title: Anatomically-aided PET reconstruction using the kernel method.
abstract: This paper extends the kernel method that was proposed previously for dynamic PET reconstruction, to incorporate anatomical side information into the PET reconstruction model. In contrast to existing methods that incorporate anatomical information using a penalized likelihood framework, the proposed method incorporates this information in the simpler maximum likelihood (ML) formulation and is amenable to ordered subsets. The new method also does not require any segmentation of the anatomical image to obtain edge information. We compare the kernel method with the Bowsher method for anatomically-aided PET image reconstruction through a simulated data set. Computer simulations demonstrate that the kernel method offers advantages over the Bowsher method in region of interest quantification. Additionally the kernel method is applied to a 3D patient data set. The kernel method results in reduced noise at a matched contrast level compared with the conventional ML expectation maximization algorithm.
authors.affiliation: Department of Biomedical Engineering, University of California-Davis, Davis, CA, USA.
authors.forename: Will
authors.initials: W
authors.lastname: Hutchcroft
authors.forename: Guobao
authors.initials: G
authors.lastname: Wang
authors.forename: Kevin T
authors.initials: KT
authors.lastname: Chen
authors.forename: Ciprian
authors.initials: C
authors.lastname: Catana
authors.forename: Jinyi
authors.initials: J
authors.lastname: Qi
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D049268:Positron-Emission Tomography / Q000379:methods*; D012984:Software