Performance evaluation of filtered backprojection reconstruction and iterative reconstruction methods for PET images

The filtered backprojection (FBP) algorithm and statistical model based iterative algorithms such as the maximum likelihood (ML) reconstruction or the maximum a posteriori (MAP) reconstruction are the two major classes of tomographic reconstruction methods. The FBP method is widely used in clinical setting while iterative methods have attracted research interests in the past decade. In this paper we studied the performance of the FBP, the ML and the MAP methods using simulated projection data. The experiment showed that the MAP algorithm generated superior image quality in terms of the bias, the variance, and the average mean squared error (MSE) measures.