Tracking Quick Start

This example shows how to perform fast fiber tracking using DIPY [Garyfallidis12].

We will use Constrained Spherical Deconvolution (CSD) [Tournier07] for local reconstruction and then generate deterministic streamlines using the fiber directions (peaks) from CSD and fractional anisotropic (FA) from DTI as a stopping criteria for the tracking.

Let’s load the necessary modules.

import numpy as np
from dipy.tracking.local import LocalTracking, ThresholdTissueClassifier
from dipy.tracking.utils import random_seeds_from_mask
from dipy.reconst.dti import TensorModel
from dipy.reconst.csdeconv import (ConstrainedSphericalDeconvModel,
                                   auto_response)
from dipy.direction import peaks_from_model
from dipy.data import fetch_stanford_hardi, read_stanford_hardi, get_sphere
from dipy.segment.mask import median_otsu
from dipy.viz import actor, window
from dipy.io.image import save_nifti
from nibabel.streamlines import save as save_trk
from nibabel.streamlines import Tractogram
from dipy.tracking.streamline import Streamlines

Enables/disables interactive visualization

interactive = False

Load one of the available datasets with 150 gradients on the sphere and 10 b0s

fetch_stanford_hardi()
img, gtab = read_stanford_hardi()

data = img.get_data()

Create a brain mask. This dataset is a bit difficult to segment with the default median_otsu parameters (see Brain segmentation with median_otsu) therefore we use here more advanced options.

maskdata, mask = median_otsu(data, 3, 1, False,
                             vol_idx=range(10, 50), dilate=2)

For the Constrained Spherical Deconvolution we need to estimate the response function (see Reconstruction with Constrained Spherical Deconvolution) and create a model.

response, ratio = auto_response(gtab, data, roi_radius=10, fa_thr=0.7)

csd_model = ConstrainedSphericalDeconvModel(gtab, response)

Next, we use peaks_from_model to fit the data and calculated the fiber directions in all voxels.

sphere = get_sphere('symmetric724')

csd_peaks = peaks_from_model(model=csd_model,
                             data=data,
                             sphere=sphere,
                             mask=mask,
                             relative_peak_threshold=.5,
                             min_separation_angle=25,
                             parallel=True)

For the tracking part, we will use the fiber directions from the csd_model but stop tracking in areas where fractional anisotropy is low (< 0.1). To derive the FA, used here as a stopping criterion, we would need to fit a tensor model first. Here, we fit the tensor using weighted least squares (WLS).

tensor_model = TensorModel(gtab, fit_method='WLS')
tensor_fit = tensor_model.fit(data, mask)

fa = tensor_fit.fa

In this simple example we can use FA to stop tracking. Here we stop tracking when FA < 0.1.

tissue_classifier = ThresholdTissueClassifier(fa, 0.1)

Now, we need to set starting points for propagating each track. We call those seeds. Using random_seeds_from_mask we can select a specific number of seeds (seeds_count) in each voxel where the mask fa > 0.3 is true.

seeds = random_seeds_from_mask(fa > 0.3, seeds_count=1)

For quality assurance we can also visualize a slice from the direction field which we will use as the basis to perform the tracking.

ren = window.Renderer()
ren.add(actor.peak_slicer(csd_peaks.peak_dirs,
                          csd_peaks.peak_values,
                          colors=None))

if interactive:
    window.show(ren, size=(900, 900))
else:
    window.record(ren, out_path='csd_direction_field.png', size=(900, 900))
../_images/csd_direction_field.png

Direction Field (peaks)

EuDX [Garyfallidis12] is a fast algorithm that we use here to generate streamlines. This algorithm is what is used here and the default option when providing the output of peaks directly in LocalTracking.

streamline_generator = LocalTracking(csd_peaks, tissue_classifier,
                                     seeds, affine=np.eye(4),
                                     step_size=0.5)

streamlines = Streamlines(streamline_generator)

The total number of streamlines is shown below.

print(len(streamlines))

To increase the number of streamlines you can change the parameter seeds_count in random_seeds_from_mask.

We can visualize the streamlines using actor.line or actor.streamtube.

ren.clear()
ren.add(actor.line(streamlines))

if interactive:
    window.show(ren, size=(900, 900))
else:
    print('Saving illustration as det_streamlines.png')
    window.record(ren, out_path='det_streamlines.png', size=(900, 900))
../_images/det_streamlines.png

Deterministic streamlines using EuDX (new framework)

To learn more about this process you could start playing with the number of seed points or, even better, specify seeds to be in specific regions of interest in the brain.

Save the resulting streamlines in a Trackvis (.trk) format and FA as Nifti (.nii.gz).

save_trk(Tractogram(streamlines, affine_to_rasmm=img.affine),
         'det_streamlines.trk')

save_nifti('fa_map.nii.gz', fa, img.affine)

In Windows if you get a runtime error about frozen executable please start your script by adding your code above in a main function and use:

if __name__ == '__main__':
    import multiprocessing
    multiprocessing.freeze_support()
    main()

References

[Garyfallidis12](1, 2) Garyfallidis E., “Towards an accurate brain tractography”, PhD thesis, University of Cambridge, 2012.
[Tournier07]J-D. Tournier, F. Calamante and A. Connelly, “Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution”, Neuroimage, vol. 35, no. 4, pp. 1459-1472, 2007.

Example source code

You can download the full source code of this example. This same script is also included in the dipy source distribution under the doc/examples/ directory.