A comparison of regularization models for few-view CT image reconstruction

Abstract In this paper I analyse some regularization models for the reconstruction of X-rays Computed Tomography images from few-view projections. It is well known that widely used low-cost Filtered Back Projection method not suitable in case low-dose data, since it produces with noise and artifacts. Iterative methods based on model discretization are preferred case. However, problem has infinite possible solutions ill-posed, necessary to obtain a good solution. Different iterative have been proposed literature, but an organized comparison among them available. We compare approaches tomography by means simulated projections both phantom real image.