An introduction to the Deterministic Tractography

Deterministic tractography follows the trajectory of the most probable pathway within the tracking constraint (e.g. max angle). It follows the direction with the highest probability from a distribution, as opposed to the probabilistic tractography which draws the direction from the distribution. Therefore, deterministic tractography is equivalent to the probabilistic tractography returning always the maximum value of the distribution.

Deterministic tractography is an alternative to EuDX deterministic tractography and unlike EuDX does not follow the peaks of the local models but uses the entire orientation distributions.

This example is an extension of the An introduction to the Probabilistic Tractography example. We begin by loading the data, fitting a Constrained Spherical Deconvolution (CSD) reconstruction model for the tractography and fitting the constant solid angle (CSA) reconstruction model to define the tracking mask (stopping criterion).

from dipy.core.gradients import gradient_table
from dipy.data import default_sphere, get_fnames
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.tracking.stopping_criterion import BinaryStoppingCriterion
from dipy.tracking.streamline import Streamlines
from dipy.tracking.tracker import deterministic_tracking
from dipy.tracking.utils import seeds_from_mask
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
seeds = seeds_from_mask(seed_mask, affine, density=2)

white_matter = (labels == 1) | (labels == 2)
sc = BinaryStoppingCriterion(white_matter)

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)

The Fiber Orientation Distribution (FOD) of the CSD model estimates the distribution of small fiber bundles within each voxel. This distribution can be used for deterministic fiber tracking. As for probabilistic tracking, there are many ways to provide those distributions to the deterministic tractography. Here, the spherical harmonic representation of the FOD is used.

streamline_generator = deterministic_tracking(
    seeds,
    sc,
    affine,
    sh=csd_fit.shm_coeff,
    random_seed=1,
    sphere=default_sphere,
    max_angle=30,
    step_size=0.5,
)

streamlines = Streamlines(streamline_generator)

sft = StatefulTractogram(streamlines, hardi_img, Space.RASMM)
save_trk(sft, "tractogram_deterministic.trk")

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

Corpus Callosum using deterministic maximum direction getter