Continuum estimation in low-resolution gamma-ray spectra based on deep learning

In this study, an end-to-end deep learning method is proposed to improve the accuracy of continuum estimation in low-resolution gamma-ray spectra. A novel process for generating the theoretical continuum of a simulated spectrum is established, and a convolutional neural network consisting of 51 layers and more than 105\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10<^>5$$\end{document} parameters is constructed to directly predict the entire continuum from the extracted global spectrum features. For testing, an in-house NaI-type whole-body counter is used, and 106\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10<^>6$$\end{document} training spectrum samples (20% of which are reserved for testing) are generated using Monte Carlo simulations. In addition, the existing fitting, step-type, and peak erosion methods are selected for comparison. The proposed method exhibits excellent performance, as evidenced by its activity error distribution and the smallest mean activity error of 1.5% among the evaluated methods. Additionally, a validation experiment is performed using a whole-body counter to analyze a human physical phantom containing four radionuclides. The largest activity error of the proposed method is -5.1%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-5.1\%$$\end{document}, which is considerably smaller than those of the comparative methods, confirming the test results. The multiscale feature extraction and nonlinear relation modeling in the proposed method establish a novel approach for accurate and convenient continuum estimation in a low-resolution gamma-ray spectrum. Thus, the proposed method is promising for accurate quantitative radioactivity analysis in practical applications.