基于主成分分析的多能谱CT图像分析方法研究

基于光子计数探测器的能谱CT，可以同时采集多个能谱通道的投影数据，并获得相应能量范围内物质的吸收特征，可以有效应用于物质识别与材料分解。主成分分析是一种很好的多元数据分析技术，可以用于处理多能谱CT数据。本文分别在投影域和图像域对能谱CT数据进行主成分分析，并对分析结果做出系统比较。为了减少噪声的影响，提高能谱CT图像的彩色表征性能，提出双域滤波与像素值平方相结合的方法，用于含噪声的主成分图像去噪，然后将所选取的主成分图像映射到RGB颜色通道。实验结果表明，无论是在投影域还是图像域进行主成分分析，都可以获取清晰的CT图像，识别出物质的不同成分。相较于在图像域的主成分分析方法，在投影域进行主成分分析能够保留物质的更多细节，获取更清晰的彩色CT图像。 

Research on Image Analysis Method of Spectral CT Based on Principal Component Analysis

Spectral computed tomography (CT) based on photon counting detector can simultaneously collect projection data of multiple spectral channels and obtain absorption characteristics of material within corresponding energy ranges, so it can be effectively applied to material identification and material decomposition. Principal component analysis is an excellent multivariate analysis technique, which can be applied to process multi-energy spectral CT data. In this paper, principal component analysis was performed on spectral CT data in projection domain and image domain respectively, and the analysis results were compared systematically. Meanwhile, in order to reduce the influence of noise and improve the color characterization performance of spectral CT images, the method of combining double domain filtering with pixel value square was proposed to denoise the noisy principal component images, and then the selected principal component images were mapped to RGB color channels. The experimental results demonstrate that the principal component analysis can obtain clear CT images and identify the different components of the substance, whether in the projection domain or the image domain. However, compared with the principal component analysis method in the image domain, principal component analysis in the projection domain can retain more details of the substance and acquire clearer color CT images.