{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T02:21:23Z","timestamp":1742955683637,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030872304"},{"type":"electronic","value":"9783030872311"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-87231-1_10","type":"book-chapter","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T15:05:11Z","timestamp":1632323111000},"page":"97-106","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Fast Magnetic Resonance Imaging on\u00a0Regions of Interest: From Sensing to\u00a0Reconstruction"],"prefix":"10.1007","author":[{"given":"Liyan","family":"Sun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyu","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinghao","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoqing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yizhou","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"10_CR1","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.1109\/TCI.2020.3006727","volume":"6","author":"CD Bahadir","year":"2020","unstructured":"Bahadir, C.D., Wang, A.Q., Dalca, A.V., Sabuncu, M.R.: Deep-learning-based optimization of the under-sampling pattern in MRI. IEEE Trans. Comput. Imaging 6, 1139\u20131152 (2020)","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"10_CR2","doi-asserted-by":"publisher","first-page":"101950","DOI":"10.1016\/j.media.2020.101950","volume":"69","author":"AE Kavur","year":"2021","unstructured":"Kavur, A.E., et al.: CHAOS challenge-combined (CT-MR) healthy abdominal organ segmentation. Med. Image Anal. 69, 101950 (2021)","journal-title":"Med. Image Anal."},{"issue":"1","key":"10_CR3","doi-asserted-by":"publisher","first-page":"11","DOI":"10.5152\/dir.2019.19025","volume":"26","author":"AE Kavur","year":"2020","unstructured":"Kavur, A.E., et al.: Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors. Diagn. Interv. Radiol. 26(1), 11 (2020)","journal-title":"Diagn. Interv. Radiol."},{"key":"10_CR4","doi-asserted-by":"publisher","unstructured":"Kavur, A.E., Selver, M.A., Dicle, O., Bari\u015f, M., Gezer, N.S.: CHAOS - combined (CT-MR) healthy abdominal organ segmentation challenge data, April 2019. https:\/\/doi.org\/10.5281\/zenodo.3362844","DOI":"10.5281\/zenodo.3362844"},{"key":"10_CR5","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2015)"},{"key":"10_CR6","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.mri.2019.11.014","volume":"67","author":"AS Konar","year":"2020","unstructured":"Konar, A.S., Vajuvalli, N.N., Rao, R., Jain, D., Babu, D.R., Geethanath, S.: Accelerated dynamic contrast enhanced MRI based on region of interest compressed sensing. Magn. Reson. Imaging 67, 18\u201323 (2020)","journal-title":"Magn. Reson. Imaging"},{"issue":"4","key":"10_CR7","doi-asserted-by":"publisher","first-page":"1215","DOI":"10.1109\/TBME.2014.2385307","volume":"62","author":"Z Li","year":"2014","unstructured":"Li, Z., et al.: Expiration-phase template-based motion correction of free-breathing abdominal dynamic contrast enhanced MRI. IEEE Trans. Biomed. Eng. 62(4), 1215\u20131225 (2014)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"6","key":"10_CR8","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":"3","key":"10_CR9","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/j.mri.2012.11.008","volume":"32","author":"H Oh","year":"2014","unstructured":"Oh, H., Lee, S.: Visually weighted reconstruction of compressive sensing MRI. Magn. Reson. Imaging 32(3), 270\u2013280 (2014)","journal-title":"Magn. Reson. Imaging"},{"issue":"6","key":"10_CR10","doi-asserted-by":"publisher","first-page":"1488","DOI":"10.1109\/TMI.2018.2820120","volume":"37","author":"TM Quan","year":"2018","unstructured":"Quan, T.M., Nguyen-Duc, T., Jeong, W.K.: Compressed sensing MRI reconstruction using a generative adversarial network with a cyclic loss. IEEE Trans. Med. Imaging 37(6), 1488\u20131497 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1007\/978-3-319-59050-9_51","volume-title":"Information Processing in Medical Imaging","author":"J Schlemper","year":"2017","unstructured":"Schlemper, J., Caballero, J., Hajnal, J.V., Price, A., Rueckert, D.: A deep cascade of convolutional neural networks for MR image reconstruction. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 647\u2013658. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59050-9_51"},{"key":"10_CR12","unstructured":"Sun, J., Li, H., Xu, Z., et al.: Deep ADMM-Net for compressive sensing MRI. In: Advances in Neural Information Processing Systems, pp. 10\u201318 (2016)"},{"key":"10_CR13","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.mri.2019.07.010","volume":"63","author":"L Sun","year":"2019","unstructured":"Sun, L., Fan, Z., Ding, X., Huang, Y., Paisley, J.: Region-of-interest undersampled MRI reconstruction: a deep convolutional neural network approach. Magn. Reson. Imaging 63, 185\u2013192 (2019)","journal-title":"Magn. Reson. Imaging"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Wang, S., et al.: Accelerating magnetic resonance imaging via deep learning. In: International Symposium on Biomedical Imaging, pp. 514\u2013517. IEEE (2016)","DOI":"10.1109\/ISBI.2016.7493320"},{"key":"10_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1007\/978-3-319-10590-1_53","volume-title":"Computer Vision \u2013 ECCV 2014","author":"MD Zeiler","year":"2014","unstructured":"Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818\u2013833. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10590-1_53"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Zhang, C., van de Giessen, M., Eisemann, E., Vilanova, A.: User-guided compressed sensing for magnetic resonance angiography. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2416\u20132419. IEEE (2014)","DOI":"10.1109\/EMBC.2014.6944109"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Zhou, B., Zhou, S.K.: DuDoRNet: learning a dual-domain recurrent network for fast MRI reconstruction with deep T1 prior. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4273\u20134282 (2020)","DOI":"10.1109\/CVPR42600.2020.00433"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87231-1_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T18:13:28Z","timestamp":1725819208000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87231-1_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030872304","9783030872311"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87231-1_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 September 2021","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":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai2021.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":"1622","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":"531","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":"33% - 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.","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)"}}]}}