Radiation therapy is afflicted by a multitude of geometric uncertainties, which must be compensated to ensure treatment success. Such mitigation is currently achieved by enlarging the apparent target volume by various safety margins. This thesis investigated uncertainty sources relating to target position and extent in oesophageal carcinoma, both static and dynamic, and evaluated their impact in a combined model. The first were errors inherent to delineation of the gross tumour volume (GTV), where computed tomography (CT) imaging, the overall modality of choice for target volume delineation (TVD), has a tendency to overestimate target extent. Two rival modalities, [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET) and endoscopic ultrasound (EUS), are generally expected to yield more accurate assessments. EUS has previously suffered from a difficulty in transferring its findings to the spatial domain in which TVD is undertaken. This limitation was overcome here through the use of endoscopically implanted fiducial markers visible on the planning CT. This has enabled their inclusion in TVD and allowed a direct comparison of FDG-PET and EUS based target extents, which were found to agree quite well on average, but showed occasional discrepancies on the order of a few cm. Recently published reports on inter-observer variability (IOV) in TVD of oesophageal carcinoma were summarised with a particular focus on its reduction afforded by the use of fiducial markers. The influence of IOV was investigated more widely in other tumour entities, where it was shown to increase during the course of treatment, mostly due to differing adaptation practices. Microscopic disease extension (MDE), undetectable prior to treatment with current imaging techniques, constituted the second uncertainty source. Reports on histopathological measurements of MDE incidence and its distance from the main tumour were reviewed and spatial measurements extracted to derive a combined estimate of the distribution of extension distances. The overall incidence was estimated as 14.6%, with individual studies reporting widely differing values. Conventional margin widths to compensate for MDE were extracted from the pooled distribution and found to largely agree with the common clinical choice of 3–5 cm, but associated with broad confidence intervals. The addition of such margins to the GTV defines the clinical target volume (CTV). Most studies acknowledged tissue deformations as a major problem, but not all implemented means to prevent or correct it. Preliminary measurements on oesophageal resection specimens were presented, wherein fiducial markers were used to measure tissue deformations, and might ultimately be used to correct spatial measurements of MDE. Fiducial markers also facilitated the study of inter-fractional target mobility in a cohort (n=23) receiving daily orthogonal X-ray imaging for target positioning verification. Markers were found capable of detecting target misalignments, which were a common occurrence with 54% and 15% of analysed markers and treatment fractions showing displacements from their planned position in excess of 5 and 10mm, respectively. Mobility amplitudes were highest in the longitudinal direction and a dependence on tumour location was hinted at, with motion more restricted for proximally located lesions. Measures of systematic and random mobility components were extracted to derive safety margins, which are added to the CTV to form the planning target volume (PTV). A radiobiological model of tumour control probability (TCP) was then evaluated under different uncertainty scenarios. It simplified the tumour system to its longitudinal dimension, which is most affected by the aforementioned phenomena, and simulated positional uncertainties, as well as MDE. The differential impact of systematic and random mobility components on TCP was demonstrated and margin widths sufficient to limit TCP reduction to 1% could best be described by a quadratic combination of their magnitudes. This composition was still applicable when MDE was introduced and mitigated by a conventional margin, but the relative impact of both components shifted. The addition of a PTV margin to the CTV afforded the MDE-positive subpopulation similar protection against positional uncertainties as the same margin achieved without consideration of MDE. The MDE-negative subpopulation attained a much improved tolerance to positional uncertainties through the CTV margin, which also propagated to the overall population. An alternative mitigation of MDE was attempted by optimising the applied dose distribution to an assumed tumour cell density distribution motivated by the literature, which decreases towards the target edge. The optimisation maximised TCP while preserving the integral dose delivered with a conventional margin, under the assumption that this translates into a similar likelihood of normal tissue toxicity. Reduced doses could be delivered to lower cell density regions without sacrificing overall TCP, but this reduction was modest despite vastly diminished cell densities. When this spared dose was redistributed so as to enlarge the treated area, negligible TCP change was observed, but redistribution to the central target did result in appreciably improved TCP in both subpopulations. These effects persisted when positional uncertainties were added and when MDE incidence was increased to the most extreme value reported in the literature.

Des optiques à longue focale ont été souvent utilisées dans le cinéma 2D et la télévision, soit dans le but de se rapprocher de la scène, soit dans le but de produire un effet esthétique grâce à la déformation de la perspective. Toutefois, dans le cinéma ou la télévision 3D, l'utilisation de longues focales crée le plus souvent un "effet carton” ou de la divergence oculaire.Pour résoudre ce problème, les méthodes de l'état de l'art utilisent des techniques de transformation de la disparité, qui sont une généralisation de l'interpolation de points de vue.Elles génèrent de nouvelles paires stéréoscopiques à partir des deux séquences d'images originales. Nous proposons d'utiliser plus de deux caméras pour résoudre les problèmes non résolus par les méthodes de transformation de la disparité.Dans la première partie de la thèse, nous passons en revue les causes de la fatigue visuelle et de l'inconfort visuel lors de la visualisation d'un film stéréoscopique. Nous modélisons alors la perception de la profondeur de la vision stéréoscopique d'une scène filmée en 3D avec deux caméras, et projetée dans une salle de cinéma ou sur un téléviseur 3D. Nous caractérisons mathématiquement cette distorsion 3D, et formulons les contraintes mathématiques associées aux causes de la fatigue visuelle et de l'inconfort. Nous illustrons ces distorsions 3D avec un nouveau logiciel interactif, la “salle de projection virtuelle".Afin de générer les images stéréoscopiques souhaitées, nous proposons d'utiliser le rendu basé image. Ces techniques comportent généralement deux étapes. Tout d'abord, les images d'entrée sont transformées vers la vue cible, puis les images transformées sont mélangées. Les transformations sont généralement calculés à l'aide d'une géométrie intermédiaire (implicite ou explicite). Le mélange d'images a été largement étudié dans la littérature et quelques heuristiques permettent d'obtenir de très bonnes performances.Cependant, la combinaison des heuristiques proposées n'est pas simple et nécessite du réglage manuel de nombreux paramètres.Dans cette thèse, nous proposons une nouvelle approche bayésienne au problème de synthèse de nouveaux points de vue, basé sur un modèle génératif.Le modèle génératif proposé tient compte de l'incertitude sur la transformation d'image. Le formalisme bayésien nous permet de déduire l'énergie du modèle génératif et de calculer les images désirées correspondant au maximum a posteriori. La méthode dépasse en termes de qualité les techniques de l'état de l'art du rendu basé image sur des jeux de données complexes. D'autre part, les équations de l'énergie fournissent une formalisation des heuristiques largement utilisés dans les techniques de rendu basé image.Le modèle génératif proposé aborde également le problème de la super-résolution, permettant de rendre des images à une résolution plus élevée que les images de départ.Dans la dernière partie de cette thèse, nous appliquons la nouvelle technique de rendu au cas du zoom stéréoscopique et nous montrons ses performances. / Optics with long focal length have been extensively used for shooting 2D cinema and television, either to virtually get closer to the scene or to produce an aesthetical effect through the deformation of the perspective. However, in 3D cinema or television, the use of long focal length either creates a “cardboard effect” or causes visual divergence. To overcome this problem, state-of-the-art methods use disparity mapping techniques, which is a generalization of view interpolation, and generate new stereoscopic pairs from the two image sequences. We propose to use more than two cameras to solve for the remaining issues in disparity mapping methods.In the first part of the thesis, we review the causes of visual fatigue and visual discomfort when viewing a stereoscopic film. We then model the depth perception from stereopsis of a 3D scene shot with two cameras, and projected in a movie theater or on a 3DTV. We mathematically characterize this 3D distortion, and derive the mathematical constraints associated with the causes of visual fatigue and discomfort. We illustrate these 3D distortions with a new interactive software, “The Virtual Projection Room”.In order to generate the desired stereoscopic images, we propose to use image-based rendering. Those techniques usually proceed in two stages. First, the input images are warped into the target view, and then the warped images are blended together. The warps are usually computed with the help of a geometric proxy (either implicit or explicit). Image blending has been extensively addressed in the literature and a few heuristics have proven to achieve very good performance. Yet the combination of the heuristics is not straightforward, and requires manual adjustment of many parameters.In this thesis, we propose a new Bayesian approach to the problem of novel view synthesis, based on a generative model taking into account the uncertainty of the image warps in the image formation model. The Bayesian formalism allows us to deduce the energy of the generative model and to compute the desired images as the Maximum a Posteriori estimate. The method outperforms state-of-the-art image-based rendering techniques on challenging datasets. Moreover, the energy equations provide a formalization of the heuristics widely used in image-based rendering techniques. Besides, the proposed generative model also addresses the problem of super-resolution, allowing to render images at a higher resolution than the initial ones.In the last part of this thesis, we apply the new rendering technique to the case of the stereoscopic zoom and show its performance.

<p>Uncertainty analysis is of great use both for calculating outputs that are more akin to real<br> flight, and for optimization to more robust shapes. However, implementation of uncertainty<br> has been a longstanding challenge in the field of aerodynamics due to the computational cost<br> of simulations. Geometric uncertainty in particular is often left unexplored in favor of uncer-<br> tainties in freestream parameters, turbulence models, or computational error. Therefore, this<br> work proposes a method of geometric uncertainty analysis for aerodynamic shapes that miti-<br> gates the barriers to its feasible computation. The process takes a two- or three-dimensional<br> shape and utilizes a combination of multifidelity meshes and Gaussian process regression<br> (GPR) surrogates in a multifidelity Monte Carlo (MFMC) algorithm. Multifidelity meshes<br> allow for finer sampling with a given budget, making the surrogates more accurate. GPR<br> surrogates are made practical to use by parameterizing major factors in geometric uncer-<br> tainty with only four variables in 2-D and five in 3-D. In both cases, two parameters control<br> the heights of steps that occur on the top and bottom of airfoils where leading and trailing<br> edge devices are attached. Two more parameters control the height and length of waves<br> that can occur in an ideally smooth shape during manufacturing. A fifth parameter controls<br> the depth of span-wise skin buckling waves along a 3-D wing. Parameters are defined to<br> be uniformly distributed with a maximum size of 0.4 mm and 0.15 mm for steps and waves<br> to remain within common manufacturing tolerances. The analysis chain is demonstrated<br> with two test cases. The first, the RAE2822 airfoil, uses transonic freestream parameters<br> set by the ADODG Benchmark Case 2. The results show a mean drag of nearly 10 counts<br> above the deterministic case with fixed lift, and a 2 count increase for a fixed angle of attack<br> version of the case. Each case also has small variations in lift and angle of attack of about<br> 0.5 counts and 0.08◦, respectively. Variances for each of the three tracked outputs show that<br> more variability is possible, and even likely. The ONERA M6 transonic wing, popular due<br> to the extensive experimental data available for computational validation, is the second test<br> case. Variation is found to be less substantial here, with a mean drag increase of 0.5 counts,<br> and a mean lift increase of 0.1 counts. Furthermore, the MFMC algorithm enables accurate<br> results with only a few hours of wall time in addition to GPR training. </p>