Bayesian image reconstruction for positron emission tomography based on anisotropic median-diffusion

Abstract

For improving the quality of positron emission tomography (PET) images, the partial differential equation median (PDEmedian) algorithm which incorporates an anisotropic diffusion (AD) filter into the median root prior (MRP) algorithm was proposed. However, due to the shortcomings of the AD filter, the PDEmedian algorithm is difficult to realize. This work aims to solve this problem by introducing a new diffusion model into the PDEmedian. The proposed algorithm shows its positive effects on image reconstruction and denoising. Experimental results present that the new algorithm can preserve sharp edges while reducing noise at the same time. Furthermore, in comparison to other similar reconstruction algorithms, the proposed method is less sensitive to the value of the gradient threshold and the adjustment of the diffusion number.