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In this paper, we presented an efficient algorithm to implement theregularization reconstruction of SPECT. Image reconstruction with prioriassumptions is usually modeled as a constrained optimization problem. However,there is no efficient algorithm to solve it due to the large scale of theproblem. In this paper, we used the superiorization of the expectationmaximization (EM) iteration to implement the regularization reconstruction ofSPECT. We first investigated the convergent conditions of the EM iteration inthe presence of perturbations. Secondly, we designed the superiorized EMalgorithm based on the convergent conditions, and then proposed a modifiedversion of it. Furthermore, we gave two methods to generate desiredperturbations for two special objective functions. Numerical experiments forSPECT reconstruction were conducted to validate the performance of the proposedalgorithms. The experiments show that the superiorized EM algorithms are morestable and robust for noised projection data and initial image than the classicEM algorithm, and outperform the classic EM algorithm in terms of mean squareerror and visual quality of the reconstructed images.

Superiorization of EM Algorithm and Its Application in Single-Photon Emission Computed Tomography(SPECT)