An improved algebraic reconstruction technique for reconstructing tomographic gamma scanning image

作者：Zheng, Honglong; Tuo, Xianguo^*; Peng, Shuming; Shi, Rui; Li, Huailiang; He, Aijing; Li, Zhigang; Han, Qiang

来源：Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment , 2018, 906: 77-82.

DOI：10.1016/j.nima.2018.07.095

• Tomographic gamma scanning
• Improved algebraic reconstruction technique
• Image reconstruction
• Total variation minimization
• Self-adaptive relaxation factor

Tomographic gamma scanning (TGS) is one of the most advanced non-destructive techniques for assaying the radioactive waste drum. When traditional algorithms are adopted to accurately reconstruct TGS images, the measurement data must be completely sampled by TGS system, which inevitably leads to a long-term assay. In order to save time, small number of data is measured by dividing the drum into several large voxels, which leads to inaccurate TGS images. In this work, an improved algebraic reconstruction technique (IART) is proposed to reconstruct TGS images. The total variation minimization method and the self-adaptive relaxation factor are applied to improve the iterative process of traditional algebraic reconstruction technique (ART). Experimental results show that this IART algorithm can accurately reconstruct TGS images simply based on small amount of measurement data. Compared with traditional technique, this method can reduce the mean square error and improve the signal-to-noise ratio of transmission images. And it can improve the positioning accuracy of radioisotope and the accuracy of reconstructed activity.

• 出版日期2018-10-21
• 单位西南科技大学; 四川轻化工大学; 中国工程物理研究院; 中国工程物理研究院核物理与化学研究所