Optical Coherence Tomography (OCT) is a coherence-gated, micrometer-resolution imaging technique that focuses a broadband near-infrared laser beam to penetrate into optical scattering media, e.g. biological tissues. The OCT resolution is split into two parts, with the axial resolution defined by half the coherence length, and the depth-dependent lateral resolution determined by the beam geometry, which is well described by a Gaussian beam model. The depth dependence of lateral resolution directly results in the defocusing effect outside the confocal region and restricts current OCT probes to small numerical aperture (NA) at the expense of lateral resolution near the focus. Another limitation on OCT development is the presence of a mixture of speckles due to multiple scatterers within the coherence length, and other random noise. Motivated by the above two challenges, a multiple scattering model based on Rytov approximation and Gaussian beam optics is proposed for the OCT setup. Some previous papers have adopted the first Born approximation with the assumption of small perturbation of the incident field in inhomogeneous media. The Rytov method of the same order with smooth phase perturbation assumption benefits from a wider spatial range of validity. A deconvolution method for solving the inverse problem associated with the first Rytov approximation is developed, significantly reducing the defocusing effect through depth and therefore extending the feasible range of NA.

Ill-posed inverse problems have wide applications in many fields such as oceanography, signal processing, machine learning, biomedical imaging, remote sensing, geophysics, and others. In this dissertation, we address the problem of solving unstable operator equations with iteratively regularized Newton-type algorithms. Important practical questions such as selection of regularization parameters, construction of generating (filtering) functions based on a priori information available for different models, algorithms for stopping rules and error estimates are investigated with equal attention given to theoretical study and numerical experiments.

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The scope of this thesis is in the time-dependent formulation of the two dimensional model of resonant electron-diatomic molecule collisions in the range of low energies. In its time independent form the model was previously numerically solved without the Born-Oppenheimer approximation with use of modern tools such as the finite element method with discrete variable representation (FEM-DVR) or exterior complex scaling (ECS). Within the scope of this model we numerically solve the evolution problem, with use of the Crank-Nicolson method and the Padé approximation. Later we evaluate the cross section of the elastic and some inelastic processes with the correlation function approach. At last we make a comparison of the evolution and the cross sections to time dependent formulation of the local complex potential approximation of the electron-molecule collisions.

Le problème de l'imagerie non itérative dans le cadre de la diffraction électromagnétique inverse utilisant la méthode d'échantillonnage direct (DSM) est considéré. Grâce à une combinaison de l'expression asymptotique du champ proche ou du champ lointain diffracté et de l'hypothèse de petits obstacles, les expressions analytiques de la fonction d'indicateur DSM sont présentées dans diverses configurations telles que des configurations 2D/3D, mono-/multi-configurations statiques, à vue limitée/complète et fréquence unique/ diversité en fréquence. Une fois l'expression analytique obtenue, sa structure est analysée et des améliorations proposées. Notre approche est validée à l’aide de données de simulation, et d’expériences le cas échéant. Premièrement, la structure mathématique du DSM à fréquence fixe en 2D dans divers problèmes de diffusion est établie, permettant une analyse théorique de son efficacité et de ses limites. Pour surmonter les limitations connues, une méthode alternative d'échantillonnage direct (DSMA) est proposée. Puis le cas multi-fréquence est investigué en introduisant et en analysant le DSM multi-fréquence (MDSM) et le DSMA multi-fréquence (MDSMA). Enfin, notre approche est étendue aux problèmes de diffraction électromagnétique inverse 3D pour lesquels le choix de la polarisation du dipôle de test est un paramètre clé. De par notre approche analytique, ce choix peut être effectué sur la base de la polarisation du champ incident. / The non-iterative imaging problem within the inverse electromagnetic scattering framework using the direct sampling method (DSM) is considered. Thanks to the combination of the asymptotic expression of the scattered near-field or far-field and of the small obstacle hypothesis the analytical expressions of the DSM indicator function are presented in various configurations such as 2D/3D configurations and/or mono-/multi-static configurations and/or limited-/full-view case and/or mono-/multi-frequency case. Once the analytical expression obtained, its structure is analyzed and improvements proposed. Our approach is validated using synthetic data and experimental ones when available. First, the mathematical structure of DSM at a fixed frequency in 2D various scattering problems is established allowing a theoretical analysis of its efficiency and limitations. To overcome the known limitations an alternative direct sampling method (DSMA) is proposed. Next, the multi-frequency case is investigated by introducing and analyzing the multi-frequency DSM (MDSM) and the multi-frequency DSMA (MDSMA).Finally, our approach is extended to 3D inverse electromagnetic scattering problems for which the choice of the polarization of the test dipole is a key parameter. Thanks to our analytical analysis it can be made based on the polarization of the incident field.

Le Contrôle Non Destructif (CND) de matériaux composites multicouches pour des problèmes de qualité, viabilité, sécurité et disponibilité des systèmes qui impliquent des pièces fabriquées dans les industries aéronautiques et de l’automobile est devenu une tâche essentielle aujourd’hui. L'objectif visé par cette thèse est l’imagerie électromagnétique de structures complexes multicouches anisotropes, de plus en plus utilisées dans des applications, et encore source de sérieux défis à l'étape de leur modélisation et encore plus à l'étape souvent en enfance de leur imagerie. En utilisant une vaste gamme de fréquences, qui va des courants de Foucault jusqu’aux micro-ondes, il y a un fort besoin de rendre disponibles des procédures de modélisation et d'imagerie qui sont robustes, rapides, précises et utiles à la décision des utilisateurs finaux sur des défauts potentiels, tant donc en basse fréquence (BF) (matériaux conducteurs, type fibre de carbone) qu’en haute fréquence (HF) (matériaux diélectriques, type fibre de verre). De plus, il est important d'obtenir des résultats en des temps brefs. Cependant, cela nécessite la connaissance d’une réponse précise à des sources externes aux multicouches, en considérant les couches des composites comme non endommagées ou endommagées : on parle donc de solution du problème direct, avec le cas particulier de sources élémentaires conduisant aux dyades de Green (DGF). La modélisation et la simulation numérique du problème direct sont gérés principalement via une solution au premier ordre de la formulation intégrale de contraste de source impliquant le tenseur de dépolarisation des défauts, quand ceux-ci sont assez petits vis-à-vis de l’épaisseur de peau locale (cas BF) ou de la longueur d'onde locale (cas HF). La précision des DGF doit nécessairement être assurée alors, même si les sources se situent loin de l'origine, ce qui donne un spectre de dyades qui oscille très rapidement. La technique d'interpolation-intégration dite de Padua-Domínguez est ainsi introduite dans le but d'évaluer de façon efficace des intégrales fortement oscillantes.Néanmoins, les matériaux composites peuvent souffrir de divers défauts, lors du processus de fabrication ou pendant leurs utilisations. Vides d’air, cavités remplies de liquide, fissures, etc., peuvent affecter le fonctionnement correct des structures composites. Il est donc indispensable de pouvoir détecter la présence des défauts. Ici, l’insistance est sur la méthode bien connue d’imagerie dite MUltiple SIgnal Classification (MUSIC), qui est basée sur la décomposition en valeurs singulières (SVD) des DGF ; celle-ci est développée afin de localiser les positions de multiples petits défauts volumiques en interaction faible enfouis dans des milieux anisotropes uniaxiaux. Le principal inconvénient de la méthode MUSIC est cependant sa sensibilité par rapport au bruit. Par conséquent, des méthodes MUSIC avec une résolution améliorée et la Recursively Applied and Projected (RAP) MUSIC sont introduites afin de surmonter un tel inconvénient de l'algorithme standard et de fournir des résultats de qualité avec une meilleure résolution. De nombreuses simulations numériques illustrent ces investigations. / Non-Destructive Testing/Evaluation (NdT/E) of multi-layered composite materials for problems of quality, viability, safety and availability of systems involving manufactured parts (in aeronautics and in automotive industry, as a good example) has become an interesting and challenging task nowadays. The focus of the PhD thesis is on the electromagnetic (EM) imaging of complex anisotropic multi-slab composite panels as increasingly encountered in applications, yet source of strong challenges at modeling stage and even more at often-in-infancy imaging stage. From eddy-currents to microwaves, there is a strong need to make available modeling and imaging procedures that are robust, fast, accurate and useful to potential end-users’ decision about potential defects both at low-frequency (LF) (conductive materials, carbon-fiber like) and high-frequency (HF) (dielectric materials, glass-fiber like). Moreover, it is important to get the results in close-to-real-time. However, this requires an accurate response to external sources of the multilayers, considering the layers which these composite structures are made of as undamaged or damaged. The modeling at forward stage is managed via a first-order solution involving the dyadic Green’s functions (DGF) of the layers along with the depolarization tensor of the assumed defects when they are small enough vis-à-vis the skin depth (LF case) or the wavelength (HF case). The accuracy of the DGF has to be ensured even if the sources lie far away from the origin, which yields a fast-oscillating spectrum of the dyads. The Padua-Domínguez interpolation-integration technique is introduced herein in order to evaluate in an effective fashion fast-oscillating integrals.Damages or disorders, which these composite structures may suffer from, are of many kinds. One could mention voids, fluid-filled cavities or uniaxial defects with obvious impacts on the electromagnetic and geometric parameters of the multilayers. That is, the task to make available to end-users imaging algorithms tailored to detect the presence of defects. The well-known standard MUltiple SIgnal Classification (MUSIC) algorithm, which is based on the Singular Value Decomposition (SVD) of such DGF, is here applied to localize the positions of small multiple defects with weak interaction embedded in anisotropic uniaxial media. The main drawback of MUSIC is its sensitivity with respect to the noise. Therefore, MUSIC with enhanced resolution and Recursively Applied and Projected (RAP) MUSIC are introduced to overcome such a drawback of the standard algorithm and to provide quality results with better resolution.

This thesis presents the research results on the development of a microwave tomography imaging algorithm capable of reconstructing the dielectric properties of the unknown object. Our focus was on the theoretical aspects of the non-linear tomographic image reconstruction problem with particular emphasis on developing efficient numerical and non-linear optimization for solving the inverse scattering problem. A detailed description of a novel microwave tomography method based on frequency dependent finite difference time domain, a numerical method for solving Maxwell's equations and Genetic Algorithm (GA) as a global optimization technique is given. The proposed technique has the ability to deal with the heterogeneous and dispersive object with complex distribution of dielectric properties and to provide a quantitative image of permittivity and conductivity profile of the object. It is shown that the proposed technique is capable of using the multi-frequency, multi-view, and multi-incident planer techniques which provide useful information for the reconstruction of the dielectric properties profile and improve image quality. In addition, we show that when a-priori information about the object under test is known, it can be easily integrated with the inversion process. This provides realistic regularization of the solution and removes or reduces the possibility of non-true solutions. We further introduced application of the GA such as binary-coded GA, real-coded GA, hybrid binary and real coded GA, and neural-network/GA for solving the inverse scattering problem which improved the quality of the images as well as the conversion rate. The implications and possible advantages of each type of optimization are discussed, and synthetic inversion results are presented. The results showed that the proposed algorithm was capable of providing the quantitative images, although more research is still required to improve the image quality. In the proposed technique the computation time for solution convergence varies from a few hours to several days. Therefore, the parallel implementation of the algorithm was carried out to reduce the runtime. The proposed technique was evaluated for application in microwave breast cancer imaging as well as measurement data from university of Manitoba and Institut Frsenel's microwave tomography systems.

This thesis presents the research results on the development of a microwave tomography imaging algorithm capable of reconstructing the dielectric properties of the unknown object. Our focus was on the theoretical aspects of the non-linear tomographic image reconstruction problem with particular emphasis on developing efficient numerical and non-linear optimization for solving the inverse scattering problem. A detailed description of a novel microwave tomography method based on frequency dependent finite difference time domain, a numerical method for solving Maxwell's equations and Genetic Algorithm (GA) as a global optimization technique is given. The proposed technique has the ability to deal with the heterogeneous and dispersive object with complex distribution of dielectric properties and to provide a quantitative image of permittivity and conductivity profile of the object. It is shown that the proposed technique is capable of using the multi-frequency, multi-view, and multi-incident planer techniques which provide useful information for the reconstruction of the dielectric properties profile and improve image quality. In addition, we show that when a-priori information about the object under test is known, it can be easily integrated with the inversion process. This provides realistic regularization of the solution and removes or reduces the possibility of non-true solutions. We further introduced application of the GA such as binary-coded GA, real-coded GA, hybrid binary and real coded GA, and neural-network/GA for solving the inverse scattering problem which improved the quality of the images as well as the conversion rate. The implications and possible advantages of each type of optimization are discussed, and synthetic inversion results are presented. The results showed that the proposed algorithm was capable of providing the quantitative images, although more research is still required to improve the image quality. In the proposed technique the computation time for solution convergence varies from a few hours to several days. Therefore, the parallel implementation of the algorithm was carried out to reduce the runtime. The proposed technique was evaluated for application in microwave breast cancer imaging as well as measurement data from university of Manitoba and Institut Frsenel's microwave tomography systems.

La caractérisation d’objets enfouis à partir de mesures d’ondes diffractées est un problème présent dans de nombreuses applications comme l’exploration géophysique, le contrôle non-destructif, l’imagerie médicale, etc. Elle peut être obtenue numériquement par la résolution d’un problème inverse. Néanmoins, c’est un problème non linéaire et mal posé, ce qui rend la tâche difficile. Une reconstruction précise nécessite un choix judicieux de plusieurs paramètres très différents, dépendant des données de la méthode numérique d’optimisation choisie.La contribution principale de cette thèse est une étude de la reconstruction complète d’obstacles élastiques immergés à partir de mesures du champ lointain diffracté. Les paramètres à reconstruire sont la frontière, les coefficients de Lamé, la densité et la position de l’obstacle. On établit tout d’abord des résultats d’existence et d’unicité pour un problème aux limites généralisé englobant le problème direct d’élasto-acoustique. On analyse la sensibilité du champ diffracté par rapport aux différents paramètres du solide, ce qui nous conduit à caractériser les dérivées partielles de Fréchet comme des solutions du problème direct avec des seconds membres modifiés. Les dérivées sont calculées numériquement grâce à la méthode de Galerkine discontinue avec pénalité intérieure et le code est validé par des comparaisons avec des solutions analytiques. Ensuite, deux méthodologies sont introduites pour résoudre le problème inverse. Toutes deux reposent sur une méthode itérative de type Newton généralisée et la première consiste à retrouver les paramètres de nature différente indépendamment, alors que la seconde reconstruit tous les paramètre en même temps. À cause du comportement différent des paramètres, on réalise des tests de sensibilité pour évaluer l’influence de ces paramètres sur les mesures. On conclut que les paramètres matériels ont une influence plus faible sur les mesures que les paramètres de forme et, ainsi, qu’une stratégie efficace pour retrouver des paramètres de nature distincte doit prendre en compte ces différents niveaux de sensibilité. On a effectué de nombreuses expériences à différents niveaux de bruit, avec des données partielles ou complètes pour retrouver certains paramètres, par exemple les coefficients de Lamé et les paramètres de forme, la densité, les paramètres de forme et la localisation. Cet ensemble de tests contribue à la mise en place d’une stratégie pour la reconstruction complète des conditions plus proches de la réalité. Dans la dernière partie de la thèse, on étend ces résultats à des matériaux plus complexes, en particulier élastiques anisotropes. / The characterization of hidden objects from scattered wave measurements arises in many applications such as geophysical exploration, non destructive testing, medical imaging, etc. It can be achieved numerically by solving an Inverse Problem. However, this is a nonlinear and ill-posed problem, thus a difficult task. A successful reconstruction requires careful selection of very different parameters depending on the data and the chosen optimization numerical method.The main contribution of this thesis is an investigation of the full reconstruction of immersed elastic scatterers from far-field pattern measurements. The sought-after parameters are the boundary, the Lamé coefficients, the density and the location of the obstacle. First, existence and uniqueness results of a generalized Boundary Value Problem including the direct elasto-acoustic problem are established. The sensitivity of the scattered field with respect to the different parametersdescribing the solid is analyzed and we end up with the characterization of the corresponding partial Fréchet derivatives as solutions to the direct problem with modified right-hand sides. These Fréchet derivatives are computed numerically thanks to the Interior Penalty Discontinuous Galerkin method and the code is validated thanks to comparison with analytical solutions. Then, two solution methodologies are introduced for solving the inverse problem. Both are based on an iterative regularized Newton-type methodology and the first one consists in retrieving the parameters of different nature independently, while the second one reconstructs all parameters together. Due to the different behavior of the parameters, sensitivity tests are performed to assess the impact of the parameters on the measurements. We conclude that material parameters have a weaker influence on the measurements than shape parameters, and therefore, a successful strategy to retrieve parameters of distinct nature should take into account these different levels of sensitivity. Various experiments at different noise levels and with full or limited aperture data are carried out to retrieve some of the physical properties, e.g. Lamé coefficients with shape parameters, density with shape parameters a, density, shape and location. This set of tests contributes to a final strategy for the full reconstruction and in more realistic conditions. In the final part of the thesis, we extend the results to more complex material parameters, in particular anisotropic elastic.

The analysis of wide-angle cellular light scattering patterns is a challenging problem. Small changes to the organization, orientation, shape, and optical properties of scatterers and scattering populations can significantly alter their complex two-dimensional scattering signatures. Because of this, it is difficult to find methods that can identify medically relevant cellular properties while remaining robust to experimental noise and sample-to-sample differences. It is an important problem. Recent work has shown that changes to the internal structure of cells---specifically, the distribution and aggregation of organelles---can indicate the progression of a number of common disorders, ranging from cancer to neurodegenerative disease, and can also predict a patient's response to treatments like chemotherapy. However, there is no direct analytical solution to the inverse wide-angle cellular light scattering problem, and available simulation and interpretation methods either rely on restrictive cell models, or are too computationally demanding for routine use. This dissertation addresses these challenges from a computational vantage point. First, it explores the theoretical limits and optical basis for wide-angle scattering pattern analysis. The result is a rapid new simulation method to generate realistic organelle scattering patterns without the need for computationally challenging or restrictive routines. Pattern analysis, image segmentation, machine learning, and iterative pattern classification methods are then used to identify novel relationships between wide-angle scattering patterns and the distribution of organelles (in this case mitochondria) within a cell. Importantly, this work shows that by parameterizing a scattering image it is possible to extract vital information about cell structure while remaining robust to changes in organelle concentration, effective size, and random placement. The result is a powerful collection of methods to simulate and interpret experimental light scattering signatures. This gives new insight into the theoretical basis for wide-angle cellular light scattering, and facilitates advances in real-time patient care, cell structure prediction, and cell morphology research.