobiliary phase of gadoxetic acid-enhanced magnetic resonance imaging using a convolutional neural network: feasibility evaluation with multi-sequence data. Comput Methods Programs Biomed 225:107032","journal-title":"Comput Methods Programs Biomed"},{"key":"12055_CR21","doi-asserted-by":"publisher","first-page":"2872","DOI":"10.1002\/mp.16140","volume":"50","author":"Q Chen","year":"2023","unstructured":"Chen Q, Zhu Y, Chen Y et al (2023) Applicability of multidimensional convolutional neural networks on automated detection of diverse focal liver lesions in multiphase CT images. Med Phys 50:2872\u20132883","journal-title":"Med Phys"},{"key":"12055_CR22","doi-asserted-by":"publisher","first-page":"820","DOI":"10.1109\/JPROC.2021.3054390","volume":"109","author":"SK Zhou","year":"2021","unstructured":"Zhou SK, Greenspan H, Davatzikos C et al (2021) A review of deep learning in medical imaging: imaging traits, technology trends, case studies with progress highlights, and future promises. Proc IEEE Inst Electr Electron Eng 109:820\u2013838","journal-title":"Proc IEEE Inst Electr Electron Eng"},{"key":"12055_CR23","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2021.785523","volume":"8","author":"X Huang","year":"2021","unstructured":"Huang X, Mao L, Wang X et al (2021) Multi-sequence MRI registration of atherosclerotic carotid arteries based on cross-scale siamese network. Front Cardiovasc Med 8:785523","journal-title":"Front Cardiovasc Med"},{"key":"12055_CR24","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1186\/s41747-019-0120-7","volume":"3","author":"KA Hasenstab","year":"2019","unstructured":"Hasenstab KA, Cunha GM, Higaki A et al (2019) Fully automated convolutional neural network-based affine algorithm improves liver registration and lesion co-localization on hepatobiliary phase T1-weighted MR images. Eur Radiol Exp 3:43","journal-title":"Eur Radiol Exp"},{"key":"12055_CR25","doi-asserted-by":"publisher","first-page":"1924","DOI":"10.1002\/mrm.28554","volume":"85","author":"Y Lee","year":"2021","unstructured":"Lee Y, Wilm BJ, Brunner DO et al (2021) On the signal-to-noise ratio benefit of spiral acquisition in diffusion MRI. Magn Reson Med 85:1924\u20131937","journal-title":"Magn Reson Med"}],"container-title":["European Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-025-12055-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-025-12055-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-025-12055-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T15:19:59Z","timestamp":1774883999000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00330-025-12055-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,1]]},"references-count":25,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["12055"],"URL":"https:\/\/doi.org\/10.1007\/s00330-025-12055-5","relation":{},"ISSN":["1432-1084"],"issn-type":[{"value":"1432-1084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,1]]},"assertion":[{"value":"23 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 August 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 September 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 November 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The scientific guarantor of this publication is Xueqian Xie.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Guarantor"}},{"value":"Lu Zhang and Xueqian Xie are radiologists at Shanghai General Hospital, who do not work or consult in the healthcare industry, and are responsible for data control and analysis. This study used the product from Shukun Technology Company. Xiaolan Zhang, Yimin Huang, and Chao Zheng are engineers at Shukun Technology Company involved in the development of the AI; they did not control or analyze the data. All authors declare no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No complex statistical methods were necessary for this paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was waived by the Institutional Review Board.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional Review Board approval was obtained.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"No subjects or cohorts overlap.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}