Abstract
Tomographic Gamma Scanning (TGS) is a non-destructive technique used to assay and characterize low-level and mediate-level (LL & ML) radioactive waste drums qualitatively and quantitatively without opening waste drums. The ideal voxelized TGS model used in this paper is based on the assumption that matrices are uniformly distributed and radioactivity from gamma emitting radioactive sources is concentrated at the mass centroid of each voxel. An “point-detector (PD)” model in which the germanium crystal surface of High Purity Germanium (HPGe) detector is divided into different pixels are taken into account to compute the average emission path length of every gamma-ray through each voxel in the drum. Different from the previously described transmission PD model that treats the transmission source as the only “point”, this emission PD model treats the mass centroid of each voxel in the drum as an independent “point” and the finite field of views (FOVs) of the collimated High Purity Germanium (HPGe) detector must be carefully considered. The difference among this emission PD model applied at different resolutions is that the germanium crystal surface of HPGe detector is divided into different numbers of pixels respectively. Algebraic Reconstruction Technique (ART) with the non-negativity constraint and Maximum Likelihood Expectation Maximization (MLEM) are respectively used as the emission reconstruction algorithm. In order to compare these different emission path length computation models and emission reconstruction algorithms, three different series of waste drums are established, allowing to calculate the count distribution for various point source positions and different matrix configurations. Results show that ART algorithm is superior to MLEM algorithm to assay high-density drums. The more pixels the germanium crystal surface of HPGe detector is divided into, the better the emission-reconstructed results when using ART to assay high-density drums.
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