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)
1
CISTIB -
Center for Computational Imaging and Simulation Technologies in Biomedicine
2 CIBER-BBN - Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine
3 ASCLEPIOS - Analysis and Simulation of Biomedical Images
4 School of Biomedical Engineering & Imaging Sciences [London]
5 Department of Computer Science [Oxford]
6 LCB - Laboratoire de chimie bactérienne
7 LSP - Laboratoire de Spectrométrie Physique
8 BMBI - Biomécanique et Bioingénierie
9 Department of Physics [Toronto]
10 Imaging Research [Sunnybrook]
11 SPMS - Laboratoire Structures, Propriétés et Modélisation des solides
12 IEMN - Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520
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 = Catalan Institution for Research and Advanced Studies
22 Institute of Simulation and Graphics [Magdeburg]
23 Computational Biomedicine Laboratory [Rochester]
24 MBP - Department of Medical Biophysics
2 CIBER-BBN - Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine
3 ASCLEPIOS - Analysis and Simulation of Biomedical Images
4 School of Biomedical Engineering & Imaging Sciences [London]
5 Department of Computer Science [Oxford]
6 LCB - Laboratoire de chimie bactérienne
7 LSP - Laboratoire de Spectrométrie Physique
8 BMBI - Biomécanique et Bioingénierie
9 Department of Physics [Toronto]
10 Imaging Research [Sunnybrook]
11 SPMS - Laboratoire Structures, Propriétés et Modélisation des solides
12 IEMN - Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520
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 = Catalan Institution for Research and Advanced Studies
22 Institute of Simulation and Graphics [Magdeburg]
23 Computational Biomedicine Laboratory [Rochester]
24 MBP - Department of Medical Biophysics
Maxime Sermesant
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Jatin Relan
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Hervé Delingette
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- ORCID : 0000-0001-6050-5949
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Nicholas Ayache
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Alejandro Frangi
- Function : Author
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.