  Dry casks for spent nuclear fuel (SNF) ensure the safe storage of SNF and
provide radiation shielding. However, the presence of the thick casks
encompassing several layers of steel and concrete makes inspection of the SNF a
challenging task. Fast neutron interrogation is a viable method for the
nondestructive assay of dry storage casks. In this study, we performed a Monte
Carlo simulation-based study associated with a machine-learning-based image
reconstruction method to verify the content of SNF dry storage casks. We
studied the use of neutron transmission and back-scattered measurements to
assess the potential damage to fuel assemblies or fuel pin diversion during
transportation of dry casks. We used Geant4 to model a realistic HI-STAR 100
cask, MPC-68 canister and basket, and GE-14 fuel assembly irradiated by a D-T
neutron generator. Several bundle diversion scenarios were simulated. The
angular distribution of the neutrons scattered by the cask was used to identify
the diversions inside the fuel cask. A fuel bundle with at least 75% of its
pins removed can be identified with a drop in the back-scattered signature
larger than 2{\sigma} compared with a fully loaded scenario. We combined an
iterative reconstruction algorithm with a convolutional neural network (CNN) to
obtain a cross-sectional image of the fuel inside the cask. The proposed
imaging approach allows locating the position of a missing fuel bundle with at
least 75% of the pins removed when performing tomographic imaging of a canister
with an overall scan time of less than two hours, when using a commercial
neutron generator with a source strength of 10^10 n/s in the 4{\pi} solid
angle.
