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
孙孟英 1蒋上海 1李相朋 1黄鑫 1汤斌 1胡新宇 1罗彬彬 1石胜辉 1赵明富 1周密2
作者信息
- 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
- 折叠
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.
Abstract
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Key words
deep learning/self-absorption correction/X-ray fluorescence computed tomography/numerical simulation引用本文复制引用
基金项目
重庆市自然科学基金面上项目(cstc2020jcyjmsxmX0362)
重庆市自然科学基金面上项目(cstc2020jcyjmsxmX0879)
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(gzlcx20223074)
出版年
2024
NETL
NSTL
万方数据