This thesis studies the relationship between Computed Tomography (CT) and the notion of copula. In X-ray tomography the objective is to (re)construct an image representing the distribution of a physical quantity (density of matter) inside of an object from the radiographs obtained all around the object called projections. The link between these images and the object is described by the X-ray transform or the Radon transform. In 2D, when only two projections at two angles 0 and pi/2 (horizontal and vertical) are available, the problem can be identified as another problem in mathematics which is the determination of a joint density from its marginals, hence the notion of copula. Both problems are ill-posed in the sense of Hadamard. It requires prior information or additional criteria or constraints. The main contribution of this thesis is the use of entropy as a constraint that provides a regularized solution to this ill-posed inverse problem. Our work covers different areas. The mathematics aspects of X-ray tomography where the fundamental model to obtain projections is based mainly on the Radon transform. In general this transform does not provide all necessary projections which need to be associated with certain regularization techniques. We have two projections, which makes the problem extremely difficult, and ill-posed but noting that if a link can be done, that is, if the two projections can be equated with marginal densities and the image to reconstruct to a probability density, the problem translates into the statistical framework via Sklar's theorem. And the tool of probability theory called "copula" that characterizes all possible reconstructed images is suitable. Hence the choice of the image that will be the best and most reliable arises. Then we must find techniques or a criterion of a priori information, one of the criteria most often used, we have chosen is a criterion of entropy. Entropy is an important scientific quantity because it is used in various areas, originally in thermodynamics, but also in information theory. Different types of entropy exist (Rényi, Tsallis, Burg, Shannon), we have chosen some as criteria. Using the Rényi entropy we have discovered new copulas. This thesis provides new contributions to CT imaging, the interaction between areas that are tomography and probability theory and statistics.
