593 articles – 2606 Notices  [english version]
HAL : inria-00616197, version 1

Voir la fiche détaillée  BibTeX,EndNote,...
Progress in Biophysics and Molecular Biology 107, 1 (2011) 122-133
Inter-Model Consistency and Complementarity: Learning from ex-vivo Imaging and Electrophysiological Data towards an Integrated Understanding of Cardiac Physiology
Oscar Camara1, 2, Maxime Sermesant3, P. Lamata4, 5, L. Wang6, 7, 8, 9, Mihaela Pop10, Jatin Relan3, M. De Craene1, 2, Hervé Delingette3, H. Liu11, 12, 13, S. Niederer4, A. Pashaei1, 2, G. Plank14, D. Romero1, 2, R. Sebastian15, Ken C.L. Wong3, H. Zhang16, 17, 18, 19, 20, Nicholas Ayache3, Alejandro Frangi1, 2, 21, 22, P. Shi23, Nic Smith4, 5, Graham Wright10, 24

Computational models of the heart at various scales and levels of complexity have been independently developed, parameterised and validated using a wide range of experimental data for over four decades. However, despite remarkable progress, the lack of coordinated efforts to compare and combine these computational models has limited their impact on the numerous open questions in cardiac physiology. To address this issue, a comprehensive dataset has previously been made available to the community that contains the cardiac anatomy and fibre orientations from magnetic resonance imaging as well as epicardial transmembrane potentials from optical mapping measured on a perfused ex-vivo porcine heart. This data was used to develop and customize four models of cardiac electrophysiology with different level of details, including a personalized fast conduction Purkinje system, a maximum a posteriori estimation of the 3D distribution of transmembrane potential, the personalization of a simplified reaction-diffusion model, and a detailed biophysical model with generic conduction parameters. This study proposes the integration of these four models into a single modelling and simulation pipeline, after analyzing their common features and discrepancies. The proposed integrated pipeline demonstrates an increase prediction power of depolarization isochrones in different pacing conditions.
1 :  CISTIB - Center for Computational Imaging and Simulation Technologies in Biomedicine
2 :  CIBER-BBN - Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine
3 :  INRIA Sophia Antipolis - ASCLEPIOS
4 :  Department of Biomedical Engineering [London]
5 :  Department of Computer Science [Oxford]
6 :  LCB - Laboratoire de chimie bactérienne
7 :  LSP - Laboratoire de Spectrométrie Physique
8 :  Biomécanique et Bioingéniérie
9 :  Physics - Department of Physics
10 :  Imaging Research [Sunnybrook]
11 :  SPMS - Laboratoire Structures, Propriétés et Modélisation des solides
12 :  IEMN - Institut d'électronique, de microélectronique et de nanotechnologie
13 :  BIT - Beijing Institute of Technology
14 :  Institute of Biophysics [Graz]
15 :  Computational Multi-Scale Physiology Lab [Valencia]
16 :  ISCR - Institut des Sciences Chimiques de Rennes
17 :  IBCP - Institut de biologie et chimie des protéines [Lyon]
18 :  Biologie moléculaire et cellulaire de la différenciation
19 :  CPPM - Centre de Physique des Particules de Marseille
20 :  Department of Chemistry [HKUST]
21 :  ICREA - Institució Catalana de Recerca i Estudis Avançats [Barcelona]
22 :  Institute of Simulation and Graphics [Magdeburg]
23 :  Computational Biomedicine Laboratory [Rochester]
24 :  MBP - Department of Medical Biophysics
Informatique/Imagerie médicale

Informatique/Modélisation et simulation

Sciences du Vivant/Ingénierie biomédicale/Imagerie

Sciences de l'ingénieur/Traitement du signal et de l'image

Informatique/Traitement du signal et de l'image