Low-Dose CT Reconstruction via Weighted Dictionary Learning Method
ZHANG Cheng1, ZHANG Jian2, DU Qiang1, LI Ming1, LIU Jingxin3
1.Suzhou Institute of Biomedical Engineering, Chinese Academy of Sciences, Suzhou Jiangsu 215163, China;
2.Changchun Institute of Metrology Verification and Testing Technology, Changchun Jilin 130012, China;
3.Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun Jilin 130033, China
Abstract：To prevent the patients from the overdose of X-ray radiation, the radiation dose should be reduced in the design of the CT
scanning device. The dictionary learning reconstruction algorithm, which is derived from the compressed sensing theory, is able to
make high quality recovery from the under-sampled scanning data. However, the regularization term of this algorithm is not able to
distinguish the noise and the low-contrast information, tending to lose the soft tissue edge details. This article proposed a weighted
dictionary learning method, which calculated the weight factors of the regularization term based on the residuals of the image
patches by subtracting the dictionary sparse representation after each iterative step. The weight factors helped preserve structural
information of the image and smooth the noise during the iterative process by changing the smooth effects upon different regions of
the image. The corresponding experiments proves that the proposed algorithm preserves the soft tissue edge details efficiently. The
quality of the reconstruction image is improved compared with the existing dictionary learning method.
章程1，张健2，杜强1，李铭1，刘景鑫3. 基于加权字典学习方法的低剂量CT图像重建[J]. 中国医疗设备, 2018, 33(6): 12-15.
ZHANG Cheng1, ZHANG Jian2, DU Qiang1, LI Ming1, LIU Jingxin3. Low-Dose CT Reconstruction via Weighted Dictionary Learning Method. China Medical Devices, 2018, 33(6): 12-15.
