Sparse-view neutron-photon computed tomography: Object reconstruction and material discrimination

Taking into account the advantages of both neutron- and photon-based systems, we propose combined neutron-photon computed tomography (CT) under a sparse-view setting and demonstrate its performance for 3D object visualization and material discrimination. We use a high-performance regularization method for CT reconstruction by combining regularization based on total variation (TV) and curvelet transform in cone beam geometry. It is coupled with proposed 2D material signatures which is pairs of photon to neutron transmission ratios and neutron transmission values per object space voxels. Classification of materials is performed by association of a voxel signature with library signatures; and per object - by majority of voxels in the object. Representation of object-material pairs, for the model in our experiment, a complex scene with group of high-Z and low-Z materials, attains the reconstruction accuracy of 92.1% and the overall high-Z discrimination accuracy of object representation is 85%, and by about 7.5% higher discrimination accuracy than that with 1D signatures which are ratios of photon to neutron transmissions. With a relative noise level of 10%, the method yields the reconstruction accuracies of 87.2%. The analyses are performed in cone beam configuration, with Monte Carlo modeling of neutron-photon transport for the model of object geometry and material contents.