Rapidly converging multigrid reconstruction of cone-beam tomographic data

Glenn R. Myers; Andrew M. Kingston; Shane J. Latham; Benoit Recur; Thomas Li; Michael L. Turner; Levi Beeching; Adrian P. Sheppard

doi:10.1117/12.2238267

3 October 2016 Rapidly converging multigrid reconstruction of cone-beam tomographic data

Glenn R. Myers, Andrew M. Kingston, Shane J. Latham, Benoit Recur, Thomas Li, Michael L. Turner, Levi Beeching, Adrian P. Sheppard

Author Affiliations +

Proceedings Volume 9967, Developments in X-Ray Tomography X; 99671M (2016) https://doi.org/10.1117/12.2238267
Event: SPIE Optical Engineering + Applications, 2016, San Diego, California, United States

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.

Citation Download Citation

Glenn R. Myers, Andrew M. Kingston, Shane J. Latham, Benoit Recur, Thomas Li, Michael L. Turner, Levi Beeching, and Adrian P. Sheppard "Rapidly converging multigrid reconstruction of cone-beam tomographic data", Proc. SPIE 9967, Developments in X-Ray Tomography X, 99671M (3 October 2016); https://doi.org/10.1117/12.2238267

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