{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T09:06:42Z","timestamp":1772010402681,"version":"3.50.1"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030338428","type":"print"},{"value":"9783030338435","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-33843-5_2","type":"book-chapter","created":{"date-parts":[[2019,10,24]],"date-time":"2019-10-24T00:13:49Z","timestamp":1571876029000},"page":"16-24","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Self-supervised Recurrent Neural Network for 4D Abdominal and In-utero MR Imaging"],"prefix":"10.1007","author":[{"given":"Tong","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laurence H.","family":"Jackson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alena","family":"Uus","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James R.","family":"Clough","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lisa","family":"Story","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mary A.","family":"Rutherford","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joseph V.","family":"Hajnal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria","family":"Deprez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,10,24]]},"reference":[{"key":"2_CR1","doi-asserted-by":"publisher","first-page":"1619","DOI":"10.1016\/S0140-6736(18)32490-5","volume":"393","author":"DFA Lloyd","year":"2018","unstructured":"Lloyd, D.F.A., et al.: Three-dimensional visualisation of the fetal heart using prenatal MRI with motion corrected slice-volume registration. Lancet 393, 1619\u20131627 (2018)","journal-title":"Lancet"},{"key":"2_CR2","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1111\/1471-0528.6_14571","volume":"124","author":"L Story","year":"2017","unstructured":"Story, L., Zhang, T., Aljabar, P., Hajnal, J., Shennan, A., Rutherford, M.: Magnetic resonance imaging assessment of lung volumes in fetuses at high risk of preterm birth. BJOG Int. J. Obstet. Gynaecol. 124, 24 (2017)","journal-title":"BJOG Int. J. Obstet. Gynaecol."},{"key":"2_CR3","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.ejogrb.2018.01.014","volume":"222","author":"L Story","year":"2018","unstructured":"Story, L., Hutter, J., Zhang, T., Shennan, A.H., Rutherford, M.: The use of antenatal fetal magnetic resonance imaging in the assessment of patients at high risk of preterm birth. Eur. J. Obstet. Gynecol. Reprod. Biol. 222, 134\u2013141 (2018)","journal-title":"Eur. J. Obstet. Gynecol. Reprod. Biol."},{"key":"2_CR4","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1111\/1471-0528.15393","volume":"126","author":"L Story","year":"2019","unstructured":"Story, L., et al.: Magnetic resonance imaging assessment of lung: body volume ratios in fetuses at high risk of preterm birth. BJOG Int. J. Obstet. Gynaecol. 126, 8 (2019)","journal-title":"BJOG Int. J. Obstet. Gynaecol."},{"issue":"10","key":"2_CR5","doi-asserted-by":"publisher","first-page":"1739","DOI":"10.1109\/TMI.2010.2051680","volume":"29","author":"A Gholipour","year":"2010","unstructured":"Gholipour, A., Estroff, J.A., Warfield, S.K.: Robust super-resolution volume reconstruction from slice acquisitions: application to fetal brain MRI. IEEE Trans. Med. Imaging 29(10), 1739\u20131758 (2010)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"8","key":"2_CR6","doi-asserted-by":"publisher","first-page":"1550","DOI":"10.1016\/j.media.2012.07.004","volume":"16","author":"M Kuklisova-Murgasova","year":"2012","unstructured":"Kuklisova-Murgasova, M., Quaghebeur, G., Rutherford, M.A., Hajnal, J.V., Schnabel, J.A.: Reconstruction of fetal brain MRI with intensity matching and complete outlier removal. Med. Image Anal. 16(8), 1550\u20131564 (2012)","journal-title":"Med. Image Anal."},{"issue":"9","key":"2_CR7","doi-asserted-by":"publisher","first-page":"1901","DOI":"10.1109\/TMI.2015.2415453","volume":"34","author":"B Kainz","year":"2015","unstructured":"Kainz, B., et al.: Fast volume reconstruction from motion corrupted stacks of 2D slices. IEEE Trans. Med. Imaging 34(9), 1901\u20131913 (2015)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1007\/978-3-030-00928-1_36","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"M Ebner","year":"2018","unstructured":"Ebner, M., et al.: An automated localization, segmentation and reconstruction framework for fetal brain MRI. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 313\u2013320. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_36"},{"key":"2_CR9","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.media.2019.03.008","volume":"54","author":"J Torrents-Barrena","year":"2019","unstructured":"Torrents-Barrena, J., et al.: Fully automatic 3D reconstruction of the placenta and its peripheral vasculature in intrauterine fetal MRI. Med. Image Anal. 54, 263\u2013279 (2019)","journal-title":"Med. Image Anal."},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Ramanathan, V., Tang, K., Mori, G., Fei-Fei, L.: Learning temporal embeddings for complex video analysis. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4471\u20134479 (2015)","DOI":"10.1109\/ICCV.2015.508"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Fernando, B., Bilen, H., Gavves, E., Gould, S.: Self-supervised video representation learning with odd-one-out networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3636\u20133645 (2017)","DOI":"10.1109\/CVPR.2017.607"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Wei, D., Lim, J., Zisserman, A., Freeman, W.T.: Learning and using the arrow of time, pp. 8052\u20138060 (2018)","DOI":"10.1109\/CVPR.2018.00840"},{"issue":"5\u20136","key":"2_CR13","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","volume":"18","author":"A Graves","year":"2005","unstructured":"Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw. 18(5\u20136), 602\u2013610 (2005)","journal-title":"Neural Netw."},{"key":"2_CR14","unstructured":"Jackson, L.H., et al.: Respiration resolved imaging using continuous steady state multiband excitation with linear frequency sweeps. In: ISMRM, Paris, ISMRM, pp. 5\u20137 (2018)"},{"issue":"2","key":"2_CR15","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2016","unstructured":"Dong, C., Loy, C.C., He, K., Tang, X.: Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295\u2013307 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"2_CR16","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1137\/090769521","volume":"3","author":"K Bredies","year":"2010","unstructured":"Bredies, K., Kunisch, K., Pock, T.: Total generalized variation. SIAM J. Imaging Sci. 3(3), 492\u2013526 (2010)","journal-title":"SIAM J. Imaging Sci."}],"container-title":["Lecture Notes in Computer Science","Machine Learning for Medical Image Reconstruction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-33843-5_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T23:03:04Z","timestamp":1729724584000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-33843-5_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030338428","9783030338435"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-33843-5_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"24 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLMIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning for Medical Image Reconstruction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmir2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmir-2019\/home","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":"32","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":"24","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":"75% - 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":"2.5","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":"3","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}