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

TL;DR

This paper introduces a superiorized EM algorithm for SPECT image reconstruction that improves stability, robustness, and image quality over the classic EM method, especially with noisy data.

Contribution

It develops a novel superiorization approach for the EM algorithm tailored for SPECT, with proven convergence and enhanced reconstruction performance.

Findings

Superiorized EM outperforms classic EM in noisy conditions

Enhanced stability and robustness of the reconstruction process

Improved mean square error and visual quality of images

Abstract

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.