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.
Dipy 0.11.0 is now available. New features include:
- New framework for contextual enhancement of ODFs.
- Compatibility with numpy (1.11).
- Compatibility with VTK 7.0 which supports Python 3.x.
- Faster PIESNO for noise estimation.
- Reorient gradient directions according to motion correction parameters.
- Supporting Python 3.3+ but not 3.2.
- Reduced memory usage in DTI.
- DSI now can use datasets with multiple b0s.
- Fixed different issues with Windows 64bit and Python 3.5.
Dipy 0.10.1 is now available. New features in this release include:
- Compatibility with new versions of scipy (0.16) and numpy (1.10).
- New cleaner visualization API, including compatibility with VTK 6, and functions to create your own interactive visualizations.
- Diffusion Kurtosis Imaging (DKI): Google Summer of Code work by Rafael Henriques.
- Mean Apparent Propagator (MAP) MRI for tissue microstructure estimation.
- Anisotropic Power Maps from spherical harmonic coefficients.
- A new framework for affine registration of images.
See older highlights.
- Dipy 0.11 released February 21, 2016.
- Dipy 0.10 released December 4, 2015.
- Dipy 0.9.2 released, March 18, 2015.
- Dipy 0.8.0 released, January 6, 2015.
- Dipy was an official exhibitor in HBM 2015.
- Dipy was featured in The Scientist Magazine, Nov, 2014.
- Dipy paper accepted in Frontiers of Neuroinformatics, January 22nd, 2014.
See some of our past announcements
Here is a simple example showing how to calculate color FA. We
use a single 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.
In this example we use only a few voxels with 101 gradient directions:
from dipy.data import get_data
fimg, fbval, fbvec = get_data('small_101D')
import nibabel as nib
img = nib.load(fimg)
data = img.get_data()
from dipy.io import read_bvals_bvecs
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
from dipy.core.gradients import gradient_table
gtab = gradient_table(bvals, bvecs)
from dipy.reconst.dti import TensorModel
ten = TensorModel(gtab)
tenfit = ten.fit(data)
from dipy.reconst.dti import fractional_anisotropy
fa = fractional_anisotropy(tenfit.evals)
from dipy.reconst.dti import color_fa
cfa = color_fa(fa, tenfit.evecs)
As an exercise try to calculate the color FA with your datasets. Here is what
a slice should look like.
You can learn more about how you to use Dipy with your datasets by reading the examples in our Documentation.
We acknowledge support from the following organizations:
- The Gordon and Betty Moore Foundation and the Alfred P. Sloan Foundation, through the
University of Washington eScience Institute Data Science Environment.
- Google supported the work of Rafael Neto Henriques and Julio Villalon through the Google
Summer of Code Program, Summer 2015.