基于能谱CT的材料组分彩色表征研究

基于光子计数探测器的X射线能谱CT，通过增加能谱分辨率实现了CT图像由灰度向彩色的转变，提高了材料识别能力。然而随着能谱通道数量的增加，通道中的噪声也显著增加，降低了材料识别的准确性。为充分利用能谱CT图像的稀疏性以及能谱通道之间图像的相关性，提出一种多约束窄谱CT迭代重建算法，可在降低噪声的同时有效保留图像的边缘及细节部分。进一步利用主成分分析对窄谱CT图像中的能谱信息进行估计，并建立主成分图像与R，G和B颜色分量之间的映射关系，最后获取彩色CT图像。该方法通过材料组分的彩色表征可以有效识别材料，同时降低图像中的背景噪声。仿真实验和实际实验结果验证了多约束窄谱CT迭代重建算法的有效性以及利用主成分分析进行材料组分彩色表征的可行性。

Research on Color Characterization of Material Components Based on Spectral CT

Photon-counting detector based X-ray spectral computed tomography (CT), realizes the transformation of CT image from gray to color by increasing energy resolution, which increases material identification capability. However, with increasing the number of energy channels, the channel’s noise increases significantly, which decreases the accuracy of material identification. In order to make full use of the sparsity of spectral CT images and the correlation between spectral CT images, a multi constraint narrow-spectral CT iterative reconstruction algorithm is proposed, which can effectively preserve the edges and details of the image while reducing the noise. Furthermore, principal component analysis (PCA) is used to estimate the spectrum information in narrow spectrum CT images, and the mapping relationship between principal component image and color components R, G, B are established. Finally, the color CT image is obtained. This method can effectively identify materials through the color representation of material components and reduce the background noise in the images. The results of simulation and practical experiments show the proposed reconstruction algorithm is effective, and it is feasible to use PCA for the color characterization of material components.