Dipy

Dipy is an international, free and open soure software project for diffusion magnetic resonance imaging analysis.

Depends on a few standard libraries: python (the core language), numpy (for numerical computation), scipy (for more specific mathematical operations), cython (for extra speed) and nibabel (for file formats). Optionally, it can use python-vtk (for visualisation), pytables (for handling large datasets), matplotlib (for scientific plotting), and ipython (for interaction with the code and its results).

Dipy is multiplatform and will run under any standard operating systems such as Windows, Linux, Mac OS X.

Just some of our state-of-the-art applications are:

• Reconstruction algorithms e.g. GQI, DTI
• Tractography generation algorithms e.g. EuDX
• Intelligent downsampling of tracks
• Ultra fast tractography clustering
• Resampling datasets with anisotropic voxels to isotropic
• Visualizing multiple brains simultaneously
• Finding track correspondence between different brains
• Warping tractographies into another space e.g. MNI space
• Reading many different file formats e.g. Trackvis or Nifti
• Dealing with huge tractographies without memory restrictions
• Playing with datasets interactively without storing
• And much more and even more to come in next releases

Join in the fun and enjoy the video we made for the Summer Exhibition in London for the celebration of the 350 years of the Royal Society.

An Example

Here is a tiny usage example for dipy

>>> import numpy as np
>>> from dipy.reconst.dti import Tensor
>>> 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()
>>> bvals=np.loadtxt(fbvals)
>>> gradients=np.loadtxt(fbvecs).T
>>> ten=dti.Tensor(data,bvals,gradients,thresh=50)
>>> FA=ten.fa()
>>> MASK = FA < 0.2

In this code snippet we loaded a small diffusion dataset with their data, b-vectors and b-values, calculated the Tensors and fractional anisotropy (FA) and then created a mask to remove the regions with low anisotropy. Download dipy and try it for yourself.

A skeleton

This is a depiction of a tractography skeleton created using dipy.

Using skeletal tracks we can very easily have a fast visual description of our datasets. If you want to learn more how you can create these with your datasets read the examples in our Documentation .