Nonlinear reconstruction of coded spectral X-ray CT based on material decomposition

作     者：Zhang, Tong Zhao, Shengjie Ma, Xu Cuadros, Angela P. Zhao, Qile Arce, Gonzalo R. 

作者机构：Tongji Univ Sch Software Engn Shanghai 201804 Peoples R China Beijing Inst Technol Sch Opt & Photon Minist Educ China Key Lab Photoelect Imaging Technol & Syst Beijing 100081 Peoples R China Univ Delaware Dept Elect & Comp Engn Newark DE 19716 USA 

出 版 物：《OPTICS EXPRESS》 (Opt. Express)

年 卷 期：2021年第29卷第13期

页      面：19319-19339页

核心收录：

学科分类：070207[理学-光学] 07[理学] 08[工学] 0803[工学-光学工程] 0702[理学-物理学] 

基　　金：National Key Research and Development Program of China [2019YFB2102300, 2019YFB2102301] National Natural Science Foundation of China Science and Technology Innovation Plan Of Shanghai Science and Technology Commission Fundamental Research Funds for the Central Universities National Science Foundation [CIF 1717578] University Dissertation Award UNIDEL/W. L. Gore grant 

主　　题：Attenuation coefficient Biological imaging Image quality Reconstruction algorithms Spectral imaging X ray computed tomography 

摘      要：Coded spectral X-ray computed tomography (CT) based on K-edge filtered illumination is a cost-effective approach to acquire both 3-dimensional structure of objects and their material composition. This approach allows sets of incomplete rays from sparse views or sparse rays with both spatial and spectral encoding to effectively reduce the inspection duration or radiation dose, which is of significance in biological imaging and medical diagnostics. However, reconstruction of spectral CT images from compressed measurements is a nonlinear and ill-posed problem. This paper proposes a material-decomposition-based approach to directly solve the reconstruction problem, without estimating the energy-binned sinograms. This approach assumes that the linear attenuation coefficient map of objects can be decomposed into a few basis materials that are separable in the spectral and space domains. The nonlinear problem is then converted to the reconstruction of the mass density maps of the basis materials. The dimensionality of the optimization variables is thus effectively reduced to overcome the ill-posedness. An alternating minimization scheme is used to solve the reconstruction with regularizations of weighted nuclear norm and total variation. Compared to the state-of-the-art reconstruction method for coded spectral CT, the proposed method can significantly improve the reconstruction quality. It is also capable of reconstructing the spectral CT images at two additional energy bins from the same set of measurements, thus providing more spectral information of the object. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement