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J. Imaging 2018, 4(3), 51; https://doi.org/10.3390/jimaging4030051

Python Non-Uniform Fast Fourier Transform (PyNUFFT): An Accelerated Non-Cartesian MRI Package on a Heterogeneous Platform (CPU/GPU)

1
Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK
2
Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, Taiwan
Received: 5 January 2018 / Revised: 13 February 2018 / Accepted: 26 February 2018 / Published: 8 March 2018
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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. View Full-Text
Keywords: heterogeneous system architecture (HSA); graphic processing unit (GPU); multi-core system; magnetic resonance imaging (MRI); total variation (TV) heterogeneous system architecture (HSA); graphic processing unit (GPU); multi-core system; magnetic resonance imaging (MRI); total variation (TV)
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Lin, J.-M. Python Non-Uniform Fast Fourier Transform (PyNUFFT): An Accelerated Non-Cartesian MRI Package on a Heterogeneous Platform (CPU/GPU). J. Imaging 2018, 4, 51.

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