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Trayanova NA, Lyon A, Shade J, Heijman J. Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation. Physiol Rev 2024; 104:1265-1333. [PMID: 38153307 DOI: 10.1152/physrev.00017.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 12/19/2023] [Accepted: 12/21/2023] [Indexed: 12/29/2023] Open
Abstract
The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.
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Affiliation(s)
- Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, United States
| | - Aurore Lyon
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Division of Heart and Lungs, Department of Medical Physiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Julie Shade
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, United States
| | - Jordi Heijman
- Department of Cardiology, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
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Jaffery OA, Melki L, Slabaugh G, Good WW, Roney CH. A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data. Arrhythm Electrophysiol Rev 2024; 13:e08. [PMID: 38807744 PMCID: PMC11131150 DOI: 10.15420/aer.2023.25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 12/27/2023] [Indexed: 05/30/2024] Open
Abstract
Computational models of cardiac electrophysiology have gradually matured during the past few decades and are now being personalised to provide patient-specific therapy guidance for improving suboptimal treatment outcomes. The predictive features of these personalised electrophysiology models hold the promise of providing optimal treatment planning, which is currently limited in the clinic owing to reliance on a population-based or average patient approach. The generation of a personalised electrophysiology model entails a sequence of steps for which a range of activation mapping, calibration methods and therapy simulation pipelines have been suggested. However, the optimal methods that can potentially constitute a clinically relevant in silico treatment are still being investigated and face limitations, such as uncertainty of electroanatomical data recordings, generation and calibration of models within clinical timelines and requirements to validate or benchmark the recovered tissue parameters. This paper is aimed at reporting techniques on the personalisation of cardiac computational models, with a focus on calibrating cardiac tissue conductivity based on electroanatomical mapping data.
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Affiliation(s)
- Ovais A Jaffery
- School of Engineering and Materials Science, Queen Mary University of London London, UK
| | - Lea Melki
- R&D Algorithms, Acutus Medical Carlsbad, CA, US
| | - Gregory Slabaugh
- Digital Environment Research Institute, Queen Mary University of London London, UK
| | | | - Caroline H Roney
- School of Engineering and Materials Science, Queen Mary University of London London, UK
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Farquhar ME, Burrage K, Weber Dos Santos R, Bueno-Orovio A, Lawson BA. Graph-based homogenisation for modelling cardiac fibrosis. JOURNAL OF COMPUTATIONAL PHYSICS 2022; 459:None. [PMID: 35959500 PMCID: PMC9352598 DOI: 10.1016/j.jcp.2022.111126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 02/15/2022] [Accepted: 03/02/2022] [Indexed: 05/02/2023]
Abstract
Fibrosis, the excess of extracellular matrix, can affect, and even block, propagation of action potential in cardiac tissue. This can result in deleterious effects on heart function, but the nature and severity of these effects depend strongly on the localisation of fibrosis and its by-products in cardiac tissue, such as collagen scar formation. Computer simulation is an important means of understanding the complex effects of fibrosis on activation patterns in the heart, but concerns of computational cost place restrictions on the spatial resolution of these simulations. In this work, we present a novel numerical homogenisation technique that uses both Eikonal and graph approaches to allow fine-scale heterogeneities in conductivity to be incorporated into a coarser mesh. Homogenisation achieves this by deriving effective conductivity tensors so that a coarser mesh can then be used for numerical simulation. By taking a graph-based approach, our homogenisation technique functions naturally on irregular grids and does not rely upon any assumptions of periodicity, even implicitly. We present results of action potential propagation through fibrotic tissue in two dimensions that show the graph-based homogenisation technique is an accurate and effective way to capture fine-scale domain information on coarser meshes in the context of sharp-fronted travelling waves of activation. As test problems, we consider excitation propagation in tissue with diffuse fibrosis and through a tunnel-like structure designed to test homogenisation, interaction of an excitation wave with a scar region, and functional re-entry.
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Affiliation(s)
- Megan E. Farquhar
- Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers, School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Kevin Burrage
- Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers, School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
- Department of Computer Science, Oxford University, Oxford, United Kingdom
| | - Rodrigo Weber Dos Santos
- Department of Computer Science and Program on Computational Modeling, Universidade Federal de Juiz de Fora, Juiz de Fora, Brazil
| | | | - Brodie A.J. Lawson
- Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers, School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
- Centre for Data Science, Queensland University of Technology, Brisbane, Australia
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Grandits T, Effland A, Pock T, Krause R, Plank G, Pezzuto S. GEASI: Geodesic-based earliest activation sites identification in cardiac models. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2021; 37:e3505. [PMID: 34170082 PMCID: PMC8459297 DOI: 10.1002/cnm.3505] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 06/19/2021] [Accepted: 06/22/2021] [Indexed: 05/18/2023]
Abstract
The identification of the initial ventricular activation sequence is a critical step for the correct personalization of patient-specific cardiac models. In healthy conditions, the Purkinje network is the main source of the electrical activation, but under pathological conditions the so-called earliest activation sites (EASs) are possibly sparser and more localized. Yet, their number, location and timing may not be easily inferred from remote recordings, such as the epicardial activation or the 12-lead electrocardiogram (ECG), due to the underlying complexity of the model. In this work, we introduce GEASI (Geodesic-based Earliest Activation Sites Identification) as a novel approach to simultaneously identify all EASs. To this end, we start from the anisotropic eikonal equation modeling cardiac electrical activation and exploit its Hamilton-Jacobi formulation to minimize a given objective function, for example, the quadratic mismatch to given activation measurements. This versatile approach can be extended to estimate the number of activation sites by means of the topological gradient, or fitting a given ECG. We conducted various experiments in 2D and 3D for in-silico models and an in-vivo intracardiac recording collected from a patient undergoing cardiac resynchronization therapy. The results demonstrate the clinical applicability of GEASI for potential future personalized models and clinical intervention.
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Affiliation(s)
- Thomas Grandits
- Institute of Computer Graphics and VisionTU GrazGrazAustria
- BioTechMed‐GrazGrazAustria
| | - Alexander Effland
- Institute of Computer Graphics and VisionTU GrazGrazAustria
- Silicon Austria Labs (TU Graz SAL DES Lab)GrazAustria
- Institute for Applied MathematicsUniversity of BonnBonnGermany
| | - Thomas Pock
- Institute of Computer Graphics and VisionTU GrazGrazAustria
- BioTechMed‐GrazGrazAustria
| | - Rolf Krause
- Center for Computational Medicine in Cardiology, Euler InstituteUniversità della Svizzera ItalianaLuganoSwitzerland
| | - Gernot Plank
- BioTechMed‐GrazGrazAustria
- Gottfried Schatz Research Center—Division of BiophysicsMedical University of GrazGrazAustria
| | - Simone Pezzuto
- Center for Computational Medicine in Cardiology, Euler InstituteUniversità della Svizzera ItalianaLuganoSwitzerland
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Camps J, Lawson B, Drovandi C, Minchole A, Wang ZJ, Grau V, Burrage K, Rodriguez B. Inference of ventricular activation properties from non-invasive electrocardiography. Med Image Anal 2021; 73:102143. [PMID: 34271532 PMCID: PMC8505755 DOI: 10.1016/j.media.2021.102143] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 06/17/2021] [Accepted: 06/18/2021] [Indexed: 12/13/2022]
Abstract
The realisation of precision cardiology requires novel techniques for the non-invasive characterisation of individual patients’ cardiac function to inform therapeutic and diagnostic decision-making. Both electrocardiography and imaging are used for the clinical diagnosis of cardiac disease. The integration of multi-modal datasets through advanced computational methods could enable the development of the cardiac ‘digital twin’, a comprehensive virtual tool that mechanistically reveals a patient's heart condition from clinical data and simulates treatment outcomes. The adoption of cardiac digital twins requires the non-invasive efficient personalisation of the electrophysiological properties in cardiac models. This study develops new computational techniques to estimate key ventricular activation properties for individual subjects by exploiting the synergy between non-invasive electrocardiography, cardiac magnetic resonance (CMR) imaging and modelling and simulation. More precisely, we present an efficient sequential Monte Carlo approximate Bayesian computation-based inference method, integrated with Eikonal simulations and torso-biventricular models constructed based on clinical CMR imaging. The method also includes a novel strategy to treat combined continuous (conduction speeds) and discrete (earliest activation sites) parameter spaces and an efficient dynamic time warping-based ECG comparison algorithm. We demonstrate results from our inference method on a cohort of twenty virtual subjects with cardiac ventricular myocardial-mass volumes ranging from 74 cm3 to 171 cm3 and considering low versus high resolution for the endocardial discretisation (which determines possible locations of the earliest activation sites). Results show that our method can successfully infer the ventricular activation properties in sinus rhythm from non-invasive epicardial activation time maps and ECG recordings, achieving higher accuracy for the endocardial speed and sheet (transmural) speed than for the fibre or sheet-normal directed speeds. Estimation of the ventricular speeds and earliest activation sites from ECG and CMR. Evaluation with twenty virtual subjects shows the effect of anatomical variability. Bayesian-inspired simultaneous estimation of continuous and discrete parameters. Efficient dynamic time warping-based comparison of electrocardiograms (ECG). Changing fibre and sheet-normal speed does not affect healthy activation sequence.
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Affiliation(s)
- Julia Camps
- Department of Computer Science, University of Oxford, Oxford, United Kingdom.
| | - Brodie Lawson
- Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), Queensland University of Technology (QUT), Brisbane, Australia; QUT Centre for Data Science (CDS), Queensland University of Technology, Brisbane, Australia
| | - Christopher Drovandi
- Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), Queensland University of Technology (QUT), Brisbane, Australia; QUT Centre for Data Science (CDS), Queensland University of Technology, Brisbane, Australia
| | - Ana Minchole
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Zhinuo Jenny Wang
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Vicente Grau
- Institute of Biomedical Engineering (IBME), University of Oxford, Oxford, United Kingdom
| | - Kevin Burrage
- Department of Computer Science, University of Oxford, Oxford, United Kingdom; Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), Queensland University of Technology (QUT), Brisbane, Australia
| | - Blanca Rodriguez
- Department of Computer Science, University of Oxford, Oxford, United Kingdom
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Grandits T, Pezzuto S, Costabal FS, Perdikaris P, Pock T, Plank G, Krause R. Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks. FUNCTIONAL IMAGING AND MODELING OF THE HEART : ... INTERNATIONAL WORKSHOP, FIMH ..., PROCEEDINGS. FIMH 2021; 2021:650-658. [PMID: 35098259 PMCID: PMC7612271 DOI: 10.1007/978-3-030-78710-3_62] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Electroanatomical maps are a key tool in the diagnosis and treatment of atrial fibrillation. Current approaches focus on the activation times recorded. However, more information can be extracted from the available data. The fibers in cardiac tissue conduct the electrical wave faster, and their direction could be inferred from activation times. In this work, we employ a recently developed approach, called physics informed neural networks, to learn the fiber orientations from electroanatomical maps, taking into account the physics of the electrical wave propagation. In particular, we train the neural network to weakly satisfy the anisotropic eikonal equation and to predict the measured activation times. We use a local basis for the anisotropic conductivity tensor, which encodes the fiber orientation. The methodology is tested both in a synthetic example and for patient data. Our approach shows good agreement in both cases and it outperforms a state of the art method in the patient data. The results show a first step towards learning the fiber orientations from electroanatomical maps with physics-informed neural networks.
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Affiliation(s)
- Thomas Grandits
- Institute of Computer Graphics and Vision, TU Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Simone Pezzuto
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland
| | - Francisco Sahli Costabal
- Department of Mechanical and Metallurgical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
- Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile
- Millennium Nucleus for Cardiovascular Magnetic Resonance
| | - Paris Perdikaris
- Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Thomas Pock
- Institute of Computer Graphics and Vision, TU Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Gernot Plank
- BioTechMed-Graz, Graz, Austria
- Gottfried Schatz Research Center - Division of Biophysics, Medical University of Graz, Graz, Austria
| | - Rolf Krause
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland
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Kunisch K, Trautmann P. An Inverse Problem Involving a Viscous Eikonal Equation with Applications in Electrophysiology. VIETNAM JOURNAL OF MATHEMATICS 2021; 50:301-317. [PMID: 34901261 PMCID: PMC8632858 DOI: 10.1007/s10013-021-00509-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 02/24/2021] [Indexed: 06/14/2023]
Abstract
In this work we discuss the reconstruction of cardiac activation instants based on a viscous Eikonal equation from boundary observations. The problem is formulated as a least squares problem and solved by a projected version of the Levenberg-Marquardt method. Moreover, we analyze the well-posedness of the state equation and derive the gradient of the least squares functional with respect to the activation instants. In the numerical examples we also conduct an experiment in which the location of the activation sites and the activation instants are reconstructed jointly based on an adapted version of the shape gradient method from (J. Math. Biol. 79, 2033-2068, 2019). We are able to reconstruct the activation instants as well as the locations of the activations with high accuracy relative to the noise level.
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Affiliation(s)
- Karl Kunisch
- Institute for Mathematics and Scientific Computing, University of Graz, Heinrichstrasse 36, A-8010 Graz, Austria
- Johann Radon Institute for Computational and Applied Mathematics, Austrian Academy of Science, Linz, Austria
| | - Philip Trautmann
- Institute for Mathematics and Scientific Computing, University of Graz, Heinrichstrasse 36, A-8010 Graz, Austria
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Electro-Mechanical Whole-Heart Digital Twins: A Fully Coupled Multi-Physics Approach. MATHEMATICS 2021. [DOI: 10.3390/math9111247] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Mathematical models of the human heart are evolving to become a cornerstone of precision medicine and support clinical decision making by providing a powerful tool to understand the mechanisms underlying pathophysiological conditions. In this study, we present a detailed mathematical description of a fully coupled multi-scale model of the human heart, including electrophysiology, mechanics, and a closed-loop model of circulation. State-of-the-art models based on human physiology are used to describe membrane kinetics, excitation-contraction coupling and active tension generation in the atria and the ventricles. Furthermore, we highlight ways to adapt this framework to patient specific measurements to build digital twins. The validity of the model is demonstrated through simulations on a personalized whole heart geometry based on magnetic resonance imaging data of a healthy volunteer. Additionally, the fully coupled model was employed to evaluate the effects of a typical atrial ablation scar on the cardiovascular system. With this work, we provide an adaptable multi-scale model that allows a comprehensive personalization from ion channels to the organ level enabling digital twin modeling.
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A Framework for the generation of digital twins of cardiac electrophysiology from clinical 12-leads ECGs. Med Image Anal 2021; 71:102080. [PMID: 33975097 DOI: 10.1016/j.media.2021.102080] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 02/15/2021] [Accepted: 04/06/2021] [Indexed: 12/21/2022]
Abstract
Cardiac digital twins (Cardiac Digital Twin (CDT)s) of human electrophysiology (Electrophysiology (EP)) are digital replicas of patient hearts derived from clinical data that match like-for-like all available clinical observations. Due to their inherent predictive potential, CDTs show high promise as a complementary modality aiding in clinical decision making and also in the cost-effective, safe and ethical testing of novel EP device therapies. However, current workflows for both the anatomical and functional twinning phases within CDT generation, referring to the inference of model anatomy and parameters from clinical data, are not sufficiently efficient, robust and accurate for advanced clinical and industrial applications. Our study addresses three primary limitations impeding the routine generation of high-fidelity CDTs by introducing; a comprehensive parameter vector encapsulating all factors relating to the ventricular EP; an abstract reference frame within the model allowing the unattended manipulation of model parameter fields; a novel fast-forward electrocardiogram (Electrocardiogram (ECG)) model for efficient and bio-physically-detailed simulation required for parameter inference. A novel workflow for the generation of CDTs is then introduced as an initial proof of concept. Anatomical twinning was performed within a reasonable time compatible with clinical workflows (<4h) for 12 subjects from clinically-attained magnetic resonance images. After assessment of the underlying fast forward ECG model against a gold standard bidomain ECG model, functional twinning of optimal parameters according to a clinically-attained 12 lead ECG was then performed using a forward Saltelli sampling approach for a single subject. The achieved results in terms of efficiency and fidelity demonstrate that our workflow is well-suited and viable for generating biophysically-detailed CDTs at scale.
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PIEMAP: Personalized Inverse Eikonal Model from Cardiac Electro-Anatomical Maps. STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART. M&MS AND EMIDEC CHALLENGES 2021. [DOI: 10.1007/978-3-030-68107-4_8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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