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Inter-model consistency and complementarity: learning from ex-vivo imaging and electrophysiological data towards an integrated understanding of cardiac physiology |
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| Authors: | Camara O Sermesant M Lamata P Wang L Pop M Relan J De Craene M Delingette H Liu H Niederer S Pashaei A Plank G Romero D Sebastian R Wong K C L Zhang H Ayache N Frangi A F Shi P Smith N P Wright G A |
| Institution: | a Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), Universitat Pompeu Fabra, Barcelona, Spain b Networking Biomedical Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona, Spain c INRIA, Asclepios Project, Sophia Antipolis, France d Department of Biomedical Engineering, King’s College London, St. Thomas Hospital, UK e Department of Computer Science, University of Oxford, UK f Computational Biomedicine Laboratory, Rochester Institute of Technology, Rochester, NY, USA g University of Toronto, Sunnybrook Health Sciences Centre, Canada h State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou, China i Institute of Biophysics, Medical University of Graz, Austria j Computational Multi-Scale Physiology Lab, Universitat de Valencia, Valencia, Spain k Chinese University of Hong Kong, Hong Kong |
| 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. |
| Keywords: | Heart electrophysiology Model integration Optical mapping data Parameter personalization Phenomenological models Fast conduction Purkinje system Detailed ionic models Maximum a posteriori estimation |
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