Using Various Tissue Classifiers for Tractography

Using Various Tissue Classifiers for Tractography

The tissue classifier determines if the tracking stops or continues at each tracking position. The tracking stops when it reaches an ending region (e.g. low FA, gray matter or corticospinal fluid regions) or exits the image boundaries. The tracking also stops if the direction getter has no direction to follow.

Each tissue classifier determines if the stopping is ‘valid’ or ‘invalid’. A streamline is ‘valid’ when the tissue classifier determines if the streamline stops in a position classified as ‘ENDPOINT’ or ‘OUTSIDEIMAGE’. A streamline is ‘invalid’ when it stops in a position classified as ‘TRACKPOINT’ or ‘INVALIDPOINT’. These conditions are described below. The ‘LocalTracking’ generator can be set to output all generated streamlines or only the ‘valid’ ones.

This example is an extension of the An introduction to the Deterministic Maximum Direction Getter example. We begin by loading the data, creating a seeding mask from white matter voxels of the corpus callosum, fitting a Constrained Spherical Deconvolution (CSD) reconstruction model and creating the maximum deterministic direction getter.

import numpy as np

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

# Enables/disables interactive visualization
interactive = False

ren = window.Renderer()

hardi_img, gtab, labels_img = read_stanford_labels()
_, _, img_pve_wm = read_stanford_pve_maps()
data = hardi_img.get_data()
labels = labels_img.get_data()
affine = hardi_img.affine
white_matter = img_pve_wm.get_data()

seed_mask = np.logical_and(labels == 2, white_matter == 1)

seeds = utils.seeds_from_mask(seed_mask, density=2, affine=affine)

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=white_matter)

dg = DeterministicMaximumDirectionGetter.from_shcoeff(csd_fit.shm_coeff,
                                                      max_angle=30.,
                                                      sphere=default_sphere)

Threshold Tissue Classifier

A scalar map can be used to define where the tracking stops. The threshold tissue classifier uses a scalar map to stop the tracking whenever the interpolated scalar value is lower than a fixed threshold. Here, we show an example using the fractional anisotropy (FA) map of the DTI model. The threshold tissue classifier uses a trilinear interpolation at the tracking position.

Parameters

  • metric_map: numpy array [:, :, :]
  • threshold: float

Stopping criterion

  • ‘ENDPOINT’: metric_map < threshold,
  • ‘OUTSIDEIMAGE’: tracking point outside of metric_map,
  • ‘TRACKPOINT’: stop because no direction is available,
  • ‘INVALIDPOINT’: N/A.
import matplotlib.pyplot as plt
import dipy.reconst.dti as dti
from dipy.reconst.dti import fractional_anisotropy
from dipy.tracking.local import ThresholdTissueClassifier

tensor_model = dti.TensorModel(gtab)
tenfit = tensor_model.fit(data, mask=labels > 0)
FA = fractional_anisotropy(tenfit.evals)

threshold_classifier = ThresholdTissueClassifier(FA, .2)

fig = plt.figure()
mask_fa = FA.copy()
mask_fa[mask_fa < 0.2] = 0
plt.xticks([])
plt.yticks([])
plt.imshow(mask_fa[:, :, data.shape[2] // 2].T, cmap='gray', origin='lower',
           interpolation='nearest')
fig.tight_layout()
fig.savefig('threshold_fa.png')
../../_images/threshold_fa.png

Thresholded fractional anisotropy map.

all_streamlines_threshold_classifier = LocalTracking(dg,
                                                     threshold_classifier,
                                                     seeds,
                                                     affine,
                                                     step_size=.5,
                                                     return_all=True)

save_trk("deterministic_threshold_classifier_all.trk",
         all_streamlines_threshold_classifier,
         affine,
         labels.shape)

streamlines = Streamlines(all_streamlines_threshold_classifier)

if have_fury:
    window.clear(ren)
    ren.add(actor.line(streamlines, cmap.line_colors(streamlines)))
    window.record(ren, out_path='all_streamlines_threshold_classifier.png',
                  size=(600, 600))
    if interactive:
        window.show(ren)
../../_images/all_streamlines_threshold_classifier.png

Deterministic tractography using a thresholded fractional anisotropy.

Binary Tissue Classifier

A binary mask can be used to define where the tracking stops. The binary tissue classifier stops the tracking whenever the tracking position is outside the mask. Here, we show how to obtain the binary tissue classifier from the white matter mask defined above. The binary tissue classifier uses a nearest-neighborhood interpolation at the tracking position.

Parameters

  • mask: numpy array [:, :, :]

Stopping criterion

  • ‘ENDPOINT’: mask = 0
  • ‘OUTSIDEIMAGE’: tracking point outside of mask
  • ‘TRACKPOINT’: no direction is available
  • ‘INVALIDPOINT’: N/A
from dipy.tracking.local import BinaryTissueClassifier

binary_classifier = BinaryTissueClassifier(white_matter == 1)

fig = plt.figure()
plt.xticks([])
plt.yticks([])
fig.tight_layout()
plt.imshow(white_matter[:, :, data.shape[2] // 2].T, cmap='gray',
           origin='lower', interpolation='nearest')

fig.savefig('white_matter_mask.png')
../../_images/white_matter_mask.png

White matter binary mask.

all_streamlines_binary_classifier = LocalTracking(dg,
                                                  binary_classifier,
                                                  seeds,
                                                  affine,
                                                  step_size=.5,
                                                  return_all=True)

save_trk("deterministic_binary_classifier_all.trk",
         all_streamlines_binary_classifier,
         affine,
         labels.shape)

streamlines = Streamlines(all_streamlines_binary_classifier)

if have_fury:
    window.clear(ren)
    ren.add(actor.line(streamlines, cmap.line_colors(streamlines)))
    window.record(ren, out_path='all_streamlines_binary_classifier.png',
                  size=(600, 600))
    if interactive:
        window.show(ren)
../../_images/all_streamlines_binary_classifier.png

Deterministic tractography using a binary white matter mask.

ACT Tissue Classifier

Anatomically-constrained tractography (ACT) [Smith2012] uses information from anatomical images to determine when the tractography stops. The include_map defines when the streamline reached a ‘valid’ stopping region (e.g. gray matter partial volume estimation (PVE) map) and the exclude_map defines when the streamline reached an ‘invalid’ stopping region (e.g. corticospinal fluid PVE map). The background of the anatomical image should be added to the include_map to keep streamlines exiting the brain (e.g. through the brain stem). The ACT tissue classifier uses a trilinear interpolation at the tracking position.

Parameters

  • include_map: numpy array [:, :, :],
  • exclude_map: numpy array [:, :, :],

Stopping criterion

  • ‘ENDPOINT’: include_map > 0.5,
  • ‘OUTSIDEIMAGE’: tracking point outside of include_map or exclude_map,
  • ‘TRACKPOINT’: no direction is available,
  • ‘INVALIDPOINT’: exclude_map > 0.5.
from dipy.tracking.local import ActTissueClassifier

img_pve_csf, img_pve_gm, img_pve_wm = read_stanford_pve_maps()

background = np.ones(img_pve_gm.shape)
background[(img_pve_gm.get_data() +
            img_pve_wm.get_data() +
            img_pve_csf.get_data()) > 0] = 0

include_map = img_pve_gm.get_data()
include_map[background > 0] = 1
exclude_map = img_pve_csf.get_data()

act_classifier = ActTissueClassifier(include_map, exclude_map)

fig = plt.figure()
plt.subplot(121)
plt.xticks([])
plt.yticks([])
plt.imshow(include_map[:, :, data.shape[2] // 2].T, cmap='gray',
           origin='lower', interpolation='nearest')

plt.subplot(122)
plt.xticks([])
plt.yticks([])
plt.imshow(exclude_map[:, :, data.shape[2] // 2].T, cmap='gray',
           origin='lower', interpolation='nearest')

fig.tight_layout()
fig.savefig('act_maps.png')
../../_images/act_maps.png

Include (left) and exclude (right) maps for ACT.

all_streamlines_act_classifier = LocalTracking(dg,
                                               act_classifier,
                                               seeds,
                                               affine,
                                               step_size=.5,
                                               return_all=True)

save_trk("deterministic_act_classifier_all.trk",
         all_streamlines_act_classifier,
         affine,
         labels.shape)

streamlines = Streamlines(all_streamlines_act_classifier)

if have_fury:
    window.clear(ren)
    ren.add(actor.line(streamlines, cmap.line_colors(streamlines)))
    window.record(ren, out_path='all_streamlines_act_classifier.png',
                  size=(600, 600))
    if interactive:
        window.show(ren)
../../_images/all_streamlines_act_classifier.png

Deterministic tractography using ACT stopping criterion.

valid_streamlines_act_classifier = LocalTracking(dg,
                                                 act_classifier,
                                                 seeds,
                                                 affine,
                                                 step_size=.5,
                                                 return_all=False)

save_trk("deterministic_act_classifier_valid.trk",
         valid_streamlines_act_classifier,
         affine,
         labels.shape)

streamlines = Streamlines(valid_streamlines_act_classifier)

if have_fury:
    window.clear(ren)
    ren.add(actor.line(streamlines, cmap.line_colors(streamlines)))
    window.record(ren, out_path='valid_streamlines_act_classifier.png',
                  size=(600, 600))
    if interactive:
        window.show(ren)
../../_images/valid_streamlines_act_classifier.png

Deterministic tractography using a anatomically-constrained tractography stopping criterion. Streamlines ending in gray matter region only.

The threshold and binary tissue classifiers use respectively a scalar map and a binary mask to stop the tracking. The ACT tissue classifier use partial volume fraction (PVE) maps from an anatomical image to stop the tracking. Additionally, the ACT tissue classifier determines if the tracking stopped in expected regions (e.g. gray matter) and allows the user to get only streamlines stopping in those regions.

Notes

Currently in ACT the proposed method that cuts streamlines going through subcortical gray matter regions is not implemented. The backtracking technique for streamlines reaching INVALIDPOINT is not implemented either.

References

[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.