An improved algebraic reconstruction technique for reconstructing tomographic gamma scanning image

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.