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# PyNUFFT: Python non-uniform fast Fourier transform

A minimal "getting start" tutorial is available at http://jyhmiinlin.github.io/pynufft/ .
 
### Summary

PyNUFFT is developed to reimplement the min-max NUFFT of Fessler and Sutton, with the following features:

- Based on Python numerical libraries, such as Numpy, Scipy (matplotlib for displaying examples).
- Multi-dimensional NUFFT.
- Support of PyCUDA and PyOpenCL with single Python source.
- Tested on the Intel OpenCL device, the Nvidia CUDA device, and AMD FirePro.
- LGPLv3
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- The single/double-precision CPU/GPU NUFFT. (fp64 is available on request)
- Under the same level of precision (1e-6 ~ 1e-9), the min-max kernel on OpenCL is generally 2x faster than the semicircle-kernel on modern Intel multi-core CPUs (Benchmarking is available on request.) -->
- The feature of "_many_" functionality is deprecated. 

If you find PyNUFFT useful, please cite:

Lin, Jyh-Miin. “Python Non-Uniform Fast Fourier Transform (PyNUFFT): An Accelerated Non-Cartesian MRI Package on a Heterogeneous Platform (CPU/GPU).” Journal of Imaging 4.3 (2018): 51. (Available at https://www.mdpi.com/2313-433X/4/3/51)

### Acknowledgements

Special thanks to the authors of MIRT, gpuNUFFT and BART, which have largely inspired the development of this package. 

The project also thanks contributors for providing their testing results and patches. 

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### Related projects

The PyNUFFT package has currently been used by signal processing experts, astronomers, and physicists for building their applications. 

1. Online PySAP-MRI reconstruction (https://github.com/CEA-COSMIC/pysap-mri, which enjoys the speed of PyNUFFT.) 
2. Accelerated tomography
3. Radiation distribution 
4. Machine learning based MRI reconstruction (https://www.researchgate.net/publication/335473585_A_deep_learning_approach_for_reconstruction_of_undersampled_Cartesian_and_Radial_data)
5. Spiral off-resonance correction
6. For motion estimation (NUFFT adjoint + SPyNET) (https://pubmed.ncbi.nlm.nih.gov/32408295/)
7. PyNUFFT was used in ISMRM reproducible study group and was mentioned in "Stikov, Nikola, Joshua D. Trzasko, and Matt A. Bernstein. "Reproducibility and the future of MRI research." Magnetic resonance in medicine 82.6 (2019): 1981-1983."

Open-source Python software is nice for delivering your products. So try PyNUFFT today!


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