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

Eikonal Model Personalisation using Invasive Data to Predict Cardiac Resynchronisation Therapy Electrophysiological Response

International audienceIn this manuscript, we personalise an Eikonal model of cardiac wave front propagation using data acquired during an invasive electro-physiological study. To this end, we use a genetic algorithm to determine the parameters that provide the best fit between simulated and recorded activation maps during sinus rhythm. We propose a way to parameterise the Eikonal simulations that take into account the Purkinje network and the septomarginal trabecula influences while keeping the computational cost low. We then re-use these parameters to predict the cardiac resynchronisation therapy electrophysiological response by adapting the simulation initialisation to the pacing locations. We experiment different divisions of the myocardium on which the propagation velocities have to be optimised. We conclude that separating both ventricles and both endocardia seems to provide a reasonable personalisation framework in terms of accuracy and predictive power

Deep Learning Formulation of ECGI Integrating Image & Signal Information with Data-driven Regularisation

International audienceAims: Electrocardiographic Imaging (ECGI) is a promising tool to map the electrical activity of the heart non-invasively using body surface potentials (BSP). However, it is still challenging due to the mathematically ill-posed nature of the inverse problem to solve. Novel approaches leveraging progress in artificial intelligence could alleviate these difficulties. Methods: We propose a Deep Learning (DL) formulation of ECGI in order to learn the statistical relation between BSP and cardiac activation. The presented method is based on Conditional Variational Autoencoders (CVAE) using deep generative neural networks. To quantify the accuracy of this method, we simulated activation maps and BSP data on six cardiac anatomies. Results: We evaluated our model by training it on five different cardiac anatomies (5 000 activation maps) and by testing it on a new patient anatomy over 200 activation maps. Due to the probabilistic property of our method, we predicted 10 distinct activation maps for each BSP data. The proposed method is able to generate volumetric activation maps with a good accuracy on the simulated data: the mean absolute error is 9.40 ms with 2.16 ms standard deviation on this testing set. Conclusion: The proposed formulation of ECGI enables to naturally include imaging information in the estimation of cardiac electrical activity from body surface potential. It naturally takes into account all the spatio-temporal correlations present in the data. We believe these features can help improve ECGI results

EP-Net 2.0: Out-of-Domain Generalisation for Deep Learning Models of Cardiac Electrophysiology

International audienceCardiac electrophysiology models achieved good progress in simulating cardiac electrical activity. However, it is still challenging to leverage clinical measurements due to the discrepancy between idealised models and patient-specific conditions. In the last few years, data-driven machine learning methods have been actively used to learn dynamics and physical model parameters from data. In this paper, we propose a principled deep learning approach to learn the cardiac electrophysiology dynamics from data in the presence of scars in the cardiac tissue slab. We demonstrate that this technique is indeed able to reproduce the transmembrane potential dynamics in situations close to the training context. We then focus on evaluating the ability of the trained networks to generalize outside their training domain. We show experimentally that our model is able to generalize to new conditions including more complex scar geometries, multiple signal onsets and various conduction velocities

SPADE: A Small Particle Detection Method Using A Dictionary Of Shapes Within The Marked Point Process Framework

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VT Scan: Towards an Efficient Pipeline from Computed Tomography Images to Ventricular Tachycardia Ablation

International audienceNon-invasive prediction of optimal targets for efficient radio-frequency ablation is a major challenge in the treatment of ventricular tachycardia.Most of the related modelling work relies on magnetic resonance imaging of the heart for patient-specific personalized electrophysiology simulations.In this study, we used high-resolution computed tomography images to personalize an Eikonal model of cardiac electrophysiology in seven patients, addressed to us for catheter ablation in the context of post-infarction arrhythmia.We took advantage of the detailed geometry offered by such images, which are also more easily available in clinical practice, to estimate a conduction speed parameter based on myocardial wall thickness.We used this model to simulate a propagation directly on voxel data, in similar conditions to the ones invasively observed during the ablation procedure.We then compared the results of our simulations to dense activation maps that recorded ventricular tachycardias during the procedures.We showed as a proof of concept that realistic re-entrant pathways responsible for ventricular tachycardia can be reproduced using our framework, directly from imaging data

Fast Personalized Electrophysiological Models from CT Images for Ventricular Tachycardia Ablation Planning

International audienceAims Clinical application of patient-specific cardiac computer models requires fast and robust processing pipelines that can be seamlessly integrated into clinical workflows. We aim at building such a pipeline from computed tomography (CT) images to personalised cardiac electrophysiology (EP) model. The simulation output could be useful in the context of post-infarct ventricular tachycardia (VT) radio-frequency ablation (RFA) planning for pre-operative targets prediction

An image based high throughput screen to identify regulators of Imp containing RNP granules

International audienceIn vivo, RNAs and proteins are frequently packaged into diverse dynamic macromolecular structures named mRNP granules. These assemblies form upon phase separation of individual RNA and protein components, a process involving the establishment of multivalent weak interactions and their regulations via post-translational modifications. Defects in their properties have been associated with several human pathologies. However, our knowledge of these dynamic structures relies essentially on the study of P bodies and stress granules. We are interested in the highly conserved RNA binding protein Imp whose mammalian counterpart's overexpression correlates with poor prognosis in several cancers. In vivo, Imp is present in cytoplasmic RNP granules, distinct from P-bodies and visible both in neuronal cell bodies and axons. They are also detected in Drosophila S2R + cultured cells. Taking advantage of this cellular model, we have undertaken a genome-wide RNAi-based visual screen to identify factors that regulate the properties of Imp-containing granules. This implies combining high throughput microscopy with the development of a computational pipeline for automatic image analysis. This pipeline first segments and discriminates healthy from dead nuclei, storing this information in an interactive SQLite database that enables experimental quality control. Then, GFP-Imp granules are detected using the SPADE algorithm in the cytoplasm of healthy cells. Data from the pilot screen we have performed to validate the experimental design and develop our pipeline for data mining are presented

Image-based Personalised Models of Cardiac Electrophysiology for Ventricular Tachycardia Therapy Planning

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.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

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