Fast Bayesian techniques for attenuation corrected whole-body PET

Erkan U. Mumcuoglu, Richard Leahy, Simon R Cherry

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We 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. To ensure non-negativity 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 18FDG 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.

Original languageEnglish (US)
Title of host publicationIEEE Nuclear Science Symposium & Medical Imaging Conference
Place of PublicationPiscataway, NJ, United States
PublisherPubl by IEEE
Pages1902-1906
Number of pages5
Editionpt 3
ISBN (Print)0780314875
StatePublished - 1994
Externally publishedYes
EventProceedings of the 1993 IEEE Nuclear Science Symposium & Medical Imaging Conference - San Francisco, CA, USA
Duration: Oct 30 1993Nov 6 1993

Other

OtherProceedings of the 1993 IEEE Nuclear Science Symposium & Medical Imaging Conference
CitySan Francisco, CA, USA
Period10/30/9311/6/93

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Mumcuoglu, E. U., Leahy, R., & Cherry, S. R. (1994). Fast Bayesian techniques for attenuation corrected whole-body PET. In IEEE Nuclear Science Symposium & Medical Imaging Conference (pt 3 ed., pp. 1902-1906). Publ by IEEE.