  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.
