Regularized Multidirections and Multiscales Anisotropic Diffusion for Sinogram Restoration of Low-Dosed Computed Tomography

Abstract

Although most of existing anisotropic diffusion (AD) methods are supported by prefect mathematical theories, they still lead to smoothed edges and anatomy details (EADs). They are caused by not considering the discrete nature of digital signal. In order to improve the performance of AD in sinogram restoration of low-dosed computed tomography (LDCT), we propose a new AD method, named regularized multidirections and multiscales anisotropic diffusion (RMDMS-AD), by extending AD to regularized AD (RAD) in multidirections and multiscales. Since the multidirections can reduce the discrete errors to the maximum extent, meanwhile multiscales and RAD make searching neighborhood of solution be as large as possible which can get more optimal solution to AD, the new proposed method can improve the performance of AD both in denoising and in stability of solution. Moreover, the discrete errors and ill-posed solutions occur mostly near the EADs; the RMDMS-AD will also preserve EADs well. Comparing the proposed new method to existing AD methods using real sinogram, the new method shows good performance in EADs preserving while denoising and suppressing artifacts.