基于Split-Bregman算法的能谱计算机层析图像重建

Spectral Computed Tomographic Image Reconstruction Based on Split-Bregman Algorithm

与传统计算机层析(CT)成像技术相比,能谱CT可在一次扫描中得到物体在不同能谱通道下的投影图像,这有利于区分物体的材质,提高信号噪声比。基于光子计数探测器的能谱CT是近年来成像领域研究的热点。由于能谱通道变窄,每个能谱通道内的噪声增加。为了有效降低通道内的噪声,采用基于全变分最小化的SplitBregman算法进行图像重建。根据重建模体的先验信息,进行能谱通道的划分;采用Split-Bregman算法对含噪声和稀疏角的能谱投影数据进行重建。仿真结果表明,基于Split-Bregman算法的能谱CT图像重建方法能够有效减少能谱通道内噪声的影响,满足物体材质区分的需求。

Compared with the traditional computed tomography （CT）, the spectral CT can obtain projection images of the object in different energy spectrum channels with a single scan, which is helpful to improve the contrast-to- noise ratio and distinguish the materials. Spectral CT based on photon counting detector is a hot research topic in recent years. As the energy spectrum channel narrows, the noise increases in each energy spectrum channel. In order to reduce the noise in the channels effectively, the Split-Bregman algorithm based on the total variation minimization is used for spectral CT image reconstruction. The spectral range is divided into different channels according to the prior information of the reconstructed model. The reconstructions are conducted for the projection data with noise and sparse angle based on the Split-Bregman algorithm. The simulation results show that the spectral CT image reconstruction based on the Split-Bregman algorithm can reduce the influence of the noise in spectral channels effectively, and satisfying the requirement of substance distinguishing.