{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:27:27Z","timestamp":1773246447615,"version":"3.50.1"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030615970","type":"print"},{"value":"9783030615987","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":"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_9","type":"book-chapter","created":{"date-parts":[[2020,10,20]],"date-time":"2020-10-20T19:04:36Z","timestamp":1603220676000},"page":"91-101","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Extending LOUPE for K-Space Under-Sampling Pattern Optimization in Multi-coil MRI"],"prefix":"10.1007","author":[{"given":"Jinwei","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qihao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mert","family":"Sabuncu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pascal","family":"Spincemaille","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thanh D.","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,21]]},"reference":[{"issue":"2","key":"9_CR1","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1109\/TMI.2018.2865356","volume":"38","author":"HK Aggarwal","year":"2018","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 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"9_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1007\/978-3-030-20351-1_61","volume-title":"Information Processing in Medical Imaging","author":"CD Bahadir","year":"2019","unstructured":"Bahadir, C.D., Dalca, A.V., Sabuncu, M.R.: Learning-based optimization of the under-sampling pattern in MRI. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 780\u2013792. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20351-1_61"},{"key":"9_CR3","unstructured":"Bengio, Y., L\u00e9onard, N., Courville, A.: Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432 (2013)"},{"issue":"1","key":"9_CR4","first-page":"1","volume":"3","author":"S Boyd","year":"2011","unstructured":"Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J., et al.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations Trends\u00ae Mach. Learn. 3(1), 1\u2013122 (2011)","journal-title":"Foundations Trends\u00ae Mach. Learn."},{"issue":"1","key":"9_CR5","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(1), 120\u2013145 (2011). https:\/\/doi.org\/10.1007\/s10851-010-0251-1","journal-title":"J. Math. Imaging Vis."},{"issue":"1","key":"9_CR6","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/s10479-007-0176-2","volume":"153","author":"B Colson","year":"2007","unstructured":"Colson, B., Marcotte, P., Savard, G.: An overview of bilevel optimization. Ann. Oper. Res. 153(1), 235\u2013256 (2007). https:\/\/doi.org\/10.1007\/s10479-007-0176-2","journal-title":"Ann. Oper. Res."},{"issue":"1","key":"9_CR7","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1137\/1019005","volume":"19","author":"JE Dennis Jr","year":"1977","unstructured":"Dennis Jr., J.E., Mor\u00e9, J.J.: Quasi-newton methods, motivation and theory. SIAM Rev. 19(1), 46\u201389 (1977)","journal-title":"SIAM Rev."},{"issue":"2","key":"9_CR8","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1006\/acha.1995.1008","volume":"2","author":"DL Donoho","year":"1995","unstructured":"Donoho, D.L., et al.: Nonlinear solution of linear inverse problems by Wavelet-Vaguelette decomposition. Appl. Comput. Harmonic Anal. 2(2), 101\u2013126 (1995)","journal-title":"Appl. Comput. Harmonic Anal."},{"issue":"3","key":"9_CR9","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1002\/mrm.24980","volume":"72","author":"L Feng","year":"2014","unstructured":"Feng, L., et al.: Golden-angle radial sparse parallel MRI: combination of compressed sensing, parallel imaging, and golden-angle radial sampling for fast and flexible dynamic volumetric MRI. Magn. Reson. Med. 72(3), 707\u2013717 (2014)","journal-title":"Magn. Reson. Med."},{"issue":"6","key":"9_CR10","doi-asserted-by":"publisher","first-page":"1394","DOI":"10.1109\/TMI.2018.2832540","volume":"37","author":"B G\u00f6zc\u00fc","year":"2018","unstructured":"G\u00f6zc\u00fc, B., et al.: Learning-based compressive MRI. IEEE Trans. Med. Imaging 37(6), 1394\u20131406 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"6","key":"9_CR11","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. Official J. Int. Soc. Magn. Reson. Med. 47(6), 1202\u20131210 (2002)","journal-title":"Magn. Reson. Med. Official J. Int. Soc. Magn. Reson. Med."},{"issue":"7","key":"9_CR12","doi-asserted-by":"publisher","first-page":"1545","DOI":"10.1109\/TMI.2019.2896180","volume":"38","author":"JP Haldar","year":"2019","unstructured":"Haldar, J.P., Kim, D.: OEDIPUS: an experiment design framework for sparsity-constrained MRI. IEEE Trans. Med. Imaging 38(7), 1545\u20131558 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"6","key":"9_CR13","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":"9_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"9_CR15","unstructured":"Hinton, G., Srivastava, N., Swersky, K.: Neural networks for machine learning. Coursera Video Lect. 264(1) (2012)"},{"key":"9_CR16","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"issue":"2","key":"9_CR17","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1002\/mrm.22595","volume":"65","author":"F Knoll","year":"2011","unstructured":"Knoll, F., Bredies, K., Pock, T., Stollberger, R.: Second order total generalized variation (TGV) for MRI. Magn. Reson. Med. 65(2), 480\u2013491 (2011)","journal-title":"Magn. Reson. Med."},{"issue":"1","key":"9_CR18","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1007\/s10334-010-0234-7","volume":"24","author":"F Knoll","year":"2011","unstructured":"Knoll, F., Clason, C., Diwoky, C., Stollberger, R.: Adapted random sampling patterns for accelerated MRI. Magn. Reson. Mater. Phys. Biol. Med. 24(1), 43\u201350 (2011)","journal-title":"Magn. Reson. Mater. Phys. Biol. Med."},{"issue":"6","key":"9_CR19","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. Official J. Int. Soc. Magn. Reson. Med. 58(6), 1182\u20131195 (2007)","journal-title":"Magn. Reson. Med. Official J. Int. Soc. Magn. Reson. Med."},{"issue":"6","key":"9_CR20","doi-asserted-by":"publisher","first-page":"1250","DOI":"10.1109\/TMI.2012.2188039","volume":"31","author":"M Murphy","year":"2012","unstructured":"Murphy, M., Alley, M., Demmel, J., Keutzer, K., Vasanawala, S., Lustig, M.: Fast $$l_1$$-spirit compressed sensing parallel imaging MRI: scalable parallel implementation and clinically feasible runtime. IEEE Trans. Med. Imaging 31(6), 1250\u20131262 (2012)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"9_CR21","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1137\/040605412","volume":"4","author":"S Osher","year":"2005","unstructured":"Osher, S., Burger, M., Goldfarb, D., Xu, J., Yin, W.: An iterative regularization method for total variation-based image restoration. Multiscale Model. Simul. 4(2), 460\u2013489 (2005)","journal-title":"Multiscale Model. Simul."},{"issue":"5","key":"9_CR22","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., Boesiger, P.: Sense: sensitivity encoding for fast MRI. Magn. Reson. Med. Official J. Int. Soc. Magn. Reson. Med. 42(5), 952\u2013962 (1999)","journal-title":"Magn. Reson. Med. Official J. Int. Soc. Magn. Reson. Med."},{"key":"9_CR23","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"},{"issue":"2","key":"9_CR24","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1109\/TMI.2017.2760978","volume":"37","author":"J Schlemper","year":"2017","unstructured":"Schlemper, J., Caballero, J., Hajnal, J.V., Price, A.N., Rueckert, D.: A deep cascade of convolutional neural networks for dynamic MR image reconstruction. IEEE Trans. Med. Imaging 37(2), 491\u2013503 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"9_CR25","doi-asserted-by":"publisher","first-page":"990","DOI":"10.1002\/mrm.24751","volume":"71","author":"M Uecker","year":"2014","unstructured":"Uecker, M., et al.: ESPIRiT-an eigenvalue approach to autocalibrating parallel MRI: where sense meets GRAPPA. Magn. Reson. Med. 71(3), 990\u20131001 (2014)","journal-title":"Magn. Reson. Med."},{"key":"9_CR26","unstructured":"Uecker, M., et al.: Berkeley advanced reconstruction toolbox. In: Proceedings of the International Society for Magnetic Resonance in Medicine, vol. 23 (2015)"},{"key":"9_CR27","unstructured":"Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: the missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022 (2016)"},{"key":"9_CR28","doi-asserted-by":"crossref","unstructured":"Vasanawala, S., et al.: Practical parallel imaging compressed sensing MRI: summary of two years of experience in accelerating body mri of pediatric patients. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1039\u20131043. IEEE (2011)","DOI":"10.1109\/ISBI.2011.5872579"},{"issue":"4","key":"9_CR29","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"9_CR30","doi-asserted-by":"publisher","first-page":"116579","DOI":"10.1016\/j.neuroimage.2020.116579","volume":"211","author":"J Zhang","year":"2020","unstructured":"Zhang, J., et al.: Fidelity imposed network edit (fine) for solving ill-posed image reconstruction. NeuroImage 211, 116579 (2020)","journal-title":"NeuroImage"}],"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_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T22:02:16Z","timestamp":1760997736000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-61598-7_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030615970","9783030615987"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-61598-7_9","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":"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)"}}]}}