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Development of High-Efficiency Multisensor Detection System for Gamma Emission Tomography in Spent Nuclear Fuel Inspection
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Development of High-Efficiency Multisensor Detection System for Gamma Emission Tomography in Spent Nuclear Fuel Inspection

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Journal Journal of Radiation Protection and Research
Volume Vol. 50(3) 183-192
Published September 2025
DOI https://doi.org/10.14407/jrpr.2025.00094

Abstract

Background

Gamma emission tomography (GET) is one of the most reliable methods for detection of partial-defects within spent nuclear fuel (SNF). In our previous study, we developed and proposed the scintillation crystal-based GET instrument named Yonsei single-photon emission computed tomography (YSECT). However, this conventional YSECT instrument has consequential limitations related to the low inspection accuracy in the central region, which fact is due to the high density of nuclear fuel rods. This study aimed to derive, as proposed in this paper and using Monte Carlo simulation, a multisensor-based YSECT for enhancement of partialdefect detection accuracy.

Materials and Methods

The gamma-ray energy spectra for gadolinium aluminum gallium garnet (GAGG), CsI(Tl), CdWO4, and PbWO4 were obtained to determine the appropriate material for image quality improvement in the central region. The shapes of the spectra and the detection efficiencies were compared among the scintillation crystal materials. The tomographic images were acquired with both conventional YSECT and the newly proposed YSECT, and their quality was compared based on the signal-to-noise ratio (SNR).

Results and Discussion

CdWO4 was found to have a detection efficiency twice as high as that of GAGG, owing to its high-density despite poor light yield. Based on these results, the optimal scintillation crystal material for enhancement of detection efficiency for high-energy (>662 keV) gamma rays was determined to be CdWO4. In accordance with the optimization study, the SNR of the CdWO4 image was calculated as 7.95, which was higher than that of the GAGG image by a factor of 2. Furthermore, the SNR of the synthesis image was determined to be 9.85, which was higher than that of the single-sensor–based YSECT image by a factor of 1.59.

Conclusion

Based on these results, we believe that the multisensor-based YSECT can enhance inspection accuracy for partial defects arising in pressurized water reactor-type SNF. In further studies, the effect and influence of neutrons will be evaluated and noise-reduction methods will be developed.

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