Skip to main content
U.S. Department of Energy
Office of Scientific and Technical Information

Fast gradient-based methods for Bayesian reconstruction of transmission and emission PET images

Journal Article · · IEEE Transactions on Medical Imaging (Institute of Electrical and Electronics Engineers); (United States)
DOI:https://doi.org/10.1109/42.363099· OSTI ID:6873587
; ;  [1];  [2]
  1. Univ. of Southern California, Los Angeles, CA (United States)
  2. Univ. of California, Los Angeles, CA (United States)

The authors describe conjugate gradient algorithms for reconstruction of transmission and emission PET images. The reconstructions are based on a Bayesian formulation, where the data are modeled as a collection of independent Poisson random variables and the image is modeled using a Markov random field. A conjugate gradient algorithm is used to compute a maximum a posteriori (MAP) estimate of the image by maximizing over the posterior density. To ensure nonnegativity of the solution, a penalty function is used to convert the problem to one of unconstrained optimization. Preconditioners are used to enhance convergence rates. These methods generally achieve effective convergence in 15--25 iterations. Reconstructions are presented of an [sup 18]FDG whole body scan from data collected using a Siemens/CTI ECAT931 whole body system. These results indicate significant improvements in emission image quality using the Bayesian approach, in comparison to filtered backprojection, particularly when reprojections of the MAP transmission image are used in place of the standard attenuation correction factors.

OSTI ID:
6873587
Journal Information:
IEEE Transactions on Medical Imaging (Institute of Electrical and Electronics Engineers); (United States), Journal Name: IEEE Transactions on Medical Imaging (Institute of Electrical and Electronics Engineers); (United States) Vol. 13:4; ISSN 0278-0062; ISSN ITMID4
Country of Publication:
United States
Language:
English