Image Reconstruction by Minimizing Second-Order Moment

Abstract

Statistical methods have received much attention in image reconstruction, especially when the projection data are not sufficient in number, since they usually give more reliable results in such a case than other methods. In this paper, we propose a new statistical method which minimizes the second-order moment of an object that indicates the variance and randomness in a statistical structure. By minimizing the second-order moment, the resulting image reflects the statistical structure imposed by the available information and is biased toward a flat gray structure in the absence of information. The computer simulation results show a good convergence behavior of the proposed method. This method was successfully applied to ultrasound attenuation CT using a sponge phantom.