PERFORMANCE EVALUATION OF FILTERED BACKPROJECTION RECONSTRUCTION AND ITERATIVE RECONSTRUCTION METHODS FOR PET IMAGES

The filtered backprojection (FBP) algorithm and statistical model base d iterative algorithms such as the maximum likelihood (ML) reconstruct ion or the maximum a posteriori (MAP) reconstruction are the two major classes of tomographic reconstruction methods. The FBP method is wide ly used in clinical setting while iterative methods have attracted res earch interests in the past decade. In this paper we studied the perfo rmance of the FBP, the ML and the MAP methods using simulated projecti on data. The experiment showed that the MAP algorithm generated superi or image quality in terms of the bias, the variance, and the average m ean squared error (MSE) measures. (C) 1998 Elsevier Science Ltd. All r ights reserved.