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In this paper, we propose an improved algorithm for PET image reconstruction using nonlocal means regularization based on structural similarity which is developed from the previously proposed algorithm LMROS-NLM and is named LMROS-NLM-SSIM. The structural similarity index (SSIM) is used to describe the similarity between the neighborhoods of two pixels of the image in nonlocal means (NLM) regularization rather than the Gaussian weighted Euclidean distance as the latter weights calculation method may lead to wrong value in noisy PET images. The performance of the proposed algorithm was compared with LMROS-NLM through reconstruct 50% of the data of human subject collected with an All-digital Brain PET system. Visual inspection reveals that the proposed algorithm can yield images with enhanced spatial resolution and suppressed image noise.

The PET system response homogeneity is closely related to the manufacturing accuracy and the SRM modeling consistency. The physical modeling errors such as the crystal septa efficiency deviation and the mechanical error always lead to response heterogeneity. We have proposed an improved CBN algorithm for 3D PET normalization, which is named the SRM based component eliminated normalization (SCEN) method. The overall idea of this work is to eliminate the image artifact caused by system modeling error and reduce the interference effects between different components. We investigated the SCEN method based on the DigitMI 910 all-digital PET/CT scanner with phantom data preliminarily. The results show that the background variability is reduced by 21.0%~24.8% using the SCEN method.