Fast algorithms and efficient GPU implementations for the Radon transform and the back-projection operator represented as convolution operators
Center for Mathematical Sciences, Lund University, Box 118, 22100 Lund, Sweden
arXiv:1506.00014 [math.NA], (29 May 2015)
@article{andersson2015fast,
title={Fast algorithms and efficient GPU implementations for the Radon transform and the back-projection operator represented as convolution operators},
author={Andersson, Fredrik and Carlsson, Marcus and Nikitin, Viktor V.},
year={2015},
month={may},
archivePrefix={"arXiv"},
primaryClass={math.NA}
}
The Radon transform and its adjoint, the back-projection operator, can both be expressed as convolutions in log-polar coordinates. Hence, fast algorithms for the application of the operators can be constructed by using FFT, if data is resampled at log-polar coordinates. Radon data is typically measured on an equally spaced grid in polar coordinates, and reconstructions are represented (as images) in Cartesian coordinates. Therefore, in addition to FFT, several steps of interpolation have to be conducted in order to apply the Radon transform and the back-projection operator by means of convolutions. Both the interpolation and the FFT operations can be efficiently implemented on Graphical Processor Units (GPUs). For the interpolation, it is possible to make use of the fact that linear interpolation is hard-wired on GPUs, meaning that it has the same computational cost as direct memory access. Cubic order interpolation schemes can be constructed by combining linear interpolation steps which provides important computation speedup. We provide details about how the Radon transform and the back-projection can be implemented efficiently as convolution operators on GPUs. For large data sizes, speedups of about 10 times are obtained in relation to the computational times of other software packages based on GPU implementations of the Radon transform and the back-projection operator. Moreover, speedups of more than a 1000 times are obtained against the CPU-implementations provided in the MATLAB image processing toolbox.
June 5, 2015 by hgpu
