La modélisation de l’électrophysiologie in silico a été un sujet de recherche important ces dernières décennies. Afin de pouvoir utiliser ces progrès importants dans les applications cliniques, il faut mettre en place des modèles macroscopiques qui peuvent être utilisés pour la planification et le guidage des procédures cliniques.L’objectif de cette thèse est de construire de tels modèles macroscopiques spécifiques à chaque patient pour le diagnostic et la prévision, dans le but d’améliorer la planification et le guidage de l’ablation par radio-fréquence (ARF) des patients souffrant de tachycardie ventriculaire (TV) après infarctus. Dans ce travail, nous avons proposé un cadre pour la personnalisation d’un modèle cardiaque 3D, le modèle de Mitchell-Schaeffer (MS), et nous avons évalué sa puissance prédictive dans plusieurs configurations de stimulation. Ceci a été réalisé sur des données ex vivo de cœurs porcins à l’aide d’images médicales et de données cartographiques optiques de l’épicarde. Ce cadre a ensuite été appliqué à un ensemble de données cliniques provenant d’imagerie hybride XMR et d’une procédure de cartographie électrophysiologique sur un patient souffrant d’insuffisance cardiaque.Ensuite, le modèle 3D MS a également été adapté pour simuler le comportement macroscopique structural de la fibrose près des cicatrices. La simulation d’une étude in silico de stimulation de TV en utilisant le modèle adapté personnalisé MS a été réalisée pour quantifier le risque de TV en termes de cartes d’inductibilité, de réentrées des modèles et de cartes de points de sortie. Une approche de modélisation pour l’ablation par RF fondée sur l’état de l’art a été proposée. Enfin, l’étude in silico de stimulation de TV a été appliquée aux données in vivo personnalisées des patients, qui ont suivi ce protocole. Ceci a permis une validation de la prévision in silico de TV post-infarctus par comparaison avec la TV clinique induite. Ler ôle de l’hétérogénéité spatiale des propriétés des tissus cardiaques estimés dans la genèse de TV ischémique a été évalué, ainsi que les caractéristiques des points de sortie, qui sont les candidats potentiels à l’ablation par RF. / Modelling cardiac electrophysiology for arrhythmias in silico has been an important research topic for the last decades. In order to translate this important progress into clinical applications, there is a requirement to make macroscopic models that can be used for the planning and guidance of clinical procedures. The objective of this thesis was to construct such macroscopic EP models specifict o each patient for study and prediction, in order to improve the planning and guidance of radio frequency ablation (RFA) the rapieson patients suffering from post infarction Ventricular Tachycardia (VT). In this work, we proposed a framework for the personalisation of a 3D cardiac EP model, the Mitchell-Schaeffer (MS) model, an devaluated its volumetric predictive power under various pacing scenarios.This was performed on ex vivo large porcine healthy heart susing Diffusion Tensor MRI (DT-MRI) and dense optical mapping data of the epicardium. This framework was then also applied to a clinical dataset derived from a hybrid XMR imaging and sparse electroanatomical mapping on a patient with heart failure. Next, the 3DMS model was also adapted to simulate the macroscopic structural behaviour of fibrosis near the scars. The simulation of an in silico VT stimulation study using the personalised adapted MS model was then performed, to quantify VT risk in terms of inducibility maps, re-entry patterns and exit point maps. A rule-based modelling approach for RF ablation lesions based on state of the art studies was proposed. Lastly, the in silico VT stimulation study was applied to in vivo personalised data of patients who underwent a clinical VT stimulation study. A validation of the in silico post-infarct VT prediction was performed against the clinically induced VT. Therole of spatial heterogeneity of the estimated patient’s cardiac tissue properties in the genesis of ischemic VT was learnt, along with their characteristics for entry/exit points, which are the potential candidates for RF ablation.

Submitted by Renata Lopes (renatasil82@gmail.com) on 2017-03-03T17:31:40Z No. of bitstreams: 1 carolinemendoncacosta.pdf: 8702151 bytes, checksum: 71f420415d76b4959dd71a63dd2b39ad (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-03-06T20:17:19Z (GMT) No. of bitstreams: 1 carolinemendoncacosta.pdf: 8702151 bytes, checksum: 71f420415d76b4959dd71a63dd2b39ad (MD5) / Made available in DSpace on 2017-03-06T20:17:19Z (GMT). No. of bitstreams: 1 carolinemendoncacosta.pdf: 8702151 bytes, checksum: 71f420415d76b4959dd71a63dd2b39ad (MD5) Previous issue date: 2011-02-28 / FAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Gerais / Há algumas décadas atrás acreditava-se que o tecido cardíaco era contínuo e uniformemente conectado. Atualmente, sabe-se que as células do tecido cardíaco são conectadas umas às outras por canais especiais chamados junções gap, por onde há fluxo de corrente entre células vizinhas. Estas células por sua vez estão arranjadas em distintas camadas formando fibras de músculo cercadas por espaços extracelulares e tecido conectivo. A modelagem da eletrofisiologia cardíaca é uma importante ferramenta na compreensão de fenômenos cardíacos, como arritmias e outras doenças. Um dos modelos mais utilizados para descrever a atividade elétrica no coração é o modelo Monodomínio, no qual considera-se um tecido contínuo e uniformemente conectado obtido através da técnica de homogeneização. Em condições normais esta é uma aproximação adequada, uma vez que a influência da microestrutura do tecido não é tão evidente. Por outro lado, sabe-se que algumas condições patológicas alteram a conectividade do tecido, como em casos de infarto do miocárdio, onde é observada uma redução no acoplamento intercelular formando uma barreira parcial à propagação elétrica e no caso de fibrose, onde é observado um aumento do tecido conectivo formando uma barreira total à propagação. Nestas circunstâncias, estudos mostram que o modelo Monodomínio não é capaz de reproduzir os efeitos destas barreiras microscópicas na propagação elétrica. Sendo assim, neste trabalho serão apresentadas algumas das limitações deste modelo em casos de acoplamento intercelular reduzido e também uma técnica numérica baseada no método dos elementos finitos para reproduzir barreiras microscópicas causadas pela presença de espaços extracelulares e tecido conectivo no tecido cardíaco. / A few decades ago the cardiac tissue was believed to be an uniformly connected continumm. Currently, it is known that the cardiac cells are connected to each other via special protein channels called gap junctions, through which the ionic current flows between neighboring cells. The cardiac cells are arranged in distinct layers of muscle fibers surrounded by extracellular space and connective tissue. The cardiac electrophysiology modeling is an important tool in understanding cardiac phenomena, such as arrythmias and other cardiac diseases. The Monodomain model is extensively used to describe the electrical activity in the heart. In this model the cardiac tissue is considered an uniformly connected continumm obtained by the application the homogenization technique. This is a reasonably approximation for normal physiological conditions, as in this case the cardiac microstructure is not so evident. On the other hand, some pathological conditions are known to modify the connectivity of the tissue. In isquemic and infarcted tissue it is observed a reduction in the intercellular coupling representing a partial barrier to the electrical propagation. In adittion, during fibrosis it is observed an excessive growth of the conective tissue, representing a total barrier to the electrical propagation. In such cases, recent simulation studies show that the Monodomain model can not reproduce such microscopic barrier effect on the electrical propagation. In this work we present some limitations of this model for the case of low intercellular coupling and also a numerical technique based on the finite element method to reproduce microscopic barrier caused by the presence of extracellular spaces and connective tissue in the cardiac tissue

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. / 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.