Image Reconstruction Based on Total Variation Minimization for Radioactive Wastes Tomographic Gamma Scanning From Sparse Projections

Rui Shi,Honglong Zheng,Jianbo Yang,Yi Cheng,Xianguo Tuo,Mingzhe Liu,Changming Wang,Songbai Zhang

doi:10.1109/access.2021.3088746

Abstract

Tomographic Gamma Scanning (TGS) is one of the most important non-destructive analyzed techniques for radioactive waste drums. By reconstructing the radioactivity distribution image, it can accurately realize the qualitative, quantitative, and positioning analysis of the radionuclides in the drum. However, the time consuming of the scanning is long and the reconstructed image is rough, which limits its good application in the practical assay of the waste drum. In this work, the total variational minimization (TVM) method was applied to improve the iterative process of the conventional algorithms of maximum likelihood expectation maximization (MLEM) and algebraic reconstruction technique (ART), then the MLEM-TVM and ART-TVM reconstruction methods were developed. The transmitted experiments were carried out where four kinds of materials were arranged in a segment whose densities ranging from 1.04 g/cm 3 to 2.02 g/cm 3 and a 152 Eu isotope was set up as a transmission source. Compared with the traditional algorithms MLEM and ART, the MLEM-TVM and the ART-TVM algorithms have a better performance on the accuracy and the signal-to-noise ratio, and the MLEM-TVM algorithm achieves the best results, which means the quality of the reconstructed image is improved. The accuracy and effectiveness of the TVM method used in the TGS image reconstruction are verified in the work, and moreover, it can save the scanning time and enhance the TGS image resolution through sparse projection sampling.

Highlights

Nuclear energy is clean energy compared with thermal power generation, but nuclear power plants will produce a large amount of low and intermediate-level solid radioactive waste with a specific concentration of radionuclides less than 4 × 106 Bq/kg and 4 × 1010 Bq/kg, respectively [1]
The reference images of the transmission with the four materials are shown in Fig. 5, in which the pixel values of the four materials equal the attenuation coefficients according to the distribution of the materials in the segment
The results with 6 energies of 152Eu are shown in Fig. 6, where (a), (b), (c), (d) correspond to the algebraic reconstruction technique (ART), maximum likelihood expectation maximization (MLEM), ART-total variation minimization (TVM), and MLEMTVM algorithms, respectively

Summary

Introduction

Nuclear energy is clean energy compared with thermal power generation, but nuclear power plants will produce a large amount of low and intermediate-level solid radioactive waste with a specific concentration of radionuclides less than 4 × 106 Bq/kg and 4 × 1010 Bq/kg, respectively [1]. Radioactive waste is different from ordinary garbage because it contains radionuclides and needs to be packaged in special nuclear waste drums to reduce radiation hazards and protect the environment. Before classifying and disposal of the radioactive waste, the radiation level must be assessed, so the spatial distribution of radionuclides and their radioactivities in the waste drums should be determined [2], [3]. Considering the radioactive hazards of the radioactive waste, gamma-ray-based non-destructive assay (γ-NDA) is a commonly used technique [4], [5].

Methods

Results

Conclusion