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Theses

Simulation de modèles personnalisés du coeur pour la prédiction de thérapies cardiaques

Abstract : The clinical understanding and treatment of cardiovascular diseases is highly complex. For each patient, cardiologists face issues in determining the pathology, choosing a therapy or selecting suitable patients for the therapy. In order to provide additional guidance to cardiologists, many research groups are investigating the possibility to plan such therapies based on biophysical models of the heart. The hypothesis is that one may combine anatomical and functional data to build patient-specific cardiac models that could have the potential to predict the benefits of different therapies. Cardiac electromechanical simulations are based on computational models that can represent the heart geometry, motion and electrophysiology patterns during a cardiac cycle with sufficient accuracy. Integration of anatomical, mechanical and electrophysiological information for a given subject is essential to build such models.In this thesis, we first introduce the geometry, kinematics and electrophysiology personalizations that are necessary inputs to mechanical modeling. We propose to use the Bestel-Cl'ement-Sorine electromechanical model of the heart, which is sufficiently accurate without being over-parametrized for the available data. We start by presenting a new implementation of this model in an efficient opensource framework for interactive medical simulation and we analyze the resulting simulations through a complete sensitivity analysis.In a second step, the goal is to personalize the mechanical parameters from medical images (MRI data). To this end, we first propose an automatic calibration algorithm that estimates global mechanical parameters from volume and pressure curves. This technique was tested on 7 volunteers and 2 heart failure cases and allowed to perform a preliminary specificity study that intends to determine the relevant parameters able to differentiate the pathological cases from the control cases.Once initialized with the calibrated values, the parameters are then locally personalized with a more complex optimization algorithm. Reduced Order Unscented Kalman Filtering is used to estimate the contractilities on all of the AHA zones of the Left Ventricle, matching the regional volumes extracted from cine MRI data. This personalization strategy was validated and tested on several pathological and healthy cases. These contributions have led to promising results through this thesis and some are already used for various research studies.
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Stephanie Marchesseau. Simulation de modèles personnalisés du coeur pour la prédiction de thérapies cardiaques. Mathématiques générales [math.GM]. Ecole Nationale Supérieure des Mines de Paris, 2013. Français. ⟨NNT : 2012ENMP0082⟩. ⟨pastel-00820082⟩

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