  We present TRex, a flexible and robust Tomographic Reconstruction framework
using proximal algorithms. We provide an overview and perform an experimental
comparison between the famous iterative reconstruction methods in terms of
reconstruction quality in sparse view situations. We then derive the proximal
operators for the four best methods. We show the flexibility of our framework
by deriving solvers for two noise models: Gaussian and Poisson; and by plugging
in three powerful regularizers. We compare our framework to state of the art
methods, and show superior quality on both synthetic and real datasets.
