title: [Tsallis entropy-based prior for PET reconstruction].
abstract: Maximum a Posteriori (MAP) method has been widely applied to the ill-posed problem of image reconstruction. The choice of prior is the crucial point on MAP methods. However, the most conventional priors will lead to a blurring of the whole image or cause ladder-like artifacts. We therefore proposed a Tsallis entropy-based prior for positron emission tomography (PET) iterative reconstruction in MAP framework. The method uses a Tsallis entropy-based prior to eliminate the uncertainty between prior information and the estimated images. We tested this method in the phantom image, compared it with the traditional prior methods. the results showed that the proposed algorithm could suppress noise and obtain better reconstructed image quality.
authors.affiliation: School of Biomedical Engineering, Southern Medical University , Guangzhou 510515, China.
authors.forename: Yuanyuan
authors.initials: Y
authors.lastname: Gao
authors.forename: Lijun
authors.initials: L
authors.lastname: Lu
authors.forename: Jianhua
authors.initials: J
authors.lastname: Ma
authors.forename: Zhaoying
authors.initials: Z
authors.lastname: Bian
authors.forename: Qingwen
authors.initials: Q
authors.lastname: Lu
authors.forename: Lei
authors.initials: L
authors.lastname: Cao
authors.forename: Shaoying
authors.initials: S
authors.lastname: Gao
mesh_terms: D000465:Algorithms; D016477:Artifacts; D019277:Entropy; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography / Q000379:methods*