Dipy is a free and open source software project for diffusion magnetic resonance imaging (dMRI) analysis.
In Dipy we care about methods which can solve complex problems efficiently and robustly. QuickBundles is one of the many state-of-the art algorithms found in Dipy. It can be used to simplify large datasets of streamlines. See our gallery of examples and try QuickBundles with your data. Here is a video of QuickBundles applied on a simple dataset.
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 how a slice should look like.
We recommend to copy and paste this example in an IPython console. IPython helps interacting with the datasets easily. For example it is easy to find the size of the dataset which is given by data.shape.