Particle Filtering Tractography

Particle Filtering Tractography

Particle Filtering Tractography (PFT) [Girard2014] uses tissue partial volume estimation (PVE) to reconstruct trajectories connecting the gray matter, and not incorrectly stopping in the white matter or in the corticospinal fluid. It relies on a tissue classifier that identifies the tissue where the streamline stopped. If the streamline correctly stopped in the gray matter, the trajectory is kept. If the streamline incorrecly stopped in the white matter or in the corticospinal fluid, PFT uses anatomical information to find an alternative streamline segment to extend the trajectory. When this segment is found, the tractography continues until the streamline correctly stops in the gray matter.

PFT finds an alternative streamline segment whenever the tissue classifier returns a position classified as ‘INVALIDPOINT’.

This example is an extension of the probabilistic_fiber_tracking example. We begin by loading the data, fitting a Constrained Spherical Deconvolution (CSD) reconstruction model and creating the probabilistic direction getter.

import numpy as np

from dipy.data import (read_stanford_labels, default_sphere,
                       read_stanford_pve_maps)
from dipy.direction import ProbabilisticDirectionGetter
from dipy.io.streamline import save_trk
from dipy.reconst.csdeconv import (ConstrainedSphericalDeconvModel,
                                   auto_response)
from dipy.tracking.local import LocalTracking, ParticleFilteringTracking
from dipy.tracking import utils
from dipy.viz import window, actor, colormap as cmap


renderer = window.Renderer()

img_pve_csf, img_pve_gm, img_pve_wm = read_stanford_pve_maps()
hardi_img, gtab, labels_img = read_stanford_labels()

data = hardi_img.get_data()
labels = labels_img.get_data()
affine = hardi_img.affine
shape = labels.shape

response, ratio = auto_response(gtab, data, roi_radius=10, fa_thr=0.7)
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit = csd_model.fit(data, mask=img_pve_wm.get_data())

dg = ProbabilisticDirectionGetter.from_shcoeff(csd_fit.shm_coeff,
                                               max_angle=20.,
                                               sphere=default_sphere)

CMC/ACT Tissue Classifiers

Continuous map criterion (CMC) [Girard2014] and Anatomically-constrained tractography (ACT) [Smith2012] both uses PVEs information from anatomical images to determine when the tractography stops. Both tissue classifiers use a trilinear interpolation at the tracking position. CMC tissue classifier uses a probability derived from the PVE maps to determine if the streamline reaches a ‘valid’ or ‘invalid’ region. ACT uses a fixed threshold on the PVE maps. Both tissue classifiers can be used in conjunction with PFT. In this example, we used CMC.

from dipy.tracking.local import CmcTissueClassifier
from dipy.tracking.streamline import Streamlines

voxel_size = np.average(img_pve_wm.get_header()['pixdim'][1:4])
step_size = 0.2

cmc_classifier = CmcTissueClassifier.from_pve(img_pve_wm.get_data(),
                                              img_pve_gm.get_data(),
                                              img_pve_csf.get_data(),
                                              step_size=step_size,
                                              average_voxel_size=voxel_size)

# seeds are place in voxel of the corpus callosum containing only white matter
seed_mask = labels == 2
seed_mask[img_pve_wm.get_data() < 0.5] = 0
seeds = utils.seeds_from_mask(seed_mask, density=2, affine=affine)

# Particle Filtering Tractography
pft_streamline_generator = ParticleFilteringTracking(dg,
                                                     cmc_classifier,
                                                     seeds,
                                                     affine,
                                                     max_cross=1,
                                                     step_size=step_size,
                                                     maxlen=1000,
                                                     pft_back_tracking_dist=2,
                                                     pft_front_tracking_dist=1,
                                                     particle_count=15,
                                                     return_all=False)

# streamlines = list(pft_streamline_generator)
streamlines = Streamlines(pft_streamline_generator)
save_trk("pft_streamline.trk", streamlines, affine, shape)


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

Particle Filtering Tractography

# Local Probabilistic Tractography
prob_streamline_generator = LocalTracking(dg,
                                          cmc_classifier,
                                          seeds,
                                          affine,
                                          max_cross=1,
                                          step_size=step_size,
                                          maxlen=1000,
                                          return_all=False)
# streamlines = list(pro)
streamlines = Streamlines(prob_streamline_generator)
save_trk("probabilistic_streamlines.trk", streamlines, affine, shape)

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

Probabilistic Tractography

References

[Girard2014](1, 2) Girard, G., Whittingstall, K., Deriche, R., & Descoteaux, M. Towards quantitative connectivity analysis: reducing tractography biases. NeuroImage, 98, 266-278, 2014.
[Smith2012]Smith, R. E., Tournier, J.-D., Calamante, F., & Connelly, A. Anatomically-constrained tractography: Improved diffusion MRI streamlines tractography through effective use of anatomical information. NeuroImage, 63(3), 1924-1938, 2012.

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.