On the Choice and Evaluation of Regularization Priors for CT/ PET Image Reconstruction

Computed Tomography and Positron Emission Tomography (CT/PET) are primary imaging tools for the clinical diagnosis. The main objective of the imaging tool is to minimize the radiation dose without sacrificing the quality of CT/PET images. To achieve this goal, a suitable regularization prior is incorporated with the iterative reconstruction method. The proposed model is a fusion of Maximum Likelihood Expectation Maximization (MLEM) with partial differential equation based anisotropic diffusion (AD) filter. The proposed method is capable enough to minimize the radiation dose, removing the staircase effects as well as handle the ill-posed issue. To check the validation of the proposed method, both qualitative and quantitative results are presented using the simulated and real test phantoms. The results shown that the proposed methods outperform the state-of-the-art methods.