Multimodal ventricular tachycardia analysis : towards the accurate parametrization of predictive HPC electrophysiological computational models

After a myocardial infarction, the affected areas of the cardiac tissue suffer changes in their electrical and mechanical properties. This post-infarction scar tissue has been related with a particular type of arrhythmia: ventricular tachycardia (VT). A thorough study on the experimental data acquired with clinical tools is presented in this thesis with the objective of defining the limitations of the clinical data towards predictive computational models. Computational models have a large potential as predictive tools for VT, but the verification, validation and uncertain quantification of the numerical results is required before they can be employed as a clinical tool. Swine experimental data from an invasive electrophysiological study and Cardiac Magnetic Resonance imaging is processed to obtain accurate characterizations of the post-infarction scar. Based on the results, the limitation of each technique is described. Furthermore, the volume of the scar is evaluated as marker for post-infarction VT induction mechanisms. A control case from the animal experimental protocol is employed to build a simulation scenario in which biventricular simulations are done using a detailed cell model adapted to the ionic currents present in the swine myocytes. The uncertainty of the model derived from diffusion and fibre orientation is quantified. Finally, the recovery of the model to an extrastimulus is compared to experimental data by computationally reproducing an S1-S2 protocol. Results from the cardiac computational model show that the propagation wave patterns from numerical results match the one described by the experimental activation maps if the DTI fibre orientations are used. The electrophysiological activation is sensitive to fibre orientation. Therefore simulations including the fibre orientations from DTI are able to reproduce a physiological wave propagation pattern. The diffusion coefficients highly determine the conduction velocity. The S1-S2 protocol produced restitution curves that have similar slopes to the experimental curves. This work is a first step forward towards validation of cardiac electrophysiology simulations. Future work will address the limitations about optimal parametrization of the O'Hara-Rudy cell model to fully validate the cardiac computational model for prediction of VT inducibility.Tras un infarto de miocardio, las zonas de tejido cardiaco afectadas sufren cambios en sus propiedades eléctricas y mecánicas. Este substrato miocárdico se ha relacionado con la taquicardia ventricular (TV), un tipo de arritmia. En esta tesis se presenta un estudio exhaustivo de los datos experimentales adquiridos con protocolos clínicos con el objetivo de definir las limitaciones de los datos clínicos antes de avanzar hacia modelos computacionales. Los modelos computacionales tienen un gran potencial como herramientas para la predicción de TV, pero es necesaria su verificación, validación y la cuantificación de la incertidumbre en los resultados numéricos antes de poderlos emplear como herramientas clínicas. La caracterización precisa del sustrato miocárdico, cicatriz, se realiza mediante el procesado de los datos experimentales porcinos obtenidos del estudio electrofisiológico invasivo y la resonancia magnética cardiaca. Como consecuencia, se describen las limitaciones de cada técnica. Ademas, se estudia si el volumen da la cicatriz puede actuar como indicador de la aparición de VT. El escenario de simulación para los modelos computacionales biventriulares se construye a partir de los datos experimentales de un caso control incluido en el protocolo experimental. En el, se realizan simulaciones electrofisiológicas empleando un modelo celular detallado adaptado a las propiedades de las corrientes iónicas en los miocitos de los cerdos. Se cuantifica la incertidumbre del modelo generada por la difusión y la orientación de las fibras. Por ultimo, se compara la recuperación del modelo a un extraestímulo con datas experimentales mediante la simulación de un protocolo S1-S2. Los resultado numéricos obtenidos muestran que los patrones de propagación de la onda de las simulación cardiaca coinciden con los descritos por los mapas de activación experimentales si la fibras incluidas en el modelo corresponden a los datos de DTI. El modelo de activación es sensible a la orientación de fibras impuesta. Las simulaciones incluyendo la orientación de fibras de DTI es capaz de reproducir los patrones fisiológicos de la onda de propagación eléctrica en ambos ventrículos. El velocidad de conducción obtenida es muy dependiente del coeficiente de difusión impuesto. El protocolo S1-S2 protocolo genera curvas de restitución con pendientes simulares a las curvas experimentales. Esta tesis es un primer paso hacia la validación de las simulaciones electrofisiológicas cardiacas. En el futuro, se mejoraran las limitaciones relacionadas con una optima parametrización del modelo celular de O?Hara-Rudy para validar por completo el modelo computacional cardiaco para avanzar hacia la predicción de la predicción de VT.Postprint (published version

Computational Physiology

This open access volume compiles student reports from the 2021 Simula Summer School in Computational Physiology. Interested readers will find herein a number of modern approaches to modeling excitable tissue. This should provide a framework for tools available to model subcellular and tissue-level physiology across scales and scientific questions. In June through August of 2021, Simula held the seventh annual Summer School in Computational Physiology in collaboration with the University of Oslo (UiO) and the University of California, San Diego (UCSD). The course focuses on modeling excitable tissues, with a special interest in cardiac physiology and neuroscience. The majority of the school consists of group research projects conducted by Masters and PhD students from around the world, and advised by scientists at Simula, UiO and UCSD. Each group then produced a report that addreses a specific problem of importance in physiology and presents a succinct summary of the findings. Reports may not necessarily represent new scientific results; rather, they can reproduce or supplement earlier computational studies or experimental findings. Reports from eight of the summer projects are included as separate chapters. The fields represented include cardiac geometry definition (Chapter 1), electrophysiology and pharmacology (Chapters 2–5), fluid mechanics in blood vessels (Chapter 6), cardiac calcium handling and mechanics (Chapter 7), and machine learning in cardiac electrophysiology (Chapter 8)

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

Non-Invasive Electrocardiographic Imaging of Ventricular Activities: Data-Driven and Model-Based Approaches

Die vorliegende Arbeit beleuchtet ausgewählte Aspekte der Vorwärtsmodellierung, so zum Beispiel die Simulation von Elektro- und Magnetokardiogrammen im Falle einer elektrisch stillen Ischämie sowie die Anpassung der elektrischen Potentiale unter Variation der Leitfähigkeiten. Besonderer Fokus liegt auf der Entwicklung neuer Regularisierungsalgorithmen sowie der Anwendung und Bewertung aktuell verwendeter Methoden in realistischen in silico bzw. klinischen Studien

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

Computational Physiology

This open access volume compiles student reports from the 2021 Simula Summer School in Computational Physiology. Interested readers will find herein a number of modern approaches to modeling excitable tissue. This should provide a framework for tools available to model subcellular and tissue-level physiology across scales and scientific questions. In June through August of 2021, Simula held the seventh annual Summer School in Computational Physiology in collaboration with the University of Oslo (UiO) and the University of California, San Diego (UCSD). The course focuses on modeling excitable tissues, with a special interest in cardiac physiology and neuroscience. The majority of the school consists of group research projects conducted by Masters and PhD students from around the world, and advised by scientists at Simula, UiO and UCSD. Each group then produced a report that addreses a specific problem of importance in physiology and presents a succinct summary of the findings. Reports may not necessarily represent new scientific results; rather, they can reproduce or supplement earlier computational studies or experimental findings. Reports from eight of the summer projects are included as separate chapters. The fields represented include cardiac geometry definition (Chapter 1), electrophysiology and pharmacology (Chapters 2–5), fluid mechanics in blood vessels (Chapter 6), cardiac calcium handling and mechanics (Chapter 7), and machine learning in cardiac electrophysiology (Chapter 8)

Modeling Human Atrial Patho-Electrophysiology from Ion Channels to ECG - Substrates, Pharmacology, Vulnerability, and P-Waves

Half of the patients suffering from atrial fibrillation (AF) cannot be treated adequately, today. This thesis presents multi-scale computational methods to advance our understanding of patho-mechanisms, to improve the diagnosis of patients harboring an arrhythmogenic substrate, and to tailor therapy. The modeling pipeline ranges from ion channels on the subcellular level up to the ECG on the body surface. The tailored therapeutic approaches carry the potential to reduce the burden of AF

Apprentissage statistique pour la personnalisation de modèles cardiaques à partir de données d’imagerie

This thesis focuses on the calibration of an electromechanical model of the heart from patient-specific, image-based data; and on the related task of extracting the cardiac motion from 4D images. Long-term perspectives for personalized computer simulation of the cardiac function include aid to the diagnosis, aid to the planning of therapy and prevention of risks. To this end, we explore tools and possibilities offered by statistical learning. To personalize cardiac mechanics, we introduce an efficient framework coupling machine learning and an original statistical representation of shape & motion based on 3D+t currents. The method relies on a reduced mapping between the space of mechanical parameters and the space of cardiac motion. The second focus of the thesis is on cardiac motion tracking, a key processing step in the calibration pipeline, with an emphasis on quantification of uncertainty. We develop a generic sparse Bayesian model of image registration with three main contributions: an extended image similarity term, the automated tuning of registration parameters and uncertainty quantification. We propose an approximate inference scheme that is tractable on 4D clinical data. Finally, we wish to evaluate the quality of uncertainty estimates returned by the approximate inference scheme. We compare the predictions of the approximate scheme with those of an inference scheme developed on the grounds of reversible jump MCMC. We provide more insight into the theoretical properties of the sparse structured Bayesian model and into the empirical behaviour of both inference schemesCette thèse porte sur un problème de calibration d'un modèle électromécanique de cœur, personnalisé à partir de données d'imagerie médicale 3D+t ; et sur celui - en amont - de suivi du mouvement cardiaque. A cette fin, nous adoptons une méthodologie fondée sur l'apprentissage statistique. Pour la calibration du modèle mécanique, nous introduisons une méthode efficace mêlant apprentissage automatique et une description statistique originale du mouvement cardiaque utilisant la représentation des courants 3D+t. Notre approche repose sur la construction d'un modèle statistique réduit reliant l'espace des paramètres mécaniques à celui du mouvement cardiaque. L'extraction du mouvement à partir d'images médicales avec quantification d'incertitude apparaît essentielle pour cette calibration, et constitue l'objet de la seconde partie de cette thèse. Plus généralement, nous développons un modèle bayésien parcimonieux pour le problème de recalage d'images médicales. Notre contribution est triple et porte sur un modèle étendu de similarité entre images, sur l'ajustement automatique des paramètres du recalage et sur la quantification de l'incertitude. Nous proposons une technique rapide d'inférence gloutonne, applicable à des données cliniques 4D. Enfin, nous nous intéressons de plus près à la qualité des estimations d'incertitude fournies par le modèle. Nous comparons les prédictions du schéma d'inférence gloutonne avec celles données par une procédure d'inférence fidèle au modèle, que nous développons sur la base de techniques MCMC. Nous approfondissons les propriétés théoriques et empiriques du modèle bayésien parcimonieux et des deux schémas d'inférenc