  Tristructural-isotropic (TRISO) fuel is one of the most mature fuel types for
candidate advanced reactor types under development. TRISO-fuel pebbles flow
continuously through the reactor core and can be reinserted into the reactor
several times until a target burnup is reached. The capability of identifying
individual fuel pebbles would allow us to calculate the fuel residence time in
the core and validate pebble flow computational models, prevent excessive
burnup accumulation or premature fuel discharge, and maintain accountability of
special nuclear materials during fuel circulation. In this work, we have
developed a 3D image reconstruction and segmentation algorithm to accurately
segment TRISO particles and extract the unique 3D distribution. We have
developed a rotation-invariant and noise-robust identification algorithm that
allows us to identify the pebble and retrieve the pebble ID in the presence of
rotations and noises. We also report the results of 200kV X-ray CT image
reconstruction of a mock-up fuel sample consisting of tungsten-carbide (WC)
kernels in a lucite matrix. The 3D distribution of TRISO particles along with
other signatures such as $^{235}$U enrichment and burnup level extracted
through neutron multiplicity counting, would enable accurate fuel
identification in a reasonable amount of time.
