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We inspect the nuclear fuel assembly by demonstrating the potential use of sparse-view neutron computed tomography. The projection images of the fuel assembly were collected at the Idaho National Laboratory hot fuel examination facility using indirect foil-film transfer technique. The radiographs were digitized using a commercial film digitizer and registered spatially for reconstruction. Digitized data were reconstructed using simultaneous algebraic reconstruction technique (SART) with total variation minimization using a dual approach for numerical solution assuming the projection data are corrupted by Poisson noise. To validate and evaluate the performance of the algorithm, visual inspections, as well as quantitative evaluation studies using a computer simulation data and the experimental data of the fuel assembly were carried out. The proposed method provides better reconstruction for both simulated and experimental case in terms of artifact reduction, higher SNR, and better spatial resolution compared to the reconstruction yielded by filtered back projection and SART reconstruction.

We present and compare the designs of three types of neutron microscopes for high-resolution neutron imaging. Like optical microscopes, and unlike standard neutron imaging instruments, these microscopes have both condenser and image-forming objective optics. The optics are glancing-incidence axisymmetric mirrors and therefore suitable for polychromatic neutron beams. The mirrors are designed to provide a magnification of 10 to achieve a spatial resolution of better than 10 μm. The resolution of the microscopes is determined by the mirrors rather than by the L/Dratio as in conventional pinhole imaging, leading to possible dramatic improvements in the signal rate. We predict the increase in the signal rate by at least two orders of magnitude for very high-resolution imaging, which is always flux limited. Furthermore, in contrast to pinhole imaging, in the microscope, the samples are placed far from the detector to allow for a bulky sample environment without sacrificing spatial resolution.

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