Published July 2008
| Version v1
Journal article
Toward patient-specific myocardial models of the heart
Contributors
Others:
- Analysis and Simulation of Biomedical Images (ASCLEPIOS) ; Centre Inria d'Université Côte d'Azur (CRISAM) ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
- University College of London [London] (UCL)
- Modelling, Simulation, Control and Optimization of Non-Smooth Dynamical Systems (BIPOP) ; Centre Inria de l'Université Grenoble Alpes ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK) ; Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)
- Division of Imaging Sciences ; King's College London
- NIHR Biomedical Research Centre [London] ; King's College London-Guy's and St Thomas' NHS Foundation Trust
Description
This article presents a framework for building patient-specific models of the myocardium, to help diagnosis, therapy planning, and procedure guidance. The aim is to be able to introduce such models in clinical applications. Thus, there is a need to design models that can be adjusted from clinical data, images, or signals, which are sparse and noisy. The authors describe the three main components of a myocardial model: the anatomy, the electrophysiology, and the biomechanics. For each of these components, the authors try to obtain the best balance between prior knowledge and observable parameters to be able to adjust these models to patient data. To achieve this, there is a need to design models with the right level of complexity and a computational cost compatible with clinical constraints.
Abstract
International audienceAdditional details
Identifiers
- URL
- https://inria.hal.science/inria-00616068
- URN
- urn:oai:HAL:inria-00616068v1
Origin repository
- Origin repository
- UNICA