Bootstrap and Closest Peak Direction Getters Example
This example shows how choices in direction-getter impact fiber tracking results by demonstrating the bootstrap direction getter (a type of probabilistic tracking, as described in [Berman2008]) and the closest peak direction getter (a type of deterministic tracking). (Amirbekian, PhD thesis, 2016)
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.streamline import save_trk
from dipy.viz import window, actor, colormap as cmap
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.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, cmap.line_colors(streamlines)))
window.record(renderer, out_path='bootstrap_dg_CSD.png', size=(600, 600))
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, cmap.line_colors(streamlines)))
window.record(renderer, out_path='closest_peak_dg_CSD.png', size=(600, 600))
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)
| [Berman2008] | Berman, J. et al., Probabilistic streamline q-ball |
tractography using the residual bootstrap, NeuroImage, vol 39, no 1, 2008
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.