Heart disease is the leading cause of death in the world. Therefore, numerous studies are undertaken to identify indicators which can be applied to discover cardiac dysfunctions at an early age. Among others, the fluid dynamics of the blood flow (hemodymanics) is considered to contain relevant information related to abnormal performance of the heart.This thesis presents a robust framework for numerical simulation of the fluid dynamics of the blood flow in the left ventricle of a human heart and the fluid-structure interaction of the blood and the aortic leaflets.We first describe a patient-specific model for simulating the intraventricular blood flow. The motion of the endocardial wall is extracted from data acquired with medical imaging and we use the incompressible Navier-Stokes equations to model the hemodynamics within the chamber. We set boundary conditions to model the opening and closing of the mitral and aortic valves respectively, and we apply a stabilized Arbitrary Lagrangian-Eulerian (ALE) space-time finite element method to simulate the blood flow. Even though it is difficult to collect in-vivo data for validation, the available data and results from other simulation models indicate that our approach possesses the potential and capability to provide relevant information about the intraventricular blood flow.To further demonstrate the robustness and clinical feasibility of our model, a semi-automatic pathway from 4D cardiac ultrasound imaging to patient-specific simulation of the blood flow in the left ventricle is developed. The outcome is promising and further simulations and analysis of large data sets are planned.In order to enhance our solver by introducing additional features, the fluid solver is extended by embedding different geometrical prototypes of both a native and a mechanical aortic valve in the outflow area of the left ventricle.Both, the contact as well as the fluid-structure interaction, are modeled as a unified continuum problem using conservation laws for mass and momentum. To use this ansatz for simulating the valvular dynamics is unique and has the expedient properties that the whole problem can be described with partial different equations and the same numerical methods for discretization are applicable.All algorithms are implemented in the high performance computing branch of Unicorn, which is part of the open source software framework FEniCS-HPC. The strong advantage of implementing the solvers in an open source software is the accessibility and reproducibility of the results which enhance the prospects of developing a method with clinical relevance. / <p>QC 20171006</p>

Ces travaux de thèse s'inscrivent dans le contexte des gestes médico-chirurgicaux assistés par ordinateur pour le traitement endovasculaire de l'anévrisme de l'aorte abdominale. La complexité de ce type de procédure et l'évolution permanente des dispositifs endovasculaires posent des difficultés liées notamment à la sélection des endoprothèses les mieux adaptées à l'anatomie du patient et à la localisation précise des outils endovasculaires lors de l'intervention. L'objectif de ces travaux thèse est d'apporter aux cliniciens une aide à la décision lors de la planification et de la réalisation de la procédure. L'approche envisagée consiste à mettre en correspondance les examens d'imagerie préopératoire et peropératoire et à les combiner avec des modèles d'interaction dispositifs / tissus pour améliorer le positionnement des endoprothèses dans des structures vasculaires déformables. Nous envisageons tout d'abord une solution permettant de positionner interactivement des modèles d'endoprothèse dans la structure vasculaire préopératoire afin de vérifier l'adéquation des endoprothèses sélectionnées avec l'anatomie du patient. La méthode s'appuie sur un modèle géométrique ou mécanique approché de l'endoprothèse placée dans une structure vasculaire non déformable. Nous proposons ensuite une solution originale de simulation des interactions outil-tissu dans le but d'anticiper les déformations vasculaires provoquées par l'insertion des outils, relativement rigides, avant le déploiement des endoprothèses. Le guidage de l'intervention par navigation endovasculaire augmentée est abordé au travers du recalage 3D/2D. Une méthode polyvalente est proposée afin de mettre en correspondance le scanner ainsi que les modèles préopératoires avec les images peropératoires. L'évaluation de ces méthodes et leur application sur données patients ont permis de montrer la compatibilité de l'approche proposée avec le workflow clinique. / This thesis deals with computer-assisted surgery in the context of endovascular repair of abdominal aortic aneurysm. The complexity of this procedure and the ongoing development of endovascular devices pose challenges such as the selection of the most appropriate stent-grafts for the patient anatomy and the precise location of endovascular tools during surgery. The aim of this thesis is to provide clinicians with decision support for the planning and the performance of the procedure. The proposed approach consists in matching preoperative and intraoperative image data and in combining them with tool-tissue interaction models in order to improve the positioning of stent-grafts in deformable vascular structures. We consider a solution for positioning stent-grafts interactively in the preoperative vascular structure to verify the adequacy of the selected stent-grafts with the patient anatomy. The method is based on a geometrical or simplified mechanical model of the stent graft placed in a rigid vascular structure. We propose an original solution for simulating tool-tissue interactions in order to anticipate vascular deformations caused by the insertion of stiff tools before stent-graft deployment. Augmented reality guidance for endovascular interventions is addressed through 3D/2D registration. A versatile method is proposed for the matching of the CT-scan and preoperative models with intraoperative images. The evaluation of these methods and the results obtained on patient data have shown the compatibility of the proposed approach with the clinical workflow.

No description available.

<div>Excessive deformation and stress of skin following reconstructive surgery plays a crucial role in wound healing, often leading to complications. Yet, despite of this concern, surgeries are still planned and executed based on each surgeon's training and experience rather than quantitative engineering tools. The limitations of current treatment planning and execution stem in part from the difficulty in predicting the mechanical behavior of skin, challenges in directly measuring stress in the operating room, and inability to predict the long term adaptation of skin following reconstructive surgery. Computational modeling of soft tissue mechanics has emerged as an ideal candidate to determine stress contours over sizable skin regions in realistic situations. Virtual surgeries with computational mechanics tools will help surgeons explore different surgeries preoperatively, make prediction of stress contours, and eventually aid the surgeon in planning for optimal wound healing. While there has been significant progress on computational modeling of both reconstructive surgery and skin mechanical and mechanobiological behavior, there remain major gaps preventing computational mechanics to be widely used in the clinical setting. At the preoperative stage, better calibration of skin mechanical properties for individual patients based on minimally invasive mechanical tests is still needed. One of the key challenges in this task is that skin is not stress-free in vivo. In many applications requiring large skin flaps, skin is further grown with the tissue expansion technique. Thus, better understanding of skin growth and the resulting stress-free state is required. The other most significant challenge is dealing with the inherent variability of mechanical properties and biological response of biological systems. Skin properties and adaptation to mechanical cues changes with patient demographic, anatomical location, and from one individual to another. Thus, the precise model parameters can never be known exactly, even if some measurements are available. Therefore, rather than expecting to know the exact model describing a patient, a probabilistic approach is needed. To bridge the gaps, this dissertation aims to advance skin biomechanics and computational mechanics tools in order to make virtual surgery for clinical use a reality in the near future. In this spirit, the dissertation constitutes three parts: skin growth and its incompatibility, acquisition of patient-specific geometry and skin mechanical properties, and uncertainty analysis of virtual surgery scenarios.</div><div>Skin growth induced by tissue expansion has been widely used to gain extra skin before reconstructive surgery. Within continuum mechanics, growth can be described with the split of the deformation gradient akin to plasticity. We propose a probabilistic framework to do uncertainty analysis of growth and remodeling of skin in tissue expansion. Our approach relies on surrogate modeling through multi-fidelity Gaussian process regression. This work is being used calibrate the computational model against animal model data. Details of the animal model and the type of data obtained are also covered in the thesis. One important aspect of the growth and remodeling process is that it leads to residual stress. It is understood that this stress arises due to the nonhomogeneous growth deformation. In this dissertation we characterize the geometry of incompatibility of the growth field borrowing concepts originally developed in the study of crystal plasticity. We show that growth produces unique incompatibility fields that increase our understanding of the development of residual stress and the stress-free configuration of tissues. We pay particular attention to the case of skin growth in tissue expansion.</div><div>Patient-specific geometry and material properties are the focus on the second part of the thesis. Minimally invasive mechanical tests based on suction have been developed which can be used in vivo, but these tests offer only limited characterization of an individual's skin mechanics. Current methods have the following limitations: only isotropic behavior can be measured, the calibration problem is done with inverse finite element methods or simple analytical calculations which are inaccurate, the calibration yields a single deterministic set of parameters, and the process ignores any previous information about the mechanical properties that can be expected for a patient. To overcome these limitations, we recast the calibration problem in a Bayesian framework. To sample from the posterior distribution of the parameters for a patient given a suction test, the method relies on an inexpensive Gaussian process surrogate. For the patient-specific geometry, techniques such as magnetic resonance imaging or computer tomography scans can be used. Such approaches, however, require specialized equipment and set up and are not affordable in many scenarios. We propose to use multi-view stereo (MVS) to capture patient-specific geometry.</div><div>The last part of the dissertation focuses on uncertainty analysis of the reconstructive procedure itself. To achieve uncertainty analysis in the clinical setting we propose to create surrogate and reduced order models, especially principal component analysis and Gaussian process regression. We first show the characterization of stress profiles under uncertainty for the three most common flap designs. For these examples we deal with idealized geometries. The probabilistic surrogates enable not only tasks such as fast prediction and uncertainty quantification, but also optimization. Based on a global sensitivity analysis we show that the direction of anisotropy of skin with respect to the flap geometry is the most important parameter controlled by the surgeon, and we show hot to optimize the flap in this idealized setting. We conclude with the application of the probabilistic surrogates to perform uncertainty analysis in patient-specific geometries. In summary, this dissertation focuses on some of the fundamental challenges that needed to be addressed to make virtual surgery models ready for clinical use. We anticipate that our results will continue to shape the way computational models continue to be incorporated in reconstructive surgery plans.</div>

Le travail présenté dans ce manuscrit concerne la fabrication de fantômes d'aorte patient spécifiques utilisant une technique de fabrication additive par impression 3D. Ces répliques sont fabriquées en matériaux synthétiques dont les caractéristiques morphologiques et les propriétés mécaniques doivent être proches de celles déterminées sur un patient. Elles permettent d'optimiser ou de développer les techniques d'imagerie médicale, de comprendre les relations entre le comportement mécanique de la paroi aortique et les caractéristiques hémodynamiques du flux sanguin mais aussi de réaliser des entrainements préopératoires aux interventions chirurgicales, telles que le traitement endovasculaire. Dans cette étude, le comportement mécanique hyper-viscoélastique de la paroi aortique est modélisé par un modèle de Maxwell solide généralisé, dont les paramètres ont permis la sélection et le développement de matériaux élastomères de type silicone aux comportements mécaniques contrôlés. Ces matériaux ont été élaborés à partir de mélanges de formulations existantes et des lois de mélange ont été comparées pour guider la définition de la composition idéale permettant d'imiter le comportement mécanique désiré. Nous avons mis au point une méthode basée sur l'imagerie médicale par ultrason, capable d'identifier les paramètres hyper-viscoélastiques d'une paroi vasculaire. Cette méthode a été validée sur des tubes réalisés avec ces formulations de silicone, dont les propriétés mécaniques ont été mesurées avec des méthodes de référence. Puis, ces silicones ont été utilisés dans un processus de fabrication additive utilisant l'impression 3D par voie indirecte. Un travail de conception assistée par ordinateur a été réalisé pour produire un fantôme d'aorte patient-spécifique présentant un anévrisme fusiforme et non-thrombosé dans la région thoracique / The present work deals with the producing of patient-specific aortic phantoms using an additive manufacturing technique by 3D printing. Phantoms are manufactured from synthetic materials with morphological and mechanical characteristics which should be close to these identified on a patient. They can be used to develop techniques of medical imaging, to understand the relationship between aortic mechanical behavior and hemodynamic properties of blood flow, as well as to perform a preoperative training of interventions, such as endovascular treatment. In this study, the hyper-viscoelastic aortic mechanical behavior was described using a generalized solid Maxwell model. Silicone materials were developed based on the model’s mechanical parameters to mimic various aortic mechanical behaviors. These materials were formulated from commercials silicones, and then mixing rules were compared to define the ideal mixture which can mimic the specific mechanical behavior. A nondestructive method based on medical imaging by ultrasound was developed to identify the parameters of a blood vessel hyper-viscoelastic model. Silicone tubes made of our formulations with known reference mechanical parameters, were used to validate this method. Then, these silicone materials were used in an additive manufacturing process using indirect 3D printing. A work of computer aided design was done to produce a patient-specific aortic phantom with a thoracic fusiform aneurysm without thrombosis

Les modèles de croissance tumorale fondés sur l'équation de réaction-diffusion Fisher Kolmogorov FK ont montré des résultats probants dans la reproduction et la prédiction de l'invasion de cellules tumorales du cerveau pour les gliomes. Nous utilisons différentes formulations du modèle FK pour évaluer la nécessité de l’imagerie de diffusion pour construire des modèles spécifiques de Gliomes de Bas Grade GBG, l'étude de l'infiltration de cellules tumorales après une résection chirurgicale de la tumeur, et définir une métrique pour quantifier l’évolution de GBG. L'imagerie en tenseur de diffusion ITD a été suggérée pour modéliser la diffusion anisotrope des cellules tumorales dans la matière blanche du cerveau. Les ITD acquises en basse résolution impactent la précision des résultats des modèles de croissance. Nous utilisons une formulation FK pour décrire l'évolution de la frontière visible de la tumeur pour étudier l'impact du remplacement de l'ITD patient par une hypothèse de diffusion isotrope ou une ITD de référence anisotrope en haute résolution formée par la moyenne des ITD de plusieurs patients. Nous quantifions l'impact du remplacement de l'ITD acquise sur un patient à aide de simulations de croissance tumorales synthétiques et des prévisions d'évolution de la tumeur d'un cas clinique. Cette étude suggère que la modélisation de la croissance du gliome à base de motilité différentielle de tissus donne des résultats un peu moins précis qu'à l'aide d'une ITD. S'abstenir d'utiliser une ITD serait suffisant lors de la modélisation de GBG. Par conséquent, toutes ces options d'ITD sont valides dans une formulation FK pour modéliser la croissance de GBG dans le but d'aider les cliniciens dans la planification du traitement. Après la résection d’une tumeur cérébrale, ils veulent savoir quel serait le meilleur traitement de suivi pour chaque patient : une chimiothérapie pour des tumeurs diffuses ou bien une deuxième résection après un laps de temps donné pour les tumeurs massives. Nous proposons une méthode pour tirer profit de modèles de croissance de gliome FK sur les cas post-opératoires montrant des distorsions du cerveau pour estimer l'infiltration des cellules tumorales au-delà des frontières visibles dans les IRM FLAIR. Notre méthode répond à 2 défis de modélisation : celui du mouvement du parenchyme cérébral après la chirurgie avec une technique de recalage non-linéaire et celui de la segmentation incomplète de la tumeur post-opératoire en combinant 2 cartes d'infiltration : une ayant été simulée à partir d'une image pré-opératoire et l’autre à partir d'une image post-opératoire. Nous avons utilisé les données de 2 patients ayant des GBG afin de démontrer l'efficacité de la méthode. Celle-ci pourrait aider les cliniciens à anticiper la récurrence de la tumeur après une résection et à caractériser l’étendue de l'infiltration non visible par la radiologie pour planifier la thérapie. Pour les GBG visibles par une IRM FLAIR/T2, il y a un débat important au sein du groupe de travail RANO Response Assessment in Neuro-Oncology sur la sélection d'un seuil pertinent des métriques basées sur l’évolution de la taille de la tumeur pour déterminer si la maladie est évolutive ME. Nous proposons une approche pour évaluer la ME du GBG en utilisant des estimations de la vitesse de croissance de la tumeur à partir d'une formulation FK qui prend en compte les irrégularités de forme de la tumeur, les différences de vitesse de croissance entre la matière grise et la matière blanche, et les changements volumétriques. En utilisant les IRM FLAIR de 9 patients, nous comparons les estimations de ME de notre approche proposée avec celles calculées en utilisant les estimations manuelles de la vitesse de croissance tumorale 1D, 2D et 3D et celles calculées en utilisant un ensemble de critères basés sur la taille critères RECIST, Macdonald et RANO. Notre approche est prometteuse pour évaluer la ME du GBG à partir d'un nombre limité d'examens par IRM. / Tumor growth models based on the Fisher Kolmogorov (FK) reaction-diffusion equation have shown convincing results in reproducing and predicting the invasion patterns of glioma brain tumors. In this thesis we use different FK model formulations to i) assess the need of patient-specific DTIs when modeling LGGs, ii) study cancer cell infiltration after tumor resections, and iii) define a metric to determine progressive disease for low-grade glimoas (LGG).Diffusion tensor images (DTIs) have been suggested to model the anisotropic diffusion of tumor cells in brain white matter. However, patient specific DTIs are expensive and often acquired with low resolution, which compromises the accuracy of the tumor growth models' results. We used a FK formulation to describe the evolution of the visible boundary of the tumor to investigate the impact of replacing the patient DTI by i) an isotropic diffusion map or ii) an anisotropic high-resolution DTI atlas formed by averaging the DTIs of multiple patients. We quantify the impact of replacing the patient DTI using synthetic tumor growth simulations and tumor evolution predictions on a clinical case. This study suggests that modeling glioma growth with tissue based differential motility (not using a DTI) yields slightly less accurate results than using a DTI. However, refraining from using a DTI would be sufficient in situations when modeling LGGs. Therefore, any of these DTI options are valid to use in a FK formulation to model LGG growth with the purpose of aiding clinicians in therapy planning.After a brain resection medical professionals want to know what the best type of follow-up treatment would be for a particular patient, i.e., chemotherapy for diffuse tumors or a second resection after a given amount of time for bulky tumors. We propose a thorough method to leverage FK reaction-diffusion glioma growth models on post-operative cases showing brain distortions to estimate tumor cell infiltration beyond the visible boundaries in FLAIR MRIs. Our method addresses two modeling challenges: i) the challenge of brain parenchyma movement after surgery with a non-linear registration technique and ii) the challenge of incomplete post-operative tumor segmentations by combining two infiltration maps, where one was simulated from a pre-operative image and one estimated from a post-operative image. We used the data of two patients with LGG to demonstrate the effectiveness of the proposed three-step method. We believe that our proposed method could help clinicians anticipate tumor regrowth after a resection and better characterize the radiological non-visible infiltrative extent of a tumor to plan therapy.For LGGs captured on FLAIR/T2 MRIs, there is a substantial amount debate on selecting a definite threshold for size-based metrics to determine progressive disease (PD) and it is still an open item for the Response Assessment in Neuro-Oncology (RANO) Working Group. We propose an approach to assess PD of LGG using tumor growth speed estimates from a FK formulation that takes into consideration irregularities in tumor shape, differences in growth speed between gray matter and white matter, and volumetric changes. Using the FLAIR MRIs of nine patients we compare the PD estimates of our proposed approach to i) the ones calculated using 1D, 2D, and 3D manual tumor growth speed estimates and ii) the ones calculated using a set of well-established size-based criteria (RECIST, Macdonald, and RANO). We conclude from our comparison results that our proposed approach is promising for assessing PD of LGG from a limited number of MRI scans. It is our hope that this model's tumor growth speed estimates could one day be used as another parameter in clinical therapy planning.

Les simulations numériques des écoulements sanguins cardiovasculaires peuvent combler d’importantes lacunes dans les capacités actuelles de traitement clinique. En effet, elles offrent des moyens non invasifs pour quantifier l’hémodynamique dans le cœur et les principaux vaisseaux sanguins chez les patients atteints de maladies cardiovasculaires. Ainsi, elles permettent de recouvrer les caractéristiques des écoulements sanguins qui ne peuvent pas être obtenues directement à partir de l’imagerie médicale. Dans ce sens, des simulations personnalisées utilisant des informations propres aux patients aideraient à une prévision individualisée des risques. Nous pourrions en effet, disposer des informations clés sur la progression éventuelle d’une maladie ou détecter de possibles anomalies physiologiques. Les modèles numériques peuvent fournir également des moyens pour concevoir et tester de nouveaux dispositifs médicaux et peuvent être utilisés comme outils prédictifs pour la planification de traitement chirurgical personnalisé. Ils aideront ainsi à la prise de décision clinique. Cependant, une difficulté dans cette approche est que, pour être fiables, les simulations prédictives spécifiques aux patients nécessitent une assimilation efficace de leurs données médicales. Ceci nécessite la solution d’un problème hémodynamique inverse, où les paramètres du modèle sont incertains et sont estimés à l’aide des techniques d’assimilation de données.Dans cette thèse, le problème inverse pour l’estimation des paramètres est résolu par une méthode d’assimilation de données basée sur un filtre de Kalman d’ensemble (EnKF). Connaissant les incertitudes sur les mesures, un tel filtre permet la quantification des incertitudes liées aux paramètres estimés. Un algorithme d’estimation de paramètres, basé sur un filtre de Kalman d’ensemble, est proposé dans cette thèse pour des calculs hémodynamiques spécifiques à un patient, dans un réseau artériel schématique et à partir de mesures cliniques incertaines. La méthodologie est validée à travers plusieurs scenarii in silico utilisant des données synthétiques. La performance de l’algorithme d’estimation de paramètres est également évaluée sur des données expérimentales pour plusieurs réseaux artériels et dans un cas provenant d’un banc d’essai in vitro et des données cliniques réelles d’un volontaire (cas spécifique du patient). Le but principal de cette thèse est l’analyse hémodynamique spécifique du patient dans le polygone de Willis, appelé aussi cercle artériel du cerveau. Les propriétés hémodynamiques communes, comme celles de la paroi artérielle (module de Young, épaisseur de la paroi et coefficient viscoélastique), et les paramètres des conditions aux limites (coefficients de réflexion et paramètres du modèle de Windkessel) sont estimés. Il est également démontré qu’un modèle appelé compartiment d’ordre réduit (ou modèle dimension zéro) permet une estimation simple et fiable des caractéristiques du flux sanguin dans le polygone de Willis. De plus, il est ressorti que les simulations avec les paramètres estimés capturent les formes attendues pour les ondes de pression et de débit aux emplacements prescrits par le clinicien. / Cardiovascular blood flow simulations can fill several critical gaps in current clinical capabilities. They offer non-invasive ways to quantify hemodynamics in the heart and major blood vessels for patients with cardiovascular diseases, that cannot be directly obtained from medical imaging. Patient-specific simulations (incorporating data unique to the individual) enable individualised risk prediction, provide key insights into disease progression and/or abnormal physiologic detection. They also provide means to systematically design and test new medical devices, and are used as predictive tools to surgical and personalize treatment planning and, thus aid in clinical decision-making. Patient-specific predictive simulations require effective assimilation of medical data for reliable simulated predictions. This is usually achieved by the solution of an inverse hemodynamic problem, where uncertain model parameters are estimated using the techniques for merging data and numerical models known as data assimilation methods.In this thesis, the inverse problem is solved through a data assimilation method using an ensemble Kalman filter (EnKF) for parameter estimation. By using an ensemble Kalman filter, the solution also comes with a quantification of the uncertainties for the estimated parameters. An ensemble Kalman filter-based parameter estimation algorithm is proposed for patient-specific hemodynamic computations in a schematic arterial network from uncertain clinical measurements. Several in silico scenarii (using synthetic data) are considered to investigate the efficiency of the parameter estimation algorithm using EnKF. The usefulness of the parameter estimation algorithm is also assessed using experimental data from an in vitro test rig and actual real clinical data from a volunteer (patient-specific case). The proposed algorithm is evaluated on arterial networks which include single arteries, cases of bifurcation, a simple human arterial network and a complex arterial network including the circle of Willis.The ultimate aim is to perform patient-specific hemodynamic analysis in the network of the circle of Willis. Common hemodynamic properties (parameters), like arterial wall properties (Young’s modulus, wall thickness, and viscoelastic coefficient) and terminal boundary parameters (reflection coefficient and Windkessel model parameters) are estimated as the solution to an inverse problem using time series pressure values and blood flow rate as measurements. It is also demonstrated that a proper reduced order zero-dimensional compartment model can lead to a simple and reliable estimation of blood flow features in the circle of Willis. The simulations with the estimated parameters capture target pressure or flow rate waveforms at given specific locations.

Cette thèse aborde l'étude de la fonction cérébrale en Imagerie par Résonance Magnétique (IRM) à l'aide de deux séquences : l'IRM fonctionnelle (IRMf) BOLD et l'imagerie de perfusion par marquage de spins (ASL). Dans ce contexte, les analyses de groupe jouent un rôle important dans l'identification des dysfonctionnements globaux associés à une pathologie. D'autre part, les études individuelles, qui fournissent des conclusions au niveau d'un sujet unique, présentent un intérêt croissant. Dans ce travail, nous abordons à la fois les études de groupe et les analyses individuelles. Dans un premier temps, nous réalisons une analyse de groupe en IRMf BOLD en vue d'étudier la dysphasie chez l'enfant, une pathologie peu explorée en neuroimagerie. Nous mettons ainsi en évidence un fonctionnement et une latéralisation atypiques des aires langagières. Ensuite, nous nous concentrons sur les analyses individuelles. Nous proposons l'utilisation d'estimateurs robustes pour calculer les cartographies de débit sanguin cérébral en ASL. Ensuite, nous étudions la validité des hypothèses qui sous-tendent les analyses statistiques standard dans le contexte de l'ASL. Finalement, nous proposons une nouvelle méthode localement multivariée basée sur une approche a contrario. La validation de cette nouvelle approche est réalisée dans deux contextes applicatifs : la détection d'anomalies de perfusion en ASL et la détection de zones d'activation en IRMf BOLD. / This thesis deals with the analysis of brain function in Magnetic Resonance Imaging (MRI) using two sequences: BOLD functional MRI (fMRI) and Arterial Spin Labelling (ASL). In this context, group statistical analyses are of great importance in order to understand the general mechanisms underlying a pathology, but there is also an increasing interest towards patient-specific analyses that draw conclusions at the patient level. Both group and patient-specific analyses are studied in this thesis. We first introduce a group analysis in BOLD fMRI for the study of specific language impairment, a pathology that was very little investigated in neuroimaging. We outline atypical patterns of functional activity and lateralisation in language regions. Then, we move forward to patient-specific analysis. We propose the use of robust estimators to compute cerebral blood flow maps in ASL. Then, we analyse the validity of the assumptions underlying standard statistical analyses in the context of ASL. Finally, we propose a new locally multivariate statistical method based on an a contrario approach and apply it to the detection of atypical patterns of perfusion in ASL and to activation detection in BOLD functional MRI.

The current clinical management of abdominal aortic aneurysm (AAA) disease is based on measuring the aneurysm maximum diameter to decide when timely intervention can be recommended to a patient. However, other parameters may also play a role in causing or predisposing the AAA to either an early or delayed rupture relative to its size. Therefore, patient-specific assessment of rupture risk based on physical principles such as individualized biomechanics can be conducive to the development of a vascular tool with translational potential. To that end, the present doctoral research materialized into a framework for image based patient-specific vascular biomechanics assessment. A robust generalized approach is described herein for image-based volume mesh generation of complex multidomain bifurcated vascular trees with the capability of incorporating regionally varying wall thickness. The developed framework is assessed for geometrical accuracy, mesh quality, and optimal computational performance. The relative influence of the shape and the constitutive wall material property on the AAA wall mechanics was explored. This study resulted in statistically insignificant differences in peak wall stress among 28 AAA geometries of similar maximum diameter (in the 50 – 55 mm range) when modeled with five different hyperelastic isotropic constitutive equations. Relative influence of regionally varying vs. uniform wall thickness distribution on the AAA wall mechanics was also assessed to find statistically significant differences in spatial maxima of wall stresses, strains, and strain energy densities among the same 28 AAA geometries modeled with patient-specific non-uniform wall thickness and two uniform wall thickness assumptions. Finally, the feasibility of estimating in vivo wall strains from individual clinical images was evaluated. Such study resulted in a framework for in vivo 3D strain distributions based on ECG gated, unenhanced, dynamic magnetic resonance images acquired for 20 phases in the cardiac cycle. Future efforts should be focused on further development of the framework for in vivo estimation of regionally varying hyperelastic, anisotropic constitutive material models with active mechanics components and the integration of such framework with an open source finite element solver with the goal of increasing the translational potential of these tools for individualized prediction of AAA rupture risk in the clinic.

Background: Single ventricle congenital heart defects with cyanotic mixing between systemic and pulmonary circulations afflict 2 per 1000 live births. Following the atriopulmonary connection proposed by Fontan and Baudet in 1971, the present procedure is the total cavopulmonary connection (TCPC), where the superior vena cava (SVC) and inferior vena cava (IVC) are sutured to the left pulmonary artery (LPA) and right pulmonary artery (RPA). However, surgeon preference dictates the implementation of the extra-cardiac and intra-atrial varieties of the TCPC. Overall efficiency and hemodynamic advantage of the competing methodologies have not been determined. Hypothesis: It is hypothesized that an understanding of the experimental fluid dynamic differences between various Fontan surgical methodologies in the TCPC allows for power loss evaluation toward improved surgical planning and design. Methods: Toward such analysis, a previously developed data processing methodology is applied to create an anatomic database of single ventricle patients from in vivo magnetic resonance imaging (MRI) to examine the gamut of TCPC anatomies. From stereolithographic models of representative cases, pressure and flow data are used to quantify control volume power loss to measure overall efficiency. particle image velocimetry (PIV) is employed to detail flow structures in the vasculature. Results are validated with dye injection flow visualization and 3-D phase contrast magnetic resonance imaging (PC-MRI) velocimetry, highlighting flow phenomena that cannot be captured with in vivo MRI due to prohibitively long scanning times. Preliminary results illustrate the variation of control volume power loss over several TCPC anatomies with varying flow conditions, the application of PIV, and validation approaches with 3-D PC-MRI velocimetry. Data from control volume power loss evaluation demonstrate a correlation with TCPC anatomy, providing added clinical knowledge of optimal TCPC design. Findings from PIV and 3-D PC-MRI velocimetry reveal a means for quantitatively comparing flow structure. Dye injection flow visualization offers qualitative insight into limitations of the selected velocimetry techniques.