CBEL Publications.
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A Data-Parallel Algorithm for Tomographic Image Recontruction
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Authors:
- Johnson CA, Sofer A
- Formats:
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This publication is available in Adobe's PostScript Adobe's Portable Document Format.
- Published:
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Proceedings of the 7th Symposium on the Frontiers of Massively Parallel Computation (1999) (IEEE Computer Society Press, Washington)
- Abstract:
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In the tomographic imaging problem
images are reconstructed from a set of measured projections
Iterative reconstruction methods are computationally intensive
alternatives to the more traditional Fourier-based methods
Despite their high cost, the popularity of these methods is
increasing because of the advantages they pose. Although
numerous iterative methods have been proposed over the years
all of these methods can be shown to have a similar
computational structure. This paper presents a parallel
algorithm that we originally developed for performing the
expectation maximization algorithm in emission tomography
This algorithm is capable of exploiting the sparsity and
symmetries of the model in a computationally efficient manner
Our parallelization scheme is based upon decomposition of the
measurement-space vectors. We demonstrate that such a
parallelization scheme is applicable to the vast majority of
iterative reconstruction algorithms proposed to date
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