Sparse-View Neutron CT Reconstruction of Irradiated Fuel Assembly Using Total Variation Minimization with Poisson Statistics
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.
M. Abir et al., "Sparse-View Neutron CT Reconstruction of Irradiated Fuel Assembly Using Total Variation Minimization with Poisson Statistics," Journal of Radioanalytical and Nuclear Chemistry, vol. 307, no. 3, pp. 1967-1979, Springer Verlag, Mar 2016.
The definitive version is available at https://doi.org/10.1007/s10967-015-4542-2
Mining and Nuclear Engineering
Keywords and Phrases
Algorithm; Article; Artifact; Computer assisted tomography; Geometry; Image analysis; Image quality; Image reconstruction; Irradiation; Nuclear fuel reprocessing; Poisson distribution; signal noise ratio; Simulation; Statistical analysis; Statistical model; CT reconstruction; Irradiated fuel assembly; Poisson statistics; Sparse-view; Total variation minimization
International Standard Serial Number (ISSN)
Article - Journal
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01 Mar 2016