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Proceedings Paper

Parallel implementation of the maximum likelihood-expectation maximization (ML-EM) reconstruction algorithm for positron emission tomography (PET) images in a visual language
Author(s): Koen Bastiaens; Ignace L. Lemahieu
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Paper 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.

Paper Details

Date Published: 1 June 1994
PDF: 8 pages
Proc. SPIE 2238, Hybrid Image and Signal Processing IV, (1 June 1994); doi: 10.1117/12.177711
Show Author Affiliations
Koen Bastiaens, Univ. of Gent (Belgium)
Ignace L. Lemahieu, Univ. of Gent (Belgium)


Published in SPIE Proceedings Vol. 2238:
Hybrid Image and Signal Processing IV
David P. Casasent; Andrew G. Tescher, Editor(s)

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