Generalized gibbs priors in positron emission tomography

Maximum A Posteriori (MAP) methods have been widely applied to the ill-posed problem of image reconstruction, such as Positron Emission Tomography (PET) imaging. In this paper, a family of new generalized Gibbs priors based on MAP method, which exploits the basic affinity structure information in an image, is proposed. The generalized Gibbs priors can suppress noise effectively while capturing sharp edges without oscillations. A binary optimal reconstruction strategy is established using a locally linearized scheme in the framework of a standard Paraboloidal Surrogate Coordinate Ascent (PSCA) algorithm. The proposed generalized Gibbs priors based MAP reconstruction algorithm has been tested on simulated and real phantom PET data. Comparisons of the new priors model with other classical methods clearly demonstrate that the proposed generalized Gibbs priors perform better in lowering the noise, and preserving the edge and detail in the image.