Optimization of single-photon emission computed tomography system for fast verification of spent fuel assembly: a Monte Carlo study - Choi, H.J.; Kang, I.S.; Kim, K.B.; Chung, Y.H.; Min, C.H. (Journal of instrumentation, 2019)
The International Atomic Energy Agency (IAEA) assessed that single-photon emission computed tomography (SPECT) technique is one of the most attractive technique for safeguards of a spent fuel because of its intuitive evaluation capability by using a tomographic image. Even though there are over a hundred kinds of the verification technique, the IAEA suggested the need for “more sensitive and less intrusive alternatives to existing nondestructive assay instruments” for partial-defect detection. The aim of this study is to optimize a SPECT system using Monte Carlo method for faster verification of spent fuel assembly than the conventional system. An optimization study of detector geometry was performed using GATE simulation program. The detector head consists of one-dimensional (1D) multi-slit tungsten collimator, Bismuth Germanate (BGO) scintillator, and silicon photomultiplier (SiPM) arrays. The collimator slit width and length, and scintillator length were optimally determined by evaluating the quality of 1D projection image for Cs-137 line source. The type of photoelectric sensor was determined by assessing the light transfer efficiency of the scintillator using DETECT2000 program. For the performance evaluation of the optimized detector, a tomographic image of twelve fuel sources in the assembly was compared with that acquired using the conventional detector developed by IAEA . The optimized scintillator length, collimator slit width, length, and septal thickness were 4 cm, 0.2 cm, 5 cm, and 0.2 cm, respectively. The light transfer efficiency in the scintillator to the 3×70 mm3 and 3×3 mm2 sensors were 23.4±0.6% and 5.3±0.3%, respectively. In the results of the image quality assessment, the optimized detector head shows about 1.5 times improved sensitivity, while the image quality is slightly poorer than the conventional one due to the bigger slit width. Nevertheless, by considering a deep-learning-based image-reconstruction-algorithm, we chose the bigger slit width because higher image intensity gives an advantage in discrimination between the fuel sources and the background noise. We expect that the combination of the detector head with higher sensitivity and the deep-learning-based image-reconstruction-algorithm will contribute to the faster verification of spent fuel assemblies compared to the conventional system.