关键词: 低剂量CT, 直觉模糊熵, 各向异性扩散, Huber先验, 最大后验（MAP）

Abstract: Low-Dose Computed Tomography（LDCT） reduces the radiation of X rays to human body. However, the artifacts and noises caused by the radiation dose decrease in the reconstructed images are very disturbing for clinical medical diagnosis. A new smoothing algorithm, called Maximum A Posteriori（MAP） projection domain denoising algorithm based on improved anisotropic weighted prior model, is proposed to solve this problem. Considering that the intuitionistic fuzzy entropy can distinguish the smooth area and the edge area, and combined with the traditional anisotropic diffusion coefficient, the algorithm constructs a new adaptive diffusion coefficient, and utilizes the local variance to realize its adjustment. Finally, the improved diffusion coefficient is fused to the Huber-based MAP, which realizes different intensity denoising for different regions in projection data. The simulations of the low-dose CT image reconstruction for pelvis model, Shepp-Logan model and thorax phantom model are used to test the effectiveness of the proposed algorithm, and compared with other three algorithms：Filter Back Projection（FBP）, Penalized Reweighted Least-Squares（PRWLS） and anisotropic weighted prior sinogram smoothing algorithm. The experimental results show that the artifacts in the reconstructed images are significantly reduced, while the edges and details of the images are well preserved. The signal-to-noise ratio of the three models is 20.5020 dB, 23.2948 dB and 21.0184 dB respectively. The required time is 49.50 s, 49.60 s and 8.59 s respectively.

Key words: low-dose CT, intuitionistic fuzzy entropy, anisotropic diffusion, Huber prior, Maximum A Posteriori（MAP）