Reconstruction of Fan Beam X-Ray Fluorescence Computed Tomography Based on Parallel Hole Collimator via Total Variation and Ordered Subsets

Abstract

In this paper, an Ordered Subsets Expectation Maximization (OSEM) reconstruction algorithm based on Total Variation (TV) constraint was applied for sparse reconstruction of X-ray fluorescence CT. First, the Geant4 Monte Carlo code was used to simulate the imaging process of fan beam X-ray fluorescence CT imaging system based on parallel hole collimator. Then, the reconstructed image quality of the proposed algorithm with varying numbers of projections was evaluated using RMSE and CNR. Finally, the relationship between the number of subsets of the algorithm and the quality of the reconstructed image and the reconstruction time was explored. The results demonstrated that, compared with the conventional OSEM algorithm, the proposed OSEM algorithm based on Total Variation constraint has higher quality of reconstructed images at different projection numbers, and the reconstruction time of the algorithm decreases with the increase of subset, which achieves the purpose of improving the quality of the reconstructed image and reducing the reconstruction time when sparse reconstruction.

Keywords

• X-ray fluorescence CT
• image reconstruction
• Monte Carlo simulation
• sparse projection
• total variation