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arXiv · 2103.10329

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
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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.

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Glenn R. Myers, Andrew M. Kingston, Shane J. Latham, Benoit Recur, Thomas Li, Michael L. Turner, Levi Beeching, Adrian P. Sheppard. 2021-03-14. Rapidly-converging multigrid reconstruction of cone-beam tomographic data. https://arxiv.org/abs/2103.10329

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