A data-parallel algorithm for iterative tomographic image reconstruction

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...

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Bibliographic Details
Published in:The Frontiers of Massively Parallel Processing: Proceedings: 7th Symposium, 1999, Annapolis, Maryland pp. 126 - 137
Main Authors: Johnson, C.A., Sofer, A.
Format: Conference Proceeding
Language:English
Published: IEEE 1999
Subjects:
Computed tomography
Extraterrestrial measurements
Geophysical measurements
Image reconstruction
Instruments
Iterative algorithms
Iterative methods
Optical imaging
Reconstruction algorithms
X-ray imaging
ISBN:0769500870, 9780769500874
Online Access:Get full text
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Summary: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.
ISBN:0769500870
9780769500874
DOI:10.1109/FMPC.1999.750592