  In this paper, we presented an efficient algorithm to implement the
regularization reconstruction of SPECT. Image reconstruction with priori
assumptions is usually modeled as a constrained optimization problem. However,
there is no efficient algorithm to solve it due to the large scale of the
problem. In this paper, we used the superiorization of the expectation
maximization (EM) iteration to implement the regularization reconstruction of
SPECT. We first investigated the convergent conditions of the EM iteration in
the presence of perturbations. Secondly, we designed the superiorized EM
algorithm based on the convergent conditions, and then proposed a modified
version of it. Furthermore, we gave two methods to generate desired
perturbations for two special objective functions. Numerical experiments for
SPECT reconstruction were conducted to validate the performance of the proposed
algorithms. The experiments show that the superiorized EM algorithms are more
stable and robust for noised projection data and initial image than the classic
EM algorithm, and outperform the classic EM algorithm in terms of mean square
error and visual quality of the reconstructed images.
