Send to

Choose Destination
IEEE Trans Med Imaging. 2012 Oct;31(10):1977-88. doi: 10.1109/TMI.2012.2212203. Epub 2012 Aug 8.

An optimization transfer algorithm for nonlinear parametric image reconstruction from dynamic PET data.

Author information

Department of Biomedical Engineering, University of California, Davis, CA 95616, USA.


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.

[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for IEEE Engineering in Medicine and Biology Society Icon for PubMed Central
Loading ...
Support Center