Improved x-ray computed tomography (CT) feature identification with complementary fast neutron CT

Abstract

Material identification is challenging for X-ray Computed Tomography (CT) when objects of interest composed of low atomic number (Z) elements are shielded by dense materials. Fast neutron CT (FNCT) can compensate for this shortcoming by providing both penetration through high-Z materials and good contrast in low-Z materials. Here we investigate improvements in X-ray CT feature identification using information from fast neutron imaging. To demonstrate the complementarity of X-ray and FNCT, simulated CT data sets were generated for two heterogenous, nested, cylindrical phantoms using the Monte Carlo N-particle (MCNP) transport code for both imaging modalities. Xray radiographs were simulated for polychromatic 300 keV and 9 MeV e-Bremsstrahlung X-ray sources, while a generic Gaussian D-T source spectrum [En(pk) = 14.10 MeV, w/ FWHM ~ 0.75 MeV] was used for the neutron radiographs. A total of 360 projections taken at 1° intervals were simulated for both modalities and phantoms. All projection data were reconstructed with filtered-back projection (FBP) using the Livermore Tomography Tools (LTT) code. Our results indicate improved material discrimination and resolution of certain features with combined X-ray and neutron CT data sets.