{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T18:04:50Z","timestamp":1776362690595,"version":"3.51.2"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031353017","type":"print"},{"value":"9783031353024","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-35302-4_26","type":"book-chapter","created":{"date-parts":[[2023,6,15]],"date-time":"2023-06-15T07:02:40Z","timestamp":1686812560000},"page":"255-264","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Whole Heart 3D Shape Reconstruction from Sparse Views: Leveraging Cardiac Computed Tomography for Cardiovascular Magnetic Resonance"],"prefix":"10.1007","author":[{"given":"Hao","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marica","family":"Muffoletto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steven A.","family":"Niederer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steven E.","family":"Williams","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michelle C.","family":"Williams","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alistair A.","family":"Young","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,16]]},"reference":[{"key":"26_CR1","unstructured":"Marcel, B., Banerjee, A., Grau, V.: Biventricular surface reconstruction from cine MRI contours using point completion networks. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 105\u2013109 (2021)"},{"key":"26_CR2","doi-asserted-by":"publisher","first-page":"3539","DOI":"10.1007\/s10554-021-02326-9","volume":"37","author":"Chen Vincent","year":"2021","unstructured":"Vincent, Chen, et al.: Effect of age and sex on fully automated deep learning assessment of left ventricular function, volumes, and contours in cardiac magnetic resonance imaging. The International Journal of Cardiovascular Imaging 37, 3539\u20133547 (2021)","journal-title":"The International Journal of Cardiovascular Imaging"},{"key":"26_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102228","volume":"74","author":"Chen Xiang","year":"2021","unstructured":"Xiang, Chen, et al.: Shape registration with learned deformations for 3D shape reconstruction from sparse and incomplete point clouds. Medical Image Analysis 74, 102228 (2021)","journal-title":"Medical Image Analysis"},{"key":"26_CR4","doi-asserted-by":"crossref","unstructured":"\u00d6zg\u00fcn, \u00c7., et al.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. Medical Image Computing and Computer-Assisted Intervention. Lecture Notes in Computer Science 9901, 424\u2013432 (2016)","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"26_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102445","volume":"79","author":"Joyce Thomas","year":"2022","unstructured":"Thomas, Joyce, et al.: Rapid inference of personalised left-ventricular meshes by deformation-based differentiable mesh voxelization. Medical Image Analysis 79, 102445 (2022)","journal-title":"Medical Image Analysis"},{"key":"26_CR6","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"26_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12968-019-0551-6","volume":"21","author":"C Mauger","year":"2019","unstructured":"Mauger, C., et al.: Right ventricular shape and function: cardiovascular magnetic resonance reference morphology and biventricular risk factor morphometrics in UK Biobank. Journal of Cardiovascular Magnetic Resonance 21, 1\u201313 (2019)","journal-title":"Journal of Cardiovascular Magnetic Resonance"},{"key":"26_CR8","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.220122","volume":"306","author":"CA Mauger","year":"2022","unstructured":"Mauger, C.A., et al.: Multi-ethnic study of atherosclerosis: relationship between left ventricular shape at cardiac MRI and 10-year outcomes. Radiology 306, e220122 (2022)","journal-title":"Radiology"},{"key":"26_CR9","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1109\/TMI.2017.2743464","volume":"37","author":"O Oktay","year":"2017","unstructured":"Oktay, O., et al.: Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation. IEEE Transactions on Medical Imaging 37, 384\u2013395 (2017)","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"SCOT-Heart Investigators: Coronary CT angiography and 5-year risk of myocardial infarction. New England Journal of Medicine 379, 924\u2013933 (2018)","DOI":"10.1056\/NEJMoa1805971"},{"key":"26_CR11","doi-asserted-by":"crossref","unstructured":"Wang, S., et al.: Joint motion correction and super resolution for cardiac segmentation via latent optimisation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Lecture Notes in Computer Science 12903, 14\u201324 (2021)","DOI":"10.1007\/978-3-030-87199-4_2"},{"key":"26_CR12","doi-asserted-by":"crossref","unstructured":"Xu, H., et al.: Ventricle surface reconstruction from cardiac MR slices using deep learning. Functional Imaging and Modeling of the Heart. Lecture Notes in Computer Science 11504, 342\u2013351 (2019)","DOI":"10.1007\/978-3-030-21949-9_37"},{"key":"26_CR13","doi-asserted-by":"crossref","unstructured":"Xu, H., et al.: Whole Heart Anatomical Refinement from CCTA Using Extrapolation and Parcellation. Functional Imaging and Modeling of the Heart. Lecture Notes in Computer Science 12738, 63\u201370 (2021)","DOI":"10.1007\/978-3-030-78710-3_7"}],"container-title":["Lecture Notes in Computer Science","Functional Imaging and Modeling of the Heart"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-35302-4_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T15:08:32Z","timestamp":1691593712000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-35302-4_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031353017","9783031353024"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-35302-4_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"16 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FIMH","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Functional Imaging and Modeling of the Heart","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lyon","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 June 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 June 2023","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":"fimh2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/fimh2023.sciencesconf.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Eqiunocs","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"80","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":"72","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":"90% - 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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}