{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T22:42:41Z","timestamp":1761000161106,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030615970"},{"type":"electronic","value":"9783030615987"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","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":[[2020]]},"DOI":"10.1007\/978-3-030-61598-7_8","type":"book-chapter","created":{"date-parts":[[2020,10,20]],"date-time":"2020-10-20T19:04:36Z","timestamp":1603220676000},"page":"82-90","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Data-Consistency in Latent Space and Online Update Strategy to Guide GAN for Fast MRI Reconstruction"],"prefix":"10.1007","author":[{"given":"Shuo","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shanhui","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoqian","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shu","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,21]]},"reference":[{"key":"8_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., Weiger, M., Scheidegger, M.B., et al.: SENSE: sensitivity encoding for fast MRI. Magn. Reson. Med. 42, 952\u2013962 (1999)","journal-title":"Magn. Reson. Med."},{"key":"8_CR2","unstructured":"King, K.F.: ASSET-parallel imaging on the GE scanner. In: International Workshop on Parallel MRI (2004)"},{"key":"8_CR3","doi-asserted-by":"publisher","first-page":"1202","DOI":"10.1002\/mrm.10171","volume":"47","author":"MA Griswold","year":"2002","unstructured":"Griswold, M.A., Jakob, P.M., Heidemann, R.M., et al.: Generalized auto-calibrating partially parallel acquisitions (GRAPPA). Magn. Reson. Med. 47, 1202\u20131210 (2002)","journal-title":"Magn. Reson. Med."},{"key":"8_CR4","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1002\/mrm.22428","volume":"64","author":"M Lustig","year":"2010","unstructured":"Lustig, M., Pauly, J.: SPIRiT: iterative self-consistent parallel imaging reconstruction from arbitrary k-space. Magn. Reson. Med. 64, 457\u2013471 (2010)","journal-title":"Magn. Reson. Med."},{"issue":"8","key":"8_CR5","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.1002\/cpa.20124","volume":"59","author":"EJ Candes","year":"2006","unstructured":"Candes, E.J., Romberg, J., Tao, T., et al.: Stable signal recovery from incomplete and inaccurate measurements. Commun. Pure Appl. Math. 59(8), 1207\u20131223 (2006)","journal-title":"Commun. Pure Appl. Math."},{"issue":"6","key":"8_CR6","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.1002\/mrm.21391","volume":"58","author":"M Lustig","year":"2007","unstructured":"Lustig, M., Donoho, D.L., Pauly, J.M., et al.: 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":"8_CR7","doi-asserted-by":"crossref","unstructured":"Wang, S., Su, Z., Ying, L., et al.: Accelerating magnetic resonance imaging via deep learning. In: International Symposium on Biomedical Imaging, pp. 514\u2013517 (2016)","DOI":"10.1109\/ISBI.2016.7493320"},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"Schlemper, J., Caballero, J., Hajnal, J.V., et al.: A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction. arXiv$$:$$ Computer Vision and Pattern Recognition (2017)","DOI":"10.1007\/978-3-319-59050-9_51"},{"key":"8_CR9","unstructured":"Yang, Y., Sun, J., Li, H., et al.: ADMM-CSNet: a deep learning approach for image compressive sensing. EEE Trans. Pattern Anal. Mach. Intell., 1 (2018)"},{"key":"8_CR10","unstructured":"Goodfellow, I., Pougetabadie, J., Mirza, M., et al.: Generative adversarial nets. In: Neural Information Processing Systems, pp. 2672\u20132680 (2014)"},{"key":"8_CR11","unstructured":"Yu, S., Dong, H., Yang, G., et al.: Deep De-Aliasing for Fast Compressive Sensing MRI. arXiv$$:$$ Computer Vision and Pattern Recognition (2017)"},{"key":"8_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"8_CR13","unstructured":"Alec, R., Luke, M., Soumith, C.: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv:1511.06434"},{"key":"8_CR14","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1109\/TMI.2018.2858752","volume":"38","author":"M Mardani","year":"2019","unstructured":"Mardani, M., Gong, E., Cheng, J.Y., et al.: Deep generative adversarial networks for compressed sensing MRI. IEEE Trans. Med. Imaging 38, 167\u2013179 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"8_CR15","doi-asserted-by":"crossref","unstructured":"Mao, X., et al.: Least squares generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision (2017)","DOI":"10.1109\/ICCV.2017.304"},{"key":"8_CR16","doi-asserted-by":"crossref","unstructured":"Zhu, J.-Y., et al.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision (2017)","DOI":"10.1109\/ICCV.2017.244"},{"key":"8_CR17","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, Yu.: 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"},{"key":"8_CR18","doi-asserted-by":"crossref","unstructured":"Isola, P., et al.: Image-to-image translation with conditional adversarial networks. In: Computer Vision and Pattern Recognition, pp. 5967\u20135976 (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"8_CR19","doi-asserted-by":"crossref","unstructured":"He, K., et al.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"}],"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\/978-3-030-61598-7_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T22:03:34Z","timestamp":1760997814000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-61598-7_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030615970","9783030615987"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-61598-7_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"21 October 2020","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":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmir2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmir2020","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":"18","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":"15","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":"83% - 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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The workshop 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)"}}]}}