{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T11:11:15Z","timestamp":1771067475700,"version":"3.50.1"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031438943","type":"print"},{"value":"9783031438950","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-43895-0_60","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:08:57Z","timestamp":1696115337000},"page":"639-648","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Gadolinium-Free Cardiac MRI Myocardial Scar Detection by\u00a04D Convolution Factorization"],"prefix":"10.1007","author":[{"given":"Amine","family":"Amyar","sequence":"first","affiliation":[]},{"given":"Shiro","family":"Nakamori","sequence":"additional","affiliation":[]},{"given":"Manuel","family":"Morales","sequence":"additional","affiliation":[]},{"given":"Siyeop","family":"Yoon","sequence":"additional","affiliation":[]},{"given":"Jennifer","family":"Rodriguez","sequence":"additional","affiliation":[]},{"given":"Jiwon","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Robert M.","family":"Judd","sequence":"additional","affiliation":[]},{"given":"Jonathan W.","family":"Weinsaft","sequence":"additional","affiliation":[]},{"given":"Reza","family":"Nezafat","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"issue":"1","key":"60_CR1","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1148\/radiol.2017170213","volume":"286","author":"B Baessler","year":"2018","unstructured":"Baessler, B., Mannil, M., Oebel, S., Maintz, D., Alkadhi, H., Manka, R.: Subacute and chronic left ventricular myocardial scar: accuracy of texture analysis on nonenhanced cine MR images. Radiology 286(1), 103\u2013112 (2018)","journal-title":"Radiology"},{"issue":"19","key":"60_CR2","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.120.016797","volume":"9","author":"I Csecs","year":"2020","unstructured":"Csecs, I., et al.: Association between left ventricular mechanical deformation and myocardial fibrosis in Nonischemic cardiomyopathy. J. Am. Heart Assoc. 9(19), e016797 (2020)","journal-title":"J. Am. Heart Assoc."},{"issue":"1","key":"60_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12968-022-00869-x","volume":"24","author":"AS Fahmy","year":"2022","unstructured":"Fahmy, A.S., Rowin, E.J., Arafati, A., Al-Otaibi, T., Maron, M.S., Nezafat, R.: Radiomics and deep learning for myocardial scar screening in hypertrophic cardiomyopathy. J. Cardiovasc. Magn. Reson. 24(1), 1\u201312 (2022)","journal-title":"J. Cardiovasc. Magn. Reson."},{"issue":"7","key":"60_CR4","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1016\/S1474-4422(17)30158-8","volume":"16","author":"V Gulani","year":"2017","unstructured":"Gulani, V., Calamante, F., Shellock, F.G., Kanal, E., Reeder, S.B., et al.: Gadolinium deposition in the brain: summary of evidence and recommendations. Lancet Neurol. 16(7), 564\u2013570 (2017)","journal-title":"Lancet Neurol."},{"issue":"8","key":"60_CR5","doi-asserted-by":"publisher","first-page":"4159","DOI":"10.1021\/acs.est.5b04322","volume":"50","author":"V Hatje","year":"2016","unstructured":"Hatje, V., Bruland, K.W., Flegal, A.R.: Increases in anthropogenic gadolinium anomalies and rare earth element concentrations in san Francisco bay over a 20 year record. Environ. Sci. Technol. 50(8), 4159\u20134168 (2016)","journal-title":"Environ. Sci. Technol."},{"key":"60_CR6","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"issue":"19","key":"60_CR7","doi-asserted-by":"publisher","first-page":"1992","DOI":"10.1161\/01.CIR.100.19.1992","volume":"100","author":"RJ Kim","year":"1999","unstructured":"Kim, R.J., et al.: Relationship of MRI delayed contrast enhancement to irreversible injury, infarct age, and contractile function. Circulation 100(19), 1992\u20132002 (1999)","journal-title":"Circulation"},{"issue":"20","key":"60_CR8","doi-asserted-by":"publisher","first-page":"1445","DOI":"10.1056\/NEJM200011163432003","volume":"343","author":"RJ Kim","year":"2000","unstructured":"Kim, R.J., et al.: The use of contrast-enhanced magnetic resonance imaging to identify reversible myocardial dysfunction. N. Engl. J. Med. 343(20), 1445\u20131453 (2000)","journal-title":"N. Engl. J. Med."},{"key":"60_CR9","doi-asserted-by":"crossref","unstructured":"Leiner, T.: Deep learning for detection of myocardial scar tissue: Goodbye to gadolinium? (2019)","DOI":"10.1148\/radiol.2019190783"},{"issue":"4","key":"60_CR10","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1093\/ehjci\/jeab056","volume":"23","author":"J Mancio","year":"2022","unstructured":"Mancio, J., et al.: Machine learning phenotyping of scarred myocardium from cine in hypertrophic cardiomyopathy. Eur. Heart J. Cardiovasc. Imaging 23(4), 532\u2013542 (2022)","journal-title":"Eur. Heart J. Cardiovasc. Imaging"},{"issue":"2","key":"60_CR11","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1148\/radiol.2018181151","volume":"289","author":"RJ McDonald","year":"2018","unstructured":"McDonald, R.J., et al.: Gadolinium retention: a research roadmap from the 2018 NIH\/ACR\/RSNA workshop on gadolinium chelates. Radiology 289(2), 517\u2013534 (2018)","journal-title":"Radiology"},{"issue":"3","key":"60_CR12","doi-asserted-by":"publisher","first-page":"772","DOI":"10.1148\/radiol.15150025","volume":"275","author":"RJ McDonald","year":"2015","unstructured":"McDonald, R.J., et al.: Intracranial gadolinium deposition after contrast-enhanced MR imaging. Radiology 275(3), 772\u2013782 (2015)","journal-title":"Radiology"},{"issue":"3","key":"60_CR13","doi-asserted-by":"publisher","first-page":"906","DOI":"10.1002\/jmri.27048","volume":"52","author":"U Neisius","year":"2020","unstructured":"Neisius, U., et al.: Texture signatures of native myocardial T1 as novel imaging markers for identification of hypertrophic cardiomyopathy patients without scar. J. Magn. Reson. Imaging 52(3), 906\u2013919 (2020)","journal-title":"J. Magn. Reson. Imaging"},{"key":"60_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1007\/978-3-030-00928-1_48","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"AG Roy","year":"2018","unstructured":"Roy, A.G., Navab, N., Wachinger, C.: Concurrent spatial and channel \u2018Squeeze & Excitation\u2019 in fully convolutional networks. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 421\u2013429. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_48"},{"key":"60_CR15","doi-asserted-by":"publisher","first-page":"1401","DOI":"10.1016\/j.scitotenv.2019.07.075","volume":"687","author":"K Schmidt","year":"2019","unstructured":"Schmidt, K., Bau, M., Merschel, G., Tepe, N.: Anthropogenic gadolinium in tap water and in tap water-based beverages from fast-food franchises in six major cities in Germany. Sci. Total Environ. 687, 1401\u20131408 (2019)","journal-title":"Sci. Total Environ."},{"key":"60_CR16","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3d convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4489\u20134497 (2015)","DOI":"10.1109\/ICCV.2015.510"},{"key":"60_CR17","doi-asserted-by":"crossref","unstructured":"Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., Paluri, M.: A closer look at spatiotemporal convolutions for action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6450\u20136459 (2018)","DOI":"10.1109\/CVPR.2018.00675"},{"key":"60_CR18","doi-asserted-by":"crossref","unstructured":"Wang, F., et al.: Residual attention network for image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3156\u20133164 (2017)","DOI":"10.1109\/CVPR.2017.683"},{"key":"60_CR19","unstructured":"Xiong, R., et al.: On layer normalization in the transformer architecture. In: International Conference on Machine Learning, pp. 10524\u201310533. PMLR (2020)"},{"key":"60_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101568","volume":"59","author":"C Xu","year":"2020","unstructured":"Xu, C., Howey, J., Ohorodnyk, P., Roth, M., Zhang, H., Li, S.: Segmentation and quantification of infarction without contrast agents via spatiotemporal generative adversarial learning. Med. Image Anal. 59, 101568 (2020)","journal-title":"Med. Image Anal."},{"key":"60_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1007\/978-3-319-66179-7_28","volume-title":"Medical Image Computing and Computer Assisted Intervention-MICCAI 2017","author":"C Xu","year":"2017","unstructured":"Xu, C., et al.: Direct detection of pixel-level myocardial infarction areas via a deep-learning algorithm. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 240\u2013249. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66179-7_28"},{"key":"60_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101668","volume":"62","author":"C Xu","year":"2020","unstructured":"Xu, C., Xu, L., Ohorodnyk, P., Roth, M., Chen, B., Li, S.: Contrast agent-free synthesis and segmentation of ischemic heart disease images using progressive sequential causal gans. Med. Image Anal. 62, 101668 (2020)","journal-title":"Med. Image Anal."},{"issue":"3","key":"60_CR23","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1148\/radiol.2019182304","volume":"291","author":"N Zhang","year":"2019","unstructured":"Zhang, N., et al.: Deep learning for diagnosis of chronic myocardial infarction on nonenhanced cardiac cine MRI. Radiology 291(3), 606\u2013617 (2019)","journal-title":"Radiology"},{"issue":"8","key":"60_CR24","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1161\/CIRCULATIONAHA.121.054432","volume":"144","author":"Q Zhang","year":"2021","unstructured":"Zhang, Q., et al.: Toward replacing late gadolinium enhancement with artificial intelligence virtual native enhancement for gadolinium-free cardiovascular magnetic resonance tissue characterization in hypertrophic cardiomyopathy. Circulation 144(8), 589\u2013599 (2021)","journal-title":"Circulation"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43895-0_60","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T14:34:37Z","timestamp":1710167677000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43895-0_60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031438943","9783031438950"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43895-0_60","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2250","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"730","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}