2017/12/13 by Weiwen Wu, Yanbo Zhang, Wu, Weiwen +9 · 1 citation
Computer Science · Engineering · Medicine · #Advanced X-ray and CT Imaging #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Medical Imaging Techniques and Applications #Photoacoustic and Ultrasonic Imaging #cs.CV
paper · pdf · doi:10.48550/arxiv.1801.01452
openalex publication_date 2017/12/13 · arxiv created 2018/07/24 · arxiv updated 2018/07/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.