  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%.
