基于深度学习的扇束X射线荧光计算断层扫描自吸收校正  

Deep-Learning-Based Self-Absorption Correction for Fan Beam X-Ray Fluorescence Computed Tomography

作　　者：孙孟英 蒋上海 李相朋 黄鑫 汤斌[1] 胡新宇[1] 罗彬彬[1] 石胜辉[1] 赵明富[1] 周密 Sun Mengying;Jiang Shanghai;Li Xiangpeng;Huang Xin;Tang Bin;Hu Xinyu;Luo Binbin;Shi Shenghui;Zhao Mingfu;Zhou Mi(Chongqing Key Laboratory of Optical Fiber Sensor and Photoelectric Detection,Chongqing University of Technology,Chongqing 400054,China;College of Science,Chongqing University of Technology,Chongqing 400054,China)

机构地区：[1]重庆理工大学光纤传感与光电检测重庆市重点实验室,重庆400054 [2]重庆理工大学理学院,重庆400054

出　　处：《激光与光电子学进展》2024年第18期60-68,共9页Laser & Optoelectronics Progress

基　　金：重庆市自然科学基金面上项目(cstc2020jcyj-msxmX0362,cstc2020jcyj-msxmX0879);重庆市教委科学技术研究计划重点项目(KJZD-K202301105);重庆理工大学科研创新团队培育计划项目(2023TDZ002);重庆理工大学研究生教育高质量发展行动计划资助成果(gzlcx20223074)。

摘　　要：在X射线荧光计算断层扫描(XFCT)成像过程中,样品本身对入射X射线以及荧光X射线的吸收衰减是制约其高质量图像重建的重要因素之一。本文提出一种基于深度学习的X射线荧光CT自吸收校正方法,利用基于U-Net的卷积神经网络学习原始投影数据中的对称结构分布,从受自吸收影响的正弦图中恢复完备的投影数据。通过数值模拟建立扇束XFCT成像系统获得20000组荧光正弦图,实现网络训练、测试与验证,并通过Geant4软件仿真获得受自吸收影响的投影数据进行进一步验证。结果表明,训练良好的神经网络能对不完整的投影数据实现自吸收校正,进而提高重建图像的质量。In X-ray fluorescence computed tomography(XFCT)imaging,the absorption attenuation of incident X-rays and fluorescent X-rays by the sample is a critical factor that restricts high-quality image reconstruction.This study proposes a deep-learning-based self-absorption correction method for XFCT,which utilizes a convolutional neural network based on U-Net to learn the symmetric structure distribution in the original projection data and recover complete projection data from the sinograms affected by self-absorption.Through numerical simulation,a fan-beam XFCT imaging system was established to obtain 20000 sets of fluorescence sinograms,which were then used for network training,testing,and validation.The projection data affected by self-absorption were further validated through a simulation using Geant4 software.The results indicate that the well-trained neural network can achieve self-absorption correction on incomplete projection data,thereby improving the quality of reconstructed images.

关 键 词：深度学习 自吸收校正 X射线荧光计算断层扫描 数值模拟 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]