Iterative reconstruction algorithm comparison using Poisson noise distributed sinogram data in passive gamma emission tomography

Abstract

ABSTRACT Gamma emission tomography (GET) detects fuel rods that emit gamma rays for potential use as verification tools in nuclear safeguards. In GET, iterative reconstruction algorithms are often used to reconstruct passive gamma-ray emitter source distributions. Generally, using noisy sinogram data, the iterative reconstruction algorithms increase the noise level on a reconstruction image with iterations. Thus, it is important to evaluate how performances of representative iterative reconstruction algorithms change with noisy sinogram data. The passive gamma-ray source distributions inside the mock-up of the water-water energetic reactor (WWER) fuel assembly having missing fuel rods were reconstructed by using the following algorithms: gradient method, steepest descent method, conjugate gradient reconstruction method, algebraic reconstruction technique, simultaneous iterative reconstruction technique, and maximum likelihood expectation maximization (MLEM). Consequently, MLEM algorithm yielded a higher contrast reconstruction image and was regarded as higher reliable algorithm to discriminate the fuel rods from the passive gamma-ray emitter source distribution.