Inter-Model Consistency and Complementarity: Learning from ex-vivo Imaging and Electrophysiological Data towards an Integrated Understanding of Cardiac Physiology

Oscar Camara; Maxime Sermesant; P. Lamata; L. Wang; Mihaela Pop; Jatin Relan; M. De Craene; Hervé Delingette; H. Liu; S. Niederer; A. Pashaei; G. Plank; D. Romero; R. Sebastian; Ken C.L. Wong; H. Zhang; Nicholas Ayache; Alejandro Frangi; P. Shi; Nic Smith; Graham Wright

inria-00616197, version 1

Inter-Model Consistency and Complementarity: Learning from ex-vivo Imaging and Electrophysiological Data towards an Integrated Understanding of Cardiac Physiology

Oscar Camara ^1, 2, Maxime Sermesant ( Auteur à contacter de préférence ) ³, P. Lamata ^4, 5, L. Wang ^{6, 7, 8, 9}, Mihaela Pop ¹⁰, Jatin Relan ³, M. De Craene ^1, 2, Hervé Delingette () ³, H. Liu ^11, 12, 13, S. Niederer ⁴, A. Pashaei ^1, 2, G. Plank ¹⁴, D. Romero ^1, 2, R. Sebastian ¹⁵, Ken C.L. Wong ³, H. Zhang ^{16, 17, 18, 19, 20}, Nicholas Ayache ( Auteur à contacter de préférence ) ³, Alejandro Frangi ^{1, 2, 21, 22}, P. Shi ²³, Nic Smith ^4, 5, Graham Wright ^10, 24

Progress in Biophysics and Molecular Biology 107, 1 (2011) 122-133

Résumé : 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 : Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB)
Pompeu Fabra University
2 : Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)
ministerio de ciencia e innovacion – Instituto de Salud Carlos III
3 : ASCLEPIOS (INRIA Sophia Antipolis)
INRIA
4 : Department of Biomedical Engineering [London]
King's College London – St Thomas Hospital
5 : Department of Computer Science [Oxford]
University of Oxford
6 : Laboratoire de chimie bactérienne (LCB)
CNRS : UPR9043
7 : Laboratoire de Spectrométrie Physique (LSP)
CNRS : UMR5588 – Université Joseph Fourier - Grenoble I
8 : Biomécanique et Bioingéniérie
CNRS : UMR6600 – Université de Technologie de Compiègne
9 : Department of Physics (Physics)
University of Toronto
10 : Imaging Research [Sunnybrook]
Sunnybrook Health Sciences Centre – University of Toronto
11 : Laboratoire Structures, Propriétés et Modélisation des solides (SPMS)
CNRS : UMR8580 – Ecole Centrale Paris
12 : Institut d'électronique, de microélectronique et de nanotechnologie (IEMN)
CNRS : UMR8520 – Institut supérieur de l'électronique et du nunérique (ISEN) – Université Lille I - Sciences et technologies – Université de Valenciennes et du Hainaut-Cambresis
13 : Beijing Institute of Technology
Beijing Institute of Technology
14 : Institute of Biophysics [Graz]
Medical University of Graz
15 : Computational Multi-Scale Physiology Lab [Valencia]
Universidad de Valencia
16 : Institut des Sciences Chimiques de Rennes
CNRS : UMR6226 – Université de Rennes 1 – Institut National des Sciences Appliquées (INSA) - Rennes – Ecole Nationale Supérieure de Chimie de Rennes
17 : Institut de biologie et chimie des protéines [Lyon] (IBCP)
CNRS : UMR5086 – Université Claude Bernard - Lyon I
18 : Biologie moléculaire et cellulaire de la différenciation
INSERM : U309 – Institut Albert Bonniot – Université Joseph Fourier - Grenoble I
19 : Centre de Physique des Particules de Marseille (CPPM)
CNRS : UMR7346 – IN2P3 – Université de la Méditerranée - Aix-Marseille II
20 : Department of Chemistry [HKUST]
Hong Kong University of Science and Technology
21 : Institució Catalana de Recerca i Estudis Avançats [Barcelona] (ICREA)
ICREA – Universitat de Barcelona – Fundació Catalana per a la Recerca i la Innovació (FCRI)
22 : Institute of Simulation and Graphics [Magdeburg]
Otto-von-Guericke-Universität Magdeburg
23 : Computational Biomedicine Laboratory [Rochester]
Rochester Institute of Technology
24 : Department of Medical Biophysics (MBP)
University of Toronto

inria-00616197, version 1
http://hal.inria.fr/inria-00616197
oai:hal.inria.fr:inria-00616197
Contributeur : Project-Team Asclepios <>
Déposé pour le compte de : Gregoire MALANDAIN <>
Soumis le : Vendredi 19 Août 2011, 19:57:07
Dernière modification le : Mercredi 4 Juillet 2012, 14:55:21

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DOI : 10.1016/j.pbiomolbio.2011.07.007

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%% inria-00616197, version 1
%% http://hal.inria.fr/inria-00616197
@article{camara:inria-00616197,
    hal_id = {inria-00616197},
    url = {http://hal.inria.fr/inria-00616197},
    title = {{Inter-Model Consistency and Complementarity: Learning from ex-vivo Imaging and Electrophysiological Data towards an Integrated Understanding of Cardiac Physiology}},
    author = {Camara, Oscar and Sermesant, Maxime and Lamata, P. and Wang, L. and Pop, Mihaela and Relan, Jatin and De Craene, M. and Delingette, Herv{\'e} and Liu, H. and Niederer, S. and Pashaei, A. and Plank, G. and Romero, D. and Sebastian, R. and Wong, Ken C.L. and Zhang, H. and Ayache, Nicholas and Frangi, Alejandro and Shi, P. and Smith, Nic and Wright, Graham},
    abstract = {{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.}},
    language = {Anglais},
    affiliation = {Center for Computational Imaging and Simulation Technologies in Biomedicine - CISTIB , Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine - CIBER-BBN , ASCLEPIOS - INRIA Sophia Antipolis , Department of Biomedical Engineering [London] , Department of Computer Science [Oxford] , Laboratoire de chimie bact{\'e}rienne - LCB , Laboratoire de Spectrom{\'e}trie Physique - LSP , Biom{\'e}canique et Bioing{\'e}ni{\'e}rie , Department of Physics - Physics , Imaging Research [Sunnybrook] , Laboratoire Structures, Propri{\'e}t{\'e}s et Mod{\'e}lisation des solides - SPMS , Institut d'{\'e}lectronique, de micro{\'e}lectronique et de nanotechnologie - IEMN , Beijing Institute of Technology , Institute of Biophysics [Graz] , Computational Multi-Scale Physiology Lab [Valencia] , Institut des Sciences Chimiques de Rennes , Institut de biologie et chimie des prot{\'e}ines [Lyon] - IBCP , Biologie mol{\'e}culaire et cellulaire de la diff{\'e}renciation , Centre de Physique des Particules de Marseille - CPPM , Department of Chemistry [HKUST] , Instituci{\'o} Catalana de Recerca i Estudis Avan{\c c}ats [Barcelona] - ICREA , Institute of Simulation and Graphics [Magdeburg] , Computational Biomedicine Laboratory [Rochester] , Department of Medical Biophysics - MBP},
    booktitle = {{Experimental and Computational Model Interactions in Bio-Research: State of the Art}},
    publisher = {Elsevier},
    pages = {122-133},
    journal = {Progress in Biophysics and Molecular Biology},
    volume = {107},
    number = {1 },
    audience = {internationale },
    doi = {10.1016/j.pbiomolbio.2011.07.007 },
    year = {2011},
}

%F inria-00616197, version 1
%0 Journal Article
%U http://hal.inria.fr/inria-00616197
%2 INFO:INFO_IM;INFO:INFO_MO;SDV:IB:IMA;SPI:SIGNAL;INFO:INFO_TS
%T Inter-Model Consistency and Complementarity: Learning from ex-vivo Imaging and Electrophysiological Data towards an Integrated Understanding of Cardiac Physiology
%A Camara, Oscar
%A Sermesant, Maxime
%A Lamata, P.
%A Wang, L.
%A Pop, Mihaela
%A Relan, Jatin
%A De Craene, M.
%A Delingette, Hervé
%A Liu, H.
%A Niederer, S.
%A Pashaei, A.
%A Plank, G.
%A Romero, D.
%A Sebastian, R.
%A Wong, Ken C.L.
%A Zhang, H.
%A Ayache, Nicholas
%A Frangi, Alejandro
%A Shi, P.
%A Smith, Nic
%A Wright, Graham
%+ Center for Computational Imaging and Simulation Technologies in Biomedicine - CISTIB , Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine - CIBER-BBN , ASCLEPIOS - INRIA Sophia Antipolis , Department of Biomedical Engineering [London] , Department of Computer Science [Oxford] , Laboratoire de chimie bactérienne - LCB , Laboratoire de Spectrométrie Physique - LSP , Biomécanique et Bioingéniérie , Department of Physics - Physics , Imaging Research [Sunnybrook] , Laboratoire Structures, Propriétés et Modélisation des solides - SPMS , Institut d'électronique, de microélectronique et de nanotechnologie - IEMN , Beijing Institute of Technology , Institute of Biophysics [Graz] , Computational Multi-Scale Physiology Lab [Valencia] , Institut des Sciences Chimiques de Rennes , Institut de biologie et chimie des protéines [Lyon] - IBCP , Biologie moléculaire et cellulaire de la différenciation , Centre de Physique des Particules de Marseille - CPPM , Department of Chemistry [HKUST] , Institució Catalana de Recerca i Estudis Avançats [Barcelona] - ICREA , Institute of Simulation and Graphics [Magdeburg] , Computational Biomedicine Laboratory [Rochester] , Department of Medical Biophysics - MBP
%X 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.
%G Anglais
%J Progress in Biophysics and Molecular Biology
%8 2011-00-00
%2 internationale
%I Elsevier
%2 Experimental and Computational Model Interactions in Bio-Research: State of the Art
%V 107
%N 1
%P 122-133
%R 10.1016/j.pbiomolbio.2011.07.007
%2 2011

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<author><persName><forename>Nic</forename><surname>Smith</surname></persName>
<affiliation>
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<orgName type="institution">St Thomas Hospital</orgName>
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<country>GB</country>
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<author><persName><forename>Graham</forename><surname>Wright</surname></persName>
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<orgName type="team">Imaging Research [Sunnybrook]</orgName>
<country>CA</country>
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<title type="main" level="a">Inter-Model Consistency and Complementarity: Learning from ex-vivo Imaging and Electrophysiological Data towards an Integrated Understanding of Cardiac Physiology</title></analytic><monogr><title level="j" type="main">Progress in Biophysics and Molecular Biology</title><imprint><publisher>Elsevier</publisher><biblScope type="publicationDate">2011-00-00</biblScope><biblScope type="vol">107</biblScope><biblScope type="issue">1</biblScope><biblScope type="pp">122-133</biblScope></imprint></monogr><idno type="doi">10.1016/j.pbiomolbio.2011.07.007</idno><idno type="HALid">http://hal.inria.fr/inria-00616197</idno></biblStruct>
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