title: Low-mAs X-ray CT image reconstruction by adaptive-weighted TV-constrained penalized re-weighted least-squares.
abstract: BACKGROUND
  The negative effects of X-ray exposure, such as inducing genetic and cancerous diseases, has arisen more attentions.
  
  
  OBJECTIVE
  This paper aims to investigate a penalized re-weighted least-square (PRWLS) strategy for low-mAs X-ray computed tomography image reconstruction by incorporating an adaptive weighted total variation (AwTV) penalty term and a noise variance model of projection data.
  
  
  METHODS
  An AwTV penalty is introduced in the objective function by considering both piecewise constant property and local nearby intensity similarity of the desired image. Furthermore, the weight of data fidelity term in the objective function is determined by our recent study on modeling variance estimation of projection data in the presence of electronic background noise.
  
  
  RESULTS
  The presented AwTV-PRWLS algorithm can achieve the highest full-width-at-half-maximum (FWHM) measurement, for data conditions of (1) full-view 10 mA acquisition and (2) sparse-view 80 mA acquisition. In comparison between the AwTV/TV-PRWLS strategies and the previous reported AwTV/TV-projection onto convex sets (AwTV/TV-POCS) approaches, the former can gain in terms of FWHM for data condition (1), but cannot gain for the data condition (2).
  
  
  CONCLUSIONS
  In the case of full-view 10 mA projection data, the presented AwTV-PRWLS shows potential improvement. However, in the case of sparse-view 80 mA projection data, the AwTV/TV-POCS shows advantage over the PRWLS strategies.
authors: Department of Radiology, State University of New York, Stony Brook, NY, USA Department of Electrical and Computer Engineering, State University of New York, Stony Brook, NY, USA.
authors: Yan
authors: Y
authors: Liu
authors: School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.
authors: Jianhua
authors: J
authors: Ma
authors: Department of Radiology, State University of New York, Stony Brook, NY, USA Department of Radiology and Biomedical Engineering, State University of New York, Stony Brook, NY, USA.
authors: Hao
authors: H
authors: Zhang
authors: Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA.
authors: Jing
authors: J
authors: Wang
authors: Department of Radiology, State University of New York, Stony Brook, NY, USA Department of Radiology and Biomedical Engineering, State University of New York, Stony Brook, NY, USA.
authors: Zhengrong
authors: Z
authors: Liang
mesh_terms: D000465:Algorithms; D006257:Head / Q000000981:diagnostic imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016018:Least-Squares Analysis; D008954:Models, Biological; D019047:Phantoms, Imaging; D011829:Radiation Dosage; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: X-ray computed tomography; adaptive weighted total variation; low-mAs protocol; penalized re-weighted least-squares; projection onto convex sets