title: [A new Bayesian reconstruction algorithm for PET images based on correction of the detected sinogram data].
abstract: OBJECTIVE
  To improve Bayesian reconstruction of positron-emission tomography (PET) images by devising a novel coupled feedback (CF) iterative model.
  
  
  METHODS
  The CF iterative algorithm was applied to update the noisy detected emission sinogram data using the latest reconstructed image in the iterative process of PET reconstruction. The relevant operations included linear filtering, wiener filtering, and projection of the reconstructed images. The sinogram data used in the study was obtained from simulated phantom data.
  
  
  RESULTS
  The experiments and corresponding visional and quantitative comparisons showed that the new method had better performance than the traditional Bayesian reconstruction of PET images for improvement of the PET images.
  
  
  CONCLUSIONS
  The proposed sinogram-correcting method allows improvement on the original measurement data, and is applicable for PET image reconstruction or other reconstruction tasks with high noise level.
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China. chenyang20021979@163.com
authors.forename: Yang
authors.initials: Y
authors.lastname: Chen
authors.forename: Qing-qi
authors.initials: QQ
authors.lastname: Wang
authors.forename: Zhi-yong
authors.initials: ZY
authors.lastname: Huang
authors.forename: Wu-fan
authors.initials: WF
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008962:Models, Theoretical; D049268:Positron-Emission Tomography / Q000379:methods*