{"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":1772010402671,"version":"3.50.1"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030322472","type":"print"},{"value":"9783030322489","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":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-32248-9_4","type":"book-chapter","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T23:08:49Z","timestamp":1570662529000},"page":"30-38","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Model-Based Convolutional De-Aliasing Network Learning for Parallel MR Imaging"],"prefix":"10.1007","author":[{"given":"Yanxia","family":"Chen","sequence":"first","affiliation":[]},{"given":"Taohui","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Cheng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Qiegen","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Shanshan","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"issue":"5","key":"4_CR1","doi-asserted-by":"publisher","first-page":"952","DOI":"10.1002\/(SICI)1522-2594(199911)42:5<952::AID-MRM16>3.0.CO;2-S","volume":"42","author":"KP Pruessmann","year":"1999","unstructured":"Pruessmann, K.P., et al.: SENSE: sensitivity encoding for fast MRI. Magn. Reson. Med. 42(5), 952\u2013962 (1999)","journal-title":"Magn. Reson. Med."},{"issue":"6","key":"4_CR2","doi-asserted-by":"publisher","first-page":"1202","DOI":"10.1002\/mrm.10171","volume":"47","author":"MA Griswold","year":"2002","unstructured":"Griswold, M.A., et al.: Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn. Reson. Med. 47(6), 1202\u20131210 (2002)","journal-title":"Magn. Reson. Med."},{"issue":"2","key":"4_CR3","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1002\/mrm.22428","volume":"64","author":"M Lustig","year":"2010","unstructured":"Lustig, M., Pauly, J.M.: SPIRiT: iterative self-consistent parallel imaging reconstruction from arbitrary k-space. Magn. Reson. Med. 64(2), 457\u2013471 (2010)","journal-title":"Magn. Reson. Med."},{"issue":"2","key":"4_CR4","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1109\/MSP.2007.914728","volume":"25","author":"M Lustig","year":"2008","unstructured":"Lustig, M., Donoho, D.L., Santos, J.M., et al.: Compressed sensing MRI. IEEE Signal Process. Mag. 25(2), 72\u201382 (2008)","journal-title":"IEEE Signal Process. Mag."},{"issue":"4","key":"4_CR5","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1002\/mrm.24997","volume":"72","author":"PJ Shin","year":"2014","unstructured":"Shin, P.J., et al.: Calibrationless parallel imaging reconstruction based on structured low-rank matrix completion. Mag. Reson. Med. 72(4), 959\u2013970 (2014)","journal-title":"Mag. Reson. Med."},{"issue":"2","key":"4_CR6","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.media.2010.08.001","volume":"15","author":"L Cha\u00e2ri","year":"2011","unstructured":"Cha\u00e2ri, L., et al.: A wavelet-based regularized reconstruction algorithm for SENSE parallel MRI with applications to neuroimaging. Med. Image Anal. 15(2), 185\u2013201 (2011)","journal-title":"Med. Image Anal."},{"issue":"6","key":"4_CR7","doi-asserted-by":"publisher","first-page":"1086","DOI":"10.1002\/mrm.21236","volume":"57","author":"KT Block","year":"2007","unstructured":"Block, K.T., et al.: Undersampled radial MRI with multiple coils. Iterative image reconstruction using a total variation constraint. Magn. Reson. Med. 57(6), 1086\u20131098 (2007)","journal-title":"Magn. Reson. Med."},{"issue":"1","key":"4_CR8","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1109\/TMI.2017.2746086","volume":"37","author":"S Wang","year":"2018","unstructured":"Wang, S., et al.: Learning joint-sparse codes for calibration-free parallel MR imaging. IEEE Trans. Med. Imaging 37(1), 251\u2013261 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"6","key":"4_CR9","doi-asserted-by":"publisher","first-page":"3055","DOI":"10.1002\/mrm.26977","volume":"79","author":"K Hammernik","year":"2018","unstructured":"Hammernik, K., et al.: Learning a variational network for reconstruction of accelerated MRI data. Magn. Reson. Med. 79(6), 3055\u20133071 (2018)","journal-title":"Magn. Reson. Med."},{"key":"4_CR10","doi-asserted-by":"crossref","unstructured":"Wang, S., et al.: Accelerating magnetic resonance imaging via deep learning. In: IEEE ISBI, Prague, Czech Republic, pp. 514\u2013517 (2016)","DOI":"10.1109\/ISBI.2016.7493320"},{"issue":"2","key":"4_CR11","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1109\/TMI.2017.2760978","volume":"37","author":"J Schlemper","year":"2018","unstructured":"Schlemper, J., Caballero, J., Hajnal, J.V., et al.: A deep cascade of convolutional neural networks for dynamic MR image reconstruction. IEEE Trans. Med. Imaging 37(2), 491\u2013503 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"4_CR12","unstructured":"Sun, J., Li, H., Xu, Z.: Deep ADMM-Net for compressive sensing MRI. In: NIPS, Barcelona, Spain, pp. 10\u201318 (2016)"},{"key":"4_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1007\/978-3-030-00928-1_21","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"P Zhang","year":"2018","unstructured":"Zhang, P., Wang, F., Xu, W., Li, Y.: Multi-channel generative adversarial network for parallel magnetic resonance image reconstruction in K-space. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 180\u2013188. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_21"},{"issue":"2","key":"4_CR14","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1109\/TMI.2018.2865356","volume":"38","author":"HK Aggarwal","year":"2019","unstructured":"Aggarwal, H.K., Mani, M.P., Jacob, M.: MoDL: model-based deep learning architecture for inverse problems. IEEE Trans. Med. Imaging 38(2), 394\u2013405 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"4_CR15","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1137\/080725891","volume":"2","author":"T Goldstein","year":"2009","unstructured":"Goldstein, T., Osher, S.: The split Bregman method for L1-regularized problems. SIAM J. Imaging Sci. 2(2), 323\u2013343 (2009)","journal-title":"SIAM J. Imaging Sci."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32248-9_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:21:16Z","timestamp":1728519676000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32248-9_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322472","9783030322489"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32248-9_4","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":"10 October 2019","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":"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":"13 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":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2019.org\/","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":"1730","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":"539","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":"31% - 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":"3.07","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":"6.31","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":"This content has been made available to all.","name":"free","label":"Free to read"}]}}