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 0.10.1 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 - tissue microstructure estimation.
- Anisotropic Power Maps from spherical harmonic coefficients.
- Affine registration.
See older highlights.
- Dipy 0.10.1 released December 4th, 2015.
- Dipy 0.9.2 released, March 18th, 2015.
- Dipy 0.8.0 released, 6 January, 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.
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.