title: Sparse representation and dictionary learning penalized image reconstruction for positron emission tomography.
abstract: Accurate and robust reconstruction of the radioactivity concentration is of great importance in positron emission tomography (PET) imaging. Given the Poisson nature of photo-counting measurements, we present a reconstruction framework that integrates sparsity penalty on a dictionary into a maximum likelihood estimator. Patch-sparsity on a dictionary provides the regularization for our effort, and iterative procedures are used to solve the maximum likelihood function formulated on Poisson statistics. Specifically, in our formulation, a dictionary could be trained on CT images, to provide intrinsic anatomical structures for the reconstructed images, or adaptively learned from the noisy measurements of PET. Accuracy of the strategy with very promising application results from Monte-Carlo simulations, and real data are demonstrated.
authors.affiliation: State Key Laboratory of Modern Optical Instrumentation, Department of Optical Engineering, Zhejiang University, Hangzhou 310027, People's Republic of China.
authors.forename: Shuhang
authors.initials: S
authors.lastname: Chen
authors.forename: Huafeng
authors.initials: H
authors.lastname: Liu
authors.forename: Pengcheng
authors.initials: P
authors.lastname: Shi
authors.forename: Yunmei
authors.initials: Y
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003952:Diagnostic Imaging / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016013:Likelihood Functions; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D009010:Monte Carlo Method; D049268:Positron-Emission Tomography / Q000379:methods*; D059629:Signal-To-Noise Ratio; D013909:Thorax / Q000000981:diagnostic imaging*