A new novel PET/CT reconstruction algorithm by using prior image model with simulated annealing process

This paper shows how to combine the prior anatomical information with projected data in the PET image reconstruction. The new proposed algorithm uses powerful penalized-likelihood regularization method with the prior image model and simulated annealing process to suppress the Poisson noise in data. From the simulation, the new algorithm demonstrates significant improvement in the quality of reconstructed images as compared with the images obtained from EM-ML and minimum cross-entropy algorithms.