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Berg LA, Rocha BM, Oliveira RS, Sebastian R, Rodriguez B, de Queiroz RAB, Cherry EM, Dos Santos RW. Enhanced optimization-based method for the generation of patient-specific models of Purkinje networks. Sci Rep 2023; 13:11788. [PMID: 37479707 PMCID: PMC10362015 DOI: 10.1038/s41598-023-38653-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 07/12/2023] [Indexed: 07/23/2023] Open
Abstract
Cardiac Purkinje networks are a fundamental part of the conduction system and are known to initiate a variety of cardiac arrhythmias. However, patient-specific modeling of Purkinje networks remains a challenge due to their high morphological complexity. This work presents a novel method based on optimization principles for the generation of Purkinje networks that combines geometric and activation accuracy in branch size, bifurcation angles, and Purkinje-ventricular-junction activation times. Three biventricular meshes with increasing levels of complexity are used to evaluate the performance of our approach. Purkinje-tissue coupled monodomain simulations are executed to evaluate the generated networks in a realistic scenario using the most recent Purkinje/ventricular human cellular models and physiological values for the Purkinje-ventricular-junction characteristic delay. The results demonstrate that the new method can generate patient-specific Purkinje networks with controlled morphological metrics and specified local activation times at the Purkinje-ventricular junctions.
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Affiliation(s)
- Lucas Arantes Berg
- Graduate Program in Computational Modeling, Federal University of Juiz de Fora, Juiz de Fora, Brazil.
- Department of Computer Science, University of Oxford, Oxford, UK.
| | - Bernardo Martins Rocha
- Graduate Program in Computational Modeling, Federal University of Juiz de Fora, Juiz de Fora, Brazil
| | - Rafael Sachetto Oliveira
- Department of Computer Science, Federal University of São João del-Rei, São João del-Rei, Brazil
| | - Rafael Sebastian
- Department of Computer Science, Universitat de Valencia, Valencia, Spain
| | - Blanca Rodriguez
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Rafael Alves Bonfim de Queiroz
- Graduate Program in Computational Modeling, Federal University of Juiz de Fora, Juiz de Fora, Brazil
- Department of Computer Science, Federal University of Ouro Preto, Ouro Preto, Brazil
| | - Elizabeth M Cherry
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Rodrigo Weber Dos Santos
- Graduate Program in Computational Modeling, Federal University of Juiz de Fora, Juiz de Fora, Brazil
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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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Barber F, Langfield P, Lozano M, Garcia-Fernandez I, Duchateau J, Hocini M, Haissaguerre M, Vigmond E, Sebastian R. Estimation of Personalized Minimal Purkinje Systems From Human Electro-Anatomical Maps. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:2182-2194. [PMID: 33856987 DOI: 10.1109/tmi.2021.3073499] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The Purkinje system is a heart structure responsible for transmitting electrical impulses through the ventricles in a fast and coordinated way to trigger mechanical contraction. Estimating a patient-specific compatible Purkinje Network from an electro-anatomical map is a challenging task, that could help to improve models for electrophysiology simulations or provide aid in therapy planning, such as radiofrequency ablation. In this study, we present a methodology to inversely estimate a Purkinje network from a patient's electro-anatomical map. First, we carry out a simulation study to assess the accuracy of the method for different synthetic Purkinje network morphologies and myocardial junction densities. Second, we estimate the Purkinje network from a set of 28 electro-anatomical maps from patients, obtaining an optimal conduction velocity in the Purkinje network of 1.95 ± 0.25 m/s, together with the location of their Purkinje-myocardial junctions, and Purkinje network structure. Our results showed an average local activation time error of 6.8±2.2 ms in the endocardium. Finally, using the personalized Purkinje network, we obtained correlations higher than 0.85 between simulated and clinical 12-lead ECGs.
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Bragard JR, Camara O, Echebarria B, Gerardo Giorda L, Pueyo E, Saiz J, Sebastián R, Soudah E, Vázquez M. Cardiac computational modelling. ACTA ACUST UNITED AC 2020; 74:65-71. [PMID: 32807708 DOI: 10.1016/j.rec.2020.05.024] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 05/25/2020] [Indexed: 12/26/2022]
Abstract
Cardiovascular diseases currently have a major social and economic impact, constituting one of the leading causes of mortality and morbidity. Personalized computational models of the heart are demonstrating their usefulness both to help understand the mechanisms underlying cardiac disease, and to optimize their treatment and predict the patient's response. Within this framework, the Spanish Research Network for Cardiac Computational Modelling (VHeart-SN) has been launched. The general objective of the VHeart-SN network is the development of an integrated, modular and multiscale multiphysical computational model of the heart. This general objective is addressed through the following specific objectives: a) to integrate the different numerical methods and models taking into account the specificity of patients; b) to assist in advancing knowledge of the mechanisms associated with cardiac and vascular diseases; and c) to support the application of different personalized therapies. This article presents the current state of cardiac computational modelling and different scientific works conducted by the members of the network to gain greater understanding of the characteristics and usefulness of these models.
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Affiliation(s)
- Jean R Bragard
- Grupo de Biofísica (BIOFIS), Departamento de Física y Matemática Aplicada, Universidad de Navarra, Pamplona, Navarra, Spain
| | - Oscar Camara
- Sensing in Physiology and Biomedicine (PhySense), Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Blas Echebarria
- Grupo de Biología Computacional y Sistemas Complejos (BIOCOM-SC), Universitat Politècnica de Catalunya, Barcelona, Spain
| | | | - Esther Pueyo
- Biomedical Signal Interpretation and Computational Simulation (BSICoS), Universidad de Zaragoza, CIBER-BBN, Zaragoza, Spain
| | - Javier Saiz
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitat Politècnica de València, Valencia, Spain.
| | - Rafael Sebastián
- Computational Multiscale Simulation Lab (CoMMLab), Universitat de València, Burjassot, Valencia, Spain
| | - Eduardo Soudah
- International Centre for Numerical Methods in Engineering (CIMNE), Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Mariano Vázquez
- Barcelona Supercomputing Center & ELEM Biotech, Barcelona, Spain
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Human Purkinje in silico model enables mechanistic investigations into automaticity and pro-arrhythmic abnormalities. J Mol Cell Cardiol 2020; 142:24-38. [PMID: 32251669 PMCID: PMC7294239 DOI: 10.1016/j.yjmcc.2020.04.001] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 03/30/2020] [Accepted: 04/01/2020] [Indexed: 02/06/2023]
Abstract
Cardiac Purkinje cells (PCs) are implicated in lethal arrhythmias caused by cardiac diseases, mutations, and drug action. However, the pro-arrhythmic mechanisms in PCs are not entirely understood, particularly in humans, as most investigations are conducted in animals. The aims of this study are to present a novel human PCs electrophysiology biophysically-detailed computational model, and to disentangle ionic mechanisms of human Purkinje-related electrophysiology, pacemaker activity and arrhythmogenicity. The new Trovato2020 model incorporates detailed Purkinje-specific ionic currents and Ca2+ handling, and was developed, calibrated and validated using human experimental data acquired at multiple frequencies, both in control conditions and following drug application. Multiscale investigations were performed in a Purkinje cell, in fibre and using an experimentally-calibrated population of PCs to evaluate biological variability. Simulations demonstrate the human Purkinje Trovato2020 model is the first one to yield: (i) all key AP features consistent with human Purkinje recordings; (ii) Automaticity with funny current up-regulation (iii) EADs at slow pacing and with 85% hERG block; (iv) DADs following fast pacing; (v) conduction velocity of 160 cm/s in a Purkinje fibre, as reported in human. The human in silico PCs population highlights that: (1) EADs are caused by ICaL reactivation in PCs with large inward currents; (2) DADs and triggered APs occur in PCs experiencing Ca2+ accumulation, at fast pacing, caused by large L-type calcium current and small Na+/Ca2+ exchanger. The novel human Purkinje model unlocks further investigations into the role of cardiac Purkinje in ventricular arrhythmias through computer modeling and multiscale simulations. A human in silico AP model was developed to investigate arrhythmia in cardiac Purkinje. The new Purkinje model enables multiscale investigations from single cell to tissue. Populations of human Purkinje models reproduce and explain experimental variability. Ca2+-current reactivation triggers EADs in virtual Purkinje cells with weak repolarisation reserve. Ca2+ accumulation caused by increased Ca2+ and NCX currents triggers DADs.
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Carpio EF, Gomez JF, Sebastian R, Lopez-Perez A, Castellanos E, Almendral J, Ferrero JM, Trenor B. Optimization of Lead Placement in the Right Ventricle During Cardiac Resynchronization Therapy. A Simulation Study. Front Physiol 2019; 10:74. [PMID: 30804805 PMCID: PMC6378298 DOI: 10.3389/fphys.2019.00074] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 01/22/2019] [Indexed: 12/18/2022] Open
Abstract
Patients suffering from heart failure and left bundle branch block show electrical ventricular dyssynchrony causing an abnormal blood pumping. Cardiac resynchronization therapy (CRT) is recommended for these patients. Patients with positive therapy response normally present QRS shortening and an increased left ventricle (LV) ejection fraction. However, around one third do not respond favorably. Therefore, optimal location of pacing leads, timing delays between leads and/or choosing related biomarkers is crucial to achieve the best possible degree of ventricular synchrony during CRT application. In this study, computational modeling is used to predict the optimal location and delay of pacing leads to improve CRT response. We use a 3D electrophysiological computational model of the heart and torso to get insight into the changes in the activation patterns obtained when the heart is paced from different regions and for different atrioventricular and interventricular delays. The model represents a heart with left bundle branch block and heart failure, and allows a detailed and accurate analysis of the electrical changes observed simultaneously in the myocardium and in the QRS complex computed in the precordial leads. Computational simulations were performed using a modified version of the O'Hara et al. action potential model, the most recent mathematical model developed for human ventricular electrophysiology. The optimal location for the pacing leads was determined by QRS maximal reduction. Additionally, the influence of Purkinje system on CRT response was assessed and correlation analysis between several parameters of the QRS was made. Simulation results showed that the right ventricle (RV) upper septum near the outflow tract is an alternative location to the RV apical lead. Furthermore, LV endocardial pacing provided better results as compared to epicardial stimulation. Finally, the time to reach the 90% of the QRS area was a good predictor of the instant at which 90% of the ventricular tissue was activated. Thus, the time to reach the 90% of the QRS area is suggested as an additional index to assess CRT effectiveness to improve biventricular synchrony.
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Affiliation(s)
- Edison F Carpio
- Centre for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, Valencia, Spain
| | - Juan F Gomez
- Centre for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, Valencia, Spain
| | - Rafael Sebastian
- Computational Multiscale Simulation Lab (CoMMLab), Department of Computer Science, Universitat de València, Valencia, Spain
| | - Alejandro Lopez-Perez
- Centre for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, Valencia, Spain
| | - Eduardo Castellanos
- Electrophysiology Laboratory and Arrhythmia Unit, Grupo HM Hospitales, Hospital Monteprincipe, University CEU-San Pablo, Madrid, Spain
| | - Jesus Almendral
- Electrophysiology Laboratory and Arrhythmia Unit, Grupo HM Hospitales, Hospital Monteprincipe, University CEU-San Pablo, Madrid, Spain
| | - Jose M Ferrero
- Centre for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, Valencia, Spain
| | - Beatriz Trenor
- Centre for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, Valencia, Spain
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