  Limited data and low dose constraints are common problems in a variety of
tomographic reconstruction paradigms which lead to noisy and incomplete data.
Over the past few years sinogram denoising has become an essential
pre-processing step for low dose Computed Tomographic (CT) reconstructions. We
propose a novel sinogram denoising algorithm inspired by the modern field of
signal processing on graphs. Graph based methods often perform better than
standard filtering operations since they can exploit the signal structure. This
makes the sinogram an ideal candidate for graph based denoising since it
generally has a piecewise smooth structure. We test our method with a variety
of phantoms and different reconstruction methods. Our numerical study shows
that the proposed algorithm improves the performance of analytical filtered
back-projection (FBP) and iterative methods ART (Kaczmarz) and SIRT
(Cimmino).We observed that graph denoised sinogram always minimizes the error
measure and improves the accuracy of the solution as compared to regular
reconstructions.
