Quantitative Imaging and Automated Fuel Pin Identification for Passive Gamma Emission Tomography

TL;DR

This paper presents a comprehensive software suite combining advanced image reconstruction and neural network techniques to accurately visualize and identify fuel pins in spent nuclear fuel, enhancing nuclear safeguards.

Contribution

It introduces a novel linear inverse problem approach for high-quality image reconstruction and an automated neural network-based method for fuel pin identification in gamma emission tomography.

Findings

Inverse-problem approach reduced mean-square-error by 50%.

Structural similarity of images improved by 200%.

Estimated activity levels had about 5% uncertainty.

Abstract

Compliance of member States to the Treaty on the Non-Proliferation of Nuclear Weapons is monitored through nuclear safeguards. The Passive Gamma Emission Tomography system is a novel instrument developed by the International Atomic Energy Agency (IAEA) for the verification of spent nuclear fuel stored in water pools. Advanced image reconstruction techniques are crucial for obtaining high-quality cross-sectional images of the spent-fuel bundle to allow inspectors of the IAEA to monitor nuclear material and promptly identify its diversion. In this work, we have developed a software suite to accurately reconstruct the spent-fuel cross sectional image, automatically identify present fuel rods, and estimate their activity. Unique image reconstruction challenges are posed by the measurement of spent fuel, due to its high activity and the self-attenuation. We implemented a linear forward model to model the detector responses to the fuel rods inside the PGET. The image reconstruction is performed by solving a regularized linear inverse problem using the fast-iterative shrinkage-thresholding algorithm. We have also implemented the traditional filtered back projection method for comparison and applied both methods to reconstruct images of simulated mockup fuel assemblies. Higher image resolution and fewer reconstruction artifacts were obtained with the inverse-problem approach, with the mean-square-error reduced by 50%, and the structural-similarity improved by 200%. We then used a convolutional neural network to automatically identify the bundle type and extract the pin locations from the images; the estimated activity levels finally being compared with the ground truth. The proposed computational methods accurately estimated the activity levels of the present pins, with an associated uncertainty of approximately 5%.