Skip to content

Latest commit

 

History

History
150 lines (99 loc) · 5.05 KB

Readme.md

File metadata and controls

150 lines (99 loc) · 5.05 KB
Raw

NOTE: This project is no longer actively maintained.

gradunwarp

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).

Installation

Prerequisites

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

Install

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

https://github.com/downloads/ksubramz/gradunwarp/gradunwarp-2.1_slice_alpha.tar.gz

https://github.com/downloads/ksubramz/gradunwarp/nibabel-1.2.0.dev.tar.gz

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

sudo python setup.py install

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

python setup.py 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/

Usage

skeleton

gradient_unwarp.py infile outfile manufacturer -g <coefficient file> [optional arguments]

typical usage

gradient_unwarp.py sonata.mgh testoutson.mgh siemens -g coeff_Sonata.grad  --fovmin -.15 --fovmax .15 --numpoints 40

gradient_unwarp.py 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

gradunwarp-2.1_slice_alpha.tar.gz

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

License

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

Credit

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