Dipy is a free and open source software project focusing mainly on diffusion magnetic resonance imaging (dMRI) analysis. Nonetheless, as we solve problems in dMRI some of the solutions are applicable to the greater medical imaging and image processing communities. See for example our registration and denoising tutorials.
Dipy 0.8 is now available for download. The new release contains state-of-the-art algorithms for diffusion MRI registration, reconstruction, denoising, statistical evaluation, fiber tracking and validation of tracking.
For more information about Dipy, read the dipy paper in Frontiers in Neuroinformatics.
So, how similar are your bundles to the real anatomy? Learn how to optimize your analysis as we did to create the fornix of the figure above, by reading the tutorials in our gallery.
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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.
You can learn more about how you to use Dipy with your datasets by reading the examples in our Documentation.