  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.
