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

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

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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms*
  • Animals
  • Brain / anatomy & histology
  • Brain / physiology
  • Computer Simulation
  • Image Processing, Computer-Assisted / methods*
  • Models, Statistical
  • Nonlinear Dynamics
  • Positron-Emission Tomography / methods*
  • Primates