Noise and resolution of Bayesian reconstruction for multiple image configurations
- Univ. of California, Los Angeles, CA (United States)
Images reconstructed by Bayesian and maximum-likelihood (ML) using a Gibbs prior with prior weight {beta} were compared with images produced by filtered back projection (FBP) from sinogram data simulated with different counts and image configurations. Bayesian images were generated by the OSL algorithm accelerated by an over relaxation parameter. For relatively low {beta}, Bayesian images can yield an overall improvement to the images compared to ML reconstruction. However, for larger {beta}, Bayesian images degrade from the standpoint of noise and quantitation. Compared to FBP, the ML images were superior in a mean square error sense in regions of low activity level and for small structures. At a comparable noise level to FBP, Bayesian reconstruction can be used to effectively recover higher resolution images. The overall performance is dependent on the image structure and the weight of the Bayesian prior.
- DOE Contract Number:
- FC03-87ER60615
- OSTI ID:
- 142401
- Journal Information:
- IEEE Transactions on Nuclear Science, Vol. 40, Issue 6; Other Information: PBD: Dec 1993
- Country of Publication:
- United States
- Language:
- English
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