光学学报, 2020, 40 (21): 2111004, 网络出版: 2020-10-25
基于双能CT图像域的DL-RTV多材料分解研究 下载: 1119次
Image-Domain Multimaterial Decomposition for Dual-Energy CT Based on Dictionary Learning and Relative Total Variation
成像系统 双能计算机断层扫描技术 多材料分解 图像域 字典学习 相对总变分 imaging systems dual-energy computed tomography multi-material decomposition image domain dictionary learning relative total variation
摘要
双能计算机断层扫描(DECT)技术因能分解和识别材料,并提供定量化的成像结果,广泛应用于医疗、安检、无损检测以及材料科学等领域。DECT技术能提供物体在两种能谱下的衰减信息,可准确分解两种基材料。但当检测对象含有三种材料时,若对DECT图像直接求逆(DIMD)分解多材料,其基图像将含较多噪声和伪影。为此,提出了一种基于双能CT图像域的字典学习(DL)和相对总变分(RTV)的多材料分解算法,简称DL-RTV算法。通过直接求逆获得初始基图像,利用字典学习挖掘基图像的稀疏性,以提高材料分解的准确性;引进RTV进一步降低基图像的噪声和伪影,并保护图像细节;同时引入各基材料质量守恒和像素边界的约束项,提高材料分解精度。仿真和实验研究表明,DL-RTV算法能较准确地分解三种材料,较好抑制基图像噪声和伪影,提高了材料区分度,从而验证了此算法的有效性和实用性,这对DECT技术的发展和应用具有重要的意义。
Abstract
Dual-energy computed tomography (DECT) has been widely used to medical imaging, security inspection, nondestructive testing, materials science and so on, with its capability to decompose and identify materials and provide quantified results. DECT technique can accurately decompose two basis materials due to its performance to acquire the attenuation information of the scanned object at low and high energies. However, when there are three basis materials, if the direct inverse material decomposition (DIMD) is used to decompose the materials, the material CT images will contain much noise and artifacts. Therefore, we propose an image domain multi-material decomposition algorithm for DECT based on dictionary learning (DL) and relative total variation(RTV), which is called DL-RTV for short. The method employs the DIMD to acquire original material images, and then trains a dictionary to explore the sparsity of the images and improve the accuracy of the material decomposition. Meanwhile, the RTV is introduced to further reduce the noise and artifacts of the images and preserve details. In addition, the constrains of mass conservation and the bounds of each pixel are added into the DL-RTV model to enhance the material decomposition accuracy. Simulation and experimental results indicate that the DL-RTV method can decompose three kinds of materials accurately, suppress the noise and artifact of the basis images and improve the material discrimination. The method is authenticated to be effective and practical, which has important significance for the development and application of DECT.
降俊汝, 余海军, 龚长城, 刘丰林. 基于双能CT图像域的DL-RTV多材料分解研究[J]. 光学学报, 2020, 40(21): 2111004. Junru Jiang, Haijun Yu, Changcheng Gong, Fenglin Liu. Image-Domain Multimaterial Decomposition for Dual-Energy CT Based on Dictionary Learning and Relative Total Variation[J]. Acta Optica Sinica, 2020, 40(21): 2111004.