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Proceedings Paper

Stochastic reconstruction of incomplete data sets using Gibbs priors in positron emission tomography
Author(s): Chin-Tu Chen; Oscar H. Kapp; Wing H. Wong
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Paper Abstract

Statistical method for image reconstruction in positron emission tomography (PET) have been utilized with increasing frequency in recent years because of their potential for yielding improved image quality. Stochastic techniques such as the inexact reconstruction technique (IRT) have provided a fruitful approach to the problem of image reconstruction with only a limited number of projection views by applying an iterative approach, with certain constraints, to the treatment of backprojected probability matrices. We have combined the use of the IRT with a new Bayesian model developed recently in our laboratories which employs a Gibbs prior that incorporate some prior information to describe the spatial correlation of neighboring regions and takes into account the effect of the limited spatial resolution as well. This model incorporates continuous values for `line sites' in order to avoid computational difficulties in the determination of point estimate of the image. In addition, we use a square-root transformation for Poisson intensity allowing ready incorporation into the Gibbs formulation. The method of iterative conditional averages was used for computing the point estimates. A preliminary study showed promising results with the use of data from only 8 projection angles.

Paper Details

Date Published: 26 June 1992
PDF: 3 pages
Proc. SPIE 1660, Biomedical Image Processing and Three-Dimensional Microscopy, (26 June 1992); doi: 10.1117/12.59542
Show Author Affiliations
Chin-Tu Chen, Univ. of Chicago (United States)
Oscar H. Kapp, Univ. of Chicago (United States)
Wing H. Wong, Univ. of Chicago (United States)


Published in SPIE Proceedings Vol. 1660:
Biomedical Image Processing and Three-Dimensional Microscopy
Raj S. Acharya; Carol J. Cogswell; Dmitry B. Goldgof, Editor(s)

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