MRI-NUFFT

MRI-NUFFT: Doing non-Cartesian MRI has never been easier - Published in JOSS (2025)

https://github.com/mind-inria/mri-nufft

Keywords

Scientific Fields

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Doing non-Cartesian MR Imaging has never been so easy.

README.rst

=========
MRI-NUFFT
=========

Doing non-Cartesian MR Imaging has never been so easy.

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        :target: https://mind-inria.github.io/mri-nufft
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        :target: https://pypi.org/project/mri-nufft/


This python package extends various NUFFT (Non-Uniform Fast Fourier Transform) python bindings used for MRI reconstruction.

In particular, it provides a unified interface for all the methods, with extra features such as coil sensitivity, density compensated adjoint and off-resonance corrections (for static B0 inhomogeneities).

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.. image:: https://github.com/mind-inria/mri-nufft/raw/master/docs/_static/mri-nufft-scheme.svg
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Modularity and Integration of MRI-nufft with the python computing libraries.

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Usage
=====

.. TODO use a include file directive.
.. code-block:: python

      from scipy.datasets import face # For demo
      import numpy as np
      import mrinufft
      from mrinufft.trajectories import display
      from mrinufft.density import voronoi

      # Create 2D Radial trajectories for demo
      samples_loc = mrinufft.initialize_2D_radial(Nc=100, Ns=500)
      # Get a 2D image for the demo (512x512)
      image = np.complex64(face(gray=True)[256:768, 256:768])

      ## The real deal starts here ##
      # Choose your NUFFT backend (installed independently from the package)
      # pip install mri-nufft[finufft] will be just fine here 
      NufftOperator = mrinufft.get_operator("finufft")

      # For improved image reconstruction, use density compensation
      density = voronoi(samples_loc.reshape(-1, 2))

      # And create the associated operator.
      nufft = NufftOperator(
          samples_loc.reshape(-1, 2), shape=image.shape, density=density, n_coils=1
      )

      kspace_data = nufft.op(image)  # Image -> Kspace
      image2 = nufft.adj_op(kspace_data)  # Kspace -> Image


.. TODO Add image

For improved image quality, embed these steps in a more complex reconstruction pipeline (for instance using `PySAP `_).

Want to see more ?

- Check the `Documentation `_

- Or go visit the `Examples `_


Installation
------------

MRI-nufft is available on Pypi and can be installed with::

  pip install mri-nufft

Additionally, you will have to install at least one NUFFT computation backend. See the `Documentation `_ for more guidance.
Typically we recommend:: 

  pip install mri-nufft[finufft] 
  pip install mri-nufft[cufinufft] # if you have a NVIDIA GPU and CUDA>=12


Benchmark
---------

A benchmark of NUFFT backend for MRI applications is available in https://github.com/mind-inria/mri-nufft-benchmark


Who is using MRI-NUFFT?
-----------------------

Here are several project that rely on MRI-NUFFT:

- `pysap-mri `_
- `snake-fmri `_
- `deepinv `_


  Add yours by opening a PR !

Owner

JOSS Publication

MRI-NUFFT: Doing non-Cartesian MRI has never been easier

Published

April 15, 2025

DOI

10.21105/joss.07743

Volume 10, Issue 108, Page 7743

Authors

Pierre-Antoine Comby
MIND, Inria, France, Université Paris-Saclay / CEA, France

Guillaume Daval-Frérot
Chipiron, France

Caini Pan
MIND, Inria, France, Université Paris-Saclay / CEA, France

Asma Tanabene
MIND, Inria, France, Université Paris-Saclay / CEA, France, Siemens Healthineers, Princeton, United States of America

Léna Oudjman
MIND, Inria, France, Université Paris-Saclay / CEA, France

Matteo Cencini
INFN, Pisa Division, Italy

Philippe Ciuciu
MIND, Inria, France, Université Paris-Saclay / CEA, France

Chaithya Gr
MIND, Inria, France, Université Paris-Saclay / CEA, France

Editor

Kevin M. Moerman

Tags

MRI NUFFT Numpy CUDA Torch

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