{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T16:07:42Z","timestamp":1789747662023,"version":"4.0.1"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"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":["82371904"],"award-info":[{"award-number":["82371904"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62476015"],"award-info":[{"award-number":["62476015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62171298"],"award-info":[{"award-number":["62171298"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009331","name":"Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support","doi-asserted-by":"publisher","award":["ZLRK202502"],"award-info":[{"award-number":["ZLRK202502"]}],"id":[{"id":"10.13039\/501100009331","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Abdom Radiol"],"DOI":"10.1007\/s00261-026-05790-w","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T15:37:19Z","timestamp":1789745839000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A knowledge-guided dual-path framework for automated liver MRI series classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9908-6374","authenticated-orcid":false,"given":"Sizhuo","family":"Han","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahui","family":"Lv","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gong","family":"Lun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8799-8042","authenticated-orcid":false,"given":"Xibin","family":"Jia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8190-6469","authenticated-orcid":false,"given":"Zhenchang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dawei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenghan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,18]]},"reference":[{"key":"5790_CR1","doi-asserted-by":"publisher","unstructured":"de Mello JPV, Paix\u00e3o, TM, Berriel, R, Reyes, M, Badue, C, De Souza, AF, et al (2021) Deep Learning-based Type Identification of Volumetric MRI Sequences. 2020 25th International Conference on Pattern Recognition (ICPR) 2021, pp. 1\u20138, 1051\u20134651. https:\/\/doi.org\/10.1109\/ICPR48806.2021.9413120","DOI":"10.1109\/ICPR48806.2021.9413120"},{"key":"5790_CR2","doi-asserted-by":"publisher","first-page":"4576","DOI":"10.1007\/s00261-021-03142-4","volume":"46","author":"SH Wang","year":"2021","unstructured":"Wang SH, Du, J, Xu, H, Yang, D, Ye, Y, Chen, Y, et al (2021) Automatic discrimination of different sequences and phases of liver mri using a dense feature fusion neural network: A preliminary study. Abdom Radiol (NY) 46:4576\u20134587. https:\/\/doi.org\/10.1007\/s00261-021-03142-4","journal-title":"Abdom Radiol (NY)"},{"key":"5790_CR3","doi-asserted-by":"publisher","first-page":"1688","DOI":"10.1109\/tpami.2020.3033990","volume":"44","author":"Z Zhu","year":"2022","unstructured":"Zhu Z, Mittendorf, A, Shropshire, E, Allen, B, Miller, C, Bashir, MR, et al (2022) 3D pyramid pooling network for abdominal MRI series classification. IEEE Trans Pattern Anal Mach Intell 44:1688\u20131698. https:\/\/doi.org\/10.1109\/tpami.2020.3033990","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5790_CR4","doi-asserted-by":"publisher","first-page":"1192","DOI":"10.1016\/j.acra.2024.11.046","volume":"32","author":"B Kim","year":"2025","unstructured":"Kim B, Mathai, TS, Helm, K, Mukherjee, P, Liu, J,Summers, RM (2025) Automated classification of body MRI sequences using convolutional neural networks. Acad Radiol 32:1192\u20131203. https:\/\/doi.org\/10.1016\/j.acra.2024.11.046","journal-title":"Acad Radiol"},{"key":"5790_CR5","doi-asserted-by":"publisher","first-page":"27044","DOI":"10.1038\/s41598-025-11985-w","volume":"15","author":"J Kim","year":"2025","unstructured":"Kim J, Chae, A, Duda, J, Borthakur, A, Rader, DJ, Gee, JC, et al (2025) Automated characterization of abdominal MRI exams using deep learning. Sci Rep 15:27044. https:\/\/doi.org\/10.1038\/s41598-025-11985-w","journal-title":"Sci Rep"},{"key":"5790_CR6","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1067\/j.cpradiol.2023.10.018","volume":"53","author":"LPG Petroianu","year":"2024","unstructured":"Petroianu LPG, Li, L, Mieloszyk, RJ, Mastrangelo, CM, Stapleton, S,Hall, C (2024) MRI log file analysis for workflow improvement. Curr Probl Diagn Radiol 53:192\u2013200. https:\/\/doi.org\/10.1067\/j.cpradiol.2023.10.018","journal-title":"Curr Probl Diagn Radiol"},{"key":"5790_CR7","doi-asserted-by":"publisher","first-page":"622951","DOI":"10.3389\/fninf.2021.622951","volume":"15","author":"S Liang","year":"2021","unstructured":"Liang S, Beaton, D, Arnott, SR, Gee, T, Zamyadi, M, Bartha, R, et al (2021) Magnetic resonance imaging sequence identification using a metadata learning approach. Front Neuroinform 15:622951. https:\/\/doi.org\/10.3389\/fninf.2021.622951","journal-title":"Front Neuroinform"},{"key":"5790_CR8","doi-asserted-by":"crossref","unstructured":"Gueld MO, Kohnen, M, Keysers, D, Schubert, H, Wein, BB, Bredno, J, et al (2002) Quality of dicom header information for image categorization. Medical imaging 2002: PACS and integrated medical information systems: design and evaluation. SPIE 2002, 4685: 280\u2013287.","DOI":"10.1117\/12.467017"},{"key":"5790_CR9","unstructured":"Radiology ACo (2018) CT\/MRI LI-RADS\u00ae v2018 CORE. American College of Radiology. https:\/\/edge.sitecorecloud.io\/americancoldf5f-acrorgf92a-productioncb02-3650\/media\/ACR\/Files\/RADS\/LI-RADS\/LI-RADS-CT-MRI-2018-Core.pdf. Accessed Dec 31:2025."},{"key":"5790_CR10","doi-asserted-by":"publisher","first-page":"1987","DOI":"10.4254\/wjh.v7.i16.1987","volume":"7","author":"AP Matos","year":"2015","unstructured":"Matos AP, Velloni, F, Ramalho, M, AlObaidy, M, Rajapaksha, A,Semelka, RC (2015) Focal liver lesions: Practical magnetic resonance imaging approach. World J Hepatol 7:1987\u20132008. https:\/\/doi.org\/10.4254\/wjh.v7.i16.1987","journal-title":"World J Hepatol"},{"key":"5790_CR11","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.14309\/ajg.0000000000002857","volume":"119","author":"C Frenette","year":"2024","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. https:\/\/doi.org\/10.14309\/ajg.0000000000002857","journal-title":"Am J Gastroenterol"},{"key":"5790_CR12","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1007\/s00261-022-03726-8","volume":"48","author":"O Kamal","year":"2023","unstructured":"Kamal O, Sy, E, Chernyak, V, Gupta, A, Yaghmai, V, Fowler, K, et al (2023) Optional MRI sequences for LI-RADS: Why, what, and how? Abdom Radiol (NY) 48:519\u2013531. https:\/\/doi.org\/10.1007\/s00261-022-03726-8","journal-title":"Abdom Radiol (NY)"},{"key":"5790_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.mric.2014.04.004","author":"FF Guglielmo","year":"2014","unstructured":"Guglielmo FF, Mitchell, DG, Roth, CG,Deshmukh, S (2014) Hepatic mr imaging techniques, optimization, and artifacts. Magn Reson Imaging Clin N Am 22:263\u2009\u2013\u200982. https:\/\/doi.org\/10.1016\/j.mric.2014.04.004","journal-title":"Magn Reson Imaging Clin N Am"},{"key":"5790_CR14","doi-asserted-by":"publisher","first-page":"1236","DOI":"10.1093\/bjr\/tqaf089","volume":"98","author":"S Keaveney","year":"2025","unstructured":"Keaveney S, McHugh, DJ, Rata, M, Dragan, A, Winfield, JM, Doran, SJ, et al (2025) An open-source repository-based tool for quality control of imaging protocol compliance: Demonstration in a multicentre mri study. Br J Radiol 98:1236\u20131244. https:\/\/doi.org\/10.1093\/bjr\/tqaf089","journal-title":"Br J Radiol"},{"key":"5790_CR15","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.ejrad.2017.05.028","volume":"93","author":"H Donato","year":"2017","unstructured":"Donato H, Fran\u00e7a, M, Candel\u00e1ria, I,Caseiro-Alves, F (2017) Liver MRI: From basic protocol to advanced techniques. Eur J Radiol 93:30\u201339. https:\/\/doi.org\/10.1016\/j.ejrad.2017.05.028","journal-title":"Eur J Radiol"},{"key":"5790_CR16","doi-asserted-by":"crossref","unstructured":"Chou Y, Remedios, S W, Butman, J A,Pham, D L(2022) Automatic classification of mri contrasts using a deep siamese network and one-shot learning. Medical imaging 2022: image processing. SPIE, 2022, 12032: 110\u2013114.","DOI":"10.1117\/12.2613052"},{"key":"5790_CR17","doi-asserted-by":"publisher","first-page":"e22435","DOI":"10.7759\/cureus.22435","volume":"14","author":"N Braeker","year":"2022","unstructured":"Braeker N, Schmitz, C, Wagner, N, Stanicki, BJ, Schr\u00f6der, C, Ehret, F, et al (2022) Classifying the acquisition sequence for brain MRIs using neural networks on single slices. Cureus 14:e22435. https:\/\/doi.org\/10.7759\/cureus.22435","journal-title":"Cureus"},{"key":"5790_CR18","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1007\/s10278-019-00282-4","volume":"33","author":"S Ranjbar","year":"2020","unstructured":"Ranjbar S, Singleton, KW, Jackson, PR, Rickertsen, CR, Whitmire, SA, Clark-Swanson, KR, et al (2020) A deep convolutional neural network for annotation of magnetic resonance imaging sequence type. J Digit Imaging 33:439\u2013446. https:\/\/doi.org\/10.1007\/s10278-019-00282-4","journal-title":"J Digit Imaging"},{"key":"5790_CR19","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1007\/s10278-018-0154-z","volume":"32","author":"DS Lituiev","year":"2019","unstructured":"Lituiev DS, Trivedi, H, Panahiazar, M, Norgeot, B, Seo, Y, Franc, B, et al (2019) Automatic labeling of special diagnostic mammography views from images and DICOM headers. J Digit Imaging 32:228\u2013233. https:\/\/doi.org\/10.1007\/s10278-018-0154-z","journal-title":"J Digit Imaging"},{"key":"5790_CR20","unstructured":"Geirhos R, Rubisch, P, Michaelis, C, Bethge, M, Wichmann, FA,Brendel, W. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. International conference on learning representations. 2018."},{"key":"5790_CR21","doi-asserted-by":"publisher","first-page":"102512","DOI":"10.1016\/j.media.2022.102512","volume":"80","author":"T Ottens","year":"2022","unstructured":"Ottens T, Barbieri, S, Orton, MR, Klaassen, R, van Laarhoven, HW, Crezee, H, et al (2022) Deep learning DCE-MRI parameter estimation: Application in pancreatic cancer. Med Image Anal 80:102512. https:\/\/doi.org\/10.1016\/j.media.2022.102512","journal-title":"Med Image Anal"},{"key":"5790_CR22","doi-asserted-by":"publisher","first-page":"963","DOI":"10.1007\/s00261-015-0623-5","volume":"41","author":"EC Ehman","year":"2016","unstructured":"Ehman EC, Behr, SC, Umetsu, SE, Fidelman, N, Yeh, BM, Ferrell, LD, et al (2016) Rate of observation and inter-observer agreement for li-rads major features at ct and mri in 184 pathology proven hepatocellular carcinomas. Abdom Radiol (NY) 41:963\u20139. https:\/\/doi.org\/10.1007\/s00261-015-0623-5","journal-title":"Abdom Radiol (NY)"},{"issue":"3","key":"5790_CR23","doi-asserted-by":"publisher","first-page":"575","DOI":"10.2214\/ajr.179.3.1790575","volume":"179","author":"LH Schwartz","year":"2002","unstructured":"Schwartz LH, Kijewski P, Lynch K, Lis E, Panicek DM. Review and interpretation of MR imaging studies with PACS: creating uniform series descriptors for radiologists and referring physicians. AJR Am J Roentgenol. 2002;179(3):575\u2013577. doi:https:\/\/doi.org\/10.2214\/ajr.179.3.1790575","journal-title":"AJR Am J Roentgenol"}],"container-title":["Abdominal Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00261-026-05790-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00261-026-05790-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00261-026-05790-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T15:37:22Z","timestamp":1789745842000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00261-026-05790-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,18]]},"references-count":23,"alternative-id":["5790"],"URL":"https:\/\/doi.org\/10.1007\/s00261-026-05790-w","relation":{},"ISSN":["2366-0058"],"issn-type":[{"value":"2366-0058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,18]]},"assertion":[{"value":"27 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 September 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 September 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}]}}