Artifact Suppressed Dictionary Learning for Low-Dose CT Image Processing

Yang Chen (1, 2) , Luyao Shi (1, 2) , Qianjing Feng (3) , Jiang Yang (4) , Huazhong Shu (1, 2) , Limin Luo (1, 2) , Jean-Louis Coatrieux (2) , Wufan Chen (3)

1 LIST - Laboratory of Image Science and Technology [Nanjing]
2 CRIBS - Centre de Recherche en Information Biomédicale sino-français
3 School of Biomedical Engineering
4 Key Laboratory of Photoelectronic Imaging Technology and System

–Low-dose CT (LDCT) images are often severely degraded by amplified mottle noise and streak artifacts. These artifacts are often hard to suppress without introducing tissue blurring effects. In this paper, we propose to process LDCT images using a novel image-domain algorithm called "artifact suppressed dictionary learning (ASDL)". In this ASDL method, orientation and scale information on artifacts is exploited to train artifact atoms, which are then combined with tissue feature atoms to build three discriminative dictionaries. The streak artifacts are cancelled via a discriminative sparse representation (DSR) operation based on these dictionaries. Then, a general dictionary learning (DL) processing is applied to further reduce the noise and residual artifacts. Qualitative and quantitative evaluations on a large set of abdominal and mediastinum CT images are carried out and the results show that the proposed method can be efficiently applied in most current CT systems. Index Terms—Low-dose CT (LDCT), dictionary learning, noise, artifact suppression, artifact suppressed dictionary learning algorithm (ASDL)

Mots clés: Artifact suppressed dictionary learning algorithm (ASDL) artifact suppression dictionary learning low-dose computed tomography (LDCT) noise

Domaines: Ingénierie biomédicale

Yang Chen, Luyao Shi, Qianjing Feng, Jiang Yang, Huazhong Shu, et al.. Artifact Suppressed Dictionary Learning for Low-Dose CT Image Processing. IEEE Transactions on Medical Imaging, 2014, 33 (12), pp.2271 - 2292. ⟨10.1109/TMI.2014.2336860⟩. ⟨hal-01096038⟩