Myers, GlennKingston, AndrewLatham, ShaneRecur, BenoitLi, Heyang (Thomas)Turner, Michael L.Beeching, LeviSheppard, Adrian2019-12-172019-12-17Glenn R. Myers, Andrew M. Kingston, Shane J. Latham, Benoit Recur, Thomas Li, Michael L. Turner, Levi Beeching, 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); doi: 10.1117/12.22382670277-786Xhttp://hdl.handle.net/1885/195616In 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.This research was supported under the Australian Research Council's Linkage Projects funding scheme (project number LP150101040), in collaboration with FEI.application/pdfen-AU© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)X-ray Computed tomographyiterative image reconstructionmultigrid2016-10-0310.1117/12.2238267