Shanghai Jiao Tong University Reports Findings in Applied Radiation Research (Transmission reconstruction algorithm by combining maximum-likelihood expectation maximization and a convolutional neural network for radioactive drum characterization)

摘要

By a News Reporter-Staff News Editor at Network Daily News – New research on Applied Radiation Research is the subject of a report. According to news reporting originating in Shanghai, People’s Republic of China, by NewsRx journalists, research stated, “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.”