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Coveney S, Corrado C, Oakley JE, Wilkinson RD, Niederer SA, Clayton RH. Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators. Front Physiol 2021; 12:693015. [PMID: 34366883 PMCID: PMC8339909 DOI: 10.3389/fphys.2021.693015] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 06/28/2021] [Indexed: 11/13/2022] Open
Abstract
Calibration of cardiac electrophysiology models is a fundamental aspect of model personalization for predicting the outcomes of cardiac therapies, simulation testing of device performance for a range of phenotypes, and for fundamental research into cardiac function. Restitution curves provide information on tissue function and can be measured using clinically feasible measurement protocols. We introduce novel "restitution curve emulators" as probabilistic models for performing model exploration, sensitivity analysis, and Bayesian calibration to noisy data. These emulators are built by decomposing restitution curves using principal component analysis and modeling the resulting coordinates with respect to model parameters using Gaussian processes. Restitution curve emulators can be used to study parameter identifiability via sensitivity analysis of restitution curve components and rapid inference of the posterior distribution of model parameters given noisy measurements. Posterior uncertainty about parameters is critical for making predictions from calibrated models, since many parameter settings can be consistent with measured data and yet produce very different model behaviors under conditions not effectively probed by the measurement protocols. Restitution curve emulators are therefore promising probabilistic tools for calibrating electrophysiology models.
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Affiliation(s)
- Sam Coveney
- Insigneo Institute for In-Silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Cesare Corrado
- Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Jeremy E. Oakley
- School of Mathematics and Statistics, University of Sheffield, Sheffield, United Kingdom
| | - Richard D. Wilkinson
- School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom
| | - Steven A. Niederer
- Division of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom
| | - Richard H. Clayton
- Insigneo Institute for In-Silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
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Vadakkumpadan F, Arevalo H, Ceritoglu C, Miller M, Trayanova N. Image-based estimation of ventricular fiber orientations for personalized modeling of cardiac electrophysiology. IEEE TRANSACTIONS ON MEDICAL IMAGING 2012; 31:1051-60. [PMID: 22271833 PMCID: PMC3518051 DOI: 10.1109/tmi.2012.2184799] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
Technological limitations pose a major challenge to acquisition of myocardial fiber orientations for patient-specific modeling of cardiac (dys)function and assessment of therapy. The objective of this project was to develop a methodology to estimate cardiac fiber orientations from in vivo images of patient heart geometries. An accurate representation of ventricular geometry and fiber orientations was reconstructed, respectively, from high-resolution ex vivo structural magnetic resonance (MR) and diffusion tensor (DT) MR images of a normal human heart, referred to as the atlas. Ventricular geometry of a patient heart was extracted, via semiautomatic segmentation, from an in vivo computed tomography (CT) image. Using image transformation algorithms, the atlas ventricular geometry was deformed to match that of the patient. Finally, the deformation field was applied to the atlas fiber orientations to obtain an estimate of patient fiber orientations. The accuracy of the fiber estimates was assessed using six normal and three failing canine hearts. The mean absolute difference between inclination angles of acquired and estimated fiber orientations was 15.4°. Computational simulations of ventricular activation maps and pseudo-ECGs in sinus rhythm and ventricular tachycardia indicated that there are no significant differences between estimated and acquired fiber orientations at a clinically observable level.
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Affiliation(s)
- Fijoy Vadakkumpadan
- Institute for Computational Medicine and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA.
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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 KCL, Zhang H, Ayache N, Frangi AF, Shi P, Smith NP, Wright GA. 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 2011; 107:122-33. [PMID: 21791225 DOI: 10.1016/j.pbiomolbio.2011.07.007] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2011] [Accepted: 07/01/2011] [Indexed: 11/27/2022]
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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Affiliation(s)
- O Camara
- Center for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), Universitat Pompeu Fabra, Barcelona, Spain.
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Smith N, de Vecchi A, McCormick M, Nordsletten D, Camara O, Frangi AF, Delingette H, Sermesant M, Relan J, Ayache N, Krueger MW, Schulze WHW, Hose R, Valverde I, Beerbaum P, Staicu C, Siebes M, Spaan J, Hunter P, Weese J, Lehmann H, Chapelle D, Rezavi R. euHeart: personalized and integrated cardiac care using patient-specific cardiovascular modelling. Interface Focus 2011; 1:349-64. [PMID: 22670205 DOI: 10.1098/rsfs.2010.0048] [Citation(s) in RCA: 95] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2010] [Accepted: 03/04/2011] [Indexed: 01/09/2023] Open
Abstract
The loss of cardiac pump function accounts for a significant increase in both mortality and morbidity in Western society, where there is currently a one in four lifetime risk, and costs associated with acute and long-term hospital treatments are accelerating. The significance of cardiac disease has motivated the application of state-of-the-art clinical imaging techniques and functional signal analysis to aid diagnosis and clinical planning. Measurements of cardiac function currently provide high-resolution datasets for characterizing cardiac patients. However, the clinical practice of using population-based metrics derived from separate image or signal-based datasets often indicates contradictory treatments plans owing to inter-individual variability in pathophysiology. To address this issue, the goal of our work, demonstrated in this study through four specific clinical applications, is to integrate multiple types of functional data into a consistent framework using multi-scale computational modelling.
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Affiliation(s)
- Nic Smith
- Imaging Sciences and Biomedical Engineering Division , St Thomas' Hospital, King's College London , London , UK
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Relan J, Chinchapatnam P, Sermesant M, Rhode K, Ginks M, Delingette H, Rinaldi CA, Razavi R, Ayache N. Coupled personalization of cardiac electrophysiology models for prediction of ischaemic ventricular tachycardia. Interface Focus 2011; 1:396-407. [PMID: 22670209 DOI: 10.1098/rsfs.2010.0041] [Citation(s) in RCA: 59] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2010] [Accepted: 03/03/2011] [Indexed: 11/12/2022] Open
Abstract
In order to translate the important progress in cardiac electrophysiology modelling of the last decades into clinical applications, there is a requirement to make macroscopic models that can be used for the planning and performance of the clinical procedures. This requires model personalization, i.e. estimation of patient-specific model parameters and computations compatible with clinical constraints. Simplified macroscopic models can allow a rapid estimation of the tissue conductivity, but are often unreliable to predict arrhythmias. Conversely, complex biophysical models are more complete and have mechanisms of arrhythmogenesis and arrhythmia sustainibility, but are computationally expensive and their predictions at the organ scale still have to be validated. We present a coupled personalization framework that combines the power of the two kinds of models while keeping the computational complexity tractable. A simple eikonal model is used to estimate the conductivity parameters, which are then used to set the parameters of a biophysical model, the Mitchell-Schaeffer (MS) model. Additional parameters related to action potential duration restitution curves for the tissue are further estimated for the MS model. This framework is applied to a clinical dataset derived from a hybrid X-ray/magnetic resonance imaging and non-contact mapping procedure on a patient with heart failure. This personalized MS model is then used to perform an in silico simulation of a ventricular tachycardia (VT) stimulation protocol to predict the induction of VT. This proof of concept opens up possibilities of using VT induction modelling in order to both assess the risk of VT for a given patient and also to plan a potential subsequent radio-frequency ablation strategy to treat VT.
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Affiliation(s)
- Jatin Relan
- INRIA, Asclepios research project, Sophia Antipolis, France
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Relan J, Pop M, Delingette H, Wright GA, Ayache N, Sermesant M. Personalization of a cardiac electrophysiology model using optical mapping and MRI for prediction of changes with pacing. IEEE Trans Biomed Eng 2011; 58:3339-49. [PMID: 21257368 DOI: 10.1109/tbme.2011.2107513] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Computer models of cardiac electrophysiology (EP) can be a very efficient tool to better understand the mechanisms of arrhythmias. Quantitative adjustment of such models to experimental data (personalization) is needed in order to test their realism and predictive power, but it remains challenging at the organ scale. In this paper, we propose a framework for the personalization of a 3-D cardiac EP model, the Mitchell-Schaeffer (MS) model, and evaluate its volumetric predictive power under various pacing scenarios. The personalization was performed on ex vivo large porcine healthy hearts using diffusion tensor MRI (DT-MRI) and optical mapping data. The MS model was simulated on a 3-D mesh incorporating local fiber orientations, built from DT-MRI. The 3-D model parameters were optimized using features such as 2-D epicardial depolarization and repolarization maps, extracted from the optical mapping. We also evaluated the sensitivity of our personalization framework to different pacing locations and showed results on its robustness. Further, we evaluated volumetric model predictions for various epi- and endocardial pacing scenarios. We demonstrated promising results with a mean personalization error around 5 ms and a mean prediction error around 10 ms (5% of the total depolarization time). Finally, we discussed the potential translation of such work to clinical data and pathological hearts.
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Affiliation(s)
- Jatin Relan
- Inria, Asclepios team, Sophia-Antipolis, 06902 France.
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