Research on Gamma Image Reconstruction Method Based on Multi-distance System Matrix Superposition

As radioactive sources are increasingly used in scientific research, industry and other fields, radioactive sources are often lost due to improper management and control. Since gamma rays have higher penetrating power and longer action distance than α particles and β rays, they can affect a larger area and have radiation effects on various substances in the environment. Therefore, the search and positioning of radioactive sources has always been the focus of radioactive source search. Although gamma detection equipment can achieve relatively precise positioning of a single radioactive source, when there are multiple radioactive sources in the radiation environment, radioactive sources with weaker energy can easily be ignored. In order to enable the gamma camera to accurately locate multiple radioactive sources in the unknown radiation field at the same time, this paper proposed a multi-distance system matrix superposition gamma image reconstruction algorithm based on the maximum likelihood expectation maximization (MLEM) algorithm. First, Monte Carlo simulation software was used to build a gamma camera simulation, and the impact of different encoding patterns on detection efficiency and the impact of radioactive sources at different distances on detection performance were analyzed. In order to be as close to the actual situation as possible, the photoelectric effect, Compton scattering, electron pair effect and decay of the radioactive source were considered in subsequent simulation experiments, and the simulation structure is consistent with the gamma camera structure. Then, simulation was used to obtain projection data, and different distance system matrices were calculated based on the impact of radioactive source distance on detector collection efficiency. Besides, in order to simultaneously located multiple radioactive sources within different distance ranges, a new system matrix was formed by superimposing multiple system matrices with different distances, and then calculated with the projection data, and through continuous iteration, a higher quality reconstructed image and the position information of multiple radioactive sources were obtained. Finally, in order to verify the feasibility of the algorithm, the algorithm was used for actual verification of 60Co and 137Cs radioactive sources in a real environment, accurately reconstructed the position information of the two radioactive sources. Experimental results show that this method can quickly locate radioactive sources. It has high convergence when using a single source, and it can accurately locate multiple radioactive sources when using multiple sources. It has a longer imaging distance and higher positioning accuracy than related decoding algorithms. This method is practical and feasible in practical applications.