{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T15:19:34Z","timestamp":1792250374372,"version":"4.4.0"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030872304","type":"print"},{"value":"9783030872311","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-87231-1_1","type":"book-chapter","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T11:05:11Z","timestamp":1632308711000},"page":"3-12","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Two-Stage Self-supervised Cycle-Consistency Network for Reconstruction of Thin-Slice MR Images"],"prefix":"10.1007","author":[{"given":"Zhiyang","family":"Lu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"issue":"6","key":"1_CR1","doi-asserted-by":"publisher","first-page":"1983","DOI":"10.1002\/mrm.24187","volume":"68","author":"E Plenge","year":"2012","unstructured":"Plenge, E., Poot, D.H., Bernsen, M., et al.: Super-resolution methods in MRI: can they improve the trade-off between resolution, signal-to-noise ratio, and acquisition time? Magn. Reson. Med. 68(6), 1983\u20131993 (2012)","journal-title":"Magn. Reson. Med."},{"issue":"5","key":"1_CR2","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.1109\/TMI.2017.2656907","volume":"36","author":"Y Jia","year":"2017","unstructured":"Jia, Y., Gholipour, A., He, Z., et al.: A new sparse representation framework for reconstruction of an isotropic high spatial resolution MR volume from orthogonal anisotropic resolution scans. IEEE Trans. Med. Imag. 36(5), 1182\u20131193 (2017)","journal-title":"IEEE Trans. Med. Imag."},{"issue":"6","key":"1_CR3","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1002\/cmr.a.21249","volume":"40","author":"E Van Reeth","year":"2012","unstructured":"Van Reeth, E., Tham, I.W., Tan, C.H., et al.: Super-resolution in magnetic resonance imaging: a review. Concepts Magn. Reson. Part A 40(6), 306\u2013325 (2012)","journal-title":"Concepts Magn. Reson. Part A"},{"issue":"12","key":"1_CR4","doi-asserted-by":"publisher","first-page":"3106","DOI":"10.1109\/TMM.2019.2919431","volume":"21","author":"W Yang","year":"2019","unstructured":"Yang, W., Zhang, X., Tian, Y., et al.: Deep learning for single image super-resolution: a brief review. IEEE Trans. Multimedia 21(12), 3106\u20133121 (2019)","journal-title":"IEEE Trans. Multimedia"},{"issue":"3","key":"1_CR5","doi-asserted-by":"publisher","first-page":"1129","DOI":"10.1109\/JBHI.2018.2843819","volume":"23","author":"J Shi","year":"2018","unstructured":"Shi, J., Li, Z., Ying, S., et al.: MR image super-resolution via wide residual networks with fixed skip connection. IEEE J. Biomed. Health inform. 23(3), 1129\u20131140 (2018)","journal-title":"IEEE J. Biomed. Health inform."},{"key":"1_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1007\/978-3-030-32248-9_2","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Z Li","year":"2019","unstructured":"Li, Z., et al.: A two-stage multi-loss super-resolution network for arterial spin labeling magnetic resonance imaging. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 12\u201320. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_2"},{"key":"1_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/978-3-319-67389-9_38","volume-title":"Machine Learning in Medical Imaging","author":"Z Li","year":"2017","unstructured":"Li, Z., Wang, Y., Yu, J.: Reconstruction of thin-slice medical images using generative adversarial network. In: Wang, Q., Shi, Y., Suk, H.-I., Suzuki, K. (eds.) MLMI 2017. LNCS, vol. 10541, pp. 325\u2013333. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67389-9_38"},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Chen, Y., Xie, Y., Zhou, Z., et al.: Brain MRI super resolution using 3D deep densely connected neural networks. In: ISBI, pp. 739\u2013742 (2018)","DOI":"10.1109\/ISBI.2018.8363679"},{"key":"1_CR9","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/j.neucom.2018.10.102","volume":"392","author":"J Du","year":"2020","unstructured":"Du, J., He, Z., Wang, L., et al.: Super-resolution reconstruction of single anisotropic 3D MR images using residual convolutional neural network. Neurocomputing 392, 209\u2013220 (2020)","journal-title":"Neurocomputing"},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Peng, C., Lin, W.A., Liao, H., et al.: SAINT: spatially aware interpolation network for medical slice synthesis. In: CVPR, pp. 7750\u20137759 (2020)","DOI":"10.1109\/CVPR42600.2020.00777"},{"key":"1_CR11","doi-asserted-by":"crossref","unstructured":"Jing, L., Tian, Y.: Self-supervised visual feature learning with deep neural networks: a survey. IEEE Trans. Pattern Anal. Mach. Intell. (2020)","DOI":"10.1109\/TPAMI.2020.2992393"},{"key":"1_CR12","doi-asserted-by":"crossref","unstructured":"Huang, J.B., Singh, A., Ahuja, N.: Single image super-resolution from transformed self-exemplars. In: CVPR, pp. 5197\u20135206 (2015)","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"1_CR13","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Liu, S., Zhang, J., et al.: Unsupervised image super-resolution using cycle-in-cycle generative adversarial networks. In: CVPR, pp. 701\u2013710 (2018)","DOI":"10.1109\/CVPRW.2018.00113"},{"key":"1_CR14","doi-asserted-by":"crossref","unstructured":"Reda, F.A., Sun, D., Dundar, A., et al.: Unsupervised video interpolation using cycle consistency. In: ICCV, pp. 892\u2013900 (2019)","DOI":"10.1109\/ICCV.2019.00098"},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"Zhao, C., Carass, A., Dewey, B.E., et al.: Self super-resolution for magnetic resonance images using deep networks. In: ISBI, pp. 365\u2013368 (2018)","DOI":"10.1109\/ISBI.2018.8363594"},{"key":"1_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1007\/978-3-030-00928-1_12","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"C Zhao","year":"2018","unstructured":"Zhao, C., et al.: A deep learning based anti-aliasing self super-resolution algorithm for MRI. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 100\u2013108. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_12"},{"issue":"3","key":"1_CR17","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1109\/TMI.2020.3037187","volume":"40","author":"C Zhao","year":"2020","unstructured":"Zhao, C., Dewey, B.E., Pham, D.L., et al.: SMORE: a self-supervised anti-aliasing and super-resolution algorithm for MRI using deep learning. IEEE Trans. Med. Imag. 40(3), 805\u2013817 (2020)","journal-title":"IEEE Trans. Med. Imag."},{"key":"1_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., et al.: Residual dense network for image super-resolution. In: CVPR, pp. 2472\u20132481 (2018)","DOI":"10.1109\/CVPR.2018.00262"},{"key":"1_CR19","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: CVPR, pp. 4681\u20134690 (2017)","DOI":"10.1109\/CVPR.2017.19"},{"issue":"4","key":"1_CR20","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1002\/jmri.21049","volume":"27","author":"CR Jack Jr","year":"2008","unstructured":"Jack,  Jr., C.R., Bernstein, M.A., Fox, N.C., et al.: The Alzheimer\u2019s disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imag. 27(4), 685\u2013691 (2008)","journal-title":"J. Magn. Reson. Imag."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87231-1_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:02:50Z","timestamp":1790035370000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87231-1_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030872304","9783030872311"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87231-1_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 September 2021","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":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai2021.org\/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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1622","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":"531","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":"33% - 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":"4","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)"}},{"value":"The conference was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}