  We propose an end-to-end differentiable architecture for tomography
reconstruction that directly maps a noisy sinogram into a denoised
reconstruction. Compared to existing approaches our end-to-end architecture
produces more accurate reconstructions while using less parameters and time. We
also propose a generative model that, given a noisy sinogram, can sample
realistic reconstructions. This generative model can be used as prior inside an
iterative process that, by taking into consideration the physical model, can
reduce artifacts and errors in the reconstructions.
