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

Guobao Wang, Jinyi Qi

Research output: Contribution to journalArticle

53 Scopus citations


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.

Original languageEnglish (US)
Article number6263303
Pages (from-to)1977-1988
Number of pages12
JournalIEEE Transactions on Medical Imaging
Issue number10
StatePublished - 2012



  • Image reconstruction
  • kinetic modeling
  • parametric imaging
  • penalized maximum likelihood

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Radiological and Ultrasound Technology
  • Software

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