The Design and Performance of Muon Scattering Tomography Reconstruction Algorithms for Applications in Nuclear Waste Identification

Abstract

Characterisation of nuclear waste in the current time is well organised and each radioac- tive waste is disposed of or stored in an interim facility depending on the type of the waste. Characterised nuclear waste is expected to be documented and kept for records to be checked regularly according to the IAEA regulations. However, uncharacteristic nuclear waste is still found, especially, old nuclear waste that was stored at a time when documentation of the materials was not required. Moreover, some historical nuclear waste might contain heterogeneous contents of different types of radioactive materials. This dictates an efficient technique to resolve these issues by characterising disposed and/or stored unrecorded nuclear waste. Such techniques to investigate these nuclear waste drums without opening them are valuable to reduce the cost and the hazard of being contacted with unidentified radioactive materials. Muon Scattering Tomogra- phy (MST) technique has significantly increased in importance as a non-destructive imaging method of nuclear waste. In the past few decades, a significant amount of research has shown the efficacy of MST as an imaging method. However, there are still several areas that require further development in MST technique to contribute to nuclear waste management. An efficient imaging algorithm is requested to image and identify nuclear waste in a few hours. This thesis shows a method of optimising imaging performances of the common algorithms. This thesis shows that dividing the volume of interest by rectangular voxels with a side length of 10 mm and height of 30 mm improves the discrimination power of the imaging method. The ASR algorithm performance increased in the ability to distinguish between a 10 cm side length of uranium cube from an equally-sized lead cube with a contrast-to-noise ratio (CNR) value of 3.2 ± 0.1, compared to the CNR value of 2.2 ± 0.07 when using cubic voxels with a side length of 10 mm. Following localising high-Z materials inside nuclear waste drums, identifying these materials in a few hours is possible. It was shown that this thesis introduces two new algorithms for material classification applications which are the Hybrid (HB) and the High Angle Statistics Reconstruction (H-ASR) algorithms. It was shown that the H-ASR algorithm is able to identify 10 cm and 5 cm cubes of uranium from lead in 3 and 4.5 hours, respectively.