Coupled Personalisation of Electrophysiology Models for Simulation of Induced Ischemic Ventricular Tachycardia

International audienceDespite recent efforts in cardiac electrophysiology modelling, there is still a strong need to make macroscopic models usable in planning and assistance of the clinical procedures. This requires model personalisation i.e. estimation of patient-specific model parameters and computations compatible with clinical constraints. Fast macroscopic models allow a quick estimation of the tissue conductivity, but are often unreliable in prediction of arrhythmias. On the other side, complex biophysical models are quite expensive for the tissue conductivity estimation, but are well suited for arrhythmia predictions. Here we present a coupled personalisation framework, which combines the benefits of the two models. A fast Eikonal (EK) 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 (APD) and APD restitution curves for the tissue are estimated for the MS model. This framework is applied to a clinical dataset provided with an hybrid X-Ray/MR imaging on an ischemic patient. This personalised MS Model is then used for in silico simulation of clinical Ventricular Tachycardia (VT) stimulation protocol to predict the induction of VT. This proof of concept opens up possibilities of using VT induction modelling directly in the intervention room, in order to plan the radio-frequency ablation lines

Three-dimensional cardiac computational modelling: methods, features and applications

[EN] The combination of computational models and biophysical simulations can help to interpret an array of experimental data and contribute to the understanding, diagnosis and treatment of complex diseases such as cardiac arrhythmias. For this reason, three-dimensional (3D) cardiac computational modelling is currently a rising field of research. The advance of medical imaging technology over the last decades has allowed the evolution from generic to patient-specific 3D cardiac models that faithfully represent the anatomy and different cardiac features of a given alive subject. Here we analyse sixty representative 3D cardiac computational models developed and published during the last fifty years, describing their information sources, features, development methods and online availability. This paper also reviews the necessary components to build a 3D computational model of the heart aimed at biophysical simulation, paying especial attention to cardiac electrophysiology (EP), and the existing approaches to incorporate those components. We assess the challenges associated to the different steps of the building process, from the processing of raw clinical or biological data to the final application, including image segmentation, inclusion of substructures and meshing among others. We briefly outline the personalisation approaches that are currently available in 3D cardiac computational modelling. Finally, we present examples of several specific applications, mainly related to cardiac EP simulation and model-based image analysis, showing the potential usefulness of 3D cardiac computational modelling into clinical environments as a tool to aid in the prevention, diagnosis and treatment of cardiac diseases.This work was partially supported by the "VI Plan Nacional de Investigacion Cientifica, Desarrollo e Innovacion Tecnologica" from the Ministerio de Economia y Competitividad of Spain (TIN2012-37546-C03-01 and TIN2011-28067) and the European Commission (European Regional Development Funds - ERDF - FEDER) and by "eTorso project" (GVA/2013-001404) from the Generalitat Valenciana (Spain). ALP is financially supported by the program "Ayudas para contratos predoctorales para la formacion de doctores" from the Ministerio de Economia y Competitividad of Spain (BES-2013-064089).López Pérez, AD.; Sebastián Aguilar, R.; Ferrero De Loma-Osorio, JM. (2015). 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Personnalisation basée sur l'imagerie de modèles cardiaques électrophysiologiques pour la planification du traitement de la tachycardie ventriculaire

Acute infarct survival rates have drastically improved over the last decades, mechanically increasing chronic infarct related affections.Among these affections, ischaemic ventricular tachycardia (VT) is a particularly serious arrhythmia that can lead to the often lethal ventricular fibrillation. VT can be treated by radio frequency ablation of the arrhythmogenic substrate.The first phase of this long and risky interventional cardiology procedure is an electrophysiological (EP) exploration of the heart.This phase aims at localising the ablation targets, notably by inducing the arrhythmia in a controlled setting. In this work we propose to re-create this exploration phase in silico, by personalising cardiac EP models.We show that key information about infarct scar location and heterogeneity can be automatically obtained by a deep learning-based automated segmentation of the myocardium on computed tomography (CT) images.Our goal is to use this information to run patient-specific simulations of depolarisation wave propagation in the myocardium, mimicking the interventional cardiology exploration phase.We start by studying the relationship between the depolarisation wave propagation velocity and the left ventricular wall thickness to personalise an Eikonal model, an approach that can successfully reproduce periodic activation maps of the left ventricle recorded during VT.We then propose efficient algorithms to detect the repolarisation wave on unipolar electrograms (UEG), that we use to analyse the UEGs embedded in such intra-cardiac recordings.Thanks to a multimodal registration between these recordings and CT images, we establish relationships between action potential durations/restitution properties and left ventricular wall thickness.These relationships are finally used to parametrise a reaction-diffusion model able to reproduce interventional cardiologists' induction protocols that trigger realistic and documented VTs. inteinterventional cardiologists' induction protocols that trigger realistic and documented VTs.La survie lors de la phase aiguë de l'infarctus du myocarde a énormément progressé au cours des dernières décennies, augmentant ainsi la mortalité des affections liées à l'infarctus chronique.Parmi ces pathologies, la tachycardie ventriculaire (TV) est une arythmie particulièrement grave qui peut conduire à la fibrillation ventriculaire, souvent fatale.La TV peut être traitée par ablation par radio-fréquences du substrat arythmogène.La première phase de cette procédure, longue et risquée, est une exploration électrophysiologique (EP) du cœur consistant à déterminer les cibles de cette ablation, notamment en provoquant l'arythmie dans un environnement contrôléDans cette thèse, nous proposons de re-créer in silico cette phase exploratoire, en personnalisation des modèles cardiaques EP.Nous montrons que des informations clefs à propos de la localisation et de l'hétérogénéité de la cicatrice d'infarctus peuvent être obtenues automatiquement par une segmentation d'images tomodensitométriques (TDM) utilisant un réseau de neurones artificiels.Notre but est alors d'utiliser ces informations pour réaliser des simulations spécifiques à un patient de la propagation de l'onde de dépolarisation dans le myocarde, reproduisant la phase exploratoire décrite plus haut.Nous commençons par étudier la relation entre la vitesse de l'onde de dépolarisation et l'épaisseur du ventricule gauche, relation qui permet de personnaliser un modèle EP Eikonal; cette approche permet fr reproduire des cartes d'activations périodiques du ventricule gauche obtenues durant des TV.Nous proposons ensuite des algorithmes efficaces pour détecter l'onde de repolarisation sur les électrogrammes unipolaires (EGU), que nous utilisons pour analyser les EGU contenus dans les enregistrements intra-cardiaques à notre disposition.Grâce à un recalage multimodal entre ces enregistrements et des images TDM, nous établissons des relations entre durées de potentiels d'action (DPA)/propriétés de restitutions de DPA et épaisseur du ventricule gauche.Enfin, ces relations sont utilisés pour paramétrer un modèle de réaction-diffusion capable de reproduire fidèlement les protocoles d'induction des cardiologues interventionnels qui provoquent des TV réalistes et documentées

When Cardiac Biophysics Meets Groupwise Statistics: Complementary Modelling Approaches for Patient-Specific Medicine

This habilitation manuscript contains research on biophysical and statistical modeling of the heart, as well as interactions between these two approaches

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

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

When Cardiac Biophysics Meets Groupwise Statistics: Complementary Modelling Approaches for Patient-Specific Medicine

This habilitation manuscript contains research on biophysical and statistical modeling of the heart, as well as interactions between these two approaches

Computer-Assisted Electroanatomical Guidance for Cardiac Electrophysiology Procedures

Cardiac arrhythmias are serious life-threatening episodes affecting both the aging population and younger patients with pre-existing heart conditions. One of the most effective therapeutic procedures is the minimally-invasive catheter-driven endovascular electrophysiology study, whereby electrical potentials and activation patterns in the affected cardiac chambers are measured and subsequent ablation of arrhythmogenic tissue is performed. Despite emerging technologies such as electroanatomical mapping and remote intraoperative navigation systems for improved catheter manipulation and stability, successful ablation of arrhythmias is still highly-dependent on the operator’s skills and experience. This thesis proposes a framework towards standardisation in the electroanatomical mapping and ablation planning by merging knowledge transfer from previous cases and patient-specific data. In particular, contributions towards four different procedural aspects were made: optimal electroanatomical mapping, arrhythmia path computation, catheter tip stability analysis, and ablation simulation and optimisation. In order to improve the intraoperative electroanatomical map, anatomical areas of high mapping interest were proposed, as learned from previous electrophysiology studies. Subsequently, the arrhythmic wave propagation on the endocardial surface and potential ablation points were computed. The ablation planning is further enhanced, firstly by the analysis of the catheter tip stability and the probability of slippage at sparse locations on the endocardium and, secondly, by the simulation of the ablation result from the computation of convolutional matrices which model mathematically the ablation process. The methods proposed by this thesis were validated on data from patients with complex congenital heart disease, who present unusual cardiac anatomy and consequently atypical arrhythmias. The proposed methods also build a generic framework for computer guidance of electrophysiology, with results showing complementary information that can be easily integrated into the clinical workflow.Open Acces

Planification de l’ablation radiofréquence des arythmies cardiaques en combinant modélisation et apprentissage automatique

Cardiac arrhythmias are heart rhythm disruptions which can lead to sudden cardiac death. They require a deeper understanding for appropriate treatment planning. In this thesis, we integrate personalized structural and functional data into a 3D tetrahedral mesh of the biventricular myocardium. Next, the Mitchell-Schaeffer (MS) simplified biophysical model is used to study the spatial heterogeneity of electrophysiological (EP) tissue properties and their role in arrhythmogenesis. Radiofrequency ablation (RFA) with the elimination of local abnormal ventricular activities (LAVA) has recently arisen as a potentially curative treatment for ventricular tachycardia but the EP studies required to locate LAVA are lengthy and invasive. LAVA are commonly found within the heterogeneous scar, which can be imaged non-invasively with 3D delayed enhanced magnetic resonance imaging (DE-MRI). We evaluate the use of advanced image features in a random forest machine learning framework to identify areas of LAVA-inducing tissue. Furthermore, we detail the dataset’s inherent error sources and their formal integration in the training process. Finally, we construct MRI-based structural patient-specific heart models and couple them with the MS model. We model a recording catheter using a dipole approach and generate distinct normal and LAVA-like electrograms at locations where they have been found in clinics. This enriches our predictions of the locations of LAVA-inducing tissue obtained through image-based learning. Confidence maps can be generated and analyzed prior to RFA to guide the intervention. These contributions have led to promising results and proofs of concepts.Les arythmies sont des perturbations du rythme cardiaque qui peuvent entrainer la mort subite et requièrent une meilleure compréhension pour planifier leur traitement. Dans cette thèse, nous intégrons des données structurelles et fonctionnelles à un maillage 3D tétraédrique biventriculaire. Le modèle biophysique simplifié de Mitchell-Schaeffer (MS) est utilisé pour étudier l’hétérogénéité des propriétés électrophysiologiques (EP) du tissu et leur rôle sur l’arythmogénèse. L’ablation par radiofréquence (ARF) en éliminant les activités ventriculaires anormales locales (LAVA) est un traitement potentiellement curatif pour la tachycardie ventriculaire, mais les études EP requises pour localiser les LAVA sont longues et invasives. Les LAVA se trouvent autour de cicatrices hétérogènes qui peuvent être imagées de façon non-invasive par IRM à rehaussement tardif. Nous utilisons des caractéristiques d’image dans un contexte d’apprentissage automatique avec des forêts aléatoires pour identifier des aires de tissu qui induisent des LAVA. Nous détaillons les sources d’erreur inhérentes aux données et leur intégration dans le processus d’apprentissage. Finalement, nous couplons le modèle MS avec des géométries du coeur spécifiques aux patients et nous modélisons le cathéter avec une approche par un dipôle pour générer des électrogrammes normaux et des LAVA aux endroits où ils ont été localisés en clinique. Cela améliore la prédiction de localisation du tissu induisant des LAVA obtenue par apprentissage sur l’image. Des cartes de confiance sont générées et peuvent être utilisées avant une ARF pour guider l’intervention. Les contributions de cette thèse ont conduit à des résultats et des preuves de concepts prometteurs

Developing a novel comprehensive framework for the investigation of cellular and whole heart electrophysiology in the in situ human heart: Historical perspectives, current progress and future prospects

Understanding the mechanisms of fatal ventricular arrhythmias is of great importance. In view of the many electrophysiological differences that exist between animal species and humans, the acquisition of basic electrophysiological data in the intact human heart is essential to drive and complement experimental work in animal and in-silico models. Over the years techniques have been developed to obtain basic electrophysiological signals directly from the patients by incorporating these measurements into routine clinical procedures which access the heart such as cardiac catheterisation and cardiac surgery. Early recordings with monophasic action potentials provided valuable information including normal values for the in vivo human heart, cycle length dependent properties, the effect of ischaemia, autonomic nervous system activity, and mechano-electric interaction. Transmural recordings addressed the controversial issue of the mid myocardial “M” cell. More recently, the technique of multielectrode mapping (256 electrodes) developed in animal models has been extended to humans, enabling mapping of activation and repolarisation on the entire left and right ventricular epicardium in patients during cardiac surgery. Studies have examined the issue of whether ventricular fibrillation was driven by a “mother” rotor with inhomogeneous and fragmented conduction as in some animal models, or by multiple wavelets as in other animal studies; results showed that both mechanisms are operative in humans. The simpler spatial organisation of human VF has important implications for treatment and prevention. To link in-vivo human electrophysiological mapping with cellular biophysics, multielectrode mapping is now being combined with myocardial biopsies. This technique enables region-specific electrophysiology changes to be related to underlying cellular biology, for example: APD alternans, which is a precursor of VF and sudden death. The mechanism is incompletely understood but related to calcium cycling and APD restitution. Multielectrode sock mapping during incremental pacing enables epicardial sites to be identified which exhibit marked APD alternans and sites where APD alternans is absent. Whole heart electrophysiology is assessed by activation repolarisation mapping and analysis is performed immediately on-site in order to guide biopsies to specific myocardial sites. Samples are analysed for ion channel expression, Ca2+-handling proteins, gap junctions and extracellular matrix. This new comprehensive approach to bridge cellular and whole heart electrophysiology allowed to identify 20 significant changes in mRNA for ion channels Ca2+-handling proteins, a gap junction channel, a Na+–K+ pump subunit and receptors (particularly Kir 2.1) between the positive and negative alternans sites

Three-dimensional Multiscale Modelling and Simulation of Atria and Torso Electrophysiology

A better understanding of the electrical activity of the heart under physiological and pathological conditions has always been key for clinicians and researchers. Over the last years, the information in the P-wave signals has been extensively analysed to un-cover the mechanisms underlying atrial arrhythmias by localizing ectopic foci or high-frequency rotors. However, the relationship between the activation of the different areas of the atria and the characteristics of the P-wave signals or body surface poten-tial maps are still far from being completely understood. Multiscale anatomical and functional models of the heart are a new technological framework that can enable the investigation of the heart as a complex system. This thesis is centred in the construction of a multiscale framework that allows the realistic simulation of atrial and torso electrophysiology and integrates all the anatom-ical and functional descriptions described in the literature. The construction of such model involves the development of heterogeneous cellular and tissue electrophysiolo-gy models fitted to empirical data. It also requires an accurate 3D representation of the atrial anatomy, including tissue fibre arrangement, and preferential conduction axes. This multiscale model aims to reproduce faithfully the activation of the atria under physiological and pathological conditions. We use the model for two main applica-tions. First, to study the relationship between atrial activation and surface signals in sinus rhythm. This study should reveal the best places for recording P-waves signals in the torso, and which are the regions of the atria that make the most significant contri-bution to the body surface potential maps and determine the main P-wave characteris-tics. Second, to spatially cluster and classify ectopic atrial foci into clearly differenti-ated atrial regions by using the body surface P-wave integral map (BSPiM) as a bi-omarker. We develop a machine-learning pipeline trained from simulations obtained from the atria-torso model aiming to validate whether ectopic foci with similar BSPiM naturally cluster into differentiated non-intersected atrial regions, and whether new BSPiM could be correctly classified with high accuracy.En la actualidad, una mejor compresión de la actividad eléctrica del corazón en condi-ciones fisiológicas y patológicas es clave para médicos e investigadores. A lo largo de los últimos años, la información derivada de la onda P se ha utilizado para intentar descubrir los mecanismos subyacentes a las arritmias auriculares mediante la localiza-ción de focos ectópicos y rotores de alta frecuencia. Sin embargo, la relación entre la activación de distintas regiones auriculares y las características tanto de las ondas P como de la distribución de potencial en la superficie del torso está lejos de entenderse completamente. Los modelos cardíacos funcionales y anatómicos son una nueva he-rramienta que puede facilitar la investigación relativa al corazón entendido como sis-tema complejo. La presente tesis se centra en la construcción de un modelo multiescala para la simula-ción realista de la electrofisiología cardíaca tanto a nivel auricular como de torso, integrando toda la información anatómica y funcional disponible en la literatura. La construcción de este modelo implica el desarrollo, en base a datos experimentales, de modelos electrofisiológicos heterogéneos tanto celulares como tisulares. Así mismo, es imprescindible una representación tridimensional precisa de la anatomía auricular, incluyendo la dirección de fibras y los haces de conducción preferentes. Este modelo multiescala busca reproducir fielmente la activación auricular en condiciones fisiológi-cas y patológicas. Su uso se ha centrado fundamentalmente en dos aplicaciones. En primer lugar, estudiar la relación entre la activación auricular en ritmo sinusal y las señales en la superficie del torso. Este estudio busca definir la mejor ubicación para el registro de las ondas P en el torso así como determinar aquellas regiones auriculares que contribuyen fundamentalmente a la formación y distribución de potenciales super-ficiales así como a las características de las ondas P. En segundo lugar, agrupar y cla-sificar espacialmente los focos ectópicos en regiones auriculares claramente diferen-ciables empleando como biomarcador los mapas superficiales de integral de la onda P (BSPiM). Se ha desarrollado para ello una metodología de aprendizaje automático en la que las simulaciones obtenidas con el modelo multiescala aurícula-torso sirven de entrenamiento, permitiendo validar si los focos ectópicos cuyos BSPiMs son similares se agrupan de forma natural en regiones auriculares no intersectadas y si BSPiMs nue-vos podrían ser clasificados prospectivamente con gran precisión.Avui en dia, una millor comprenssió de l'activitat elèctrica del cor en condicions fisio-lògiques i patològiques és clau per a metges i investigadors. Al llarg dels últims anys, la informació derivada de l'ona P s'ha utilitzat per intentar descobrir els mecanismes subjacents a les arítmies auriculars mitjançant la localització de focus ectòpics i rotors d'alta freqüència. No obstant això, la relació entre l'activació de diferents regions auri-culars i les característiques tant de les ones P com de la distribució de potencial en la superfície del tors està lluny d'entendre's completament. Els models cardíacs funcionals i anatòmics són una nova eina que pot facilitar la recerca relativa al cor entès com a sistema complex. La present tesi es centra en la construcció d'un model multiescala per a la simulació realista de la electrofisiologia cardíaca tant a nivell auricular com de tors, integrant tota la informació anatòmica i funcional disponible en la literatura. La construcció d'aquest model implica el desenvolupament, sobre la base de dades experimentals, de models electrofisiològics heterogenis, tant cel·lulars com tissulars. Així mateix, és imprescindible una representació tridimensional precisa de l'anatomia auricular, in-cloent la direcció de fibres i els feixos de conducció preferents. Aquest model multies-cala busca reproduir fidelment l'activació auricular en condicions fisiològiques i pa-tològiques. El seu ús s'ha centrat fonamentalment en dues aplicacions. En primer lloc, estudiar la relació entre l'activació auricular en ritme sinusal i els senyals en la superfí-cie del tors. A més a més, amb aquest estudi també es busca definir la millor ubicació per al registre de les ones P en el tors, així com, determinar aquelles regions auriculars que contribueixen fonamentalment a la formació i distribució de potencials superfi-cials a l'hora que es caracteritzen les ones P. En segon lloc, agrupar i classificar espa-cialment els focus ectòpics en regions auriculars clarament diferenciables emprant com a biomarcador els mapes superficials d'integral de l'ona P (BSPiM). És per això que s'ha desenvolupat una metodologia d'aprenentatge automàtic en la qual les simulacions obtingudes amb el model multiescala aurícula-tors serveixen d'entrenament, la qual cosa permet validar si els focus ectòpics, llurs BSPiMs són similars, s'agrupen de for-ma natural en regions auriculars no intersectades i si BSPiMs nous podrien ser classifi-cats de manera prospectiva amb precisió.Ferrer Albero, A. (2017). Three-dimensional Multiscale Modelling and Simulation of Atria and Torso Electrophysiology [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/88402TESI