This library provides a higher performance CPU/GPU NUFFT for Python.

This library started as a port of the Matlab NUFFT code in the Michigan image reconstruction toolbox written by Jeff Fessler and his students, but has been substantially overhauled and GPU support has been added.

The library does not implement all NUFFT variants, but only the following two cases:

1.) transformation from a uniform spatial grid to non-uniformly sampled frequency domain.

2.) Inverse transformation from non-uniform Fourier samples to a uniformly spaced spatial grid.

Those interested in other NUFFT types may want to consider the NFFT library which has an unofficial python wrapper via pyNFFT.

The transforms are implemented in both single and double precision variants.

Both a low memory lookup table-based implementation and a fully precomputed sparse matrix-based implementations are available.

See Copying and LICENSES_bundled.txt for full license info.

Another Python-based implementation that has both CPU and GPU support is available in the sigpy package. The sigpy implementation of the NUFFT is fairly compact as it uses Numba to provide just-in-time compilation for both the CPU and GPU variants from a common code base.

In contrast mrrt.nufft uses pre-compiled C code for the CPU variant and the GPU kernels are compiled at run time using NVIDIA's run-time compilation (NVRTC) as provided by cupy.RawKernel.

The NFFT library implements a more extensive set of non-uniform Fourier transform variants. It has an unofficial python wrapper via pyNFFT. At the time of writing it is CPU only.

A Matlab-based CPU-based implementation of the NUFFT is available in the Michigan image reconstruction toolbox

A GPU based implementation with a Matlab interface is avialable as gpuNUFFT.

The Flatiron Institute implemented FINUFFT which is a C++ library with Fortran, Matlab and Python interfaces.

Some C/C++ MRI image reconstruction toolboxes also provide NUFFT implementations: Gadgetron and the Berkley Advanced Reconstruction Toolbox (BART).

For those interested in iterative MR image reconstruction it is recommended to use the simplified interface provided by:

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Binary packages have not yet been built and uploaded to PyPI or conda-forge, but the package can be built from source tarballs hosted on PyPI.

pip install mrrt.nufft

• NumPy (>=1.14)
• SciPy (>=0.19)
• Cython (>=0.29.13)
• mrrt.utils

• Matplotlib (for plotting)
• pyFFTW (>=0.11) (enable faster FFTS than numpy.fft)
• CuPy (>=6.1) (required for the GPU implementation)
• jinja2 (required for GPU implementation)