Background Novel theranostic options for high-risk non-muscle invasive bladder cancer are urgently needed. This requires a thorough evaluation of experimental approaches in animal models best possibly reflecting human disease before entering clinical studies. Although several bladder cancer xenograft models were used in the literature, the establishment of an orthotopic bladder cancer model in mice remains challenging. Methods Luciferase-transduced UM-UC-3LUCK1 bladder cancer cells were instilled transurethrally via 24G permanent venous catheters into athymic NMRI and BALB/c nude mice as well as into SCID-beige mice. Besides the mouse strain, the pretreatment of the bladder wall (trypsin or poly-L-lysine), tumor cell count (0.5 × 106–5.0 × 106) and tumor cell dwell time in the murine bladder (30 min – 2 h) were varied. Tumors were morphologically and functionally visualized using bioluminescence imaging (BLI), magnetic resonance imaging (MRI), and positron emission tomography (PET). Results Immunodeficiency of the mouse strains was the most important factor influencing cancer cell engraftment, whereas modifying cell count and instillation time allowed fine-tuning of the BLI signal start and duration – both representing the possible treatment period for the evaluation of new therapeutics. Best orthotopic tumor growth was achieved by transurethral instillation of 1.0 × 106 UM-UC-3LUCK1 bladder cancer cells into SCID-beige mice for 2 h after bladder pretreatment with poly-L-lysine. A pilot PET experiment using 68Ga-cetuximab as transurethrally administered radiotracer revealed functional expression of epidermal growth factor receptor as representative molecular characteristic of engrafted cancer cells in the bladder. Conclusions With the optimized protocol in SCID-beige mice an applicable and reliable model of high-risk non-muscle invasive bladder cancer for the development of novel theranostic approaches was established.

Diffuse optical tomography uses near infrared (NIR) light as the probing media to re-cover the distributions of tissue optical properties with an ability to provide functional information of the tissue under investigation. As NIR light propagation in the tissue is dominated by scattering, the image reconstruction problem (inverse problem) is non-linear and ill-posed, requiring usage of advanced computational methods to compensate this. Diffuse optical image reconstruction problem is always rank-deficient, where finding the independent measurements among the available measurements becomes challenging problem. Knowing these independent measurements will help in designing better data acquisition set-ups and lowering the costs associated with it. An optimal measurement selection strategy based on incoherence among rows (corresponding to measurements) of the sensitivity (or weight) matrix for the near infrared diffuse optical tomography is proposed. As incoherence among the measurements can be seen as providing maximum independent information into the estimation of optical properties, this provides high level of optimization required for knowing the independency of a particular measurement on its counterparts. The utility of the proposed scheme is demonstrated using simulated and experimental gelatin phantom data set comparing it with the state-of-the-art methods. The traditional image reconstruction methods employ ℓ2-norm in the regularization functional, resulting in smooth solutions, where the sharp image features are absent. The sparse recovery methods utilize the ℓp-norm with p being between 0 and 1 (0 ≤ p1), along with an approximation to utilize the ℓ0-norm, have been deployed for the reconstruction of diffuse optical images. These methods are shown to have better utility in terms of being more quantitative in reconstructing realistic diffuse optical images compared to traditional methods. Utilization of ℓp-norm based regularization makes the objective (cost) function non-convex and the algorithms that implement ℓp-norm minimization utilizes approximations to the original ℓp-norm function. Three methods for implementing the ℓp-norm were con-sidered, namely Iteratively Reweigthed ℓ1-minimization (IRL1), Iteratively Reweigthed Least-Squares (IRLS), and Iteratively Thresholding Method (ITM). These results in-dicated that IRL1 implementation of ℓp-minimization provides optimal performance in terms of shape recovery and quantitative accuracy of the reconstructed diffuse optical tomographic images. Photoacoustic tomography (PAT) is an emerging hybrid imaging modality combining optics with ultrasound imaging. PAT provides structural and functional imaging in diverse application areas, such as breast cancer and brain imaging. A model-based iterative reconstruction schemes are the most-popular for recovering the initial pressure in limited data case, wherein a large linear system of equations needs to be solved. Often, these iterative methods requires regularization parameter estimation, which tends to be a computationally expensive procedure, making the image reconstruction process to be performed off-line. To overcome this limitation, a computationally efficient approach that computes the optimal regularization parameter is developed for PAT. This approach is based on the least squares-QR (LSQR) decomposition, a well-known dimensionality reduction technique for a large system of equations. It is shown that the proposed framework is effective in terms of quantitative and qualitative reconstructions of initial pressure distribution.

Background Novel theranostic options for high-risk non-muscle invasive bladder cancer are urgently needed. This requires a thorough evaluation of experimental approaches in animal models best possibly reflecting human disease before entering clinical studies. Although several bladder cancer xenograft models were used in the literature, the establishment of an orthotopic bladder cancer model in mice remains challenging. Methods Luciferase-transduced UM-UC-3LUCK1 bladder cancer cells were instilled transurethrally via 24G permanent venous catheters into athymic NMRI and BALB/c nude mice as well as into SCID-beige mice. Besides the mouse strain, the pretreatment of the bladder wall (trypsin or poly-L-lysine), tumor cell count (0.5 × 106–5.0 × 106) and tumor cell dwell time in the murine bladder (30 min – 2 h) were varied. Tumors were morphologically and functionally visualized using bioluminescence imaging (BLI), magnetic resonance imaging (MRI), and positron emission tomography (PET). Results Immunodeficiency of the mouse strains was the most important factor influencing cancer cell engraftment, whereas modifying cell count and instillation time allowed fine-tuning of the BLI signal start and duration – both representing the possible treatment period for the evaluation of new therapeutics. Best orthotopic tumor growth was achieved by transurethral instillation of 1.0 × 106 UM-UC-3LUCK1 bladder cancer cells into SCID-beige mice for 2 h after bladder pretreatment with poly-L-lysine. A pilot PET experiment using 68Ga-cetuximab as transurethrally administered radiotracer revealed functional expression of epidermal growth factor receptor as representative molecular characteristic of engrafted cancer cells in the bladder. Conclusions With the optimized protocol in SCID-beige mice an applicable and reliable model of high-risk non-muscle invasive bladder cancer for the development of novel theranostic approaches was established.

Dans ce travail, les s´séquences Imagerie par Résonance Magnétique (IRM) radiales à temps d’écho ultra-court (UTE) sont analysées pour évaluer leur potentiel dans l’étude non-invasive de différents modèles expérimentaux de maladies pulmonaires chez la souris. Chez le petit animal, les séquences radiales UTE peuvent efficacement limiter l’impact négatif sur la qualité de l’image dû au déphasage rapide des spins causé par les nombreuses interfaces air/tissu. En plus, les séquences radiales UTE sont moins sensibles aux artefacts de mouvement par rapport aux séquences Cartésiennes classiques. En conséquence, chez le petit animal, les séquences radiales UTE peuvent permettre d’obtenir des images du poumon avec une résolution bien inférieure au millimètre avec des rapports signal/bruit importants dans le parenchyme pulmonaire, tout en travaillant en conditions physiologiques (animaux en respiration spontanée). Dans cette thèse, il sera démontré que les séquences d’IRM protonique UTE sont outils efficaces dans l’étude quantitative et non-invasive de différents marqueurs distinctifs de certaines pathologies pulmonaires d’intérêt général. Les protocoles développés serontsimples, rapides et non-invasifs, faciles à implémenter, avec une interférence minimale sur la pathologie pulmonaire étudiée et, en définitive, potentiellement applicables chez l’homme. Il sera ainsi démontré que l’emploi des agents de contraste, administrés via les voies aériennes, permet d’augmenter la sensibilité des protocoles développés. Parallèlement, dans cette thèse des protocoles suffisamment flexibles seront implémentés afin de permettre l’étude d’un agent de contraste paramagnétique générique pour des applications aux poumons. / In this work, ultra-short echo time (UTE) Magnetic Resonance Imaging (MRI) sequences are investigated as flexible tools for the noninvasive study of experimental models of lung diseases in mice. In small animals radial UTE sequences can indeed efficiently limit the negative impact on lung image quality due to the fast spin dephasing caused by the multiple air/tissue interfaces. In addition, radial UTE sequences are less sensitive to motion artifacts compared to standard Cartesian acquisitions. As a result, radial UTE acquisitions can provide lung images in small animals at sub-millimetric resolution with significant signal to noise ratio in the lung parenchyma, while working with physiological conditions (freely-breathing animals). In this thesis, UTE proton MRI sequences were shown to be efficient instruments to quantitatively investigate a number of hallmarks in longitudinal models of relevant lung diseases with minimal interference with the lung pathophysiology, employing easilyimplementable fast protocols. The synergic use of positive contrast agents, along with anadvantageous administration modality, was shown to be a valuable help in the increase of sensitivity of UTE MRI. At the same time, UTE MRI was shown to be an extremely useful and efficacious sequence for studying positive contrast agents in lungs

Le but de cette thèse est de proposer une modélisation mathématique des stimulations visuelles afin d'analyser finement des données expérimentales en psychophysique et en électrophysiologie. Plus précis\'ement, afin de pouvoir exploiter des techniques d'analyse de données issues des statistiques Bayésiennes et de l'apprentissage automatique, il est nécessaire de développer un ensemble de stimulations qui doivent être dynamiques, stochastiques et d'une complexité paramétrée. Il s'agit d'un problème important afin de comprendre la capacité du système visuel à intégrer et discriminer différents stimuli. En particulier, les mesures effectuées à de multiples échelles (neurone, population de neurones, cognition) nous permette d'étudier les sensibilités particulières des neurones, leur organisation fonctionnelle et leur impact sur la prise de décision. Dans ce but, nous proposons un ensemble de contributions théoriques, numériques et expérimentales, organisées autour de trois axes principaux : (1) un modèle de synthèse de textures dynamiques Gaussiennes spécialement paramétrée pour l'étude de la vision; (2) un modèle d'observateur Bayésien rendant compte du biais positif induit par fréquence spatiale sur la perception de la vitesse; (3) l'utilisation de méthodes d'apprentissage automatique pour l'analyse de données obtenues en imagerie optique par colorant potentiométrique et au cours d'enregistrements extra-cellulaires. Ce travail, au carrefour des neurosciences, de la psychophysique et des mathématiques, est le fruit de plusieurs collaborations interdisciplinaires. / The goal of this thesis is to propose a mathematical model of visual stimulations in order to finely analyze experimental data in psychophysics and electrophysiology. More precisely, it is necessary to develop a set of dynamic, stochastic and parametric stimulations in order to exploit data analysis techniques from Bayesian statistics and machine learning. This problem is important to understand the visual system capacity to integrate and discriminate between stimuli. In particular, the measures performed at different scales (neurons, neural population, cognition) allow to study the particular sensitivities of neurons, their functional organization and their impact on decision making. To this purpose, we propose a set of theoretical, numerical and experimental contributions organized around three principal axes: (1) a Gaussian dynamic texture synthesis model specially crafted to probe vision; (2) a Bayesian observer model that accounts for the positive effect of spatial frequency over speed perception; (3) the use of machine learning techniques to analyze voltage sensitive dye optical imaging and extracellular data. This work, at the crossroads of neurosciences, psychophysics and mathematics is the fruit of several interdisciplinary collaborations.

There is a difference between the raw sensory input to the brain and our stable perception of entities in the environment. A first approach to investigate perception is to study relationships between properties of currently presented stimuli and biological correlates of perceptual processes. However, it is known that such processes are not only dependent on the current stimulus. Sampling of information and the concurrent neuronal processing of stimulus content rely on contextual relationships in the environment, and between the environment and the body. Perceptual processes dynamically adjust to relevant context, such as the current task of the organism and its immediate history. To understand perception, we have to study how processing of current stimulus content is influenced by such contextual factors. This thesis investigates the influence of such factors on visual processing. In particular, it investigates effects of temporal context in early visual processing and the effect of task context in eye movement control. To investigate effects of contextual factors on early visual processing of current stimulus content, we study neuronal processing of visual information in the primary visual cortex. We use real-time optical imaging with voltage sensitive dyes to capture neuronal population activity in the millisecond range across several millimeters of cortical area. To characterize the cortical layout concerning the mapping of orientation, previous to further investigations, we use smoothly moving grating stimuli. Investigating responses to this stimulus type systematically, we find independent encoding of local contrast and orientation, and a direct mapping of current stimulus content onto cortical activity (Study 1). To investigate the influence of the previous stimulus as context on processing of current stimulus content, we use abrupt visual changes in sequences of modified natural images. In earlier studies, investigating relatively fast timescales, it was found that the primary visual cortex continuously represents current input (ongoing encoding), with little interference from past stimuli. We investigate whether this coding scheme generalizes to cases in which stimuli change more slowly, as frequently encountered in natural visual input. We use sequences of natural scene contours, comprised of vertically and horizontally filtered natural images, their superpositions, and a blank stimulus, presented with 10 or 33 Hz. We show that at the low temporal frequency, cortical activity patterns do not encode the present orientations but instead reflect their relative changes in time. For example, when a stimulus with horizontal orientation is followed by the superposition of both orientations, the pattern of cortical activity represents the newly added vertical orientations instead of the full sum of orientations. Correspondingly, contour removal from the superposition leads to the representation of orientations that have disappeared rather than those that remain. This is in sharp contrast to more rapid sequences for which we find an ongoing representation of present input, consistent with earlier studies. In summary, we find that for slow stimulus sequences, populations of neurons in the primary visual cortex are no longer tuned to orientations within individual stimuli but instead represent the difference between consecutive stimuli. Our results emphasize the influence of the temporal context on early visual processing and consequentially on information transmission to higher cortical areas (Study 2). To study effects of contextual factors on the sampling of visual information, we focus on human eye movement control. The eyes are actively moved to sample visual information from the environment. Some traditional approaches predict eye movements solely on simple stimulus properties, such as local contrasts (stimulus-driven factors). Recent arguments, however, emphasize the influence of tasks (task context) and bodily factors (spatial bias). To investigate how contextual factors affect eye movement control, we quantify the relative influences of the task context, spatial biases and stimulus-driven factors. Participants view and classify natural scenery and faces while their eye movements are recorded. The stimuli are composed of small image patches. For each of these patches we derive a measure that quantifies stimulus-driven factors, based on the image content of a patch, and spatial viewing biases, based on the location of the patch. Utilizing the participants’ classification responses, we additionally derive a measure, which reflects the information content of a patch in the context of a given task. We show that the effect of spatial biases is highest, that task context is a close runner-up, and that stimulus-driven factors have, on average, a smaller influence. Remarkably, all three factors make independent and significant contributions to the selection of viewed locations. Hence, in addition to stimulus-driven factors and spatial biases, the task context contributes to visual sampling behavior and has to be considered in a model of human eye movements. Visual processing of current stimulus content, in particular visual sampling behavior and early processing, is inherently dependent on context. We show that already in the first cortical stage, temporal context strongly affects the processing of new visual information and that visual sampling by eye movements is significantly influenced by the task context, independently of spatial factors and stimulus-driven factors. The empirical results presented provide foundations for an improved theoretical understanding of the role of context in perceptual processes.