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Proceedings Paper

Rapidly converging multigrid reconstruction of cone-beam tomographic data
Author(s): Glenn R. Myers; Andrew M. Kingston; Shane J. Latham; Benoit Recur; Thomas Li; Michael L. Turner; Levi Beeching; Adrian P. Sheppard
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Paper Abstract

In the context of large-angle cone-beam tomography (CBCT), we present a practical iterative reconstruction (IR) scheme designed for rapid convergence as required for large datasets. The robustness of the reconstruction is provided by the “space-filling” source trajectory along which the experimental data is collected. The speed of convergence is achieved by leveraging the highly isotropic nature of this trajectory to design an approximate deconvolution filter that serves as a pre-conditioner in a multi-grid scheme. We demonstrate this IR scheme for CBCT and compare convergence to that of more traditional techniques.

Paper Details

Date Published: 3 October 2016
PDF: 7 pages
Proc. SPIE 9967, Developments in X-Ray Tomography X, 99671M (3 October 2016); doi: 10.1117/12.2238267
Show Author Affiliations
Glenn R. Myers, The Australian National Univ. (Australia)
Andrew M. Kingston, The Australian National Univ. (Australia)
Shane J. Latham, The Australian National Univ. (Australia)
Benoit Recur, Institut National de la Santé et de la Recherche Médicale (France)
Thomas Li, The Australian National Univ. (Australia)
Michael L. Turner, The Australian National Univ. (Australia)
Levi Beeching, The Australian National Univ. (Australia)
Adrian P. Sheppard, The Australian National Univ. (Australia)

Published in SPIE Proceedings Vol. 9967:
Developments in X-Ray Tomography X
Stuart R. Stock; Bert Müller; Ge Wang, Editor(s)

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