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

• Published in: Journal of imaging Vol. 4; no. 3; p. 51
• Main Author: Lin, Jyh-Miin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2018
• Subjects: Algorithms; Cartesian coordinates; Central processing units; Conjugate gradient method; CPUs; Fast Fourier transformations; Fourier transforms; graphic processing unit (GPU); Graphics processing units; heterogeneous system architecture (HSA); Image reconstruction; Libraries; magnetic resonance imaging (MRI); Microprocessors; multi-core system; NMR; Nuclear magnetic resonance; Software; Solvers; total variation (TV); United States > US
• ISSN: 2313-433X; 2313-433X
• DOI: 10.3390/jimaging4030051

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, ℓ1 total variation regularized ordinary least square (L1TV-OLS), and ℓ1 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.