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

Abstract:

: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.

journal_name

IEEE Trans Med Imaging

authors

Wang G,Qi J

doi

10.1109/TMI.2012.2212203

subject

Has Abstract

pub_date

2012-10-01 00:00:00

pages

1977-88

issue

10

eissn

0278-0062

issn

1558-254X

journal_volume

31

pub_type

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