A Forward Regridding Method With Minimal Oversampling for Accurate and Efficient Iterative Tomographic Algorithms

Filippo Arcadu, Masih Nilchian, Alain Studer, Marco Stampanoni, Federica Marone
2016 IEEE Transactions on Image Processing  
Reconstruction of underconstrained tomographic data sets remains a major challenge. Standard analytical techniques frequently lead to unsatisfactory results due to insufficient information. Several iterative algorithms, which can easily integrate a priori knowledge, have been developed to tackle this problem during the last few decades. Most of these iterative algorithms are based on an implementation of the Radon transform that acts as forward projector. This operator and its adjoint, the
more » ... s adjoint, the backprojector, are typically called few times per iteration and represent the computational bottleneck of the reconstruction process. Here, we present a Fourier-based forward projector, founded on the regridding method with minimal oversampling. We show that this implementation of the Radon transform significantly outperforms in efficiency other state-of-the-art operators with O(N 2 log 2 N) complexity. Despite its reduced computational cost, this regridding method provides comparable accuracy to more sophisticated projectors and can, therefore, be exploited in iterative algorithms to substantially decrease the time required for the reconstruction of underconstrained tomographic data sets without loss in the quality of the results. Index Terms-Tomography, iterative reconstruction algorithms, Radon transform, forward and backprojector, regridding method, minimal oversampling, kaiser-bessel, prolate-spheroidal wavefunctions.
doi:10.1109/tip.2016.2516945 pmid:26800537 fatcat:b6euvsnk5zcz5jnnjto2aenabe