Bootstrap and Closest Peak Direction Getters ExampleΒΆ

This example shows how choices in direction-getter impact fiber tracking results by demonstrating the bootstrap direction getter (a type of probabilistic tracking) and the closest peak direction getter (a type of deterministic tracking).

Let’s load the necessary modules for executing this tutorial.

from dipy.data import read_stanford_labels
from dipy.tracking import utils
from dipy.tracking.local import (ThresholdTissueClassifier, LocalTracking)
from dipy.io.trackvis import save_trk
from dipy.viz import window, actor
from dipy.viz.colormap import line_colors

renderer = window.Renderer()

Now we import the CSD model

from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel

First we load our images and establish seeds. See the Introduction to Basic Tracking tutorial for more background on these steps.

hardi_img, gtab, labels_img = read_stanford_labels()
data = hardi_img.get_data()
labels = labels_img.get_data()
affine = hardi_img.get_affine()

seed_mask = labels == 2
white_matter = (labels == 1) | (labels == 2)
seeds = utils.seeds_from_mask(seed_mask, density=1, affine=affine)

Next, we fit the CSD model

csd_model = ConstrainedSphericalDeconvModel(gtab, None, sh_order=6)
csd_fit = csd_model.fit(data, mask=white_matter)

we use the CSA fit to calculate GFA, which will serve as our tissue classifier

from dipy.reconst.shm import CsaOdfModel
csa_model = CsaOdfModel(gtab, sh_order=6)
gfa = csa_model.fit(data, mask=white_matter).gfa
classifier = ThresholdTissueClassifier(gfa, .25)

Next, we need to set up our two direction getters

Example #1: Bootstrap direction getter with CSD Model

from dipy.direction import BootDirectionGetter
from dipy.tracking.streamline import Streamlines
from dipy.data import small_sphere

boot_dg_csd = BootDirectionGetter.from_data(data, csd_model, max_angle=30.,
                                            sphere=small_sphere)
boot_streamline_generator = LocalTracking(boot_dg_csd, classifier, seeds,
                                          affine, step_size=.5)
streamlines = Streamlines(boot_streamline_generator)

renderer.clear()
renderer.add(actor.line(streamlines, line_colors(streamlines)))
window.record(renderer, out_path='bootstrap_dg_CSD.png', size=(600, 600))
../_images/bootstrap_dg_CSD.png

Corpus Callosum Bootstrap Probabilistic Direction Getter

We have created a bootstrapped probabilistic set of streamlines. If you repeat the fiber tracking (keeping all inputs the same) you will NOT get exactly the same set of streamlines. We can save the streamlines as a Trackvis file so it can be loaded into other software for visualization or further analysis.

save_trk("bootstrap_dg_CSD.trk", streamlines, affine, labels.shape)

Example #2: Closest peak direction getter with CSD Model

from dipy.direction import ClosestPeakDirectionGetter

pmf = csd_fit.odf(small_sphere).clip(min=0)
peak_dg = ClosestPeakDirectionGetter.from_pmf(pmf, max_angle=30.,
                                              sphere=small_sphere)
peak_streamline_generator = LocalTracking(peak_dg, classifier, seeds, affine,
                                          step_size=.5)
streamlines = Streamlines(peak_streamline_generator)

renderer.clear()
renderer.add(actor.line(streamlines, line_colors(streamlines)))
window.record(renderer, out_path='closest_peak_dg_CSD.png', size=(600, 600))
../_images/closest_peak_dg_CSD.png

Corpus Callosum Closest Peak Deterministic Direction Getter

We have created a set of streamlines using the closest peak direction getter, which is a type of deterministic tracking. If you repeat the fiber tracking (keeping all inputs the same) you will get exactly the same set of streamlines. We can save the streamlines as a Trackvis file so it can be loaded into other software for visualization or further analysis.

save_trk("closest_peak_dg_CSD.trk", streamlines, affine, labels.shape)
[Berman_boot]Berman, J. et al. Probabilistic streamline q-ball

tractography using the residual bootstrap

[Jeurissen_boot]Jeurissen, B. et al. Probabilistic fiber tracking

using the residual bootstrap with constrained spherical deconvolution.

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.