Improved Reconstruction Quality of PGET Using a Linear-Inverse Approach

Ming Fang, P. Dendooven, R. Virta, Y. Altmann, A. Di Fulvio

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The Passive Gamma Emission Tomography (PGET) instrument has received authorization from the International Atomic Energy Agency (IAEA) for inspecting spent fuel assemblies (SFAs) to detect any diversion of fuel rods. The accuracy of fuel rod identification hinges critically on the quality of the reconstructed image. In comparison to the traditional filtered back-projection (FBP) method, the linear inverse approach has demonstrated superior image quality and identification accuracy. This approach frames the image reconstruction process as an inverse problem, relying on a precise forward model of the PGET system. In this work, we have built upon our previously developed forward model, which accounts for the gamma-ray down-scattering in the SFA, and developed a new point spread function model incorporating collimator septal penetration and detector scattering effects. A unique benefit of our approach is the direct estimate of the source strength, unattainable otherwise. This information can be directly compared with the operator’s declaration. The enhanced forward model was validated by comparing against MCNP simulation, and a 2.95% relative difference between the counts obtained using MCNP and our new forward model was achieved. Using this enhanced model, we successfully reconstructed the image of a simulated hexagonal assembly, identified 100% of the fuel rods, and achieved a 2.87% uncertainty in activity estimation. Additionally, we applied the model to an actual measured fuel assembly, successfully imaging all the rods, including the innermost ones, and identifying the water channels within.
Original languageEnglish
Title of host publication2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD)
PublisherIEEE
ISBN (Electronic)9798350388152
DOIs
Publication statusPublished - 25 Sept 2024

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