An optimization transfer algorithm for nonlinear parametric image reconstruction from dynamic PET data.
Direct reconstruction of kinetic parameters from raw projection data is a challenging task in molecular imaging using dynamic positron emission tomography (PET). This paper presents a new optimization transfer algorithm for penalized likelihood direct reconstruction of nonlinear parametric images that is easy to use and has a fast convergence rate. Each iteration of the proposed algorithm can be implemented in three simple steps: a frame-by-frame maximum likelihood expectation-maximization (EM)-like image update, a frame-by-frame image smoothing, and a pixel-by-pixel time activity curve fitting. Computer simulation shows that the direct algorithm can achieve a better bias-variance performance than the indirect reconstruction algorithm. The convergence rate of the new algorithm is substantially faster than our previous algorithm that is based on a separable paraboloidal surrogate function. The proposed algorithm has been applied to real 4-D PET data.
Department of Biomedical Engineering, University of California, Davis, CA 95616, USA. gbwang@ucdavis.edu
Guobao
G
Wang
Jinyi
J
Qi
D000465:Algorithms; D000818:Animals; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology; D003198:Computer Simulation; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D049268:Positron-Emission Tomography / Q000379:methods*; D011323:Primates