The Effect of Regularization on the MAP-OSEM Algorithm for PET Reconstruction

In this paper, we study the algorithm of MAP-OSEM for PET reconstruction which is a well known iterative algorithm. It is desired to use a spatial regularization technique can improve the quality of reconstructed images and help to provide accurate diagnosis. The MAP-OSEM algorithm is a powerful image reconstruction algorithm that has been used in a variety of medical imaging applications, including PET reconstruction. In this work, we use the regularized MAP-OSEM algorithm that incorporates a regularization term into the objective function. The regularization term is used to promote smoothness in the reconstructed image, and it is typically chosen based on prior knowledge about the image. The MAP-OSEM algorithm is a gradient ascent optimization method which seeks to maximize the posterior distribution of an image by taking into account a Poisson-Gaussian noise model for the likelihood and a uniform prior to reduce bias. The objective function is maximized by the gradient ascent optimization method.