title: [Edge-detecting operator-based selection of Huber regularization threshold for low-dose computed tomography imaging].
abstract: OBJECTIVE
  To compare two methods for threshold selection in Huber regularization for low-dose computed tomography imaging.
  
  
  METHODS
  Huber regularization-based iterative reconstruction (IR) approach was adopted for low-dose CT image reconstruction and the threshold of Huber regularization was selected based on global versus local edge-detecting operators.
  
  
  RESULTS
  The experimental results on the simulation data demonstrated that both of the two threshold selection methods in Huber regularization could yield remarkable gains in terms of noise suppression and artifact removal.
  
  
  CONCLUSIONS
  Both of the two methods for threshold selection in Huber regularization can yield high-quality images in low-dose CT image iterative reconstruction.
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.E-mail: slzhang0170@gmail.com.
authors.forename: Shanli
authors.initials: S
authors.lastname: Zhang
authors.forename: Hua
authors.initials: H
authors.lastname: Zhang
authors.forename: Debin
authors.initials: D
authors.lastname: Hu
authors.forename: Dong
authors.initials: D
authors.lastname: Zeng
authors.forename: Zhaoying
authors.initials: Z
authors.lastname: Bian
authors.forename: Lijun
authors.initials: L
authors.lastname: Lu
authors.forename: Jianhua
authors.initials: J
authors.lastname: Ma
authors.forename: Jing
authors.initials: J
authors.lastname: Huang
mesh_terms: D016477:Artifacts; D006801:Humans; D007091:Image Processing, Computer-Assisted; D014057:Tomography, X-Ray Computed