能谱CT图像重建算法研究

Research on basis material decomposition algorithms for spectral CT

能谱CT所获取的是多个能谱(或能段)的X射线穿过被测物体的扫描数据。与传统CT扫描数据相比,这些数据中包含着更多的被测物体信息。基于基材料分解算法,能谱CT可重建被测物体的多个基材料图像,以及由基图像线性组合表示的虚拟单能图像,并由此进行物体成分识别和定量化分析。近年来,能谱CT成像的相关理论、技术、器件和系统都在快速发展,其应用领域也在日益扩大。本文在介绍能谱CT的数据采集模式、基材料分解数学模型的基础上,综述基于基材料分解的能谱CT图像重建算法的分类及各类算法的优缺点和适用性等,并探讨需要进一步研究的相关问题。

Spectral CT obtains scanning data by passing the X-rays of multiple energy spectra or energy bands through a measured object.Compared with traditional CT scanning data,these data contain more information about the measured object.Based on the basis material decomposition algorithm,the spectral CT can reconstruct multiple basis material images of the measured object,as well as the virtual single energy image represented by the linear combination of the basis images,so as to realize the component recognition and quantitative analysis of the object.In recent decades,the theory,technology,devices and systems related to the spectral CT imaging have been developing rapidly,and its application fields are also expanding.On the basis of introducing the data acquisition mode of spectral CT and the mathematical model of basis material decomposition,this paper summarizes the classification of basis material decomposition algorithms,the advantages,disadvantages and applicability of various algorithms.The related further research problems are also discussed.