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Brass Material Analysis With Deep-Learning-Based CdTe Semiconductor X-Ray Fluorescence System
- Authors
- Jo, Ajin; Lee, Wonho
- Issue Date
- 5월-2022
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Phantoms; Training; Zinc; Deep learning; Data models; X-ray imaging; Imaging; CdTe semiconductor detector array; convolutional neural network (CNN); deep learning; X-ray fluorescence (XRF) imaging
- Citation
- IEEE TRANSACTIONS ON NUCLEAR SCIENCE, v.69, no.5, pp.1085 - 1091
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE TRANSACTIONS ON NUCLEAR SCIENCE
- Volume
- 69
- Number
- 5
- Start Page
- 1085
- End Page
- 1091
- ISSN
- 0018-9499
- Abstract
- An X-ray fluorescence (XRF) imaging system is a material analysis system that can represent the material distribution and types of elements by detecting characteristic X-rays emitted from each element. A CdTe semiconductor detector array whose detection efficiency is significantly higher than a silicon drift detector (SDD) is utilized with a deep learning method to improve the energy spectral analysis. In this study, deep learning models for material discrimination and quantitation were applied based on 20 000 energy spectra obtained from Fe, Ni, Cu, and Zn rod phantoms, and a brass phantom was analyzed to verify that Cu, Zn, and brass can be distinguished from each other and that the amount of Cu and Zn in each phantom can be quantitatively analyzed.
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