Diffusion Imaging In Python

DIPY is a free and open source software project for computational neuroanatomy, focusing mainly on diffusion magnetic resonance imaging (dMRI) analysis. It implements a broad range of algorithms for denoising, registration, reconstruction, tracking, clustering, visualization, and statistical analysis of MRI data.

Highlights

DIPY 0.16.0 is now available. New features include:

  • Horizon, medical visualization interface powered by QuickBundlesX.
  • New Tractometry tools: Bundle Analysis / Bundle Profiles.
  • New reconstruction model: IVIM MIX (Variable Projection).
  • New command line interface: Affine and Diffeomorphic Registration.
  • New command line interface: Probabilistic, Deterministic and PFT Tracking.
  • Integration of Cython Guidelines for developers.
  • Replacement of Nose by Pytest.
  • Documentation update.
  • Closed 103 issues and merged 41 pull requests.

See Older Highlights.

Announcements

  • DIPY Workshop - Titanium Edition (March 11-15, 2019) is now open for registration:
  • DIPY 0.16 released March 10, 2019.
  • DIPY 0.15 released December 12, 2018.
  • DIPY 0.14 released May 1, 2018.
  • DIPY 0.13 released October 24, 2017.

See some of our Past Announcements

Getting Started

Here is a quick snippet showing how to calculate color FA also known as the DEC map. We use a Tensor model to reconstruct the datasets which are saved in a Nifti file along with the b-values and b-vectors which are saved as text files. Finally, we save our result as a Nifti file

fdwi = 'dwi.nii.gz'
fbval = 'dwi.bval'
fbvec = 'dwi.bvec'

from dipy.io.image import load_nifti, save_nifti
from dipy.io import read_bvals_bvecs
from dipy.core.gradients import gradient_table
from dipy.reconst.dti import TensorModel

data, affine = load_nifti(fdwi)
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
gtab = gradient_table(bvals, bvecs)

tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data)

save_nifti('colorfa.nii.gz', tenfit.color_fa, affine)

As an exercise, you can try to calculate color FA with your datasets. You will need to replace the filepaths fdwi, fbval and fbvec. Here is what a slice should look like.

_images/colorfa.png

Next Steps

You can learn more about how you to use DIPY with your datasets by reading the examples in our Documentation.

Support

We acknowledge support from the following organizations:

  • The department of Intelligent Systems Engineering of Indiana University.
  • The National Institute of Biomedical Imaging and Bioengineering, NIH.
  • The Gordon and Betty Moore Foundation and the Alfred P. Sloan Foundation, through the University of Washington eScience Institute Data Science Environment.
  • Google supported DIPY through the Google Summer of Code Program during Summer 2015, 2016 and 2018.