{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T21:03:28Z","timestamp":1761253408080,"version":"3.40.3"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030322472"},{"type":"electronic","value":"9783030322489"}],"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_3","type":"book-chapter","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T23:08:49Z","timestamp":1570662529000},"page":"21-29","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Model Learning: Primal Dual Networks for Fast MR Imaging"],"prefix":"10.1007","author":[{"given":"Jing","family":"Cheng","sequence":"first","affiliation":[]},{"given":"Haifeng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Leslie","family":"Ying","sequence":"additional","affiliation":[]},{"given":"Dong","family":"Liang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"issue":"6","key":"3_CR1","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.1002\/mrm.21391","volume":"58","author":"M Lustig","year":"2007","unstructured":"Lustig, M., Donoho, D., Pauly, J.: Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn. Reson. Med. 58(6), 1182\u20131195 (2007)","journal-title":"Magn. Reson. Med."},{"key":"3_CR2","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1126\/science.aaa8415","volume":"349","author":"M Jordan","year":"2015","unstructured":"Jordan, M., Mitchell, T.: Machine learning: trends, perspectives, and prospects. Science 349, 255\u2013260 (2015)","journal-title":"Science"},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Wang, S., Su, Z., Ying, L., Peng, X., Zhu, S., Liang, F., et al.: Accelerating magnetic resonance imaging via deep learning. In: 13th International Symposium on Biomedical Imaging, Prague, Czech Republic, pp. 514\u2013517. IEEE (2016)","DOI":"10.1109\/ISBI.2016.7493320"},{"key":"3_CR4","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1038\/nature25988","volume":"555","author":"B Zhu","year":"2018","unstructured":"Zhu, B., Liu, J., Cauley, S., Rosen, B., Rosen, M.: Image reconstruction by domain-transform manifold learning. Nature 555, 487\u2013492 (2018)","journal-title":"Nature"},{"key":"3_CR5","doi-asserted-by":"publisher","first-page":"2188","DOI":"10.1002\/mrm.27201","volume":"80","author":"T Eo","year":"2018","unstructured":"Eo, T., Jun, Y., Kim, T., Jang, J., Lee, H., Hwang, D.: KIKI-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images. Magn. Reson. Med. 80, 2188\u20132201 (2018)","journal-title":"Magn. Reson. Med."},{"key":"3_CR6","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., Price, A., Rueckert, D.: A deep cascade of convolutional neural networks for dynamic MR image reconstruction. IEEE Trans. Med. Imaging 37, 491\u2013503 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"3_CR7","doi-asserted-by":"publisher","first-page":"1488","DOI":"10.1109\/TMI.2018.2820120","volume":"37","author":"T Quan","year":"2018","unstructured":"Quan, T., Nguyen-Duc, T., Jeong, W.: Compressed sensing MRI reconstruction using a generative adversarial network with a cyclic loss. IEEE Trans. Med. Imaging 37, 1488\u20131497 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"3_CR8","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1109\/TMI.2018.2865356","volume":"38","author":"H Aggarwal","year":"2019","unstructured":"Aggarwal, H., Mani, M., Jacob, M.: MoDL: model-based deep learning architecture for inverse problems. IEEE Trans. Med. Imaging 38, 394\u2013405 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"3_CR9","doi-asserted-by":"publisher","first-page":"3055","DOI":"10.1002\/mrm.26977","volume":"79","author":"K Hammernik","year":"2018","unstructured":"Hammernik, K., Klatzer, T., Kobler, E., Recht, M., Sodickson, D., Pock, T.: Learning a variational network for reconstruction of accelerated MRI data. Magn. Reson. Med. 79, 3055\u20133071 (2018)","journal-title":"Magn. Reson. Med."},{"key":"3_CR10","doi-asserted-by":"publisher","unstructured":"Yang, Y., Sun, J., Li, H., Xu, Z.: ADMM-CSNet: a deep learning approach for image compressive sensing. IEEE Trans. Pattern Anal. Mach. Intell. (2018). https:\/\/doi.org\/10.1109\/TPAMI.2018.2883941","DOI":"10.1109\/TPAMI.2018.2883941"},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Zhang, J., Ghanem, B.: ISTA-Net: interpretable optimization-inspired deep network for image compressive sensing. In: Computer Vision and Pattern Recognition, pp. 1828\u20131837 (2018)","DOI":"10.1109\/CVPR.2018.00196"},{"key":"3_CR12","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1007\/s10851-010-0251-1","volume":"40","author":"A Chambolle","year":"2011","unstructured":"Chambolle, A., Pock, T.: A first-order primal-dual algorithm for convex problems with applications to imaging. J. Math. Imaging Vis. 40, 120\u2013145 (2011)","journal-title":"J. Math. Imaging Vis."},{"key":"3_CR13","doi-asserted-by":"publisher","first-page":"1322","DOI":"10.1109\/TMI.2018.2799231","volume":"37","author":"J Adler","year":"2018","unstructured":"Adler, J., Oktem, O.: Learned primal-dual reconstruction. IEEE Trans. Med. Imaging 37, 1322\u20131332 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"3_CR14","unstructured":"Cheng, J., Wang, H., Ying, L., Liang, D.: Learning primal dual network for fast MR imaging. In: 27th Annual Meeting of ISMRM, Montreal, QC, Canada (2019)"},{"issue":"2","key":"3_CR15","first-page":"288","volume":"4","author":"J Yang","year":"2010","unstructured":"Yang, J., Zhang, Y., Yin, W.: A fast alternating direction method for TVL1-L2 signal reconstruction from partial fourier data. IEEE J. STSP 4(2), 288\u2013297 (2010)","journal-title":"IEEE J. STSP"}],"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_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:21:22Z","timestamp":1728519682000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32248-9_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322472","9783030322489"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32248-9_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"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"}]}}