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

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中文摘要：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.

外文摘要：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.

外文关键词：

deep learningself-absorption correctionX-ray fluorescence computed tomographynumerical simulation

作者：

孙孟英、蒋上海、李相朋、黄鑫、汤斌、胡新宇、罗彬彬、石胜辉、赵明富、周密

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作者单位：

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

重庆理工大学理学院,重庆 400054

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

基金：

重庆市自然科学基金面上项目重庆市自然科学基金面上项目重庆市教委科学技术研究计划重点项目重庆理工大学科研创新团队培育计划项目重庆理工大学研究生教育高质量发展行动计划

项目编号：

cstc2020jcyjmsxmX0362cstc2020jcyjmsxmX0879KJZD-K2023011052023TDZ002gzlcx20223074

出版年：

2024

DOI：

10.3788/LOP232787

激光与光电子学进展

中国科学院上海光学精密机械研究所

激光与光电子学进展

CSTPCD北大核心

影响因子：1.153

ISSN：1006-4125

年,卷(期)：2024.61(18)

参考文献量4