title: [Reconstruction of dynamic positron emission tomographic images by exploiting low rank and sparse penalty].
abstract: OBJECTIVE
  To propose a new method for dynamic positron emission tomographic (PET) image reconstruction using low rank and sparse penalty (L&S).
  
  
  METHODS
  The L&S reconstruction model was established and the split Bregman method was used to solve the optimal cost function. The one-tissue compartment model was used to simulate a set of PET 82Rb myocardial perfusion image. The L&S reconstruction method was compared with maximum likelihood expectation maximization (MLEM) method, low-rank penalty method and sparse penalty method.
  
  
  RESULTS
  The L&S reconstruction method had the smallest MSE and well maintained the feature information. The polar map created by L&S method was the most similar with the reference actual polar map.
  
  
  CONCLUSIONS
  L&S reconstruction method is better than the other three methods in both visual and quantitative analysis of the PET images.
authors.affiliation: 1School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China; Yantai Yuhuangding Hospital, Yantai 264000, China; 3Department of Radiology, Cancer Center, Guangdong General Hospital/Guangdong Academy of Medical Science, Guangzhou 510080, China. E-mail: wei-xia-ping@163.com.
authors.forename: Xia-Ping
authors.initials: XP
authors.lastname: Wei
authors.forename: Xue-Wen
authors.initials: XW
authors.lastname: Jiang
authors.forename: Xiao-Mian
authors.initials: XM
authors.lastname: Ma
authors.forename: Li-Jun
authors.initials: LJ
authors.lastname: Lu
mesh_terms: D000465:Algorithms; D006801:Humans; D016013:Likelihood Functions; D049268:Positron-Emission Tomography / Q000379:methods*