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Tri-Structural Isotropic (TRISO) fuel particles are a key component of nextgeneration nuclear fuels. Using X-ray computed tomography (CT) to characterizeTRISO particles is challenging because of the strong attenuation of the X-raybeam by the uranium core leading to severe photon starvation in a substantialfraction of the measurements. Furthermore, the overall acquisition time for ahigh-resolution CT scan can be very long when using conventional lab-basedX-ray systems and reconstruction algorithms. Specifically, when analyticmethods like the Feldkamp-Davis-Kress (FDK) algorithm is used forreconstruction, it results in severe streaks artifacts and noise in thecorresponding 3D volume which make subsequent analysis of the particleschallenging. In this article, we develop and apply model-based imagereconstruction (MBIR) algorithms for improving the quality of CTreconstructions for TRISO particles in order to facilitate bettercharacterization. We demonstrate that the proposed MBIR algorithms cansignificantly suppress artifacts with minimal pre-processing compared to theconventional approaches. Furthermore, we demonstrate the proposed MBIR approachcan obtain high-quality reconstruction compared to the FDK approach even whenusing a fraction of the typically acquired measurements, thereby enablingdramatically faster measurement times for TRISO particles.

Model-based Reconstruction for Enhanced X-ray CT of Tri-structural Isotropic (TRISO) Particles