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NOTE: This project is no longer actively maintained.


gradunwarp is a Python/Numpy package used to unwarp the distorted volumes (due to the gradient field іnhomogenities). Currently, it can unwarp Siemens data (and GE support very soon).



gradunwarp needs

  • Python (>2.7)
  • Numpy (preferably, the latest)
  • Scipy (preferably, the latest)
  • Numpy devel package (to compile external modules written in C)
  • nibabel (latest trunk, which has the MGH support)

requirements for nibabel.

  • PyDICOM 0.9.5 or greater (for DICOM support)
  • nose 0.11 or greater (to run the tests)
  • sphinx (to build the documentation)

The installation of these in Ubuntu is as simple as

sudo apt-get install python-numpy
sudo apt-get install python-scipy


For convenience both the gradunwarp and nibabel tarballs can be downloaded from

They are extracted and the following step is the same for gradunwarp and nibabel installation. First, change to the respective directory. Then,

sudo python install

Note: It is possible that you don’t have superuser permissions. In that case, you can use the --prefix switch of install.

python install --prefix=/home/foo/

In that case, make sure your PATH has /home/foo/bin and make sure the PYTHONPATH has /home/foo/bin/lib/python-2.7/site-packages/


skeleton infile outfile manufacturer -g <coefficient file> [optional arguments]

typical usage sonata.mgh testoutson.mgh siemens -g coeff_Sonata.grad  --fovmin -.15 --fovmax .15 --numpoints 40 avanto.mgh testoutava.mgh siemens -g coeff_AS05.grad -n

Positional Arguments

The input file (in Nifti or MGH formats) followed by the output file name (which has the Nifti or MGH extensions — .nii/.nii.gz/.mgh/.mgz) followed by the vendor name.

Required Options

-c <coef_file>
-g <grad_file>

The coefficient file (which is acquired from the vendor) is specified using a -g option, to be used with files of type .grad.

Or it can be specified using a -c in the case you have the .coef file.

These two options are mutually exclusive.

Other Options

-n : If you want to suppress the jacobian intensity correction
-w : if the volume is to be warped rather than unwarped

--fovmin <fovmin> : a float argument which specifies the minimum extent of the grid where spherical harmonics are evaluated. (in meters). Default is -.3
--fovmax <fovmax> : a float argument which specifies the maximum extent of the grid where spherical harmonics are evaluated. (in meters). Default is .3
--numpoints <numpoints> : an int argument which specifies the number of points in the grid. (in each direction). Default is 60

--interp_order <order of interpolation> : takes values from 1 to 4. 1 means the interpolation is going to be linear which is a faster method but not as good as higher order interpolations. 

--help : display help

Memory Considerations

gradunwarp tends to use quite a bit of memory because of the intense spherical harmonics calculation and interpolations performed multiple times. For instance, it uses almost 85% memory of a 2GB memory 2.2GHz DualCore system to perform unwarping of a 256^3 volume with 40^3 spherical harmonics grid. (It typically takes 4 to 5 minutes for the entire unwarping)

Some thoughts:

  • Use lower resolution volumes if possible
  • Run gradunwarp in a computer with more memory
  • Use —numpoints to reduce the grid size. —fovmin and —fovmax can be used to move the grid close to your data extents.
  • Use non-compressed source volumes. i.e. .mgh and .nii instead of .mgz/.nii.gz
  • Recent versions of Python, numpy and scipy

Future Work

  • support for GE processing (near future)
  • better support for high res volumes (process it slice-by-slice?)
  • report statistics
  • explore removal of Numpy-devel dependency if the speedup is not that significant

Release Notes


  • slice by slice processing
  • x-y flip bug fix
  • force 32-bit output in 64-bit systems


gradunwarp is licensed under the terms of the MIT license. Please see the COPYING file in the distribution. gradunwarp also bundles Nibabel ( ) which is licensed under the MIT license as well.


  • Jon Polimeni - gradunwarp follows his original MATLAB code
  • Karl Helmer - Project Incharge
  • Nibabel team