POSITRON EMISSION TOMOGRAPHY RECOSTRUCTION BASEDON A PIECE-WISE LINEARITY MEMBRANE PRIORI
基于分段线性模板先验的正电子成像重建

Positron Emission Tomography(PET) is much noisy than many other imaging methods. In order to improve the resolution and noise properties of PET reconstruction results, generally Bayesian method is used. The premise of the Bayesian method is to select a proper prior distribution. The a priori should coincide with the principle of the positron emission and remain the breaks of the density data. The piecewise linearity membrane prior obtained from the reconstruction of other imaging models can be used. Due to the non-convex of the prior function and hyper-parameters, we use the dynamic posterior simulation rather than the general optimization methods to get reconstruction image.