Low-dose spectral CT reconstruction using L0 image gradient and tensor dictionary

Weiwen Wu1,2, Yanbo Zhang2, Qian Wang2, Fenglin Liu1,3,*, Peijun Chen1 and Hengyong Yu2,* 1Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China 2Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA 01854, USA 3Engineering Research Center of Industrial Computed Tomography Nondestructive Testing, Ministry of Education, Chongqing University, Chongqing 400044, China *Corresponding author: Fenglin Liu and Hengyong Yu E-mail: [email protected] and [email protected]. Abstract: Spectral computed tomography (CT) has a great superiority in lesion detection, tissue characterization and material decomposition. To further extend its potential clinical applications, in this work, we propose an improved tensor dictionary learning method for low-dose spectral CT reconstruction with a constraint of image gradient l0-norm, which is named as l0TDL. The l0TDL method inherits the advantages of tensor dictionary learning (TDL) by employing the similarity of spectral CT images. On the other hand, by introducing the l0-norm constraint in gradient image domain, the proposed method emphasizes the spatial sparsity to overcome the weakness of TDL on preserving edge information. The alternative direction minimization method (ADMM) is employed to solve the proposed method. Both numerical simulations and real mouse studies are perform to evaluate the proposed method. The results show that the proposed l0TDL method outperforms other competing methods, such as total variation (TV) minimization, TV with low rank (TV+LR), and TDL methods.