title: A Model of Regularization Parameter Determination in Low-Dose X-Ray CT Reconstruction Based on Dictionary Learning.
abstract: In recent years, X-ray computed tomography (CT) is becoming widely used to reveal patient's anatomical information. However, the side effect of radiation, relating to genetic or cancerous diseases, has caused great public concern. The problem is how to minimize radiation dose significantly while maintaining image quality. As a practical application of compressed sensing theory, one category of methods takes total variation (TV) minimization as the sparse constraint, which makes it possible and effective to get a reconstruction image of high quality in the undersampling situation. On the other hand, a preliminary attempt of low-dose CT reconstruction based on dictionary learning seems to be another effective choice. But some critical parameters, such as the regularization parameter, cannot be determined by detecting datasets. In this paper, we propose a reweighted objective function that contributes to a numerical calculation model of the regularization parameter. A number of experiments demonstrate that this strategy performs well with better reconstruction images and saving of a large amount of time.
authors.affiliation: Medical Imaging Laboratory, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China ; Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China ; University of Chinese Academy of Sciences, Beijing 100049, China.
authors.forename: Cheng
authors.initials: C
authors.lastname: Zhang
authors.affiliation: Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
authors.forename: Tao
authors.initials: T
authors.lastname: Zhang
authors.affiliation: Medical Imaging Laboratory, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.
authors.forename: Jian
authors.initials: J
authors.lastname: Zheng
authors.affiliation: Medical Imaging Laboratory, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China ; Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China ; University of Chinese Academy of Sciences, Beijing 100049, China.
authors.forename: Ming
authors.initials: M
authors.lastname: Li
authors.affiliation: Medical Imaging Laboratory, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China ; Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China ; University of Chinese Academy of Sciences, Beijing 100049, China.
authors.forename: Yanfei
authors.initials: Y
authors.lastname: Lu
authors.affiliation: Medical Imaging Laboratory, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China ; Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China ; University of Chinese Academy of Sciences, Beijing 100049, China.
authors.forename: Jiali
authors.initials: J
authors.lastname: You
authors.affiliation: PET Center, Huashan Hospital, Fudan University, Shanghai 200235, China.
authors.forename: Yihui
authors.initials: Y
authors.lastname: Guan
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data; D000069550:Machine Learning; D015233:Models, Statistical; D019047:Phantoms, Imaging; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D014057:Tomography, X-Ray Computed / Q000379:methods* / Q000706:statistics & numerical data