Machine Learning-based reconstruction of single and double radiation source distribution using plastic scintillation optical fiber

Abstract

In this study, we propose a novel method for reconstructing the radiation distribution of a one-dimensional radioactive source using Machine Learning (ML) algorithms and plastic scintillation optical fiber. The wavelength spectrum unfolding technique is used to estimate the source position accurately. We compare the accuracy and time efficiency of three different algorithms, namely, Generalized reduced gradient (GRG), Maximum likelihood expectation maximization (MLEM), and ML, in the single-source and dual-source cases. Our results demonstrate that MLEM algorithm has a shorter reconstruction time with comparable accuracy of position and intensity compared to GRG algorithm for single-source case. For dual-source case, ML algorithm provides real-time estimation of position and intensity with acceptable errors, while GRG algorithm has a larger error in intensity estimation and longer computation time. Our proposed ML algorithms offer useful guidance for practical applications in radiation source location.