Metadata-Version: 2.1
Name: tfkbnufft
Version: 0.1.0
Summary: A robust, easy-to-deploy non-uniform Fast Fourier Transform in TensorFlow.
Home-page: https://github.com/zaccharieramzi/tfkbnufft
Author: Zaccharie Ramzi
Author-email: zaccharie.ramzi@inria.fr
License: MIT
Download-URL: https://github.com/zaccharieramzi/tfkbnufft
Description: # TF KB-NUFFT
        
        [GitHub](https://github.com/zaccharieramzi/tfkbnufft) | [![Build Status](https://travis-ci.com/zaccharieramzi/tfkbnufft.svg?branch=master)](https://travis-ci.com/zaccharieramzi/tfkbnufft)
        
        
        Simple installation from pypi:
        ```
        pip install tfkbnufft
        ```
        
        ## About
        
        This package is a verly early-stage and modest adaptation to TensorFlow of the [torchkbnufft](https://github.com/mmuckley/torchkbnufft) package written by Matthew Muckley for PyTorch.
        Please cite his work appropriately if you use this package.
        
        ## Computation speed
        
        The computation speeds are given in seconds, for a 256x256 image with a spokelength of 512 and 405 spokes.
        These numbers are not to be directly compared to those of [torchkbnufft](https://github.com/mmuckley/torchkbnufft#computation-speed), since the computation is not the same.
        They are just to give a sense of the time required for computation.
        
        | Operation     | CPU    | GPU    |
        |---------------|--------|--------|
        | Forward NUFFT | 0.1676 | 0.0626 |
        | Adjoint NUFFT | 0.7005 | 0.0635 |
        
        To obtain these numbers for your machine, run the following commands, after installing this package:
        ```
        pip install scikit-image Pillow
        python profile_tfkbnufft.py
        ```
        
        These numbers were obtained with a Quadro P5000.
        
        ## References
        
        1. Fessler, J. A., & Sutton, B. P. (2003). Nonuniform fast Fourier transforms using min-max interpolation. *IEEE transactions on signal processing*, 51(2), 560-574.
        
        2. Beatty, P. J., Nishimura, D. G., & Pauly, J. M. (2005). Rapid gridding reconstruction with a minimal oversampling ratio. *IEEE transactions on medical imaging*, 24(6), 799-808.
        
        3. Feichtinger, H. G., Gr, K., & Strohmer, T. (1995). Efficient numerical methods in non-uniform sampling theory. Numerische Mathematik, 69(4), 423-440.
        
        ## Citation
        
        If you want to cite the package, you can use any of the following:
        
        ```bibtex
        @conference{muckley:20:tah,
          author = {M. J. Muckley and R. Stern and T. Murrell and F. Knoll},
          title = {{TorchKbNufft}: A High-Level, Hardware-Agnostic Non-Uniform Fast Fourier Transform},
          booktitle = {ISMRM Workshop on Data Sampling \& Image Reconstruction},
          year = 2020
        }
        
        @misc{Muckley2019,
          author = {Muckley, M.J. et al.},
          title = {Torch KB-NUFFT},
          year = {2019},
          publisher = {GitHub},
          journal = {GitHub repository},
          howpublished = {\url{https://github.com/mmuckley/torchkbnufft}}
        }
        ```
        
Keywords: MRI,tensorflow
Platform: UNKNOWN
Classifier: Environment :: Console
Classifier: Natural Language :: English
Classifier: Development Status :: 4 - Beta
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.5
Description-Content-Type: text/markdown
