Transmission reconstruction algorithm by combining maximum-likelihood expectation maximization and a convolutional neural network for radioactive drum characterization

One of the most critical factors that affect reconstructions of the activity of a radioactive drum is the accuracy of tomographic gamma scanning transmission reconstruction. The traditional algorithms applied for reconstructing the density map, such as maximum-likelihood expectation maximization (MLEM), algebraic reconstruction technique, or filtered back-projection, produce a grid distribution with severe grid artifacts and a high level of noise, which significantly degrade the detail of the transmission image, thereby increasing the reconstruction error for both the density map and the activity. Thus, we propose a novel algorithm for transmission reconstruction by combining MLEM and a deep convolutional neural network (CNN). The CNN is trained using a supervised learning approach with image pairs obtained from the drum's ground truth (target) and the result constructed using MLEM (input). Our experimental results indicate that the proposed reconstruction algorithm can significantly improve the spatial resolution while also removing grid artifacts. In addition, the algorithm is sufficiently robust when dealing with a noisy input image. When the input image contained 20% noise, the mean squared error of the output image decreased by 78.51% compared with the conventional reconstruction method, and the peak signal-to-noise ratio and structural similarity index measure for the output image improved by 81.19% and 71.74%, respectively. The new algorithm improves the accuracy of the radioactive drum density map reconstruction as well as decreasing the measurement time required. We consider that the proposed algorithm is an effective new method for use in the field of radioactive waste drum transmission image reconstruction.