{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:10:50Z","timestamp":1783437050136,"version":"3.54.6"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030597122","type":"print"},{"value":"9783030597139","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/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":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-59713-9_19","type":"book-chapter","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T05:06:21Z","timestamp":1601615181000},"page":"188-198","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Model-Driven Deep Attention Network for Ultra-fast Compressive Sensing MRI Guided by Cross-contrast MR Image"],"prefix":"10.1007","author":[{"given":"Yan","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Na","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heran","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zongben","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"key":"19_CR1","doi-asserted-by":"publisher","first-page":"170117","DOI":"10.1038\/sdata.2017.117","volume":"4","author":"S Bakas","year":"2017","unstructured":"Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Nat. Sci. data 4, 170117 (2017)","journal-title":"Nat. Sci. data"},{"key":"19_CR2","unstructured":"Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv (2018)"},{"issue":"6","key":"19_CR3","doi-asserted-by":"publisher","first-page":"1086","DOI":"10.1002\/mrm.21236","volume":"57","author":"KT Block","year":"2007","unstructured":"Block, K.T., Uecker, M., Frahm, J.: 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":"3","key":"19_CR4","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1109\/TMI.2017.2764326","volume":"37","author":"A Chartsias","year":"2017","unstructured":"Chartsias, A., Joyce, T., Giuffrida, M.V., Tsaftaris, S.A.: Multimodal MR synthesis via modality-invariant latent representation. IEEE Trans. Med. Imaging 37(3), 803\u2013814 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"10","key":"19_CR5","doi-asserted-by":"publisher","first-page":"2375","DOI":"10.1109\/TMI.2019.2901750","volume":"38","author":"SU Dar","year":"2019","unstructured":"Dar, S.U., Yurt, M., Karacan, L., Erdem, A., Erdem, E., \u00c7ukur, T.: Image synthesis in multi-contrast MRI with conditional generative adversarial networks. IEEE Trans. Med. Imaging 38(10), 2375\u20132388 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"19_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1007\/978-3-030-32251-9_78","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"J Duan","year":"2019","unstructured":"Duan, J., et al.: VS-Net: variable splitting network for accelerated parallel MRI reconstruction. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 713\u2013722. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32251-9_78"},{"issue":"3","key":"19_CR7","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1007\/s10851-016-0647-7","volume":"56","author":"EM Eksioglu","year":"2016","unstructured":"Eksioglu, E.M.: Decoupled algorithm for MRI reconstruction using nonlocal block matching model: BM3D-MRI. J. Math. Imaging Vis. 56(3), 430\u2013440 (2016). https:\/\/doi.org\/10.1007\/s10851-016-0647-7","journal-title":"J. Math. Imaging Vis."},{"issue":"7","key":"19_CR8","doi-asserted-by":"publisher","first-page":"932","DOI":"10.1109\/83.392335","volume":"4","author":"D Geman","year":"1995","unstructured":"Geman, D., Yang, C.: Nonlinear image recovery with half-quadratic regularization. IEEE Trans. Image Process. 4(7), 932\u2013946 (1995)","journal-title":"IEEE Trans. Image Process."},{"issue":"6","key":"19_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."},{"issue":"10","key":"19_CR10","doi-asserted-by":"publisher","first-page":"1344","DOI":"10.1016\/j.mri.2014.08.025","volume":"32","author":"J Huang","year":"2014","unstructured":"Huang, J., Chen, C., Axel, L.: Fast multi-contrast MRI reconstruction. Magn. Reson. Imaging 32(10), 1344\u20131352 (2014)","journal-title":"Magn. Reson. Imaging"},{"issue":"3","key":"19_CR11","doi-asserted-by":"publisher","first-page":"815","DOI":"10.1109\/TMI.2017.2781192","volume":"37","author":"Y Huang","year":"2017","unstructured":"Huang, Y., Shao, L., Frangi, A.F.: Cross-modality image synthesis via weakly coupled and geometry co-regularized joint dictionary learning. IEEE Trans. Med. Imaging 37(3), 815\u2013827 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"19_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1007\/978-3-319-66179-7_40","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2017","author":"T Joyce","year":"2017","unstructured":"Joyce, T., Chartsias, A., Tsaftaris, S.A.: Robust multi-modal MR image synthesis. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 347\u2013355. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66179-7_40"},{"key":"19_CR13","doi-asserted-by":"crossref","unstructured":"Lee, D., Yoo, J., Ye, J.C.: Deep residual learning for compressed sensing MRI. In: IEEE ISBI, pp. 15\u201318 (2017)","DOI":"10.1109\/ISBI.2017.7950457"},{"key":"19_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1007\/978-3-030-32251-9_87","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"H Li","year":"2019","unstructured":"Li, H., et al.: DiamondGAN: unified multi-modal generative adversarial networks for MRI sequences synthesis. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 795\u2013803. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32251-9_87"},{"issue":"6","key":"19_CR15","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.M.: Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn. Reson. Med. 58(6), 1182\u20131195 (2007)","journal-title":"Magn. Reson. Med."},{"issue":"2","key":"19_CR16","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., Pauly, J.M.: Compressed sensing MRI. IEEE Signal Process. Mag. 25(2), 72\u201382 (2008)","journal-title":"IEEE Signal Process. Mag."},{"key":"19_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"732","DOI":"10.1007\/978-3-030-32251-9_80","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"N Meng","year":"2019","unstructured":"Meng, N., Yang, Y., Xu, Z., Sun, J.: A prior learning network for joint image and sensitivity estimation in\u00a0parallel MR imaging. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 732\u2013740. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32251-9_80"},{"issue":"10","key":"19_CR18","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"19_CR19","doi-asserted-by":"crossref","unstructured":"Qin, X., Wang, Z., Bai, Y., Xie, X., Jia, H.: FFA-Net: feature fusion attention network for single image dehazing. In: AAAI (2020)","DOI":"10.1609\/aaai.v34i07.6865"},{"issue":"6","key":"19_CR20","doi-asserted-by":"publisher","first-page":"843","DOI":"10.1016\/j.media.2013.09.007","volume":"18","author":"X Qu","year":"2014","unstructured":"Qu, X., Hou, Y., Lam, F., Guo, D., Zhong, J., Chen, Z.: Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator. Med. Image Anal. 18(6), 843\u2013856 (2014)","journal-title":"Med. Image Anal."},{"issue":"5","key":"19_CR21","doi-asserted-by":"publisher","first-page":"1028","DOI":"10.1109\/TMI.2010.2090538","volume":"30","author":"S Ravishankar","year":"2010","unstructured":"Ravishankar, S., Bresler, Y.: MR image reconstruction from highly undersampled k-space data by dictionary learning. IEEE Trans. Med. Imaging 30(5), 1028\u20131041 (2010)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"12","key":"19_CR22","doi-asserted-by":"publisher","first-page":"2348","DOI":"10.1109\/TMI.2013.2282126","volume":"32","author":"S Roy","year":"2013","unstructured":"Roy, S., Carass, A., Prince, J.L.: Magnetic resonance image example-based contrast synthesis. IEEE Trans. Med. Imaging 32(12), 2348\u20132363 (2013)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"19_CR23","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1109\/TMI.2017.2760978","volume":"37","author":"J Schlemper","year":"2018","unstructured":"Schlemper, J., 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":"19_CR24","doi-asserted-by":"crossref","unstructured":"Wang, S., Su, Z., Ying, L., Xi, P., Dong, L.: Accelerating magnetic resonance imaging via deep learning. In: IEEE ISBI, pp. 514\u2013517 (2016)","DOI":"10.1109\/ISBI.2016.7493320"},{"issue":"10","key":"19_CR25","doi-asserted-by":"publisher","first-page":"5357","DOI":"10.1118\/1.4962032","volume":"43","author":"L Weizman","year":"2016","unstructured":"Weizman, L., Eldar, Y.C., Ben, B.D.: Reference-based MRI. Med. Phys. 43(10), 5357 (2016)","journal-title":"Med. Phys."},{"key":"19_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1007\/978-3-030-00928-1_25","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"L Xiang","year":"2018","unstructured":"Xiang, L., et al.: Ultra-fast T2-weighted MR reconstruction using complementary T1-weighted information. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 215\u2013223. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_25"},{"key":"19_CR27","unstructured":"Yang, Y., Sun, J., Li, H., Xu, Z.: Deep ADMM-Net for compressive sensing MRI. In: NIPS, pp. 10\u201318 (2016)"},{"issue":"9","key":"19_CR28","doi-asserted-by":"publisher","first-page":"1850","DOI":"10.1109\/TBME.2015.2503756","volume":"63","author":"Z Zhan","year":"2015","unstructured":"Zhan, Z., Cai, J.F., Guo, D., Liu, Y., Chen, Z., Qu, X.: Fast multiclass dictionaries learning with geometrical directions in MRI reconstruction. IEEE Trans. Biomed. Eng. 63(9), 1850\u20131861 (2015)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"19_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1007\/978-3-030-01234-2_18","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Zhang","year":"2018","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 294\u2013310. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_18"},{"key":"19_CR30","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV, pp. 2223\u20132232 (2017)","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59713-9_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:05:32Z","timestamp":1759356332000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59713-9_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030597122","9783030597139"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59713-9_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 September 2020","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":"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":"4 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":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2020.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":"1809","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":"542","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":"30% - 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 due to the COVID-19 pandemic.","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)"}}]}}