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 [1] and the closest peak direction getter (a type of deterministic tracking) [2].

This example is an extension of the Introduction to Basic Tracking example. Let’s start by loading the necessary modules for executing this tutorial.

from dipy.core.gradients import gradient_table
from dipy.data import get_fnames, small_sphere
from dipy.direction import BootDirectionGetter, ClosestPeakDirectionGetter
from dipy.io.gradients import read_bvals_bvecs
from dipy.io.image import load_nifti, load_nifti_data
from dipy.io.stateful_tractogram import Space, StatefulTractogram
from dipy.io.streamline import save_trk
from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel, auto_response_ssst
from dipy.reconst.shm import CsaOdfModel
from dipy.tracking import utils
from dipy.tracking.local_tracking import LocalTracking
from dipy.tracking.stopping_criterion import ThresholdStoppingCriterion
from dipy.tracking.streamline import Streamlines
from dipy.viz import actor, colormap, has_fury, window

# Enables/disables interactive visualization
interactive = False

hardi_fname, hardi_bval_fname, hardi_bvec_fname = get_fnames(name="stanford_hardi")
label_fname = get_fnames(name="stanford_labels")

data, affine, hardi_img = load_nifti(hardi_fname, return_img=True)
labels = load_nifti_data(label_fname)
bvals, bvecs = read_bvals_bvecs(hardi_bval_fname, hardi_bvec_fname)
gtab = gradient_table(bvals, bvecs=bvecs)


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

Next, we fit the CSD model.

response, ratio = auto_response_ssst(gtab, data, roi_radii=10, fa_thr=0.7)
csd_model = ConstrainedSphericalDeconvModel(gtab, response, sh_order_max=6)
csd_fit = csd_model.fit(data, mask=white_matter)

we use the CSA fit to calculate GFA, which will serve as our stopping criterion.

csa_model = CsaOdfModel(gtab, sh_order_max=6)
gfa = csa_model.fit(data, mask=white_matter).gfa
stopping_criterion = ThresholdStoppingCriterion(gfa, 0.25)

Next, we need to set up our two direction getters

Example #1: Bootstrap direction getter with CSD Model

boot_dg_csd = BootDirectionGetter.from_data(
    data, csd_model, max_angle=30.0, sphere=small_sphere
)
boot_streamline_generator = LocalTracking(
    boot_dg_csd, stopping_criterion, seeds, affine, step_size=0.5
)
streamlines = Streamlines(boot_streamline_generator)
sft = StatefulTractogram(streamlines, hardi_img, Space.RASMM)
save_trk(sft, "tractogram_bootstrap_dg.trk")

if has_fury:
    scene = window.Scene()
    scene.add(actor.line(streamlines, colors=colormap.line_colors(streamlines)))
    window.record(scene=scene, out_path="tractogram_bootstrap_dg.png", size=(800, 800))
    if interactive:
        window.show(scene)

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.

Example #2: Closest peak direction getter with CSD Model

pmf = csd_fit.odf(small_sphere).clip(min=0)
peak_dg = ClosestPeakDirectionGetter.from_pmf(pmf, max_angle=30.0, sphere=small_sphere)
peak_streamline_generator = LocalTracking(
    peak_dg, stopping_criterion, seeds, affine, step_size=0.5
)
streamlines = Streamlines(peak_streamline_generator)
sft = StatefulTractogram(streamlines, hardi_img, Space.RASMM)
save_trk(sft, "closest_peak_dg_CSD.trk")

if has_fury:
    scene = window.Scene()
    scene.add(actor.line(streamlines, colors=colormap.line_colors(streamlines)))
    window.record(
        scene=scene, out_path="tractogram_closest_peak_dg.png", size=(800, 800)
    )
    if interactive:
        window.show(scene)

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.

References

[1]

Jeffrey I. Berman, SungWon Chung, Pratik Mukherjee, Christopher P. Hess, Eric T. Han, and Roland G. Henry. Probabilistic streamline q-ball tractography using the residual bootstrap. NeuroImage, 39(1):215–222, 2008. URL: https://doi.org/10.1016/j.neuroimage.2007.08.021, doi:10.1016/j.neuroimage.2007.08.021.

[2]

Bagrat Amirbekian. The Modeling of White Matter Architecture and Networks Using Diffusion MRI: Methods, Tools and Applications. PhD thesis, University of California San Francisco, San Francisco, CA, USA, 2016. URL:, doi:.