{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T10:28:40Z","timestamp":1750415320789,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030787097"},{"type":"electronic","value":"9783030787103"}],"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.springer.com\/tdm"},{"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.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-78710-3_1","type":"book-chapter","created":{"date-parts":[[2021,6,17]],"date-time":"2021-06-17T18:04:00Z","timestamp":1623953040000},"page":"3-11","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Population-Based Personalization of Geometric Models of Myocardial Infarction"],"prefix":"10.1007","author":[{"given":"Kannara","family":"Mom","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrick","family":"Clarysse","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicolas","family":"Duchateau","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,6,18]]},"reference":[{"key":"1_CR1","first-page":"27","volume":"10","author":"AJ Connolly","year":"2016","unstructured":"Connolly, A.J., Bishop, M.J.: Computational representations of myocardial infarct scars and implications for arrhythmogenesis. Clin. Med. Insights Cardiol. 10, 27\u201340 (2016)","journal-title":"Clin. Med. Insights Cardiol."},{"key":"1_CR2","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. arXiv (2014)"},{"key":"1_CR3","doi-asserted-by":"publisher","first-page":"362","DOI":"10.1016\/j.media.2008.06.013","volume":"13","author":"RC Kerckhoffs","year":"2009","unstructured":"Kerckhoffs, R.C., McCulloch, A.D., Omens, J.H., et al.: Effects of biventricular pacing and scar size in a computational model of the failing heart with left bundle branch block. Med. Image Anal. 13, 362\u2013369 (2009). https:\/\/doi.org\/10.1016\/j.media.2008.06.013","journal-title":"Med. Image Anal."},{"key":"1_CR4","doi-asserted-by":"publisher","first-page":"2340","DOI":"10.1109\/TMI.2016.2562181","volume":"35","author":"N Duchateau","year":"2016","unstructured":"Duchateau, N., De Craene, M., Allain, P., et al.: Infarct localization from myocardial deformation: prediction and uncertainty quantification by regression from a low-dimensional space. IEEE Trans. Med. Imaging 35, 2340\u20132352 (2016). https:\/\/doi.org\/10.1109\/TMI.2016.2562181","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1_CR5","doi-asserted-by":"publisher","first-page":"e02794","DOI":"10.1002\/cnm.2794","volume":"33","author":"CN Leong","year":"2017","unstructured":"Leong, C.N., Lim, E., Andriyana, A., et al.: The role of infarct transmural extent in infarct extension: a computational study. Int. J. Numer. Method Biomed. Eng. 33, e02794 (2017). https:\/\/doi.org\/10.1002\/cnm.2794","journal-title":"Int. J. Numer. Method Biomed. Eng."},{"key":"1_CR6","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1161\/01.cir.56.5.786","volume":"56","author":"KA Reimer","year":"1977","unstructured":"Reimer, K.A., Lowe, J.E., Rasmussen, M.M., et al.: The wavefront phenomenon of ischemic cell death. 1. Myocardial infarct size vs duration of coronary occlusion in dogs. Circulation 56, 786\u2013794 (1977). https:\/\/doi.org\/10.1161\/01.cir.56.5.786","journal-title":"Circulation"},{"key":"1_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1007\/978-3-642-21028-0_54","volume-title":"Functional Imaging and Modeling of the Heart","author":"A Pashaei","year":"2011","unstructured":"Pashaei, A., Hoogendoorn, C., Sebasti\u00e1n, R., Romero, D., C\u00e1mara, O., Frangi, A.F.: Effect of scar development on fast electrophysiological models of the human heart: in-silico study on atlas-based virtual populations. In: Metaxas, D.N., Axel, L. (eds.) FIMH 2011. LNCS, vol. 6666, pp. 427\u2013436. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-21028-0_54"},{"key":"1_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1007\/978-3-319-59448-4_11","volume-title":"Functional Imaging and Modelling of the Heart","author":"GK Rumindo","year":"2017","unstructured":"Rumindo, G.K., Duchateau, N., Croisille, P., Ohayon, J., Clarysse, P.: Strain-based parameters for infarct localization: evaluation via a learning algorithm on a synthetic database of pathological hearts. In: Pop, M., Wright, G.A. (eds.) FIMH 2017. LNCS, vol. 10263, pp. 106\u2013114. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59448-4_11"},{"key":"1_CR9","doi-asserted-by":"publisher","first-page":"e003388","DOI":"10.1161\/CIRCINTERVENTIONS.115.003388","volume":"9","author":"L Belle","year":"2016","unstructured":"Belle, L., Motreff, P., Mangin, L., et al.: Comparison of immediate with delayed stenting using the minimalist immediate mechanical intervention approach in acute ST-segment-elevation myocardial infarction: the MIMI study. Circ. Cardiovasc. Interv. 9, e003388 (2016). https:\/\/doi.org\/10.1161\/CIRCINTERVENTIONS.115.003388","journal-title":"Circ. Cardiovasc. Interv."},{"key":"1_CR10","doi-asserted-by":"publisher","unstructured":"Hansen, N., Ostermeier, A.: Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation. In: Proceedings ICEC, pp. 312\u2013317 (1996). https:\/\/doi.org\/10.1109\/ICEC.1996.542381","DOI":"10.1109\/ICEC.1996.542381"},{"issue":"1","key":"1_CR11","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1007\/s10237-017-0960-0","volume":"17","author":"R Moll\u00e9ro","year":"2017","unstructured":"Moll\u00e9ro, R., Pennec, X., Delingette, H., Garny, A., Ayache, N., Sermesant, M.: Multifidelity-CMA: a multifidelity approach for efficient personalisation of 3D cardiac electromechanical models. Biomech. Model. Mechanobiol 17(1), 285\u2013300 (2017). https:\/\/doi.org\/10.1007\/s10237-017-0960-0","journal-title":"Biomech. Model. Mechanobiol"},{"key":"1_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1007\/978-3-642-40020-9_64","volume-title":"Geometric Science of Information","author":"N Duchateau","year":"2013","unstructured":"Duchateau, N., De Craene, M., Sitges, M., Caselles, V.: Adaptation of multiscale function extension to inexact matching: application to the mapping of individuals to a learnt manifold. In: Nielsen, F., Barbaresco, F. (eds.) GSI 2013. LNCS, vol. 8085, pp. 578\u2013586. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40020-9_64"},{"key":"1_CR13","first-page":"723","volume":"13","author":"A Gretton","year":"2012","unstructured":"Gretton, A., Borgwardt, K.M., Rasch, M.J., et al.: A kernel two-sample test. J. Mach. Learn. Res. 13, 723\u201373 (2012)","journal-title":"J. Mach. Learn. Res."}],"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\/978-3-030-78710-3_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T17:24:05Z","timestamp":1710264245000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-78710-3_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030787097","9783030787103"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-78710-3_1","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":"18 June 2021","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":"Stanford, CA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","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":"21 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"fimh2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/fimh2021.github.io\/","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":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"68","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":"65","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":"96% - 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":"2-4","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":"3","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 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)"}}]}}