A Multiscale Cardiac Model for Fast Personalisation and Exploitation

Roch Molléro 1 Xavier Pennec 1 Hervé Delingette 1 Nicholas Ayache 1 Maxime Sermesant 1
1 ASCLEPIOS - Analysis and Simulation of Biomedical Images
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Computer models of the heart are of increasing interest for clinical applications due to their discriminative and predictive abilities. However a single 3D simulation can be computationally expensive and long, which can make some practical applications such as the personalisation phase, or a sensitivity analysis of mechanical parameters over the simulated behaviour quite slow. In this manuscript we present a multiscale 0D/3D model which allows us to have a reliable (and extremely fast) approximation of the behaviour of the 3D model under a few simplifying assumptions. We first detail the two different models, then explain the coupling of the two models to get fast 0D approximation of 3D simulations. Finally we demonstrated how the multiscale model can speed-up an efficient optimization algorithm, which enables a fast personalisation of the 3D simulations by leveraging on the advantages of each scale.
Type de document :
Communication dans un congrès
Medical Image Computing and Computer Assisted Intervention (MICCAI), Oct 2016, Athens, Greece. 9902, pp.174-182, 2015, MICCAI 2016, Lecture Notes in Computer Science. 〈http://www.miccai2016.org/〉. 〈10.1007/978-3-319-46726-9_21〉
Liste complète des métadonnées

Littérature citée [8 références]  Voir  Masquer  Télécharger
https://hal.inria.fr/hal-01360908
Contributeur : Roch Mollero <>
Soumis le : mardi 6 septembre 2016 - 14:21:34
Dernière modification le : mercredi 30 mai 2018 - 13:56:03
Document(s) archivé(s) le : mercredi 7 décembre 2016 - 13:26:59

Fichier

paper_725.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

INRIA | SOFA | UNIV-COTEDAZUR

Citation

Roch Molléro, Xavier Pennec, Hervé Delingette, Nicholas Ayache, Maxime Sermesant. A Multiscale Cardiac Model for Fast Personalisation and Exploitation. Medical Image Computing and Computer Assisted Intervention (MICCAI), Oct 2016, Athens, Greece. 9902, pp.174-182, 2015, MICCAI 2016, Lecture Notes in Computer Science. 〈http://www.miccai2016.org/〉. 〈10.1007/978-3-319-46726-9_21〉. 〈hal-01360908〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

261

Téléchargements de fichiers

182

Contact


archive-ouverte@inria.fr