能谱计算机断层扫描在数据采集过程中可以区分光子能量,并同时得到多个能量通道的投影。由于单个能量通道只包含了总光子数的一小部分,且大多数光子计数探测器只能承受有限的计数率,所以多通道投影通常含有较大的噪声。为了从噪声投影中重建出高质量的能谱图像,利用不同能量通道下重建图像具有结构相似性,提出一种基于结构先验的加权非局部全变分(NLTV)重建算法。设计了简单和复杂两种模型进行仿真,比较了TV算法、NLTV算法、加权NLTV算法,以及基于结构先验的加权NLTV等去噪算法的重建效果,结果表明,本文算法对复杂模型和高噪声模型的重建具有明显优势。

Spectral computed tomography can distinguish the different photon energy in the data acquisition process, and get the projections of multiple energy channels simultaneously. As a single energy channel, it only contains a small part of the total number of photons, and most of the photon counting detectors can only carry a limited count rate, multi-channel projections often contain large amounts of noise. In order to rebuild the high-quality energy spectrum images from noise projections, and to reconstruct the images with different energy channels, we propose a weighted non-local total variation (NLTV) reconstruction algorithm based on the structural prior information. We design a simple model and a complex one, and both of them are simulated by TV algorithm, NLTV algorithm, weighted NLTV algorithm and weighted NLTV algorithm based on the structural prior information, then compare their reconstruction effects. Results show that this algorithm has obvious advantages for the reconstruction of complex model and high noise model.