{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T12:19:40Z","timestamp":1742991580234,"version":"3.40.3"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030937218"},{"type":"electronic","value":"9783030937225"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-030-93722-5_2","type":"book-chapter","created":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T15:04:40Z","timestamp":1642172680000},"page":"12-20","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Quality-Aware Cine Cardiac MRI Reconstruction and\u00a0Analysis from\u00a0Undersampled K-Space Data"],"prefix":"10.1007","author":[{"given":"In\u00eas","family":"Machado","sequence":"first","affiliation":[]},{"given":"Esther","family":"Puyol-Ant\u00f3n","sequence":"additional","affiliation":[]},{"given":"Kerstin","family":"Hammernik","sequence":"additional","affiliation":[]},{"given":"Gast\u00e3o","family":"Cruz","sequence":"additional","affiliation":[]},{"given":"Devran","family":"Ugurlu","sequence":"additional","affiliation":[]},{"given":"Bram","family":"Ruijsink","sequence":"additional","affiliation":[]},{"given":"Miguel","family":"Castelo-Branco","sequence":"additional","affiliation":[]},{"given":"Alistair","family":"Young","sequence":"additional","affiliation":[]},{"given":"Claudia","family":"Prieto","sequence":"additional","affiliation":[]},{"given":"Julia A.","family":"Schnabel","sequence":"additional","affiliation":[]},{"given":"Andrew P.","family":"King","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,14]]},"reference":[{"key":"2_CR1","doi-asserted-by":"publisher","first-page":"17","DOI":"10.3389\/fcvm.2020.00017","volume":"7","author":"A Bustin","year":"2020","unstructured":"Bustin, A., Fuin, N., Botnar, R.M., Prieto, C.: From compressed-sensing to artificial intelligence-based cardiac MRI reconstruction. Front. Cardiovasc. Med. 7, 17 (2020)","journal-title":"Front. Cardiovasc. Med."},{"issue":"1","key":"2_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13244-019-0754-2","volume":"10","author":"RM Mench\u00f3n-Lara","year":"2019","unstructured":"Mench\u00f3n-Lara, R.M., Simmross-Wattenberg, F., Casaseca-de-la Higuera, P., Mart\u00edn-Fern\u00e1ndez, M., Alberola-L\u00f3pez, C.: Reconstruction techniques for cardiac cine MRI. Insights Imaging 10(1), 1\u201316 (2019)","journal-title":"Insights Imaging"},{"issue":"2","key":"2_CR3","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":"6","key":"2_CR4","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":"1","key":"2_CR5","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1186\/s12968-016-0227-4","volume":"18","author":"SE Petersen","year":"2015","unstructured":"Petersen, S.E., et al.: UK biobank\u2019s cardiovascular magnetic resonance protocol. J. Cardiovasc. Magn. Reson. 18(1), 8 (2015)","journal-title":"J. Cardiovasc. Magn. Reson."},{"issue":"1","key":"2_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12968-017-0327-9","volume":"19","author":"SE Petersen","year":"2017","unstructured":"Petersen, S.E., et al.: Reference ranges for cardiac structure and function using cardiovascular magnetic resonance (CMR) in caucasians from the UK biobank population cohort. J. Cardiovasc. Magn. Reson. 19(1), 1\u201319 (2017)","journal-title":"J. Cardiovasc. Magn. Reson."},{"issue":"3","key":"2_CR7","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1109\/TMI.2013.2293974","volume":"33","author":"JP Haldar","year":"2013","unstructured":"Haldar, J.P.: Low-rank modeling of local $$ k $$-space neighborhoods (loraks) for constrained MRI. IEEE Trans. Med. Imaging 33(3), 668\u2013681 (2013)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2_CR8","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":"2_CR9","doi-asserted-by":"publisher","first-page":"105","DOI":"10.3389\/fcvm.2020.00105","volume":"7","author":"C Chen","year":"2020","unstructured":"Chen, C., et al.: Improving the generalizability of convolutional neural network-based segmentation on CMR images. Front. Cardiovasc. Med. 7, 105 (2020)","journal-title":"Front. Cardiovasc. Med."},{"issue":"1","key":"2_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12968-019-0523-x","volume":"21","author":"R Robinson","year":"2019","unstructured":"Robinson, R., et al.: Automated quality control in image segmentation: application to the UK biobank cardiovascular magnetic resonance imaging study. J. Cardiovasc. Magn. Reson. 21(1), 1\u201314 (2019)","journal-title":"J. Cardiovasc. Magn. Reson."},{"key":"2_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1007\/978-3-030-78710-3_11","volume-title":"Functional Imaging and Modeling of the Heart","author":"F Galati","year":"2021","unstructured":"Galati, F., Zuluaga, M.A.: Efficient model monitoring for quality control in cardiac image segmentation. In: Ennis, D.B., Perotti, L.E., Wang, V.Y. (eds.) FIMH 2021. LNCS, vol. 12738, pp. 101\u2013111. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-78710-3_11"},{"key":"2_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1007\/978-3-030-00937-3_66","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"R Robinson","year":"2018","unstructured":"Robinson, R., et al.: Real-time prediction of segmentation quality. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 578\u2013585. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00937-3_66"}],"container-title":["Lecture Notes in Computer Science","Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-93722-5_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T14:33:31Z","timestamp":1651156411000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-93722-5_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030937218","9783030937225"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-93722-5_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"14 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"STACOM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Statistical Atlases and Computational Models of the Heart","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":"27 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"stacom2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/stacom2021.cardiacatlas.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":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"48","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":"40","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":"2","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","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 accepted papers split in 25 regular papers and 15 Challenge papers. The workshop took place 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)"}}]}}