1 June 1994 Parallel implementation of the maximum likelihood-expectation maximization (ML-EM) reconstruction algorithm for positron emission tomography (PET) images in a visual language
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Abstract
Due to its iterative nature, the execution of the maximum likelihood expectation maximization (ML-EM) reconstruction algorithm requires a long computation time. To overcome this problem, multiprocessor machines could be used. In this paper, a parallel implementation of the algorithm for positron emission tomography (PET) images is presented. To cope with the difficulties involved with parallel programming a programming environment based on a visual language has been used.
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Koen Bastiaens, Ignace L. Lemahieu, "Parallel implementation of the maximum likelihood-expectation maximization (ML-EM) reconstruction algorithm for positron emission tomography (PET) images in a visual language", Proc. SPIE 2238, Hybrid Image and Signal Processing IV, (1 June 1994); doi: 10.1117/12.177711; https://doi.org/10.1117/12.177711
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KEYWORDS
Positron emission tomography

Scanners

Reconstruction algorithms

Expectation maximization algorithms

Visualization

Sensors

Computer programming

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