Lin, J-M;
(2018)
Python Non-Uniform Fast Fourier Transform (PyNUFFT): An Accelerated Non-Cartesian MRI Package on a Heterogeneous Platform (CPU/GPU).
Journal of Imaging
, 4
(3)
, Article 51. 10.3390/jimaging4030051.
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Abstract
A Python non-uniform fast Fourier transform (PyNUFFT) package has been developed to accelerate multidimensional non-Cartesian image reconstruction on heterogeneous platforms. Since scientific computing with Python encompasses a mature and integrated environment, the time efficiency of the NUFFT algorithm has been a major obstacle to real-time non-Cartesian image reconstruction with Python. The current PyNUFFT software enables multi-dimensional NUFFT accelerated on a heterogeneous platform, which yields an efficient solution to many non-Cartesian imaging problems. The PyNUFFT also provides several solvers, including the conjugate gradient method, `1 total variation regularized ordinary least square (L1TV-OLS), and `1 total variation regularized least absolute deviation (L1TV-LAD). Metaprogramming libraries have been employed to accelerate PyNUFFT. The PyNUFFT package has been tested on multi-core central processing units (CPUs) and graphic processing units (GPUs), with acceleration factors of 6.3–9.5× on a 32-thread CPU platform and 5.4–13× on a GPU.
Type: | Article |
---|---|
Title: | Python Non-Uniform Fast Fourier Transform (PyNUFFT): An Accelerated Non-Cartesian MRI Package on a Heterogeneous Platform (CPU/GPU) |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.3390/jimaging4030051 |
Publisher version: | http://dx.doi.org/10.3390/jimaging4030051 |
Language: | English |
Additional information: | This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0). |
Keywords: | heterogeneous system architecture (HSA); graphic processing unit (GPU); multi-core system; magnetic resonance imaging (MRI); total variation (TV) |
UCL classification: | UCL |
URI: | https://discovery.ucl.ac.uk/id/eprint/10049714 |
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