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

Personalization of Cardiac Electrophysiology Model using the Unscented Kalman Filtering

International audienceCardiac electrophysiology mapping techniques now allow to record denser intra-operative electrograms (ECG). The patient-specific information extracted from these clinical recordings is extremely valuable. A growing field of research focuses on the personalization of electro-physiology models using this patient-specific information. The modeling in silico of a patient electrophysiology is needed to better understand the mechanism of cardiac arrhythmia. In the scope of ischemic cardiomyopa-thy, the predictive power of patient-specific simulations may also provide a substantial guidance in defining the optimal location of the implantable defibrillator, since all possible configurations could be tested in silico. This article describes an innovative personalization approach based on an unscented Kalman filter. Following an iterative process, the apparent conductivity is efficiently estimated in specific regions. A sensitivity analysis is performed to assess the filter parameters. With three patient cases, we finally demonstrate the accuracy and efficiency of our algorithm

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

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

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

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

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

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

Characterization and modeling of the purkinje system for biophysical simulations

The usability of computer models of the heart depends mostly on their capacity to accurately represent heart anatomy, microstructure and function. However, integrating such a variety of biological data is often not possible. This is the case of the cardiac conduction system (CCS), which is responsible for the fast and coordinated distribution of the electrical impulses. The CCS cannot be observed in-vivo but it is mandatory in several cardiac modeling applications involving arrhythmias. The aims of this thesis are to show the importance of explicitly modeling the CCS structure and function for an accurate description of the electrical activation of the ventricles and to present a novel technique to build automatically a CCS structure that meets physiological observations. Pursuing that goal has required a multidisciplinary effort to build models for cardiac electrophysiology, and imaging techniques to acquire and analyze data of the CCS at different scales.La usabilidad de modelos computacionales cardíacos depende del poder representar con precisión la anatomía del corazón, su microestructura y su función. Sin embargo, la integración de tal variedad de datos biológicos no siempre es posible. Este es el caso del sistema de conducción cardiaco (CCS), que es responsable de la distribución rápida y coordinada de los impulsos eléctricos. El CCS no puede ser observado in vivo pero es imprescindible en los modelos del corazón que involucran las arritmias. Los objetivos de esta tesis son el modelar la estructura y función del CCS para obtener una descripción precisa de la activación eléctrica del corazón y el construir la estructura de un CCS que cumpla con las observaciones fisiológicas. La persecución de este objetivo ha requerido un esfuerzo multidisciplinar para construir modelos de la electrofisiología cardiaca y las técnicas de imagen necesarias para adquirir y analizar datos del CCS a diferentes escalas

Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy

Introduction: The 30–50% non-response rate to cardiac resynchronization therapy (CRT) calls for improved patient selection and optimized pacing lead placement. The study aimed to develop a novel technique using patient-specific cardiac models and machine learning (ML) to predict an optimal left ventricular (LV) pacing site (ML-PS) that maximizes the likelihood of LV ejection fraction (LVEF) improvement in a given CRT candidate. To validate the approach, we evaluated whether the distance DPS between the clinical LV pacing site (ref-PS) and ML-PS is associated with improved response rate and magnitude.Materials and methods: We reviewed retrospective data for 57 CRT recipients. A positive response was defined as a more than 10% LVEF improvement. Personalized models of ventricular activation and ECG were created from MRI and CT images. The characteristics of ventricular activation during intrinsic rhythm and biventricular (BiV) pacing with ref-PS were derived from the models and used in combination with clinical data to train supervised ML classifiers. The best logistic regression model classified CRT responders with a high accuracy of 0.77 (ROC AUC = 0.84). The LR classifier, model simulations and Bayesian optimization with Gaussian process regression were combined to identify an optimal ML-PS that maximizes the ML-score of CRT response over the LV surface in each patient.Results: The optimal ML-PS improved the ML-score by 17 ± 14% over the ref-PS. Twenty percent of the non-responders were reclassified as positive at ML-PS. Selection of positive patients with a max ML-score >0.5 demonstrated an improved clinical response rate. The distance DPS was shorter in the responders. The max ML-score and DPS were found to be strong predictors of CRT response (ROC AUC = 0.85). In the group with max ML-score > 0.5 and DPS< 30 mm, the response rate was 83% compared to 14% in the rest of the cohort. LVEF improvement in this group was higher than in the other patients (16 ± 8% vs. 7 ± 8%).Conclusion: A new technique combining clinical data, personalized heart modelling and supervised ML demonstrates the potential for use in clinical practice to assist in optimizing patient selection and predicting optimal LV pacing lead position in HF candidates for CRT