Reconstruction of tomographic gamma scanning transmission image from sparse projections based on convolutional neural networks

Abstract

Tomographic Gamma Scanning (TGS) is an important Non-Destructive Assay (NDA) method for radioactive waste, and transmission imaging is the basis of the TGS. Traditional algorithms reconstruct the transmission image require the provision of the same amount of projection data as the pixel value of the transmission image, which requires a long time to scan and severely limits the industrial application of TGS. Sparse angle scanning is an optimal approach to improve the efficiency of the TGS system, but the amount of data generated by sparse angle scanning is difficult to support traditional algorithms, and blurs and artifacts will appear in the reconstructed image. In this work, Algebraic Reconstruction Technique (ART) combined with the Residual Network(ResNet) to reconstruct TGS transmission images was proposed. The training data set was built with simulation data by the Monte Carlo method, and the self-developed TGS equipment was used for the experimental test. Experimental results show that ResNet can be used to reconstruct high-resolution TGS transmission images from sparse projection data. And compared with traditional algorithms, the proposed method has faster reconstruction speed and higher image quality.