光学学报, 2018, 38 (11): 1111002, 网络出版: 2019-05-09
基于图像总变分和张量字典的多能谱CT材料识别研究 下载: 912次
Material Discrimination by Multi-Spectral CT Based on Image Total Variation and Tensor Dictionary
成像系统 多能谱计算机断层成像技术 材料识别 总变分 张量字典 图像重建 imaging systems multi-spectral computed tomography material discrimination total variation tensor dictionary image reconstruction
摘要
基于光子计数探测器的多能谱计算机断层成像技术(CT),能够获得多个能量段的能谱信息,在材料识别方面有着独特的优势。由于窄能谱探测及光子计数探测器存在一致性差的问题,多能谱CT图像中含有较多的噪声和伪影,这不利于材料的分解与识别。因此从重建的角度出发,改进了传统张量字典学习(TDL)方法,提出一种基于图像总变分(TV)和TDL的图像重建算法,简称TV+TDL。该算法不但继承了TDL 算法在刻画各个能量通道图像之间相似性的优势,而且通过引进TV作为正则项,可进一步恢复图像微小结构和细节并有效地抑制噪声,提高材料分解精度。仿真实验结果表明,TV+TDL算法能够有效重建高质量的多能谱CT图像,并成功实现基材料模型下的材料分解与识别,从而验证了该方法的有效性和实用性。
Abstract
The spectral computed tomography (CT) based on the photon-counting detector has a great potential in material discrimination for its ability to obtain the energy spectral information at multiple energy bands. Due to the poor consistency between the narrow energy-bin detection and the photon-counting detector, there are lots of noises and artifacts in the multi-spectral CT images, which is not beneficial to material decomposition and discrimination. Thus, from the point of view of reconstruction, the traditional study method based on tensor dictionary learning (TDL) is improved and a new image reconstruction method based on image total variation (TV) and TDL is developed, which is called TV+TDL for short. This method not only inherits the advantage of the TDL method in enforcing the similarity among all energy channel images, but also further recovers the slim structures and details, effectively suppresses noises, and thus improves the accuracy of material decomposition by introducing the image TV as a regularization term. The simulation results show that the TV+TDL method can effectively reconstruct high-quality multi-spectral CT images and successfully realize material decomposition and discrimination based on the base material model. The validity and practicability of this method are verified.
陈佩君, 冯鹏, 伍伟文, 吴晓川, 傅翔, 魏彪, 何鹏. 基于图像总变分和张量字典的多能谱CT材料识别研究[J]. 光学学报, 2018, 38(11): 1111002. Peijun Chen, Peng Feng, Weiwen Wu, Xiaochuan Wu, Xiang Fu, Biao Wei, Peng He. Material Discrimination by Multi-Spectral CT Based on Image Total Variation and Tensor Dictionary[J]. Acta Optica Sinica, 2018, 38(11): 1111002.