Tomographic reconstruction of axially symmetric objects: Regularization by a Markovian modelization

Tomographic reconstruction of an axially symmetric object using a Bayesian regularization which introduces local geometric and physical information through Markovian modeling is presented. The algorithm is tested on simulated and experimental data. A way of using the excess of observations in a projection to reconstruct some deviations from the axial symmetry of the object is presented. A method to determine some parameters of the model is proposed