Longitudinal Analysis Using Personalised 3D Cardiac Models with Population-Based Priors: Application to Paediatric Cardiomyopathies
| Autor: | Dilveer Panesar, Xavier Pennec, Jakob A. Hauser, Andrew M. Taylor, Marcus Kelm, Alexander Jones, Nicholas Ayache, Manasi Datar, Gabriele Rinelli, Titus Kuehne, Maxime Sermesant, Marcello Chinali, Hervé Delingette, Roch Molléro, Tobias Heimann |
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| Přispěvatelé: | Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Siemens Healthcare Technology Center [Erlangen], Cardiac Unit, Institute of Child Health (UCL), University College of London [London] (UCL), University of Oxford, Great Ormond Street Hospital for Children [London] (GOSH), DHZB, German Heart Institute Berlin, IRCCS Ospedale Pediatrico Bambino Gesù [Roma], SOFA, University of Oxford [Oxford] |
| Rok vydání: | 2017 |
| Předmět: |
Cardiac function curve
Pediatric cardiomyopathy Computer science 0206 medical engineering 02 engineering and technology Population based Variance (accounting) 030204 cardiovascular system & hematology computer.software_genre 020601 biomedical engineering 03 medical and health sciences 0302 clinical medicine Prior probability [INFO.INFO-IM]Computer Science [cs]/Medical Imaging Data mining computer |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783319661841 MICCAI (2) Medical Image Computing and Computer Assisted Intervention (MICCAI) Medical Image Computing and Computer Assisted Intervention (MICCAI), Sep 2017, Québec City, Canada. pp.350-358, ⟨10.1007/978-3-319-66185-8_40⟩ |
| DOI: | 10.1007/978-3-319-66185-8_40 |
| Popis: | International audience; Personalised 3D modelling of the heart is of increasing interest in order to better characterise pathologies and predict evolution. The personalisation consists in estimating the parameter values of an electromechanical model in order to reproduce the observed cardiac motion. However, the number of parameters in these models can be high and their estimation may not be unique. This variability can be an obstacle to further analyse the estimated parameters and for their clinical interpretation. In this paper we present a method to perform consistent estimations of electromechanical parameters with prior probabilities on the estimated values, which we apply on a large database of 84 different heartbeats. We show that the use of priors reduces considerably the variance in the estimated parameters, enabling better conditioning of the parameters for further analysis of the cardiac function. This is demonstrated by the application to longitudinal data of paediatric cardiomyopathies, where the estimated parameters provide additional information on the pathology and its evolution. |
| Databáze: | OpenAIRE |
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