Note
Go to the end to download the full example code
Making videos the different stages of tractometry#
Two-dimensional figures of anatomical data are somewhat limited, because of the complex three-dimensional configuration of the brain. Therefored, dynamic videos of anatomical data are useful for exploring the data, as well as for creating dynamic presentations of the results. This example visualizes various stages of tractometry, from preprocessed diffusion data to the final tract profiles. We will use the Fury software library to visualize individual frames of the results of each stage, and then create videos of each stage of the process using the Python Image Library (PIL, also known as pillow).
Imports#
import os
import os.path as op
import nibabel as nib
import numpy as np
import tempfile
from dipy.io.streamline import load_trk
from dipy.tracking.streamline import (transform_streamlines,
set_number_of_points)
from dipy.core.gradients import gradient_table
from dipy.align import resample
from fury import actor, window
from fury.actor import colormap_lookup_table
from fury.colormap import create_colormap
from matplotlib.cm import tab20
import AFQ.data.fetch as afd
from AFQ.viz.utils import gen_color_dict
from PIL import Image
Define a function that makes videos#
The PIL library has a function that can be used to create animated GIFs from a series of images. We will use this function to create videos.
Note
This function is not part of the AFQ library, but is included here for convenience. It is not necessary to understand this function in order to understand the rest of the example. If you are interested in learning more about this function, you can read the PIL documentation. The function is based on the PIL.Image.save function.
def make_video(frames, out):
"""
Make a video from a series of frames.
Parameters
----------
frames : list of str
A list of file names of the frames to be included in the video.
out : str
The name of the output file. Format is determined by the file
extension.
"""
video = []
for nn in frames:
frame = Image.open(nn)
video.append(frame)
# Save the frames as an animated GIF
video[0].save(
out,
save_all=True,
append_images=video[1:],
duration=300,
loop=1)
tmp = tempfile.mkdtemp()
n_frames = 72
Get data from HBN POD2#
We get the same data that is used in the visualization tutorials.
afd.fetch_hbn_preproc(["NDARAA948VFH"])
study_path = afd.fetch_hbn_afq(["NDARAA948VFH"])[1]
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Visualize the processed dMRI data#
The HBN POD2 dataset was processed using the qsiprep pipeline. The
results from this processing are stored within a sub-folder of the
derivatives folder wthin the study folder.
Here, we will start by visualizing the diffusion data. We read in the
diffusion data, as well as the gradient table, using the nibabel library.
We then extract the b0, b1000, and b2000 volumes from the diffusion data.
We will use the actor.slicer function from fury to visualize these. This
function takes a 3D volume as input and returns a slicer actor, which can
then be added to a window.Scene object. We create a helper function that
will create a slicer actor for a given volume and a given slice along the x,
y, or z dimension. We then call this function three times, once for each of
the b0, b1000, and b2000 volumes, and add the resulting slicer actors to a
scene. We set the camera on the scene to a view that we like, and then we
record the scene into png files and subsequently gif animations. We do this
for each of the three volumes.
deriv_path = op.join(
study_path, "derivatives")
qsiprep_path = op.join(
deriv_path,
'qsiprep',
'sub-NDARAA948VFH',
'ses-HBNsiteRU')
dmri_img = nib.load(op.join(
qsiprep_path,
'dwi',
'sub-NDARAA948VFH_ses-HBNsiteRU_acq-64dir_space-T1w_desc-preproc_dwi.nii.gz'))
gtab = gradient_table(*[op.join(
qsiprep_path,
'dwi',
f'sub-NDARAA948VFH_ses-HBNsiteRU_acq-64dir_space-T1w_desc-preproc_dwi.{ext}') for ext in ['bval', 'bvec']])
dmri_data = dmri_img.get_fdata()
dmri_b0 = dmri_data[..., 0]
dmri_b1000 = dmri_data[..., 1]
dmri_b2000 = dmri_data[..., 65]
def slice_volume(data, x=None, y=None, z=None):
slicer_actors = []
slicer_actor_z = actor.slicer(data)
if z is not None:
slicer_actor_z.display_extent(
0, data.shape[0] - 1,
0, data.shape[1] - 1,
z, z)
slicer_actors.append(slicer_actor_z)
if y is not None:
slicer_actor_y = slicer_actor_z.copy()
slicer_actor_y.display_extent(
0, data.shape[0] - 1,
y, y,
0, data.shape[2] - 1)
slicer_actors.append(slicer_actor_y)
if x is not None:
slicer_actor_x = slicer_actor_z.copy()
slicer_actor_x.display_extent(
x, x,
0, data.shape[1] - 1,
0, data.shape[2] - 1)
slicer_actors.append(slicer_actor_x)
return slicer_actors
slicers_b0 = slice_volume(
dmri_b0,
x=dmri_b0.shape[0] // 2,
y=dmri_b0.shape[1] // 2,
z=dmri_b0.shape[-1] // 3)
slicers_b1000 = slice_volume(
dmri_b1000,
x=dmri_b0.shape[0] // 2,
y=dmri_b0.shape[1] // 2,
z=dmri_b0.shape[-1] // 3)
slicers_b2000 = slice_volume(
dmri_b2000,
x=dmri_b0.shape[0] // 2,
y=dmri_b0.shape[1] // 2,
z=dmri_b0.shape[-1] // 3)
for bval, slicers in zip([0, 1000, 2000],
[slicers_b0, slicers_b1000, slicers_b2000]):
scene = window.Scene()
for slicer in slicers:
scene.add(slicer)
scene.set_camera(position=(721.34, 393.48, 97.03),
focal_point=(96.00, 114.00, 96.00),
view_up=(-0.01, 0.02, 1.00))
scene.background((1, 1, 1))
window.record(scene, out_path=f'{tmp}/b{bval}',
size=(2400, 2400),
n_frames=n_frames, path_numbering=True)
make_video(
[f'{tmp}/b{bval}{ii:06d}.png' for ii in range(n_frames)], f'b{bval}.gif')
Visualizing whole-brain tractography#
One of the first steps of the pyAFQ pipeline is to generate whole-brain tractography. We will visualize the results of this step. We start by reading in the FA image, which is used as a reference for the tractography. We then load the whole brain tractography, and transform the coordinates of the streamlines into the coordinate frame of the T1-weighted data.
afq_path = op.join(
deriv_path,
'afq',
'sub-NDARAA948VFH',
'ses-HBNsiteRU')
fa_img = nib.load(op.join(afq_path,
'sub-NDARAA948VFH_ses-HBNsiteRU_acq-64dir_space-T1w_desc-preproc_dwi_model-DKI_FA.nii.gz'))
sft_whole_brain = load_trk(op.join(afq_path,
'sub-NDARAA948VFH_ses-HBNsiteRU_acq-64dir_space-T1w_desc-preproc_dwi_space-RASMM_model-CSD_desc-prob-afq_tractography.trk'), fa_img)
t1w_img = nib.load(op.join(deriv_path,
'qsiprep/sub-NDARAA948VFH/anat/sub-NDARAA948VFH_desc-preproc_T1w.nii.gz'))
t1w = t1w_img.get_fdata()
sft_whole_brain.to_rasmm()
whole_brain_t1w = transform_streamlines(
sft_whole_brain.streamlines,
np.linalg.inv(t1w_img.affine))
Visualize the streamlines#
The streamlines are visualized in the context of the T1-weighted data.
def lines_as_tubes(sl, line_width, **kwargs):
line_actor = actor.line(sl, **kwargs)
line_actor.GetProperty().SetRenderLinesAsTubes(1)
line_actor.GetProperty().SetLineWidth(line_width)
return line_actor
whole_brain_actor = lines_as_tubes(whole_brain_t1w, 2)
slicers = slice_volume(t1w, y=t1w.shape[1] // 2 - 5, z=t1w.shape[-1] // 3)
scene = window.Scene()
scene.add(whole_brain_actor)
for slicer in slicers:
scene.add(slicer)
scene.set_camera(position=(721.34, 393.48, 97.03),
focal_point=(96.00, 114.00, 96.00),
view_up=(-0.01, 0.02, 1.00))
scene.background((1, 1, 1))
window.record(scene, out_path=f'{tmp}/whole_brain', size=(2400, 2400),
n_frames=n_frames, path_numbering=True)
make_video([f"{tmp}/whole_brain{ii:06d}.png" for ii in range(n_frames)],
"whole_brain.gif")
Whole brain with waypoints#
We can also generate a gif video with the whole brain tractography and the waypoints that are used to define the bundles. We will use the same scene as before, but we will add the waypoints as contours to the scene.
scene.clear()
whole_brain_actor = lines_as_tubes(whole_brain_t1w, 2)
scene.add(whole_brain_actor)
for slicer in slicers:
scene.add(slicer)
scene.background((1, 1, 1))
waypoint1 = nib.load(
op.join(
afq_path,
"ROIs", "sub-NDARAA948VFH_ses-HBNsiteRU_acq-64dir_space-T1w_desc-preproc_dwi_desc-ROI-ARC_L-1-include.nii.gz"))
waypoint2 = nib.load(
op.join(
afq_path,
"ROIs", "sub-NDARAA948VFH_ses-HBNsiteRU_acq-64dir_space-T1w_desc-preproc_dwi_desc-ROI-ARC_L-2-include.nii.gz"))
waypoint1_xform = resample(waypoint1, t1w_img)
waypoint2_xform = resample(waypoint2, t1w_img)
waypoint1_data = waypoint1_xform.get_fdata() > 0
waypoint2_data = waypoint2_xform.get_fdata() > 0
surface_color = tab20.colors[0]
waypoint1_actor = actor.contour_from_roi(waypoint1_data,
color=surface_color,
opacity=0.5)
waypoint2_actor = actor.contour_from_roi(waypoint2_data,
color=surface_color,
opacity=0.5)
scene.add(waypoint1_actor)
scene.add(waypoint2_actor)
window.record(scene, out_path=f'{tmp}/whole_brain_with_waypoints', size=(2400, 2400),
n_frames=n_frames, path_numbering=True)
make_video([f"{tmp}/whole_brain_with_waypoints{ii:06d}.png" for ii in range(n_frames)],
"whole_brain_with_waypoints.gif")
bundle_path = op.join(afq_path,
'bundles')
Visualize the arcuate bundle#
Now visualize only the arcuate bundle that is selected with these waypoints.
fa_img = nib.load(op.join(afq_path,
'sub-NDARAA948VFH_ses-HBNsiteRU_acq-64dir_space-T1w_desc-preproc_dwi_model-DKI_FA.nii.gz'))
fa = fa_img.get_fdata()
sft_arc = load_trk(op.join(bundle_path,
'sub-NDARAA948VFH_ses-HBNsiteRU_acq-64dir_space-T1w_desc-preproc_dwi_space-RASMM_model-CSD_desc-prob-afq-ARC_L_tractography.trk'), fa_img)
sft_arc.to_rasmm()
arc_t1w = transform_streamlines(sft_arc.streamlines,
np.linalg.inv(t1w_img.affine))
bundles = [
"ARC_R",
"ATR_R",
"CST_R",
"IFO_R",
"ILF_R",
"SLF_R",
"UNC_R",
"CGC_R",
"Orbital", "AntFrontal", "SupFrontal", "Motor",
"SupParietal", "PostParietal", "Temporal", "Occipital",
"CGC_L",
"UNC_L",
"SLF_L",
"ILF_L",
"IFO_L",
"CST_L",
"ATR_L",
"ARC_L",
]
color_dict = gen_color_dict(bundles)
arc_actor = lines_as_tubes(arc_t1w, 8, colors=color_dict['ARC_L'])
scene.clear()
scene.add(arc_actor)
for slicer in slicers:
scene.add(slicer)
scene.add(waypoint1_actor)
scene.add(waypoint2_actor)
window.record(scene, out_path=f'{tmp}/arc1', size=(2400, 2400),
n_frames=n_frames, path_numbering=True)
make_video([f"{tmp}/arc1{ii:06d}.png" for ii in range(n_frames)], "arc1.gif")
Clean bundle#
The next step in processing would be to clean the bundle by removing streamlines that are outliers. We will visualize the cleaned bundle.
scene.clear()
scene.add(arc_actor)
for slicer in slicers:
scene.add(slicer)
window.record(scene, out_path=f'{tmp}/arc2', size=(2400, 2400),
n_frames=n_frames, path_numbering=True)
make_video([f"{tmp}/arc2{ii:06d}.png" for ii in range(n_frames)], "arc2.gif")
clean_bundles_path = op.join(afq_path,
'clean_bundles')
sft_arc = load_trk(op.join(clean_bundles_path,
'sub-NDARAA948VFH_ses-HBNsiteRU_acq-64dir_space-T1w_desc-preproc_dwi_space-RASMM_model-CSD_desc-prob-afq-ARC_L_tractography.trk'), fa_img)
sft_arc.to_rasmm()
arc_t1w = transform_streamlines(sft_arc.streamlines,
np.linalg.inv(t1w_img.affine))
arc_actor = lines_as_tubes(arc_t1w, 8, colors=tab20.colors[18])
scene.clear()
scene.add(arc_actor)
for slicer in slicers:
scene.add(slicer)
window.record(scene, out_path=f'{tmp}/arc3', size=(2400, 2400),
n_frames=n_frames, path_numbering=True)
make_video([f"{tmp}/arc3{ii:06d}.png" for ii in range(n_frames)], "arc3.gif")
Show the values of tissue properties along the bundle#
We can also visualize the values of tissue properties along the bundle. Here we will visualize the fractional anisotropy (FA) along the arcuate bundle. This is done by using a colormap to color the streamlines according to the values of the tissue property, with fury.colormap.create_colormap.
lut_args = dict(scale_range=(0, 1),
hue_range=(1, 0),
saturation_range=(0, 1),
value_range=(0, 1))
arc_actor = lines_as_tubes(arc_t1w, 8,
colors=resample(fa_img, t1w_img).get_fdata(),
lookup_colormap=colormap_lookup_table(**lut_args))
scene.clear()
scene.add(arc_actor)
for slicer in slicers:
scene.add(slicer)
window.record(scene, out_path=f'{tmp}/arc4', size=(2400, 2400),
n_frames=n_frames, path_numbering=True)
make_video([f"{tmp}/arc4{ii:06d}.png" for ii in range(n_frames)], "arc4.gif")
Core of the bundle and tract profile#
Finally, we can visualize the core of the bundle and the tract profile. The core of the bundle is the median of the streamlines, and the tract profile is the values of the tissue property along the core of the bundle.
core_arc = np.median(np.asarray(set_number_of_points(arc_t1w, 20)), axis=0)
from dipy.stats.analysis import afq_profile
sft_arc.to_vox()
arc_profile = afq_profile(fa, sft_arc.streamlines, affine=np.eye(4),
n_points=20)
core_arc_actor = lines_as_tubes(
[core_arc],
40,
colors=create_colormap(arc_profile, 'viridis')
)
arc_actor = lines_as_tubes(arc_t1w, 1,
colors=resample(fa_img, t1w_img).get_fdata(),
lookup_colormap=colormap_lookup_table(**lut_args))
scene.clear()
for slicer in slicers:
scene.add(slicer)
scene.add(arc_actor)
scene.add(core_arc_actor)
window.record(scene, out_path=f'{tmp}/arc5', size=(2400, 2400),
n_frames=n_frames, path_numbering=True)
make_video([f"{tmp}/arc5{ii:06d}.png" for ii in range(n_frames)], "arc5.gif")
Core of all bundles and their tract profiles#
Same as before, but for all bundles.
scene.clear()
for slicer in slicers:
scene.add(slicer)
for bundle in bundles:
sft = load_trk(op.join(clean_bundles_path,
f'sub-NDARAA948VFH_ses-HBNsiteRU_acq-64dir_space-T1w_desc-preproc_dwi_space-RASMM_model-CSD_desc-prob-afq-{bundle}_tractography.trk'), fa_img)
sft.to_rasmm()
bundle_t1w = transform_streamlines(sft.streamlines,
np.linalg.inv(t1w_img.affine))
bundle_actor = lines_as_tubes(bundle_t1w, 8, colors=color_dict[bundle])
scene.add(bundle_actor)
window.record(scene, out_path=f'{tmp}/all_bundles', size=(2400, 2400),
n_frames=n_frames, path_numbering=True)
make_video(
[f"{tmp}/all_bundles{ii:06d}.png" for ii in range(n_frames)], "all_bundles.gif")
scene.clear()
for slicer in slicers:
scene.add(slicer)
tract_profiles = []
for bundle in bundles:
sft = load_trk(op.join(clean_bundles_path,
f'sub-NDARAA948VFH_ses-HBNsiteRU_acq-64dir_space-T1w_desc-preproc_dwi_space-RASMM_model-CSD_desc-prob-afq-{bundle}_tractography.trk'), fa_img)
sft.to_rasmm()
bundle_t1w = transform_streamlines(sft.streamlines,
np.linalg.inv(t1w_img.affine))
core_bundle = np.median(np.asarray(
set_number_of_points(bundle_t1w, 20)), axis=0)
sft.to_vox()
tract_profiles.append(
afq_profile(fa, sft.streamlines, affine=np.eye(4),
n_points=20))
core_actor = lines_as_tubes(
[core_bundle],
40,
colors=create_colormap(tract_profiles[-1], 'viridis')
)
scene.add(core_actor)
window.record(scene,
out_path=f'{tmp}/all_tract_profiles',
size=(2400, 2400),
n_frames=n_frames,
path_numbering=True)
make_video([f"{tmp}/all_tract_profiles{ii:06d}.png" for ii in range(n_frames)],
"all_tract_profiles.gif")
Tract profiles as a table#
Finally, we can visualize the tract profiles as a table. This is done by plotting the tract profiles for each bundle as a line plot, with the x-axis representing the position along the bundle, and the y-axis representing the value of the tissue property. We will use the matplotlib library to create this plot.
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
for ii, bundle in enumerate(bundles):
ax.plot(np.arange(ii * 20, (ii + 1) * 20),
tract_profiles[ii],
color=color_dict[bundle],
linewidth=3)
ax.set_xticks(np.arange(0, 20 * len(bundles), 20))
ax.set_xticklabels(bundles, rotation=45, ha='right')
fig.set_size_inches(10, 5)
plt.subplots_adjust(bottom=0.2)
fig.savefig('tract_profiles_as_table.png')
Total running time of the script: (24 minutes 53.043 seconds)
Estimated memory usage: 4131 MB