Low-dose spectral CT reconstruction using L0 image gradient and tensor dictionary
This work addresses image quality issues in low-dose spectral CT for medical imaging applications, representing an incremental improvement over existing methods.
The authors tackled low-dose spectral CT reconstruction by proposing the L0TDL method, which combines tensor dictionary learning with an L0-norm constraint on image gradients to improve edge preservation; results from simulations and mouse studies show it outperforms competing methods like TV minimization and TDL.
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.