Interactive real time simulation of cardiac radio-frequency ablation

Best Paper AwardInternational audienceVirtual reality based therapy simulation meets a growing interest from the medical community due to its potential impact for the training of medical residents and the planning of therapies. In this paper, we describe a prototype for rehearsing radio-frequency ablation of the myocardium in the context of cardiac arrhythmia. Our main focus has been on the real-time modeling of electrophysiology which is suitable for representing simple cases of arrhythmia (ectopic focus, ventricular tachycardia). To this end, we use an anisotropic multi-front fast marching method to simulate transmembrane potential propagation in cardiac tissues. The electric propagation is coupled with a pre-recorded beating heart model. Thanks to a 3D user interface, the user can interactively measure the local extracellular potential, pace locally the myocardium or simulate the burning of cardiac tissue as done in radiofrequency ablation interventions. To illustrate this work, we show the simulation of various arrhythmias cases built from patient specific medical images including the right and left ventricles, the fiber orientation and the location of ischemic regions

Quantitative comparison of two cardiac electrophysiology models using personalisation to optical and MR data

International audienceIn order to translate the important modelling work into clinical tools, the selection of the best model for a given application is crucial. In this paper, we quantitatively compare personalisation of two different cardiac electrophysiology models on the same dataset, in order to help such a selection. One is a phenomenological model, the AlievPanfilov model (1996), and the other one is a simplified ionic model, the Mitchell-Schaeffer model (2003). In the preliminary steps of model personalisation, we optimise the forward problem with the determination of an optimum time integration scheme for each model, which could result in stable and accurate simulations without the use of unnecessary expensive high temporal and spatial resolutions. Next, we personalise the two models by optimising their respective parameters, to match the depolarisation and repolarisation maps obtained ex-vivo from optical imaging of large porcine healthy heart. Last, we compare the personalisation results of the two different model

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

Adaptive Tetrahedral Meshing for Personalized Cardiac Simulations

International audiencePersonalized simulation for therapy planning in the clinical routine requires fast and accurate computations. Finite-element (FE) simulations belong to the most commonly used approaches. Based on medical images the geometry of the patient's anatomy must be faithfully represented and discretized in a way to find a reasonable compromise between accuracy and speed. This can be achieved by adapting the mesh resolution, and by providing well-shaped elements to improve the convergence of iterative solvers. We present a pipeline for generating high-quality, adaptive meshes, and show how the framework can be applied to specific cardiac simulations. Our aim is to analyze the meshing requirements for applications in electrophysiological modeling of ventricular tachycardia and electromechanical modeling of Tetralogy of Fallot

Interactive Electromechanical Model of the Heart for Patient-Specific Therapy Planning and Training using SOFA

International audienceThe contributions of this work are twofold. First, we developed an electrophysiological training simulator in SOFA which tackles the interactive issue in the context of cardiac arrhythmias. Coupled with this electrophysiology, we developed a mechanical model of the heart that can be personalized from MRI datasets. Our simulations are based on the SOFA platform. SOFA is an open-source framework targeted at real-time simulation with an emphasis on medical simulation, mainly developed at Inria. A large choice of efficient solvers, hyperelastic or viscous material laws are already implemented in SOFA. Moreover, it enables interactivity during the simulation (pacing, surgery planning, ...) and gives a good trade-off between accuracy and computational efficiency

Confidence-based Training for Clinical Data Uncertainty in Image-based Prediction of Cardiac Ablation Targets

International audienceVentricular radio-frequency ablation (RFA) can have a critical impact on preventing sudden cardiac arrest but is challenging due to a highly complex arrhythmogenic substrate. This work aims to identify local image characteristics capable of predicting the presence of local abnormal ventricular activities (LAVA). This can allow, pre-operatively and non-invasively, to improve and accelerate the procedure. To achieve this, intensity and texture-based local image features are computed and random forests are used for classification. However using machine-learning approaches on such complex multimodal data can prove difficult due to the inherent errors in the training set. In this manuscript we present a detailed analysis of these error sources due in particular to catheter motion and the data fusion process. We derived a principled analysis of confidence impact on classification. Moreover, we demonstrate how formal integration of these uncertainties in the training process improves the algorithm's performance, opening up possibilities for non-invasive image-based prediction of RFA targets

Construction of 3D MR image-based computer models of pathologic hearts, augmented with histology and optical fluorescence imaging to characterize action potential propagation

International audienceCardiac computer models can help us understand and predict the propagation of excitation waves (i.e., action potential, AP) in healthy and pathologic hearts. Our broad aim is to develop accurate 3D MR image-based computer models of electrophysiology in large hearts (translatable to clinical applications) and to validate them experimentally. The specific goals of this paper were to match models with maps of the propagation of optical AP on the epicardial surface using large porcine hearts with scars, estimating several parameters relevant to macroscopic reaction-diffusion electrophysiological models. We used voltage-sensitive dyes to image AP in large porcine hearts with scars (3 specimens had chronic myocardial infarct, and 3 had radiofrequency RF acute scars). We first analyzed the main AP waves' characteristics: duration (APD) and propagation under controlled pacing locations and frequencies as recorded from 2D optical images. We further built 3D MR image-based computer models that have information derived from the optical measures, as well as morphologic MRI data (i.e., myocardial anatomy, fiber directions and scar definition). The scar morphology from MR images was validated against corresponding whole-mount histology. We also compared the measured 3D isochronal maps of depolarization to simulated isochrones (the latter replicating precisely the experimental conditions), performing model customization and 3D volumetric adjustments of the local conductivity. Our results demonstrated that mean APD in the border zone (BZ) of the infarct scars was reduced by ~13% (compared to ~318 ms measured in normal zone, NZ), but APD did not change significantly in the thin BZ of the ablation scars. A generic value for velocity ratio (1:2.7) in healthy myocardial tissue was derived from measured values of transverse and longitudinal conduction velocities relative to fibers direction (22cm/s and 60cm/s, respectively). The model customization and 3D volumetric adjustment reduced the differences between measurements and simulations; for example, from one pacing location, the adjustment reduced the absolute error in local depolarization times by a factor of 5 (i.e., from 58 ms to 11 ms) in the infarcted heart, and by a factor of 6 (i.e., from 60 ms to 9 ms) in the heart with the RF scar. Moreover, the sensitivity of adjusted conductivity maps to different pacing locations was tested, and the errors in activation times were found to be of approximately 10-12 ms independent of pacing location used to adjust model parameters, suggesting that any location can be used for model predictions

Automated Quantification of Right Ventricular Fat at Contrast-enhanced Cardiac Multidetector CT in Arrhythmogenic Right Ventricular Cardiomyopathy

International audiencePurpose: To evaluate an automated method for the quantification of fat in the right ventricular (RV) free wall on multidetector computed tomography (CT) images and assess its diagnostic value in arrhythmogenic RV cardiomyopathy (ARVC). Materials and Methods: This study was approved by the institutional review board, and all patients gave informed consent. Thirty-six patients with ARVC (mean age 6 standard deviation, 46 years 6 15; seven women) were compared with 36 age-and sex-matched subjects with no structural heart disease (control group), as well as 36 patients with ischemic cardiomyopathy (ischemic group). Patients underwent contrast material– enhanced electrocardiography-gated cardiac multidetector CT. A 2-mm-thick RV free wall layer was automatically segmented and myocardial fat, expressed as percentage of RV free wall, was quantified as pixels with attenuation less than 210 HU. Patient-specific segmentations were registered to a template to study fat distribution. Receiver operating characteristic (ROC) analysis was performed to assess the diagnostic value of fat quantification by using task force criteria as a reference. Results: Fat extent was 16.5% 6 6.1 in ARVC and 4.6% 6 2.7 in non-ARVC (P , .0001). No significant difference was observed between control and ischemic groups (P = .23). A fat extent threshold of 8.5% of RV free wall was used to diagnose ARVC with 94% sensitivity (95% confidence interval [CI]: 82%, 98%) and 92% specificity (95% CI: 83%, 96%). This diagnostic performance was higher than the one for RV volume (mean area under the ROC curve, 0.96 6 0.02 vs 0.88 6 0.04; P = .009). In patients with ARVC, fat correlated to RV volume (R = 0.63, P , .0001), RV function (R = 20.67, P = .001), epsilon waves (R = 0.39, P = .02), inverted T waves in V 1 –V 3 (R = 0.38, P = .02), and presence of PKP2 mutations (R = 0.59, P = .02). Fat distribution differed between patients with ARVC and those without, with posterolateral RV wall being the most ARVC-specific area

Modèles électrophysiologiques personnalisés de tachycardie ventriculaire pour la planification de la thérapie par ablation radio-fréquence

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.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

Personalised Electrophysiological Models of Ventricular Tachycardia for Radio Frequency Ablation Therapy Planning

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