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We propose a stable and fast reconstruction technique for parallel-beam (PB)tomographic X-ray imaging, relying on the discrete pseudo-polar (PP) Radontransform. Our main contribution is a resampling method, based on modernsampling theory, that transforms the acquired PB measurements to a PP grid. Theresampling process is both fast and accurate, and in addition, simultaneouslydenoises the measurements, by exploiting geometrical properties of thetomographic scan. The transformed measurements are then reconstructed using aniterative solver with total variation (TV) regularization. We show thatreconstructing from measurements on the PP grid, leads to improved recovery,due to the inherent stability and accuracy of the PP Radon transform, comparedwith the PB Radon transform. We also demonstrate recovery from a reduced numberof PB acquisition angles, and high noise levels. Our approach is shown toachieve superior results over other state-of-the-art solutions, that operatedirectly on the given PB measurements. The proposed method can benefit fan-beamand/or cone-beam projections by coupling it with a rebinning process.

RAPToR: A Resampling Algorithm for Pseudo-Polar based Tomographic Reconstruction