ML-EM reconstruction model including total variation for low dose PET high resolution data

Data acquired through the PET system tend to be very noisy, partly due to low radiation doses. In this paper, a new reconstruction strategy based on a combination of the MLEM and total variation (TV) is presented. A comparision between the MLEM and MLEM-TV algorithms for three numbers of counts were done: (1) at 15 M counts; (2) at 35 M counts; and (3) at 55 M counts. The proposed method MLEM-TV can yield better result for image reconstruction, having a higher ability than the MLEM method to recover the spatial distribution of the counts, at low number of counts. Furthermore, an adaptive regularization parameters are embedded within the method. Experimental results, for the performance evaluation using PET simulated data, demostrate the efficiency of the MLEM-TV reconstruction method proposed, significantly improving the image quality and accuracy from the first iteration of the method, in comparison with that obtained using MLEM. For each reconstruction model under investigation, studies on the effects of image quality were addressed, using the SSIM index. Simulations were addressed using the small mouse MOBY phantom with SimSet.