Concerning the serious recession problems of the low-dose Computed Tomography (CT) reconstruction images, a low-dose CT reconstruction method of MLEM based on non-locality and variable exponent was presented. Considering the traditional anisotropic diffusion noise reduction is insufficient, variable exponent which could effectively compromise between heat conduction and anisotropic diffusion P-M models, and the similarity function which could detect the edge and details instead of gradient were applied to the traditional anisotropic diffusion, so as to achieve the desired effect. In each iteration, firstly, the basic MLEM algorithm was used to reconstruct the low-dose projection data. And then the diffusion function was improved by the non-local similarity measure, variable index and fuzzy mathematics theory, and the improved anisotropic diffusion was used to denoise the reconstructed image. Finally median filter was used to eliminate impulse noise points in the image. The experimental results show the proposed algorithm has a smaller numerical value than OS-PLS (Ordered Subsets-Penalized Least Squares), OS-PML-OSL (Ordered Subsets-Penalized Maximum Likelihood-One Step Late), and the algorithm based on the traditional PM, in the variance of Mean Absolute Error (MAE), and Normalized Mean Square Distance (NMSD), especially its Signal-to-Noise Ratio (SNR) is up to 10.52. This algorithm can effectively eliminate the bar of artifacts, and can keep image edges and details information better.
