{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T06:27:07Z","timestamp":1792477627117,"version":"4.5.0"},"reference-count":33,"publisher":"Elsevier BV","issue":"3","license":[{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T00:00:00Z","timestamp":1772150400000},"content-version":"am","delay-in-days":363,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000098","name":"National Institutes of Health Clinical Center","doi-asserted-by":"publisher","award":["1Z01 CL040004"],"award-info":[{"award-number":["1Z01 CL040004"]}],"id":[{"id":"10.13039\/100000098","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.es","clinicalkey.com.au","academicradiology.org","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Academic Radiology"],"published-print":{"date-parts":[[2025,3]]},"DOI":"10.1016\/j.acra.2024.11.046","type":"journal-article","created":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T21:06:23Z","timestamp":1733519183000},"page":"1192-1203","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":6,"title":["Automated Classification of Body MRI Sequences Using Convolutional Neural Networks"],"prefix":"10.1016","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6178-9357","authenticated-orcid":false,"given":"Boah","family":"Kim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tejas Sudharshan","family":"Mathai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9323-9832","authenticated-orcid":false,"given":"Kimberly","family":"Helm","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pritam","family":"Mukherjee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9885-1695","authenticated-orcid":false,"given":"Jianfei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ronald M.","family":"Summers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.acra.2024.11.046_bib1","doi-asserted-by":"crossref","unstructured":"A. Anand, J. Liu, T.C. Shen, W.M. Linehan, P.A. Pinto, R.M. Summers, Automated classification of intravenous contrast enhancement phase of ct scans using residual networks 12465 (2023).","DOI":"10.1117\/12.2655263"},{"issue":"4","key":"10.1016\/j.acra.2024.11.046_bib2","first-page":"1688","article-title":"3D\u00a0pyramid pooling network for abdominal MRI series classification","volume":"44","author":"Zhu","year":"2022","journal-title":"IEEE Transac\u00a0Pattern Anal\u00a0Mach\u00a0Intell"},{"key":"10.1016\/j.acra.2024.11.046_bib3","series-title":"Medical Imaging 2002: PACS and Integrated Medical Information Systems: De- sign and Evaluation, Vol. 4685","first-page":"280","article-title":"Quality of DICOM header information for image categorization","author":"Gueld","year":"2002"},{"key":"10.1016\/j.acra.2024.11.046_bib4","article-title":"A foundation model utilizing chest CT volumes and radiology reports for supervised-level zero-shot detection of abnormalities","author":"Hamamci","year":"2024","journal-title":"arXiv"},{"issue":"5","key":"10.1016\/j.acra.2024.11.046_bib5","article-title":"Totalsegmentator: robust segmentation of 104 anatomic structures in CT images","volume":"5","author":"Wasserthal","year":"2023","journal-title":"Radiol\u00a0Artif\u00a0Intell"},{"key":"10.1016\/j.acra.2024.11.046_bib6","article-title":"Metadata-independent classification of MRI sequences using convolutional neural networks: successful application to prostate MRI","volume":"166","author":"Baumga\u00a8rtner","year":"2023","journal-title":"Eur\u00a0J\u00a0Radiol"},{"issue":"5","key":"10.1016\/j.acra.2024.11.046_bib7","article-title":"Duke liver dataset: a\u00a0publicly available liver MRI dataset with liver segmentation masks and series labels","volume":"5","author":"Macdonald","year":"2023","journal-title":"Radiol\u00a0Artif\u00a0Intell"},{"key":"10.1016\/j.acra.2024.11.046_bib8","first-page":"06463","article-title":"Mrsegmentator: robust multi-modality segmentation of 40 classes in MRI and CT sequences","volume":"2405","author":"Hantze","year":"2024","journal-title":"arXiv"},{"key":"10.1016\/j.acra.2024.11.046_bib9","first-page":"19492","article-title":"Totalsegmentator MRI: sequence-independent segmentation of 59 anatomical structures in MR\u00a0images","volume":"2405","author":"D\u2019Antonoli","year":"2024","journal-title":"arXiv"},{"key":"10.1016\/j.acra.2024.11.046_bib10","first-page":"05944","article-title":"MRIsegmentator-abdomen: a\u00a0fully automated multi-organ and structure segmentation tool for T1-weighted abdominal MRI","volume":"2405","author":"Zhuang","year":"2024","journal-title":"arXiv"},{"key":"10.1016\/j.acra.2024.11.046_bib11","first-page":"36722","article-title":"Amos: a\u00a0large-scale abdominal multi-organ benchmark for versatile medical image segmentation","volume":"35","author":"Ji","year":"2022","journal-title":"Adv\u00a0Neural Inf\u00a0Process\u00a0Syst"},{"key":"10.1016\/j.acra.2024.11.046_bib12","article-title":"Magnetic resonance imaging seuqnce identification using a metadata learning approach, Frontiers in","volume":"15","author":"Liang","year":"2021","journal-title":"Neuroinformatics"},{"key":"10.1016\/j.acra.2024.11.046_bib13","first-page":"439","article-title":"A deep convolutional neural network for annotation of magnetic resonance imaging seuqence type","volume":"33","author":"Ranjbar","year":"2020","journal-title":"J\u00a0Digit\u00a0Imaging"},{"key":"10.1016\/j.acra.2024.11.046_bib14","unstructured":"P.V. d Mello., Paixao,T.M.,\u00a0Berriel, R.,\u00a0et al. Deep learning-based type identification of volu- metric mri sequence, arXiv (2021)."},{"key":"10.1016\/j.acra.2024.11.046_bib15","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1007\/s11604-018-0779-3","article-title":"Artificial intelligence using neural network archi- tecture for radiology (AINNAR): classification of MR\u00a0imaging sequences","volume":"36","author":"Noguchi","year":"2018","journal-title":"Jpn J Radiol"},{"key":"10.1016\/j.acra.2024.11.046_bib16","first-page":"105752I","article-title":"Classifying magnetic resonance image modalities with convolutional neural networks","volume":"10575","author":"Remedios","year":"2018"},{"key":"10.1016\/j.acra.2024.11.046_bib17","series-title":"Medical Imaging 2022: Image Processing, Vol. 12032","first-page":"120320G","article-title":"Automatic classification of MRI contrasts using a deep Siamese network and one-shot learning","author":"Chou","year":"2022"},{"key":"10.1016\/j.acra.2024.11.046_bib18","article-title":"Advancing the cancer genome atlas glioma MRI\u00a0collections with expert segmentation labels and radiomic features, Nature Scientific","volume":"4","author":"Bakas","year":"2017","journal-title":"Data"},{"key":"10.1016\/j.acra.2024.11.046_bib19","article-title":"Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BraTS\u00a0challenge","author":"Bakas","year":"2018","journal-title":"arXiv"},{"key":"10.1016\/j.acra.2024.11.046_bib20","series-title":"Medical Imaging 2024: Computer-Aided Diagnosis, Vol. 12927","first-page":"129270K","article-title":"Automated classification of body MRI sequence type using convolutional neural networks","author":"Helm","year":"2024"},{"key":"10.1016\/j.acra.2024.11.046_bib21","doi-asserted-by":"crossref","unstructured":"B. Kim, T.S. Mathai, K. Helm\u00a0et al. Automated classification of multi-parametric body MRI series. 2024. IEEE International Symposium on Biomedical Imaging ((ISBI)); 2024, 1\u20135.","DOI":"10.1109\/ISBI56570.2024.10635686"},{"key":"10.1016\/j.acra.2024.11.046_bib22","first-page":"1","article-title":"Classification of multi-parametric body MRI series using deep learning","volume":"28","author":"Kim","year":"2024","journal-title":"IEEE J\u00a0Biomed\u00a0Health Inform"},{"key":"10.1016\/j.acra.2024.11.046_bib23","article-title":"Does image normalization and intensity resolution impact texture classification?","volume":"81","author":"Kocio-lek","year":"2020","journal-title":"Comput Med\u00a0Imaging Graph"},{"key":"10.1016\/j.acra.2024.11.046_bib24","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016","journal-title":"IEEE Conf\u00a0Comput\u00a0Vision Pattern Recognit"},{"key":"10.1016\/j.acra.2024.11.046_bib25","first-page":"2261","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017","journal-title":"Proc\u00a0IEEE Conf\u00a0Comput\u00a0Vis\u00a0Pattern Recognit"},{"key":"10.1016\/j.acra.2024.11.046_bib26","first-page":"6105","article-title":"Efficientnet: rethinking model scaling for convolutional neural networks","author":"Tan","year":"2019","journal-title":"Int Conf Mach Learn\u00a0PMLR"},{"key":"10.1016\/j.acra.2024.11.046_bib27","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012"},{"key":"10.1016\/j.acra.2024.11.046_bib28","unstructured":"Cardoso M., Li W., Brown R., Ma N., Kerfoot E., et al., MONAI: An open-source framework for deep learning in healthcare (November 2022)."},{"issue":"2","key":"10.1016\/j.acra.2024.11.046_bib29","first-page":"17","article-title":"The \u201dknee of a curve\u201d\u2014 useful clue but incomplete support","volume":"4","author":"Thomas","year":"1999","journal-title":"Mil\u00a0Oper\u00a0Res"},{"key":"10.1016\/j.acra.2024.11.046_bib30","article-title":"An overview of overfitting and its solutions","volume":"1168","author":"Ying","year":"2019"},{"key":"10.1016\/j.acra.2024.11.046_bib31","first-page":"618","article-title":"Grad-cam: visual explanations from deep networks via gradient-based localization","author":"Selvaraju","year":"2017","journal-title":"Proc\u00a0IEEE\u00a0Int Conf\u00a0Comput\u00a0Vis"},{"key":"10.1016\/j.acra.2024.11.046_bib32","unstructured":"K. Yan, L. Lu, R.M. Summers, Unsupervised body part regression via spatially self-ordering convolutional neural networks (2018). arXiv: 1707.03891."},{"key":"10.1016\/j.acra.2024.11.046_bib33","first-page":"09063","article-title":"A large annotated medical image dataset for the development and evaluation of segmentation algorithms","volume":"1902","author":"Simpson","year":"2019","journal-title":"arXiv\u00a0Preprint arXiv"}],"container-title":["Academic Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633224008912?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633224008912?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,2,27]],"date-time":"2025-02-27T18:24:31Z","timestamp":1740680671000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1076633224008912"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3]]},"references-count":33,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["S1076633224008912"],"URL":"https:\/\/doi.org\/10.1016\/j.acra.2024.11.046","relation":{},"ISSN":["1076-6332"],"issn-type":[{"value":"1076-6332","type":"print"}],"subject":[],"published":{"date-parts":[[2025,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Automated Classification of Body MRI Sequences Using Convolutional Neural Networks","name":"articletitle","label":"Article Title"},{"value":"Academic Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.acra.2024.11.046","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Association of Academic Radiology. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}