  Tri-Structural Isotropic (TRISO) fuel particles are a key component of next
generation nuclear fuels. Using X-ray computed tomography (CT) to characterize
TRISO particles is challenging because of the strong attenuation of the X-ray
beam by the uranium core leading to severe photon starvation in a substantial
fraction of the measurements. Furthermore, the overall acquisition time for a
high-resolution CT scan can be very long when using conventional lab-based
X-ray systems and reconstruction algorithms. Specifically, when analytic
methods like the Feldkamp-Davis-Kress (FDK) algorithm is used for
reconstruction, it results in severe streaks artifacts and noise in the
corresponding 3D volume which make subsequent analysis of the particles
challenging. In this article, we develop and apply model-based image
reconstruction (MBIR) algorithms for improving the quality of CT
reconstructions for TRISO particles in order to facilitate better
characterization. We demonstrate that the proposed MBIR algorithms can
significantly suppress artifacts with minimal pre-processing compared to the
conventional approaches. Furthermore, we demonstrate the proposed MBIR approach
can obtain high-quality reconstruction compared to the FDK approach even when
using a fraction of the typically acquired measurements, thereby enabling
dramatically faster measurement times for TRISO particles.
