Object-dependent performance comparison of two iterative reconstruction algorithms

Iterative algorithms are of interest for both positron-emission tomography (PET) and single-photon-emission computed tomography (SPECT) because they permit accurate modeling of the imaging system, and they can be derived to satisfy certain statistical performance criteria. The convergence process, h...

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Bibliographic Details
Published in:IEEE transactions on nuclear science 1988-02, Vol.35 (1), p.611-614
Main Authors: Stamos, J.A., Rogers, W.L., Clinthorne, N.H., Koral, K.F.
Format: Article
Language:English
Subjects:
Convergence
Detectors
Gaussian noise
Image reconstruction
Iterative algorithms
Maximum likelihood detection
Maximum likelihood estimation
Pixel
Reconstruction algorithms
Subspace constraints
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Summary:Iterative algorithms are of interest for both positron-emission tomography (PET) and single-photon-emission computed tomography (SPECT) because they permit accurate modeling of the imaging system, and they can be derived to satisfy certain statistical performance criteria. The convergence process, however, is influenced by the object distribution and noise level, so that different algorithms demonstrate a wide range of convergence phenomena. This object dependence is described for two widely accepted image-reconstruction algorithms; ART and maximum-likelihood estimation.< >
ISSN:0018-9499
1558-1578
DOI:10.1109/23.12797