502 research outputs found

    Uncertainty Quantification and Reduction in Cardiac Electrophysiological Imaging

    Get PDF
    Cardiac electrophysiological (EP) imaging involves solving an inverse problem that infers cardiac electrical activity from body-surface electrocardiography data on a physical domain defined by the body torso. To avoid unreasonable solutions that may fit the data, this inference is often guided by data-independent prior assumptions about different properties of cardiac electrical sources as well as the physical domain. However, these prior assumptions may involve errors and uncertainties that could affect the inference accuracy. For example, common prior assumptions on the source properties, such as fixed spatial and/or temporal smoothness or sparseness assumptions, may not necessarily match the true source property at different conditions, leading to uncertainties in the inference. Furthermore, prior assumptions on the physical domain, such as the anatomy and tissue conductivity of different organs in the thorax model, represent an approximation of the physical domain, introducing errors to the inference. To determine the robustness of the EP imaging systems for future clinical practice, it is important to identify these errors/uncertainties and assess their impact on the solution. This dissertation focuses on the quantification and reduction of the impact of uncertainties caused by prior assumptions/models on cardiac source properties as well as anatomical modeling uncertainties on the EP imaging solution. To assess the effect of fixed prior assumptions/models about cardiac source properties on the solution of EP imaging, we propose a novel yet simple Lp-norm regularization method for volumetric cardiac EP imaging. This study reports the necessity of an adaptive prior model (rather than fixed model) for constraining the complex spatiotemporally changing properties of the cardiac sources. We then propose a multiple-model Bayesian approach to cardiac EP imaging that employs a continuous combination of prior models, each re-effecting a specific spatial property for volumetric sources. The 3D source estimation is then obtained as a weighted combination of solutions across all models. Including a continuous combination of prior models, our proposed method reduces the chance of mismatch between prior models and true source properties, which in turn enhances the robustness of the EP imaging solution. To quantify the impact of anatomical modeling uncertainties on the EP imaging solution, we propose a systematic statistical framework. Founded based on statistical shape modeling and unscented transform, our method quantifies anatomical modeling uncertainties and establish their relation to the EP imaging solution. Applied on anatomical models generated from different image resolutions and different segmentations, it reports the robustness of EP imaging solution to these anatomical shape-detail variations. We then propose a simplified anatomical model for the heart that only incorporates certain subject-specific anatomical parameters, while discarding local shape details. Exploiting less resources and processing for successful EP imaging, this simplified model provides a simple clinically-compatible anatomical modeling experience for EP imaging systems. Different components of our proposed methods are validated through a comprehensive set of synthetic and real-data experiments, including various typical pathological conditions and/or diagnostic procedures, such as myocardial infarction and pacing. Overall, the methods presented in this dissertation for the quantification and reduction of uncertainties in cardiac EP imaging enhance the robustness of EP imaging, helping to close the gap between EP imaging in research and its clinical application

    Personalized noninvasive imaging of volumetric cardiac electrophysiology

    Get PDF
    Three-dimensionally distributed electrical functioning is the trigger of mechanical contraction of the heart. Disturbance of this electrical flow is known to predispose to mechanical catastrophe but, due to its amenability to certain intervention techniques, a detailed understanding of subject-specific cardiac electrophysiological conditions is of great medical interest. In current clinical practice, body surface potential recording is the standard tool for diagnosing cardiac electrical dysfunctions. However, successful treatments normally require invasive catheter mapping for a more detailed observation of these dysfunctions. In this dissertation, we take a system approach to pursue personalized noninvasive imaging of volumetric cardiac electrophysiology. Under the guidance of existing scientific knowledge of the cardiac electrophysiological system, we extract the subject specific cardiac electrical information from noninvasive body surface potential mapping and tomographic imaging data of individual subjects. In this way, a priori knowledge of system physiology leads the physiologically meaningful interpretation of personal data; at the same time, subject-specific information contained in the data identifies parameters in individual systems that differ from prior knowledge. Based on this perspective, we develop a physiological model-constrained statistical framework for the quantitative reconstruction of the electrical dynamics and inherent electrophysiological property of each individual cardiac system. To accomplish this, we first develop a coupled meshfree-BE (boundary element) modeling approach to represent existing physiological knowledge of the cardiac electrophysiological system on personalized heart-torso structures. Through a state space system approach and sequential data assimilation techniques, we then develop statistical model-data coupling algorithms for quantitative reconstruction of volumetric transmembrane potential dynamics and tissue property of 3D myocardium from body surface potential recoding of individual subjects. We also introduce a data integration component to build personalized cardiac electrophysiology by fusing tomographic image and BSP sequence of the same subject. In addition, we develop a computational reduction strategy that improves the efficiency and stability of the framework. Phantom experiments and real-data human studies are performed for validating each of the framework’s major components. These experiments demonstrate the potential of our framework in providing quantitative understanding of volumetric cardiac electrophysiology for individual subjects and in identifying latent threats in individual’s heart. This may aid in personalized diagnose, treatment planning, and fundamentally, prevention of fatal cardiac arrhythmia

    Personalized Multi-Scale Modeling of the Atria: Heterogeneities, Fiber Architecture, Hemodialysis and Ablation Therapy

    Get PDF
    This book targets three fields of computational multi-scale cardiac modeling. First, advanced models of the cellular atrial electrophysiology and fiber orientation are introduced. Second, novel methods to create patient-specific models of the atria are described. Third, applications of personalized models in basic research and clinical practice are presented. The results mark an important step towards the patient-specific model-based atrial fibrillation diagnosis, understanding and treatment

    Bayesian Active Learning for Personalization and Uncertainty Quantification in Cardiac Electrophysiological Model

    Get PDF
    Cardiacvascular disease is the top death causing disease worldwide. In recent years, high-fidelity personalized models of the heart have shown an increasing capability to supplement clinical cardiology for improved patient-specific diagnosis, prediction, and treatment planning. In addition, they have shown promise to improve scientific understanding of a variety of disease mechanisms. However, model personalization by estimating the patient-specific tissue properties that are in the form of parameters of a physiological model is challenging. This is because tissue properties, in general, cannot be directly measured and they need to be estimated from measurements that are indirectly related to them through a physiological model. Moreover, these unknown tissue properties are heterogeneous and spatially varying throughout the heart volume presenting a difficulty of high-dimensional (HD) estimation from indirect and limited measurement data. The challenge in model personalization, therefore, summarizes to solving an ill-posed inverse problem where the unknown parameters are HD and the forward model is complex with a non-linear and computationally expensive physiological model. In this dissertation, we address the above challenge with following contributions. First, to address the concern of a complex forward model, we propose the surrogate modeling of the complex target function containing the forward model – an objective function in deterministic estimation or a posterior probability density function in probabilistic estimation – by actively selecting a set of training samples and a Bayesian update of the prior over the target function. The efficient and accurate surrogate of the expensive target function obtained in this manner is then utilized to accelerate either deterministic or probabilistic parameter estimation. Next, within the framework of Bayesian active learning we enable active surrogate learning over a HD parameter space with two novel approaches: 1) a multi-scale optimization that can adaptively allocate higher resolution to heterogeneous tissue regions and lower resolution to homogeneous tissue regions; and 2) a generative model from low-dimensional (LD) latent code to HD tissue properties. Both of these approaches are independently developed and tested within a parameter optimization framework. Furthermore, we devise a novel method that utilizes the surrogate pdf learned on an estimated LD parameter space to improve the proposal distribution of Metropolis Hastings for an accelerated sampling of the exact posterior pdf. We evaluate the presented methods on estimating local tissue excitability of a cardiac electrophysiological model in both synthetic data experiments and real data experiments. Results demonstrate that the presented methods are able to improve the accuracy and efficiency in patient-specific model parameter estimation in comparison to the existing approaches used for model personalization

    Meshless electrophysiological modeling of cardiac resynchronization therapy—benchmark analysis with finite-element methods in experimental data

    Get PDF
    Computational models of cardiac electrophysiology are promising tools for reducing the rates of non-response patients suitable for cardiac resynchronization therapy (CRT) by optimizing electrode placement. The majority of computational models in the literature are mesh-based, primarily using the finite element method (FEM). The generation of patient-specific cardiac meshes has traditionally been a tedious task requiring manual intervention and hindering the modeling of a large number of cases. Meshless models can be a valid alternative due to their mesh quality independence. The organization of challenges such as the CRT-EPiggy19, providing unique experimental data as open access, enables benchmarking analysis of different cardiac computational modeling solutions with quantitative metrics. We present a benchmark analysis of a meshless-based method with finite-element methods for the prediction of cardiac electrical patterns in CRT, based on a subset of the CRT-EPiggy19 dataset. A data assimilation strategy was designed to personalize the most relevant parameters of the electrophysiological simulations and identify the optimal CRT lead configuration. The simulation results obtained with the meshless model were equivalent to FEM, with the most relevant aspect for accurate CRT predictions being the parameter personalization strategy (e.g., regional conduction velocity distribution, including the Purkinje system and CRT lead distribution). © 2022 by the authors. Licensee MDPI, Basel, Switzerland

    Mathematical modeling approaches for the diagnosis and treatment of reentrant atrial tachyarrhythmias

    Full text link
    [EN] Atrial tachyarrhythmias present a high prevalence in the developed world, and several studies predict that in the coming decades it will be increased. Micro or macro-reentrant mechanisms of the electrical wavefronts that govern the mechanical behavior of the heart are one of the main responsibles for the maintenance of these arrhythmias. Atrial flutter is maintained by a macro-reentry around an anatomical or functional obstacle located in the atria. In the case of atrial fibrillation, the hypothesis which describes high frequency rotors as dominant sources of the fibrillation and responsible for the maintenance of the arrhythmia, has been gaining relevance in the last years. However, the therapies that target high frequency sources have a limited efficacy with current techniques. Radiofrequency ablation allows the destruction of parts of the cardiac tissue resulting in the interruption of the reentrant circuit in case of macro-reentries or the isolation of micro-reentrant circuits. The non-invasive location of reentrant circuits would increment the efficacy of these therapies and would shorten surgery interventions. In parallel, pharmacological therapies modify ionic expressions associated to the excitability and electrical refractoriness of the cardiac tissue with the objective of hindering the maintenance of reentrant behaviors. These therapies require a deep knowledge of the ionic mechanisms underlying the reentrant behavior and its properties in order to be effective. The research in these mechanisms allows the evaluation of new targets for the treatment and thus may improve the efficacy in atrial fibrillation termination. In this thesis, mathematical modeling is used to go forward in the minimization of the limitations associated to these treatments. Body surface potential mapping has been evaluated, both clinically and by means of mathematical simulations for the diagnosis and location of macro-reentrant circuits. The analysis of phase maps obtained from multiple lead electrocardiographic recordings distributed in the whole torso allowed the discrimination between different reentrant circuits. It is the reason why this technique is presented as a tool for the non-invasive location of macro and micro-reentrant circuits. A population of mathematical models designed in this thesis based on the action potentials recordings of atrial cardiomyocites from 149 patients, allowed the evaluation of the ionic mechanisms defining the properties of reentrant behaviors. This study has allowed us defining the blockade of ICaL as a target for the pharmacological treatment. The blockade of this current is associated with the increase of the movement in the core of the rotor which easies the collision of the rotor with other wavefronts or anatomical obstacles promoting the extinction of the reentry. The variability observed between patients modeled in our population has allowed showing and explaining the mechanisms promoting divergent results of a single treatment. This is why the introduction of populations of models will allow the prevention of side effects associated to inter-subject variability and to go forward in the development of individualized therapies. These works are built through a simulation platform of cardiac electrophysiology based in Graphic Processing Units (GPUs) and developed in this thesis. The platform allows the simulation of cellular models, tissues and organs with a realistic geometry and shows features comparable to that of the platforms used by the most relevant electrophysiology research groups at the moment.[ES] Las taquiarritmias auriculares tienen una alta prevalencia en el mundo desarrollado, además diversos estudios poblacionales indican que en las próximas décadas ésta se verá incrementada. Los mecanismos de micro o macro-reentrada de los frentes de onda eléctricos que rigen el comportamiento mecánico del corazón, se presentan como una de las principales causas del mantenimiento de estas arritmias. El flutter auricular es mantenido por un macro-reentrada alrededor de un obstáculo anatómico o funcional en las aurículas, mientras que en el caso de la fibrilación auricular la hipótesis que define a los rotores de alta frecuencia como elementos dominantes y responsables del mantenimiento de la arritmia se ha ido imponiendo al resto en los últimos años. Sin embargo, las terapias que tienen como objetivo finalizar o aislar estas reentradas tienen todavía una eficacia limitada. La ablación por radiofrecuencia permite eliminar zonas del tejido cardiaco resultando en la interrupción del circuito de reentrada en el caso de macro-reentradas o el aislamiento de comportamientos micro-reentrantes. La localización no invasiva de los circuitos reentrantes incrementaría la eficacia de estas terapias y reduciría la duración de las intervenciones quirúrgicas. Por otro lado, las terapias farmacológicas alteran las expresiones iónicas asociadas a la excitabilidad y la refractoriedad del tejido con el fin de dificultar el mantenimiento de comportamientos reentrantes. Este tipo de terapias exigen incrementar el conocimiento de los mecanismos subyacentes que explican el proceso de reentrada y sus propiedades, la investigación de estos mecanismos permite definir las dianas terapéuticas que mejoran la eficacia en la extinción de estos comportamientos. En esta tesis el modelado matemático se utiliza para dar un paso importante en la minimización de las limitaciones asociadas a estos tratamientos. La cartografía eléctrica de superficie ha sido testada, clínicamente y con simulaciones matemática,s como técnica de diagnóstico y localización de circuitos macro-reentrantes. El análisis de mapas de fase obtenidos a partir de los registros multicanal de derivaciones electrocardiográficas distribuidas en la superficie del torso permite diferenciar distintos circuitos de reentrada. Es por ello que esta técnica de registro y análisis se presenta como una herramienta para la localización no invasiva de circuitos macro y micro-reentrantes. Una población de modelos matemáticos, diseñada en esta tesis a partir de los registros de los potenciales de acción de 149 pacientes, ha permitido evaluar los mecanismos iónicos que definen las propiedades asociadas a los procesos de reentrada. Esto ha permitido apuntar al bloqueo de la corriente ICaL como diana terapéutica. Ésta se asocia al incremento del movimiento del núcleo que facilita el impacto del rotor con otros frentes de onda u obstáculos extinguiéndose así el comportamiento reentrante. La variabilidad entre pacientes reflejada en la población de modelos ha permitido además mostrar los mecanismos por los cuales un mismo tratamiento puede mostrar efectos divergentes, así el uso de poblaciones de modelos matemáticos permitirá prevenir efectos secundarios asociados a la variabilidad entre pacientes y profundizar en el desarrollo de terapias individualizadas. Estos trabajos se cimientan sobre una plataforma de simulación de electrofisiología cardiaca de basado en Unidades de Procesado Gráfico (GPUs) y desarrollada en esta tesis. La plataforma permite la simulación de modelos celulares cardiacos así como de tejidos u órganos con geometría realista, mostrando unas prestaciones comparables con las de las utilizadas por los grupos de investigación más potentes en el campo de la electrofisiología.[CA] Les taquiarítmies auriculars tenen una alta prevalença en el món desenvolupat, a més diversos estudis poblacionals indiquen que en les pròximes dècades aquesta es veurà incrementada. Els mecanismes de micro o macro-reentrada dels fronts d'ona elèctrics que regeixen el comportament mecànic del cor, es presenten com una de les principals causes del manteniment d'aquestes arítmies. El flutter auricular és mantingut per una macro-reentrada al voltant d'un obstacle anatòmic o funcional en les aurícules, mentre que en el cas de la fibril·lació auricular la hipòtesi que defineix als rotors d'alta freqüència com a elements dominants i responsables del manteniment de l'arítmia s'ha anat imposant a la resta en els últims anys. No obstant això, les teràpies que tenen com a objectiu finalitzar o aïllar aquestes reentrades tenen encara una eficàcia limitada. L'ablació per radiofreqüència permet eliminar zones del teixit cardíac resultant en la interrupció del circuit de reentrada en el cas de macro-reentrades o l'aïllament de comportaments micro-reentrants. La localització no invasiva dels circuits reentrants incrementaria l'eficàcia d'aquestes teràpies i reduiria la durada de les intervencions quirúrgiques. D'altra banda, les teràpies farmacològiques alteren les expressions iòniques associades a la excitabilitat i la refractaritat del teixit amb la finalitat de dificultar el manteniment de comportaments reentrants. Aquest tipus de teràpies exigeixen incrementar el coneixement dels mecanismes subjacents que expliquen el procés de reentrada i les seues propietats, la recerca d'aquests mecanismes permet definir les dianes terapèutiques que milloren l'eficàcia en l'extinció d'aquests comportaments. En aquesta tesi el modelatge matemàtic s'utilitza per a fer un pas important en la minimització de les limitacions associades a aquests tractaments. La cartografia elèctrica de superfície ha sigut testada, clínicament i amb simulacions matemàtiques com a tècnica de diagnòstic i localització de circuits macro-reentrants. L'anàlisi de mapes de fase obtinguts a partir dels registres multicanal de derivacions electrocardiogràfiques distribuïdes en la superfície del tors permet diferenciar diferents circuits de reentrada. És per açò que aquesta tècnica de registre i anàlisi es presenta com una eina per a la localització no invasiva de circuits macro i micro-reentrants. Una població de models matemàtics, dissenyada en aquesta tesi a partir dels registres dels potencials d'acció de 149 pacients, ha permès avaluar els mecanismes iònics que defineixen les propietats associades als processos de reentrada. Açò ha permès apuntar al bloqueig del corrent ICaL com a diana terapèutica. Aquesta s'associa a l'increment del moviment del nucli que facilita l'impacte del rotor amb altres fronts d'ona o obstacles extingint-se així el comportament reentrant. La variabilitat entre pacients reflectida en la població de models ha permès a més mostrar els mecanismes pels quals un mateix tractament pot mostrar efectes divergents, així l'ús de poblacions de models matemàtics permetrà prevenir efectes secundaris associats a la variabilitat entre pacients i aprofundir en el desenvolupament de teràpies individualitzades. Aquests treballs es fonamenten sobre una plataforma de simulació de electrofisiologia cardíaca basat en Unitats de Processament Gràfic (GPUs) i desenvolupada en aquesta tesi. La plataforma permet la simulació de models cel·lulars cardíacs així com de teixits o òrgans amb geometria realista, mostrant unes prestacions comparables amb les de les utilitzades per els grups de recerca més importants en aquesta área.Liberos Mascarell, A. (2016). Mathematical modeling approaches for the diagnosis and treatment of reentrant atrial tachyarrhythmias [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/62166TESI

    Multiscale Cohort Modeling of Atrial Electrophysiology : Risk Stratification for Atrial Fibrillation through Machine Learning on Electrocardiograms

    Get PDF
    Patienten mit Vorhofflimmern sind einem fünffach erhöhten Risiko für einen ischämischen Schlaganfall ausgesetzt. Eine frühzeitige Erkennung und Diagnose der Arrhythmie würde ein rechtzeitiges Eingreifen ermöglichen, um möglicherweise auftretende Begleiterkrankungen zu verhindern. Eine Vergrößerung des linken Vorhofs sowie fibrotisches Vorhofgewebe sind Risikomarker für Vorhofflimmern, da sie die notwendigen Voraussetzungen für die Aufrechterhaltung der chaotischen elektrischen Depolarisation im Vorhof erfüllen. Mithilfe von Techniken des maschinellen Lernens könnten Fibrose und eine Vergrößerung des linken Vorhofs basierend auf P Wellen des 12-Kanal Elektrokardiogramms im Sinusrhythmus automatisiert identifiziert werden. Dies könnte die Basis für eine nicht-invasive Risikostrat- ifizierung neu auftretender Vorhofflimmerepisoden bilden, um anfällige Patienten für ein präventives Screening auszuwählen. Zu diesem Zweck wurde untersucht, ob simulierte Vorhof-Elektrokardiogrammdaten, die dem klinischen Trainingssatz eines maschinellen Lernmodells hinzugefügt wurden, zu einer verbesserten Klassifizierung der oben genannten Krankheiten bei klinischen Daten beitra- gen könnten. Zwei virtuelle Kohorten, die durch anatomische und funktionelle Variabilität gekennzeichnet sind, wurden generiert und dienten als Grundlage für die Simulation großer P Wellen-Datensätze mit genau bestimmbaren Annotationen der zugrunde liegenden Patholo- gie. Auf diese Weise erfüllen die simulierten Daten die notwendigen Voraussetzungen für die Entwicklung eines Algorithmus für maschinelles Lernen, was sie von klinischen Daten unterscheidet, die normalerweise nicht in großer Zahl und in gleichmäßig verteilten Klassen vorliegen und deren Annotationen möglicherweise durch unzureichende Expertenannotierung beeinträchtigt sind. Für die Schätzung des Volumenanteils von linksatrialem fibrotischen Gewebe wurde ein merkmalsbasiertes neuronales Netz entwickelt. Im Vergleich zum Training des Modells mit nur klinischen Daten, führte das Training mit einem hybriden Datensatz zu einer Reduzierung des Fehlers von durchschnittlich 17,5 % fibrotischem Volumen auf 16,5 %, ausgewertet auf einem rein klinischen Testsatz. Ein Long Short-Term Memory Netzwerk, das für die Unterscheidung zwischen gesunden und P Wellen von vergrößerten linken Vorhöfen entwickelt wurde, lieferte eine Genauigkeit von 0,95 wenn es auf einem hybriden Datensatz trainiert wurde, von 0,91 wenn es nur auf klinischen Daten trainiert wurde, die alle mit 100 % Sicherheit annotiert wurden, und von 0,83 wenn es auf einem klinischen Datensatz trainiert wurde, der alle Signale unabhängig von der Sicherheit der Expertenannotation enthielt. In Anbetracht der Ergebnisse dieser Arbeit können Elektrokardiogrammdaten, die aus elektrophysiologischer Modellierung und Simulationen an virtuellen Patientenkohorten resul- tieren und relevante Variabilitätsaspekte abdecken, die mit realen Beobachtungen übereinstim- men, eine wertvolle Datenquelle zur Verbesserung der automatisierten Risikostratifizierung von Vorhofflimmern sein. Auf diese Weise kann den Nachteilen klinischer Datensätze für die Entwicklung von Modellen des maschinellen Lernens entgegengewirkt werden. Dies trägt letztendlich zu einer frühzeitigen Erkennung der Arrhythmie bei, was eine rechtzeitige Auswahl geeigneter Behandlungsstrategien ermöglicht und somit das Schlaganfallrisiko der betroffenen Patienten verringert

    Parameter variations in personalized electrophysiological models of human heart ventricles

    Full text link
    The objectives of this study were to evaluate the accuracy of personalized numerical simulations of the electrical activity in human ventricles by comparing simulated electrocardiograms (ECGs) with real patients' ECGs and analyzing the sensitivity of the model output to variations in the model parameters. We used standard 12-lead ECGs and up to 224 unipolar body-surface ECGs to record three patients with cardiac resynchronization therapy devices and three patients with focal ventricular tachycardia. Patient-tailored geometrical models of the ventricles, atria, large vessels, liver, and spine were created using computed tomography data. Ten cases of focal ventricular activation were simulated using the bidomain model and the TNNP 2006 cellular model. The population-based values of electrical conductivities and other model parameters were used for accuracy analysis, and their variations were used for sensitivity analysis. The mean correlation coefficient between the simulated and real ECGs varied significantly (from r = 0.29 to r = 0.86) among the simulated cases. A strong mean correlation (r > 0.7) was found in eight of the ten model cases. The accuracy of the ECG simulation varied widely in the same patient depending on the localization of the excitation origin. The sensitivity analysis revealed that variations in the anisotropy ratio, blood conductivity, and cellular apicobasal heterogeneity had the strongest influence on transmembrane potential, while variation in lung conductivity had the greatest influence on body-surface ECGs. Futhermore, the anisotropy ratio predominantly affected the latest activation time and repolarization time dispersion, while the cellular apicobasal heterogeneity mainly affected the dispersion of action potential duration, and variation in lung conductivity mainly led to changes in the amplitudes of ECGs and cardiac electrograms. We also found that the effects of certain parameter variations had specific regional patterns on the cardiac and body surfaces. These observations are useful for further developing personalized cardiac models

    MRI-Based Computational Torso/Biventricular Multiscale Models to Investigate the Impact of Anatomical Variability on the ECG QRS Complex

    Get PDF
    Aims:Patient-to-patient anatomical differences are an important source of variability in the electrocardiogram, and they may compromise the identification of pathological electrophysiological abnormalities. This study aims at quantifying the contribution of variability in ventricular and torso anatomies to differences in QRS complexes of the 12-lead ECG using computer simulations. Methods:A computational pipeline is presented that enables computer simulations using human torso/biventricular anatomically based electrophysiological models from clinically standard magnetic resonance imaging (MRI). The ventricular model includes membrane kinetics represented by the biophysically detailed O’Hara Rudy model modified for tissue heterogeneity and includes fiber orientation based on the Streeter rule. A population of 265 torso/biventricular models was generated by combining ventricular and torso anatomies obtained from clinically standard MRIs, augmented with a statistical shape model of the body. 12-lead ECGs were simulated on the 265 human torso/biventricular electrophysiology models, and QRS morphology,duration and amplitude were quantified in each ECG lead for each of the human torso-biventricular models. Results:QRS morphologies in limb leads are mainly determined by ventricular anatomy,while in the precordial leads, and especially V1 to V4, they are determined by heart position within the torso. Differences in ventricular orientation within the torso can explain morphological variability from monophasic to biphasic QRS complexes. QRS duration ismainly influenced by myocardial volume, while it is hardly affected by the torso anatomyor position. An average increase of 0.12±0.05 ms in QRS duration is obtained for eachcm3of myocardial volume across all the leads while it hardly changed due to changes in torso volume. Conclusion:Computer simulations using populations of human torso/biventricular models based on clinical MRI enable quantification of anatomical causes of variability in the QRS complex of the 12-lead ECG. The human models presented also pave theway toward their use as testbeds in silico clinical trial
    corecore