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 3, * P. Lamata 4, 5 L. Wang 6, 7, 8, 9 Mihaela Pop 10 Jatin Relan 3 Mathieu De Craene 1, 2 Hervé Delingette 3, * Hong 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 3, * Alejandro Frangi 1, 2, 21, 22 P. Shi 23 Nic Smith 4, 5 Graham Wright 10, 24
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.
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Journal articles
Progress in Biophysics and Molecular Biology, Elsevier, 2011, Experimental and Computational Model Interactions in Bio-Research: State of the Art, 107 (1), pp.122-133. <10.1016/j.pbiomolbio.2011.07.007>


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Submitted on : Friday, August 19, 2011 - 7:57:07 PM
Last modification on : Wednesday, July 4, 2012 - 2:55:21 PM

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Oscar Camara, Maxime Sermesant, P. Lamata, L. Wang, Mihaela Pop, et al.. Inter-Model Consistency and Complementarity: Learning from ex-vivo Imaging and Electrophysiological Data towards an Integrated Understanding of Cardiac Physiology. Progress in Biophysics and Molecular Biology, Elsevier, 2011, Experimental and Computational Model Interactions in Bio-Research: State of the Art, 107 (1), pp.122-133. <10.1016/j.pbiomolbio.2011.07.007>. <inria-00616197>

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