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Connectivity Matrices, ROI Intersections and Density Maps
This example is meant to be an introduction to some of the streamline tools
available in DIPY_. Some of the functions covered in this example are
target, connectivity_matrix and density_map. target allows one
to filter streamlines that either pass through or do not pass through some
region of the brain, connectivity_matrix groups and counts streamlines
based on where in the brain they begin and end, and finally, density map counts
the number of streamlines that pass though every voxel of some image.
To get started we’ll need to have a set of streamlines to work with. We’ll use EuDX along with the CsaOdfModel to make some streamlines. Let’s import the modules and download the data we’ll be using.
import numpy as np
from scipy.ndimage.morphology import binary_dilation
from dipy.core.gradients import gradient_table
from dipy.data import get_fnames
from dipy.io.gradients import read_bvals_bvecs
from dipy.io.image import load_nifti_data, load_nifti, save_nifti
from dipy.direction import peaks
from dipy.reconst import shm
from dipy.tracking import utils
from dipy.tracking.local_tracking import LocalTracking
from dipy.tracking.stopping_criterion import BinaryStoppingCriterion
from dipy.tracking.streamline import Streamlines
hardi_fname, hardi_bval_fname, hardi_bvec_fname = get_fnames('stanford_hardi')
label_fname = get_fnames('stanford_labels')
t1_fname = get_fnames('stanford_t1')
data, affine, hardi_img = load_nifti(hardi_fname, return_img=True)
labels = load_nifti_data(label_fname)
t1_data = load_nifti_data(t1_fname)
bvals, bvecs = read_bvals_bvecs(hardi_bval_fname, hardi_bvec_fname)
gtab = gradient_table(bvals, bvecs)
We’ve loaded an image called labels_img which is a map of tissue types
such that every integer value in the array labels represents an
anatomical structure or tissue type 1. For this example, the image was
created so that white matter voxels have values of either 1 or 2. We’ll use
peaks_from_model to apply the CsaOdfModel to each white matter voxel
and estimate fiber orientations which we can use for tracking. We will also
dilate this mask by 1 voxel to ensure streamlines reach the grey matter.
white_matter = binary_dilation((labels == 1) | (labels == 2))
csamodel = shm.CsaOdfModel(gtab, 6)
csapeaks = peaks.peaks_from_model(model=csamodel,
data=data,
sphere=peaks.default_sphere,
relative_peak_threshold=.8,
min_separation_angle=45,
mask=white_matter)
Now we can use EuDX to track all of the white matter. To keep things
reasonably fast we use density=1 which will result in 1 seeds per voxel.
The stopping criterion, determining when the tracking stops, is set to stop
when the tracking exit the white matter.
affine = np.eye(4)
seeds = utils.seeds_from_mask(white_matter, affine, density=1)
stopping_criterion = BinaryStoppingCriterion(white_matter)
streamline_generator = LocalTracking(csapeaks, stopping_criterion, seeds,
affine=affine, step_size=0.5)
streamlines = Streamlines(streamline_generator)
The first of the tracking utilities we’ll cover here is target. This
function takes a set of streamlines and a region of interest (ROI) and
returns only those streamlines that pass though the ROI. The ROI should be
an array such that the voxels that belong to the ROI are True and all
other voxels are False (this type of binary array is sometimes called a
mask). This function can also exclude all the streamlines that pass though an
ROI by setting the include flag to False. In this example we’ll
target the streamlines of the corpus callosum. Our labels array has a
sagittal slice of the corpus callosum identified by the label value 2. We’ll
create an ROI mask from that label and create two sets of streamlines, those
that intersect with the ROI and those that don’t.
cc_slice = labels == 2
cc_streamlines = utils.target(streamlines, affine, cc_slice)
cc_streamlines = Streamlines(cc_streamlines)
other_streamlines = utils.target(streamlines, affine, cc_slice,
include=False)
other_streamlines = Streamlines(other_streamlines)
assert len(other_streamlines) + len(cc_streamlines) == len(streamlines)
We can use some of DIPY_’s visualization tools to display the ROI we targeted above and all the streamlines that pass though that ROI. The ROI is the yellow region near the center of the axial image.
from dipy.viz import window, actor, colormap as cmap
# Enables/disables interactive visualization
interactive = False
# Make display objects
color = cmap.line_colors(cc_streamlines)
cc_streamlines_actor = actor.line(cc_streamlines,
cmap.line_colors(cc_streamlines))
cc_ROI_actor = actor.contour_from_roi(cc_slice, color=(1., 1., 0.),
opacity=0.5)
vol_actor = actor.slicer(t1_data)
vol_actor.display(x=40)
vol_actor2 = vol_actor.copy()
vol_actor2.display(z=35)
# Add display objects to canvas
scene = window.Scene()
scene.add(vol_actor)
scene.add(vol_actor2)
scene.add(cc_streamlines_actor)
scene.add(cc_ROI_actor)
# Save figures
window.record(scene, n_frames=1, out_path='corpuscallosum_axial.png',
size=(800, 800))
if interactive:
window.show(scene)
scene.set_camera(position=[-1, 0, 0], focal_point=[0, 0, 0], view_up=[0, 0, 1])
window.record(scene, n_frames=1, out_path='corpuscallosum_sagittal.png',
size=(800, 800))
if interactive:
window.show(scene)
Out:
/Users/koudoro/miniconda3/envs/dipy-env-37/lib/python3.7/site-packages/vtkmodules/util/numpy_support.py:66: DeprecationWarning: Converting `np.character` to a dtype is deprecated. The current result is `np.dtype(np.str_)` which is not strictly correct. Note that `np.character` is generally deprecated and 'S1' should be used.
if numpy_array_type == key or \
/Users/koudoro/miniconda3/envs/dipy-env-37/lib/python3.7/site-packages/vtkmodules/util/numpy_support.py:68: DeprecationWarning: Converting `np.character` to a dtype is deprecated. The current result is `np.dtype(np.str_)` which is not strictly correct. Note that `np.character` is generally deprecated and 'S1' should be used.
numpy_array_type == numpy.dtype(key):
/Users/koudoro/miniconda3/envs/dipy-env-37/lib/python3.7/site-packages/fury/utils.py:97: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
data = np.array(data)
Once we’ve targeted on the corpus callosum ROI, we might want to find out
which regions of the brain are connected by these streamlines. To do this we
can use the connectivity_matrix function. This function takes a set of
streamlines and an array of labels as arguments. It returns the number of
streamlines that start and end at each pair of labels and it can return the
streamlines grouped by their endpoints. Notice that this function only
considers the endpoints of each streamline.
M, grouping = utils.connectivity_matrix(cc_streamlines, affine,
labels.astype(np.uint8),
return_mapping=True,
mapping_as_streamlines=True)
M[:3, :] = 0
M[:, :3] = 0
We’ve set return_mapping and mapping_as_streamlines to True so
that connectivity_matrix returns all the streamlines in
cc_streamlines grouped by their endpoint.
Because we’re typically only interested in connections between gray matter regions, and because the label 0 represents background and the labels 1 and 2 represent white matter, we discard the first three rows and columns of the connectivity matrix.
We can now display this matrix using matplotlib, we display it using a log scale to make small values in the matrix easier to see.
import numpy as np
import matplotlib.pyplot as plt
plt.imshow(np.log1p(M), interpolation='nearest')
plt.savefig("connectivity.png")
In our example track there are more streamlines connecting regions 11 and 54 than any other pair of regions. These labels represent the left and right superior frontal gyrus respectively. These two regions are large, close together, have lots of corpus callosum fibers and are easy to track so this result should not be a surprise to anyone.
# However, the interpretation of streamline counts can be tricky. The
# relationship between the underlying biology and the streamline counts will
# depend on several factors, including how the tracking was done, and the
# correct way to interpret these kinds of connectivity matrices is still an
# open question in the diffusion imaging literature.
# The next function we'll demonstrate is ``density_map``. This function allows
# one to represent the spatial distribution of a track by counting the density
# of streamlines in each voxel. For example, let's take the track connecting
# the left and right superior frontal gyrus.
lr_superiorfrontal_track = grouping[11, 54]
shape = labels.shape
dm = utils.density_map(lr_superiorfrontal_track, affine, shape)
Let’s save this density map and the streamlines so that they can be visualized together. In order to save the streamlines in a “.trk” file we’ll need to move them to “trackvis space”, or the representation of streamlines specified by the trackvis Track File format.
from dipy.io.stateful_tractogram import Space, StatefulTractogram
from dipy.io.streamline import save_trk
# Save density map
save_nifti("lr-superiorfrontal-dm.nii.gz", dm.astype("int16"), affine)
lr_sf_trk = Streamlines(lr_superiorfrontal_track)
# Save streamlines
sft = StatefulTractogram(lr_sf_trk, hardi_img, Space.VOX)
save_trk(sft, "lr-superiorfrontal.trk")
Footnotes
- 1
The image aparc-reduced.nii.gz, which we load as
labels_img, is a modified version of label map aparc+aseg.mgz created by FreeSurfer. The corpus callosum region is a combination of the FreeSurfer labels 251-255. The remaining FreeSurfer labels were re-mapped and reduced so that they lie between 0 and 88. To see the FreeSurfer region, label and name, represented by each value see label_info.txt in ~/.dipy/stanford_hardi.- 2
An affine transformation is a mapping between two coordinate systems that can represent scaling, rotation, sheer, translation and reflection. Affine transformations are often represented using a 4x4 matrix where the last row of the matrix is
[0, 0, 0, 1].
Total running time of the script: ( 0 minutes 53.118 seconds)