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

Quantifying Registration Uncertainty with Sparse Bayesian Modelling

International audienceWe investigate uncertainty quantification under a sparse Bayesian model of medical image registration. Bayesian modelling has proven powerful to automate the tuning of registration hyperparameters, such as the trade-off between the data and regularization functionals. Sparsity-inducing priors have recently been used to render the parametrization itself adaptive and data-driven. The sparse prior on transformation parameters effectively favors the use of coarse basis functions to capture the global trends in the visible motion while finer, highly localized bases are introduced only in the presence of coherent image information and motion. In earlier work, approximate inference under the sparse Bayesian model was tackled in an efficient Variational Bayes (VB) framework. In this paper we are interested in the theoretical and empirical quality of uncertainty estimates derived under this approximate scheme vs. under the exact model. We implement an (asymptotically) exact inference scheme based on reversible jump Markov Chain Monte Carlo (MCMC) sampling to characterize the posterior distribution of the transformation and compare the predictions of the VB and MCMC based methods. The true posterior distribution under the sparse Bayesian model is found to be meaningful: orders of magnitude for the estimated uncertainty are quantitatively reasonable, the uncertainty is higher in textureless regions and lower in the direction of strong intensity gradients

Reduced order parameterized viscous optimal flow control problems and applications in coronary artery bypass grafts with patient-specific geometrical reconstruction and data assimilation

Coronary artery bypass graft surgery is an invasive procedure performed to circumvent partial or complete blood flow blockage in coronary artery disease (CAD). In this thesis, we will construct a numerical framework combining parametrized optimal flow control and reduced order methods and will apply to real-life clinical case of triple coronary artery bypass grafts surgery. In this mathematical framework, we will propose patient-specific physiological data assimilation in the optimal flow control part, with the aim to minimize the discrepancies between the patient-specific physiological data and the computational hemodynamics. The optimal flow control paradigm proves to be a handy tool for the purpose and is being commonly used in the scientific community. However, the discrepancies between clinical measurements and computational hemodynamics modeling are usually due to unrealistic quantification of hard-to-quantify outflow conditions and computational inefficiency. In this work, we will utilize the unknown control in the optimal flow control pipeline to automatically quantify the boundary flux, specifically the outflux, required to minimize the data misfit, subject to different parametrized scenarios. Furthermore, the challenge of attaining reliable solutions in a time-efficient manner for such many-query parameter dependent problems will be addressed by reduced order methods

Towards development of automatic path planning system in image-guided neurosurgery

With the advent of advanced computer technology, many computer-aided systems have evolved to assist in medical related work including treatment, diagnosis, and even surgery. In modern neurosurgery, Magnetic Resonance Image guided stereotactic surgery exactly complies with this trend. It is a minimally invasive operation being much safer than the traditional open-skull surgery, and offers higher precision and more effective operating procedures compared to conventional craniotomy. However, such operations still face significant challenges of planning the optimal neurosurgical path in order to reach the ideal position without damage to important internal structures. This research aims to address this major challenge. The work begins with an investigation of the problem of distortion induced by MR images. It then goes on to build a template of the Circle of Wills brain vessels, realized from a collection of Magnetic Resonance Angiography images, which is needed to maintain operating standards when, as in many cases, Magnetic Resonance Angiography images are not available for patients. Demographic data of brain tumours are also studied to obtain further understanding of diseased human brains through the development of an effect classifier. The developed system allows the internal brain structure to be ‘seen’ clearly before the surgery, giving surgeons a clear picture and thereby makes a significant contribution to the eventual development of a fully automatic path planning system

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