In K-edge decomposition imaging for the multienergy system with the photon counting detectors (PCDs), the energy bins significantly affect the intensity of the extracted K-edge signal. Optimized energy bins can provide a better K-edge signal to improve the quality of the decomposition images and have the potential to reduce the amount of contrast agents. In this article, we present the Gaussian spectrum selection method (GSSM) for the multienergy K-edge decomposition imaging which can extract an optimized K-edge signal by optimizing energy bins compared with the conventional theoretical attenuation selection method (TASM). GSSM decides the width and locations of the energy bins using a simple but effective model of the imaging system, which takes the degraded energy resolution of the detector and the continuous x-ray spectrum into consideration. Besides, we establish the objective function, difference of attenuation to relative standard deviation ratio (DAR), to determine the optimal energy bins which maximize the K-edge signal. The results show that GSSM gets a better K-edge signal than TASM especially at the lower concentration level of contrast agents. The new method has the potential to improve the contrast and reduce the amount of contrast agents.

在具有光子计数检测器（PCD）的多能量系统的K边缘分解成像中，能量仓会显着影响提取的K边缘信号的强度。优化的能量箱可以提供更好的K边缘信号，以改善分解图像的质量，并有可能减少造影剂的数量。在本文中，我们提出了一种用于多能量K边缘分解成像的高斯谱选择方法（GSSM），与传统的理论衰减选择方法（TASM）相比，该方法可以通过优化能量仓来提取优化的K边缘信号。GSSM使用简单但有效的成像系统模型确定能量箱的宽度和位置，该模型考虑了探测器能量分辨率下降和连续X射线光谱的问题。此外，我们建立目标函数，即衰减与相对标准偏差比（DAR）的差，以确定使K边缘信号最大化的最佳能量仓。结果表明，GSSM比TASM获得更好的K边缘信号，特别是在较低的造影剂浓度下。新方法具有改善对比度并减少造影剂数量的潜力。