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Limited data and low dose constraints are common problems in a variety oftomographic reconstruction paradigms which lead to noisy and incomplete data.Over the past few years sinogram denoising has become an essentialpre-processing step for low dose Computed Tomographic (CT) reconstructions. Wepropose a novel sinogram denoising algorithm inspired by the modern field ofsignal processing on graphs. Graph based methods often perform better thanstandard filtering operations since they can exploit the signal structure. Thismakes the sinogram an ideal candidate for graph based denoising since itgenerally has a piecewise smooth structure. We test our method with a varietyof phantoms and different reconstruction methods. Our numerical study showsthat the proposed algorithm improves the performance of analytical filteredback-projection (FBP) and iterative methods ART (Kaczmarz) and SIRT(Cimmino).We observed that graph denoised sinogram always minimizes the errormeasure and improves the accuracy of the solution as compared to regularreconstructions.

Graph Based Sinogram Denoising for Tomographic Reconstructions