def select_by_vol_rois(streamlines, rois, include, mode=None, affine=None, tol=None):
"""
Include or exclude the streamlines according to some ROIs
example
>>>selection = select_by_vol_rois(streamlines, [mask1, mask2], [True, False], mode="both_end", tol=1.0)
>>>selection = list(selection)
"""
rois_selection = streamline.select_by_rois(streamline=streamlines, rois=rois,
include=include, mode=mode, affine=affine, tol=tol)
rois_streamlines = list(rois_selection)
return rois_streamlines
def select_streamlines_between_peaks_from_spheres(streamlines,
peaks_rois,
affine,
index_peak1,
index_peak2,
radius=5):
from dipy.tracking.streamline import select_by_rois
import numpy as np
roi1 = peaks_rois[index_peak1]
roi2 = peaks_rois[index_peak2]
roi = roi1 + roi2
roi = np.expand_dims(roi, axis=0)
selected_tracks = select_by_rois(streamlines, affine=affine,rois=roi, \
mode='both_end',
include=np.array([True]),
tol=radius)
return selected_tracks
def test_select_by_rois():
streamlines = [np.array([[0, 0., 0.9],
[1.9, 0., 0.]]),
np.array([[0.1, 0., 0],
[0, 1., 1.],
[0, 2., 2.]]),
np.array([[2, 2, 2],
[3, 3, 3]])]
# Make two ROIs:
mask1 = np.zeros((4, 4, 4), dtype=bool)
mask2 = np.zeros_like(mask1)
mask1[0, 0, 0] = True
mask2[1, 0, 0] = True
selection = select_by_rois(streamlines, [mask1], [True],
tol=1)
npt.assert_array_equal(list(selection), [streamlines[0],
streamlines[1]])
selection = select_by_rois(streamlines, [mask1, mask2], [True, True],
tol=1)
npt.assert_array_equal(list(selection), [streamlines[0],
streamlines[1]])
selection = select_by_rois(streamlines, [mask1, mask2], [True, False])
npt.assert_array_equal(list(selection), [streamlines[1]])
# Setting tolerance too low gets overridden:
selection = select_by_rois(streamlines, [mask1, mask2], [True, False],
tol=0.1)
npt.assert_array_equal(list(selection), [streamlines[1]])
selection = select_by_rois(streamlines, [mask1, mask2], [True, True],
tol=0.87)
npt.assert_array_equal(list(selection), [streamlines[1]])
mask3 = np.zeros_like(mask1)
mask3[0, 2, 2] = 1
selection = select_by_rois(streamlines, [mask1, mask2, mask3],
[True, True, False], tol=1.0)
npt.assert_array_equal(list(selection), [streamlines[0]])
# Select using only one ROI
selection = select_by_rois(streamlines, [mask1], [True], tol=0.87)
npt.assert_array_equal(list(selection), [streamlines[1]])
selection = select_by_rois(streamlines, [mask1], [True], tol=1.0)
npt.assert_array_equal(list(selection), [streamlines[0],
streamlines[1]])
# Use different modes:
selection = select_by_rois(streamlines, [mask1, mask2, mask3],
[True, True, False],
mode="all",
tol=1.0)
npt.assert_array_equal(list(selection), [streamlines[0]])
selection = select_by_rois(streamlines, [mask1, mask2, mask3],
[True, True, False],
mode="either_end",
tol=1.0)
npt.assert_array_equal(list(selection), [streamlines[0]])
selection = select_by_rois(streamlines, [mask1, mask2, mask3],
[True, True, False],
mode="both_end",
tol=1.0)
npt.assert_array_equal(list(selection), [streamlines[0]])
mask2[0, 2, 2] = True
selection = select_by_rois(streamlines, [mask1, mask2, mask3],
[True, True, False],
mode="both_end",
tol=1.0)
npt.assert_array_equal(list(selection), [streamlines[0],
streamlines[1]])
# Test with generator input:
selection = select_by_rois(generate_sl(streamlines), [mask1], [True],
tol=1.0)
npt.assert_array_equal(list(selection), [streamlines[0],
streamlines[1]])
def test_select_by_rois():
streamlines = [
np.array([[0, 0., 0.9], [1.9, 0., 0.]]),
np.array([[0.1, 0., 0], [0, 1., 1.], [0, 2., 2.]]),
np.array([[2, 2, 2], [3, 3, 3]])
]
# Make two ROIs:
mask1 = np.zeros((4, 4, 4), dtype=bool)
mask2 = np.zeros_like(mask1)
mask1[0, 0, 0] = True
mask2[1, 0, 0] = True
selection = select_by_rois(streamlines, [mask1], [True], tol=1)
assert_arrays_equal(list(selection), [streamlines[0], streamlines[1]])
selection = select_by_rois(streamlines, [mask1, mask2], [True, True],
tol=1)
assert_arrays_equal(list(selection), [streamlines[0], streamlines[1]])
selection = select_by_rois(streamlines, [mask1, mask2], [True, False])
assert_arrays_equal(list(selection), [streamlines[1]])
# Setting tolerance too low gets overridden:
selection = select_by_rois(streamlines, [mask1, mask2], [True, False],
tol=0.1)
assert_arrays_equal(list(selection), [streamlines[1]])
selection = select_by_rois(streamlines, [mask1, mask2], [True, True],
tol=0.87)
assert_arrays_equal(list(selection), [streamlines[1]])
mask3 = np.zeros_like(mask1)
mask3[0, 2, 2] = 1
selection = select_by_rois(streamlines, [mask1, mask2, mask3],
[True, True, False],
tol=1.0)
assert_arrays_equal(list(selection), [streamlines[0]])
# Select using only one ROI
selection = select_by_rois(streamlines, [mask1], [True], tol=0.87)
assert_arrays_equal(list(selection), [streamlines[1]])
selection = select_by_rois(streamlines, [mask1], [True], tol=1.0)
assert_arrays_equal(list(selection), [streamlines[0], streamlines[1]])
# Use different modes:
selection = select_by_rois(streamlines, [mask1, mask2, mask3],
[True, True, False],
mode="all",
tol=1.0)
assert_arrays_equal(list(selection), [streamlines[0]])
selection = select_by_rois(streamlines, [mask1, mask2, mask3],
[True, True, False],
mode="either_end",
tol=1.0)
assert_arrays_equal(list(selection), [streamlines[0]])
selection = select_by_rois(streamlines, [mask1, mask2, mask3],
[True, True, False],
mode="both_end",
tol=1.0)
assert_arrays_equal(list(selection), [streamlines[0]])
mask2[0, 2, 2] = True
selection = select_by_rois(streamlines, [mask1, mask2, mask3],
[True, True, False],
mode="both_end",
tol=1.0)
assert_arrays_equal(list(selection), [streamlines[0], streamlines[1]])
# Test with generator input:
selection = select_by_rois(generate_sl(streamlines), [mask1], [True],
tol=1.0)
assert_arrays_equal(list(selection), [streamlines[0], streamlines[1]])
def run_tracking(step_curv_combinations,
recon_shelved,
n_seeds_per_iter,
traversal,
maxcrossing,
max_length,
pft_back_tracking_dist,
pft_front_tracking_dist,
particle_count,
roi_neighborhood_tol,
min_length,
track_type,
min_separation_angle,
sphere,
tiss_class,
tissue_shelved,
verbose=False):
"""
Create a density map of the list of streamlines.
Parameters
----------
step_curv_combinations : list
List of tuples representing all pair combinations of step sizes and
curvature thresholds from which to sample streamlines.
recon_path : str
File path to diffusion reconstruction model.
n_seeds_per_iter : int
Number of seeds from which to initiate tracking for each unique
ensemble combination. By default this is set to 250.
directget : str
The statistical approach to tracking. Options are: det (deterministic),
closest (clos), boot (bootstrapped), and prob (probabilistic).
maxcrossing : int
Maximum number if diffusion directions that can be assumed per voxel
while tracking.
max_length : int
Maximum number of steps to restrict tracking.
pft_back_tracking_dist : float
Distance in mm to back track before starting the particle filtering
tractography. The total particle filtering tractography distance is
equal to back_tracking_dist + front_tracking_dist. By default this is
set to 2 mm.
pft_front_tracking_dist : float
Distance in mm to run the particle filtering tractography after the
the back track distance. The total particle filtering tractography
distance is equal to back_tracking_dist + front_tracking_dist. By
default this is set to 1 mm.
particle_count : int
Number of particles to use in the particle filter.
roi_neighborhood_tol : float
Distance (in the units of the streamlines, usually mm). If any
coordinate in the streamline is within this distance from the center
of any voxel in the ROI, the filtering criterion is set to True for
this streamline, otherwise False. Defaults to the distance between
the center of each voxel and the corner of the voxel.
waymask_data : ndarray
Tractography constraint mask array in native diffusion space.
min_length : int
Minimum fiber length threshold in mm to restrict tracking.
track_type : str
Tracking algorithm used (e.g. 'local' or 'particle').
min_separation_angle : float
The minimum angle between directions [0, 90].
sphere : obj
DiPy object for modeling diffusion directions on a sphere.
tiss_class : str
Tissue classification method.
tissue_shelved : str
File path to joblib-shelved 4D T1w tissue segmentations in native
diffusion space.
Returns
-------
streamlines : ArraySequence
DiPy list/array-like object of streamline points from tractography.
"""
import gc
import time
import numpy as np
from dipy.tracking import utils
from dipy.tracking.streamline import select_by_rois
from dipy.tracking.local_tracking import LocalTracking, \
ParticleFilteringTracking
from dipy.direction import (ProbabilisticDirectionGetter,
ClosestPeakDirectionGetter,
DeterministicMaximumDirectionGetter)
from nilearn.image import index_img, math_img
from pynets.dmri.utils import generate_seeds, random_seeds_from_mask
from nibabel.streamlines.array_sequence import ArraySequence
start_time = time.time()
if verbose is True:
print("%s%s%s" % ('Preparing tissue constraints:',
np.round(time.time() - start_time, 1), 's'))
start_time = time.time()
tissue_img = tissue_shelved.get()
# Order:
B0_mask = index_img(tissue_img, 0)
atlas_img = index_img(tissue_img, 1)
t1w2dwi = index_img(tissue_img, 3)
gm_in_dwi = index_img(tissue_img, 4)
vent_csf_in_dwi = index_img(tissue_img, 5)
wm_in_dwi = index_img(tissue_img, 6)
tissue_img.uncache()
tiss_classifier = prep_tissues(t1w2dwi, gm_in_dwi, vent_csf_in_dwi,
wm_in_dwi, tiss_class, B0_mask)
# if verbose is True:
# print("%s%s%s" % (
# 'Fitting tissue classifier:',
# np.round(time.time() - start_time, 1), 's'))
# start_time = time.time()
if verbose is True:
print("%s%s%s" % ('Loading reconstruction:',
np.round(time.time() - start_time, 1), 's'))
start_time = time.time()
print("%s%s" % ("Curvature: ", step_curv_combinations[1]))
# Instantiate DirectionGetter
if traversal.lower() in ["probabilistic", "prob"]:
dg = ProbabilisticDirectionGetter.from_shcoeff(
recon_shelved.get(),
max_angle=float(step_curv_combinations[1]),
sphere=sphere,
min_separation_angle=min_separation_angle,
)
elif traversal.lower() in ["closestpeaks", "cp"]:
dg = ClosestPeakDirectionGetter.from_shcoeff(
recon_shelved.get(),
max_angle=float(step_curv_combinations[1]),
sphere=sphere,
min_separation_angle=min_separation_angle,
)
elif traversal.lower() in ["deterministic", "det"]:
maxcrossing = 1
dg = DeterministicMaximumDirectionGetter.from_shcoeff(
recon_shelved.get(),
max_angle=float(step_curv_combinations[1]),
sphere=sphere,
min_separation_angle=min_separation_angle,
)
else:
raise ValueError("ERROR: No valid direction getter(s) specified.")
if verbose is True:
print("%s%s%s" % ('Extracting directions:',
np.round(time.time() - start_time, 1), 's'))
start_time = time.time()
print("%s%s" % ("Step: ", step_curv_combinations[0]))
# Perform wm-gm interface seeding, using n_seeds at a time
seeds = generate_seeds(
random_seeds_from_mask(np.asarray(
math_img("img > 0.01", img=index_img(
tissue_img, 2)).dataobj).astype("bool").astype("int16") > 0,
seeds_count=n_seeds_per_iter,
random_seed=42))
if verbose is True:
print("%s%s%s" % ('Drawing random seeds:',
np.round(time.time() - start_time, 1), 's'))
start_time = time.time()
# print(seeds)
# Perform tracking
if track_type == "local":
streamline_generator = LocalTracking(dg,
tiss_classifier,
np.stack([i for i in seeds]),
np.eye(4),
max_cross=int(maxcrossing),
maxlen=int(max_length),
step_size=float(
step_curv_combinations[0]),
fixedstep=False,
return_all=True,
random_seed=42)
elif track_type == "particle":
streamline_generator = ParticleFilteringTracking(
dg,
tiss_classifier,
np.stack([i for i in seeds]),
np.eye(4),
max_cross=int(maxcrossing),
step_size=float(step_curv_combinations[0]),
maxlen=int(max_length),
pft_back_tracking_dist=pft_back_tracking_dist,
pft_front_tracking_dist=pft_front_tracking_dist,
pft_max_trial=20,
particle_count=particle_count,
return_all=True,
random_seed=42)
else:
raise ValueError("ERROR: No valid tracking method(s) specified.")
if verbose is True:
print("%s%s%s" % ('Instantiating tracking:',
np.round(time.time() - start_time, 1), 's'))
start_time = time.time()
# print(seeds)
del dg
# Filter resulting streamlines by those that stay entirely
# inside the brain
try:
roi_proximal_streamlines = utils.target(
streamline_generator,
np.eye(4),
np.asarray(B0_mask.dataobj).astype('bool'),
include=True)
except BaseException:
print('No streamlines found inside the brain! ' 'Check registrations.')
#return None
if verbose is True:
print("%s%s%s" % ('Drawing streamlines:',
np.round(time.time() - start_time, 1), 's'))
start_time = time.time()
del seeds, tiss_classifier, streamline_generator
B0_mask.uncache()
atlas_img.uncache()
t1w2dwi.uncache()
gm_in_dwi.uncache()
vent_csf_in_dwi.uncache()
wm_in_dwi.uncache()
gc.collect()
# Filter resulting streamlines by roi-intersection
# characteristics
atlas_data = np.array(atlas_img.dataobj).astype("uint16")
# Build mask vector from atlas for later roi filtering
parcels = [
atlas_data == roi_val
for roi_val in [i for i in np.unique(atlas_data) if i != 0]
]
try:
roi_proximal_streamlines = \
select_by_rois(
roi_proximal_streamlines,
affine=np.eye(4),
rois=parcels,
include=list(np.ones(len(parcels)).astype("bool")),
mode="any",
tol=roi_neighborhood_tol,
)
except BaseException:
print('No streamlines found to connect any parcels! '
'Check registrations.')
#return None
del atlas_data
if verbose is True:
print("%s%s%s" % ('Selecting by parcellation:',
np.round(time.time() - start_time, 1), 's'))
start_time = time.time()
del parcels
gc.collect()
if verbose is True:
print("%s%s%s" % ('Selecting by minimum length criterion:',
np.round(time.time() - start_time, 1), 's'))
gc.collect()
return ArraySequence([
s.astype("float32") for s in roi_proximal_streamlines
if len(s) > float(min_length)
])
def run_tracking(step_curv_combinations,
recon_path,
n_seeds_per_iter,
directget,
maxcrossing,
max_length,
pft_back_tracking_dist,
pft_front_tracking_dist,
particle_count,
roi_neighborhood_tol,
waymask,
min_length,
track_type,
min_separation_angle,
sphere,
tiss_class,
tissues4d,
cache_dir,
min_seeds=100):
import gc
import os
import h5py
from dipy.tracking import utils
from dipy.tracking.streamline import select_by_rois
from dipy.tracking.local_tracking import LocalTracking, \
ParticleFilteringTracking
from dipy.direction import (ProbabilisticDirectionGetter,
ClosestPeakDirectionGetter,
DeterministicMaximumDirectionGetter)
from nilearn.image import index_img
from pynets.dmri.track import prep_tissues
from nibabel.streamlines.array_sequence import ArraySequence
from nipype.utils.filemanip import copyfile, fname_presuffix
import uuid
from time import strftime
run_uuid = f"{strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4()}"
recon_path_tmp_path = fname_presuffix(
recon_path,
suffix=f"_{'_'.join([str(i) for i in step_curv_combinations])}_"
f"{run_uuid}",
newpath=cache_dir)
copyfile(recon_path, recon_path_tmp_path, copy=True, use_hardlink=False)
tissues4d_tmp_path = fname_presuffix(
tissues4d,
suffix=f"_{'_'.join([str(i) for i in step_curv_combinations])}_"
f"{run_uuid}",
newpath=cache_dir)
copyfile(tissues4d, tissues4d_tmp_path, copy=True, use_hardlink=False)
if waymask is not None:
waymask_tmp_path = fname_presuffix(
waymask,
suffix=f"_{'_'.join([str(i) for i in step_curv_combinations])}_"
f"{run_uuid}",
newpath=cache_dir)
copyfile(waymask, waymask_tmp_path, copy=True, use_hardlink=False)
else:
waymask_tmp_path = None
tissue_img = nib.load(tissues4d_tmp_path)
# Order:
B0_mask = index_img(tissue_img, 0)
atlas_img = index_img(tissue_img, 1)
seeding_mask = index_img(tissue_img, 2)
t1w2dwi = index_img(tissue_img, 3)
gm_in_dwi = index_img(tissue_img, 4)
vent_csf_in_dwi = index_img(tissue_img, 5)
wm_in_dwi = index_img(tissue_img, 6)
tiss_classifier = prep_tissues(t1w2dwi, gm_in_dwi, vent_csf_in_dwi,
wm_in_dwi, tiss_class, B0_mask)
B0_mask_data = np.asarray(B0_mask.dataobj).astype("bool")
seeding_mask = np.asarray(
seeding_mask.dataobj).astype("bool").astype("int16")
with h5py.File(recon_path_tmp_path, 'r+') as hf:
mod_fit = hf['reconstruction'][:].astype('float32')
print("%s%s" % ("Curvature: ", step_curv_combinations[1]))
# Instantiate DirectionGetter
if directget.lower() in ["probabilistic", "prob"]:
dg = ProbabilisticDirectionGetter.from_shcoeff(
mod_fit,
max_angle=float(step_curv_combinations[1]),
sphere=sphere,
min_separation_angle=min_separation_angle,
)
elif directget.lower() in ["closestpeaks", "cp"]:
dg = ClosestPeakDirectionGetter.from_shcoeff(
mod_fit,
max_angle=float(step_curv_combinations[1]),
sphere=sphere,
min_separation_angle=min_separation_angle,
)
elif directget.lower() in ["deterministic", "det"]:
maxcrossing = 1
dg = DeterministicMaximumDirectionGetter.from_shcoeff(
mod_fit,
max_angle=float(step_curv_combinations[1]),
sphere=sphere,
min_separation_angle=min_separation_angle,
)
else:
raise ValueError("ERROR: No valid direction getter(s) specified.")
print("%s%s" % ("Step: ", step_curv_combinations[0]))
# Perform wm-gm interface seeding, using n_seeds at a time
seeds = utils.random_seeds_from_mask(
seeding_mask > 0,
seeds_count=n_seeds_per_iter,
seed_count_per_voxel=False,
affine=np.eye(4),
)
if len(seeds) < min_seeds:
print(
UserWarning(
f"<{min_seeds} valid seed points found in wm-gm interface..."))
return None
# print(seeds)
# Perform tracking
if track_type == "local":
streamline_generator = LocalTracking(dg,
tiss_classifier,
seeds,
np.eye(4),
max_cross=int(maxcrossing),
maxlen=int(max_length),
step_size=float(
step_curv_combinations[0]),
fixedstep=False,
return_all=True,
random_seed=42)
elif track_type == "particle":
streamline_generator = ParticleFilteringTracking(
dg,
tiss_classifier,
seeds,
np.eye(4),
max_cross=int(maxcrossing),
step_size=float(step_curv_combinations[0]),
maxlen=int(max_length),
pft_back_tracking_dist=pft_back_tracking_dist,
pft_front_tracking_dist=pft_front_tracking_dist,
pft_max_trial=20,
particle_count=particle_count,
return_all=True,
random_seed=42)
else:
raise ValueError("ERROR: No valid tracking method(s) specified.")
# Filter resulting streamlines by those that stay entirely
# inside the brain
try:
roi_proximal_streamlines = utils.target(streamline_generator,
np.eye(4),
B0_mask_data.astype('bool'),
include=True)
except BaseException:
print('No streamlines found inside the brain! ' 'Check registrations.')
return None
del mod_fit, seeds, tiss_classifier, streamline_generator, \
B0_mask_data, seeding_mask, dg
B0_mask.uncache()
atlas_img.uncache()
t1w2dwi.uncache()
gm_in_dwi.uncache()
vent_csf_in_dwi.uncache()
wm_in_dwi.uncache()
atlas_img.uncache()
tissue_img.uncache()
gc.collect()
# Filter resulting streamlines by roi-intersection
# characteristics
atlas_data = np.array(atlas_img.dataobj).astype("uint16")
# Build mask vector from atlas for later roi filtering
parcels = []
i = 0
intensities = [i for i in np.unique(atlas_data) if i != 0]
for roi_val in intensities:
parcels.append(atlas_data == roi_val)
i += 1
parcel_vec = list(np.ones(len(parcels)).astype("bool"))
try:
roi_proximal_streamlines = \
nib.streamlines.array_sequence.ArraySequence(
select_by_rois(
roi_proximal_streamlines,
affine=np.eye(4),
rois=parcels,
include=parcel_vec,
mode="any",
tol=roi_neighborhood_tol,
)
)
print("%s%s" % ("Filtering by: \nNode intersection: ",
len(roi_proximal_streamlines)))
except BaseException:
print('No streamlines found to connect any parcels! '
'Check registrations.')
return None
try:
roi_proximal_streamlines = nib.streamlines. \
array_sequence.ArraySequence(
[
s for s in roi_proximal_streamlines
if len(s) >= float(min_length)
]
)
print(f"Minimum fiber length >{min_length}mm: "
f"{len(roi_proximal_streamlines)}")
except BaseException:
print('No streamlines remaining after minimal length criterion.')
return None
if waymask is not None and os.path.isfile(waymask_tmp_path):
waymask_data = np.asarray(
nib.load(waymask_tmp_path).dataobj).astype("bool")
try:
roi_proximal_streamlines = roi_proximal_streamlines[utils.near_roi(
roi_proximal_streamlines,
np.eye(4),
waymask_data,
tol=int(round(roi_neighborhood_tol * 0.50, 1)),
mode="all")]
print("%s%s" %
("Waymask proximity: ", len(roi_proximal_streamlines)))
del waymask_data
except BaseException:
print('No streamlines remaining in waymask\'s vacinity.')
return None
hf.close()
del parcels, atlas_data
tmp_files = [tissues4d_tmp_path, waymask_tmp_path, recon_path_tmp_path]
for j in tmp_files:
if j is not None:
if os.path.isfile(j):
os.system(f"rm -f {j} &")
if len(roi_proximal_streamlines) > 0:
return ArraySequence(
[s.astype("float32") for s in roi_proximal_streamlines])
else:
return None
def track_ensemble(dwi_data,
target_samples,
atlas_data_wm_gm_int,
parcels,
mod_fit,
tiss_classifier,
sphere,
directget,
curv_thr_list,
step_list,
track_type,
maxcrossing,
max_length,
roi_neighborhood_tol,
min_length,
waymask,
n_seeds_per_iter=100,
pft_back_tracking_dist=2,
pft_front_tracking_dist=1,
particle_count=15):
"""
Perform native-space ensemble tractography, restricted to a vector of ROI masks.
dwi_data : array
4D array of dwi data.
target_samples : int
Total number of streamline samples specified to generate streams.
atlas_data_wm_gm_int : array
3D int32 numpy array of atlas parcellation intensities from Nifti1Image in T1w-warped native diffusion space,
restricted to wm-gm interface.
parcels : list
List of 3D boolean numpy arrays of atlas parcellation ROI masks from a Nifti1Image in T1w-warped native
diffusion space.
mod : obj
Connectivity reconstruction model.
tiss_classifier : str
Tissue classification method.
sphere : obj
DiPy object for modeling diffusion directions on a sphere.
directget : str
The statistical approach to tracking. Options are: det (deterministic), closest (clos), boot (bootstrapped),
and prob (probabilistic).
curv_thr_list : list
List of integer curvature thresholds used to perform ensemble tracking.
step_list : list
List of float step-sizes used to perform ensemble tracking.
track_type : str
Tracking algorithm used (e.g. 'local' or 'particle').
maxcrossing : int
Maximum number if diffusion directions that can be assumed per voxel while tracking.
max_length : int
Maximum fiber length threshold in mm to restrict tracking.
roi_neighborhood_tol : float
Distance (in the units of the streamlines, usually mm). If any
coordinate in the streamline is within this distance from the center
of any voxel in the ROI, the filtering criterion is set to True for
this streamline, otherwise False. Defaults to the distance between
the center of each voxel and the corner of the voxel.
min_length : int
Minimum fiber length threshold in mm.
waymask : str
Path to a Nifti1Image in native diffusion space to constrain tractography.
n_seeds_per_iter : int
Number of seeds from which to initiate tracking for each unique ensemble combination.
By default this is set to 200.
particle_count
pft_back_tracking_dist : float
Distance in mm to back track before starting the particle filtering
tractography. The total particle filtering tractography distance is
equal to back_tracking_dist + front_tracking_dist. By default this is set to 2 mm.
pft_front_tracking_dist : float
Distance in mm to run the particle filtering tractography after the
the back track distance. The total particle filtering tractography
distance is equal to back_tracking_dist + front_tracking_dist. By
default this is set to 1 mm.
particle_count : int
Number of particles to use in the particle filter.
Returns
-------
streamlines : ArraySequence
DiPy list/array-like object of streamline points from tractography.
"""
from colorama import Fore, Style
from dipy.tracking import utils
from dipy.tracking.streamline import Streamlines, select_by_rois
from dipy.tracking.local_tracking import LocalTracking, ParticleFilteringTracking
from dipy.direction import ProbabilisticDirectionGetter, BootDirectionGetter, ClosestPeakDirectionGetter, DeterministicMaximumDirectionGetter
if waymask:
waymask_data = nib.load(waymask).get_fdata().astype('bool')
# Commence Ensemble Tractography
parcel_vec = list(np.ones(len(parcels)).astype('bool'))
streamlines = nib.streamlines.array_sequence.ArraySequence()
ix = 0
circuit_ix = 0
stream_counter = 0
while int(stream_counter) < int(target_samples):
for curv_thr in curv_thr_list:
print("%s%s" % ('Curvature: ', curv_thr))
# Instantiate DirectionGetter
if directget == 'prob':
dg = ProbabilisticDirectionGetter.from_shcoeff(
mod_fit, max_angle=float(curv_thr), sphere=sphere)
elif directget == 'boot':
dg = BootDirectionGetter.from_data(dwi_data,
mod_fit,
max_angle=float(curv_thr),
sphere=sphere)
elif directget == 'clos':
dg = ClosestPeakDirectionGetter.from_shcoeff(
mod_fit, max_angle=float(curv_thr), sphere=sphere)
elif directget == 'det':
dg = DeterministicMaximumDirectionGetter.from_shcoeff(
mod_fit, max_angle=float(curv_thr), sphere=sphere)
else:
raise ValueError(
'ERROR: No valid direction getter(s) specified.')
for step in step_list:
print("%s%s" % ('Step: ', step))
# Perform wm-gm interface seeding, using n_seeds at a time
seeds = utils.random_seeds_from_mask(
atlas_data_wm_gm_int > 0,
seeds_count=n_seeds_per_iter,
seed_count_per_voxel=False,
affine=np.eye(4))
if len(seeds) == 0:
raise RuntimeWarning(
'Warning: No valid seed points found in wm-gm interface...'
)
print(seeds)
# Perform tracking
if track_type == 'local':
streamline_generator = LocalTracking(
dg,
tiss_classifier,
seeds,
np.eye(4),
max_cross=int(maxcrossing),
maxlen=int(max_length),
step_size=float(step),
return_all=True)
elif track_type == 'particle':
streamline_generator = ParticleFilteringTracking(
dg,
tiss_classifier,
seeds,
np.eye(4),
max_cross=int(maxcrossing),
step_size=float(step),
maxlen=int(max_length),
pft_back_tracking_dist=pft_back_tracking_dist,
pft_front_tracking_dist=pft_front_tracking_dist,
particle_count=particle_count,
return_all=True)
else:
raise ValueError(
'ERROR: No valid tracking method(s) specified.')
# Filter resulting streamlines by roi-intersection characteristics
roi_proximal_streamlines = Streamlines(
select_by_rois(streamline_generator,
affine=np.eye(4),
rois=parcels,
include=parcel_vec,
mode='any',
tol=roi_neighborhood_tol))
print("%s%s" %
('Qualifying Streamlines by node intersection: ',
len(roi_proximal_streamlines)))
roi_proximal_streamlines = nib.streamlines.array_sequence.ArraySequence(
[
s for s in roi_proximal_streamlines
if len(s) > float(min_length)
])
print("%s%s" %
('Qualifying Streamlines by minimum length criterion: ',
len(roi_proximal_streamlines)))
if waymask:
roi_proximal_streamlines = roi_proximal_streamlines[
utils.near_roi(roi_proximal_streamlines,
np.eye(4),
waymask_data,
tol=roi_neighborhood_tol,
mode='any')]
print("%s%s" %
('Qualifying Streamlines by waymask proximity: ',
len(roi_proximal_streamlines)))
# Repeat process until target samples condition is met
ix = ix + 1
for s in roi_proximal_streamlines:
stream_counter = stream_counter + len(s)
streamlines.append(s)
if int(stream_counter) >= int(target_samples):
break
else:
continue
# Cleanup memory
del seeds, roi_proximal_streamlines, streamline_generator
del dg
circuit_ix = circuit_ix + 1
print(
"%s%s%s%s%s" %
('Completed hyperparameter circuit: ', circuit_ix,
'...\nCumulative Streamline Count: ', Fore.CYAN, stream_counter))
print(Style.RESET_ALL)
print('\n')
return streamlines
def track_ensemble(target_samples, atlas_data_wm_gm_int, parcels, mod_fit, tiss_classifier, sphere, directget,
curv_thr_list, step_list, track_type, maxcrossing, roi_neighborhood_tol, min_length, waymask,
B0_mask, max_length=1000, n_seeds_per_iter=500, pft_back_tracking_dist=2, pft_front_tracking_dist=1,
particle_count=15, min_separation_angle=20):
"""
Perform native-space ensemble tractography, restricted to a vector of ROI masks.
target_samples : int
Total number of streamline samples specified to generate streams.
atlas_data_wm_gm_int : array
3D int32 numpy array of atlas parcellation intensities from Nifti1Image in T1w-warped native diffusion space,
restricted to wm-gm interface.
parcels : list
List of 3D boolean numpy arrays of atlas parcellation ROI masks from a Nifti1Image in T1w-warped native
diffusion space.
mod : obj
Connectivity reconstruction model.
tiss_classifier : str
Tissue classification method.
sphere : obj
DiPy object for modeling diffusion directions on a sphere.
directget : str
The statistical approach to tracking. Options are: det (deterministic), closest (clos), boot (bootstrapped),
and prob (probabilistic).
curv_thr_list : list
List of integer curvature thresholds used to perform ensemble tracking.
step_list : list
List of float step-sizes used to perform ensemble tracking.
track_type : str
Tracking algorithm used (e.g. 'local' or 'particle').
maxcrossing : int
Maximum number if diffusion directions that can be assumed per voxel while tracking.
roi_neighborhood_tol : float
Distance (in the units of the streamlines, usually mm). If any
coordinate in the streamline is within this distance from the center
of any voxel in the ROI, the filtering criterion is set to True for
this streamline, otherwise False. Defaults to the distance between
the center of each voxel and the corner of the voxel.
min_length : int
Minimum fiber length threshold in mm.
waymask : str
Path to a Nifti1Image in native diffusion space to constrain tractography.
B0_mask : str
File path to B0 brain mask.
max_length : int
Maximum number of steps to restrict tracking.
n_seeds_per_iter : int
Number of seeds from which to initiate tracking for each unique ensemble combination.
By default this is set to 200.
particle_count
pft_back_tracking_dist : float
Distance in mm to back track before starting the particle filtering
tractography. The total particle filtering tractography distance is
equal to back_tracking_dist + front_tracking_dist. By default this is set to 2 mm.
pft_front_tracking_dist : float
Distance in mm to run the particle filtering tractography after the
the back track distance. The total particle filtering tractography
distance is equal to back_tracking_dist + front_tracking_dist. By
default this is set to 1 mm.
particle_count : int
Number of particles to use in the particle filter.
min_separation_angle : float
The minimum angle between directions [0, 90].
Returns
-------
streamlines : ArraySequence
DiPy list/array-like object of streamline points from tractography.
References
----------
.. [1] Takemura, H., Caiafa, C. F., Wandell, B. A., & Pestilli, F. (2016).
Ensemble Tractography. PLoS Computational Biology.
https://doi.org/10.1371/journal.pcbi.1004692
"""
import gc
import time
from colorama import Fore, Style
from dipy.tracking import utils
from dipy.tracking.streamline import Streamlines, select_by_rois
from dipy.tracking.local_tracking import LocalTracking, ParticleFilteringTracking
from dipy.direction import (ProbabilisticDirectionGetter, ClosestPeakDirectionGetter,
DeterministicMaximumDirectionGetter)
start = time.time()
B0_mask_data = nib.load(B0_mask).get_fdata()
if waymask:
waymask_data = np.asarray(nib.load(waymask).dataobj).astype('bool')
# Commence Ensemble Tractography
parcel_vec = list(np.ones(len(parcels)).astype('bool'))
streamlines = nib.streamlines.array_sequence.ArraySequence()
circuit_ix = 0
stream_counter = 0
while int(stream_counter) < int(target_samples):
for curv_thr in curv_thr_list:
print("%s%s" % ('Curvature: ', curv_thr))
# Instantiate DirectionGetter
if directget == 'prob':
dg = ProbabilisticDirectionGetter.from_shcoeff(mod_fit, max_angle=float(curv_thr), sphere=sphere,
min_separation_angle=min_separation_angle)
elif directget == 'clos':
dg = ClosestPeakDirectionGetter.from_shcoeff(mod_fit, max_angle=float(curv_thr), sphere=sphere,
min_separation_angle=min_separation_angle)
elif directget == 'det':
dg = DeterministicMaximumDirectionGetter.from_shcoeff(mod_fit, max_angle=float(curv_thr), sphere=sphere,
min_separation_angle=min_separation_angle)
else:
raise ValueError('ERROR: No valid direction getter(s) specified.')
for step in step_list:
print("%s%s" % ('Step: ', step))
# Perform wm-gm interface seeding, using n_seeds at a time
seeds = utils.random_seeds_from_mask(atlas_data_wm_gm_int > 0, seeds_count=n_seeds_per_iter,
seed_count_per_voxel=False, affine=np.eye(4))
if len(seeds) == 0:
raise RuntimeWarning('Warning: No valid seed points found in wm-gm interface...')
# print(seeds)
# Perform tracking
if track_type == 'local':
streamline_generator = LocalTracking(dg, tiss_classifier, seeds, np.eye(4),
max_cross=int(maxcrossing), maxlen=int(max_length),
step_size=float(step), fixedstep=False, return_all=True)
elif track_type == 'particle':
streamline_generator = ParticleFilteringTracking(dg, tiss_classifier, seeds, np.eye(4),
max_cross=int(maxcrossing),
step_size=float(step),
maxlen=int(max_length),
pft_back_tracking_dist=pft_back_tracking_dist,
pft_front_tracking_dist=pft_front_tracking_dist,
particle_count=particle_count,
return_all=True)
else:
raise ValueError('ERROR: No valid tracking method(s) specified.')
# Filter resulting streamlines by those that stay entirely inside the brain
roi_proximal_streamlines = utils.target(streamline_generator, np.eye(4), B0_mask_data,
include=True)
# Filter resulting streamlines by roi-intersection characteristics
roi_proximal_streamlines = Streamlines(select_by_rois(roi_proximal_streamlines, affine=np.eye(4),
rois=parcels, include=parcel_vec,
mode='both_end',
tol=roi_neighborhood_tol))
print("%s%s" % ('Filtering by: \nnode intersection: ', len(roi_proximal_streamlines)))
if str(min_length) != '0':
roi_proximal_streamlines = nib.streamlines.array_sequence.ArraySequence([s for s in
roi_proximal_streamlines
if len(s) >=
float(min_length)])
print("%s%s" % ('Minimum length criterion: ', len(roi_proximal_streamlines)))
if waymask:
roi_proximal_streamlines = roi_proximal_streamlines[utils.near_roi(roi_proximal_streamlines,
np.eye(4),
waymask_data,
tol=roi_neighborhood_tol,
mode='any')]
print("%s%s" % ('Waymask proximity: ', len(roi_proximal_streamlines)))
out_streams = [s.astype('float32') for s in roi_proximal_streamlines]
streamlines.extend(out_streams)
stream_counter = stream_counter + len(out_streams)
# Cleanup memory
del seeds, roi_proximal_streamlines, streamline_generator, out_streams
gc.collect()
del dg
circuit_ix = circuit_ix + 1
print("%s%s%s%s%s%s" % ('Completed Hyperparameter Circuit: ', circuit_ix,
'\nCumulative Streamline Count: ', Fore.CYAN, stream_counter, "\n"))
print(Style.RESET_ALL)
print('Tracking Complete:\n', str(time.time() - start))
return streamlines
def segment(fdata,
fbval,
fbvec,
streamlines,
bundles,
reg_template=None,
mapping=None,
as_generator=True,
clip_to_roi=True,
**reg_kwargs):
"""
Segment streamlines into bundles.
Parameters
----------
fdata, fbval, fbvec : str
Full path to data, bvals, bvecs
streamlines : list of 2D arrays
Each array is a streamline, shape (3, N).
bundles: dict
The format is something like::
{'name': {'ROIs':[img, img], 'rules':[True, True]}}
reg_template : str or nib.Nifti1Image, optional.
Template to use for registration (defaults to the MNI T2)
mapping : DiffeomorphicMap object, str or nib.Nifti1Image, optional
A mapping between DWI space and a template. Defaults to generate this.
as_generator : bool, optional
Whether to generate the streamlines here, or return generators.
Default: True.
clip_to_roi : bool, optional
Whether to clip the streamlines between the ROIs
"""
img, data, gtab, mask = ut.prepare_data(fdata, fbval, fbvec)
xform_sl = [
s for s in dtu.move_streamlines(streamlines, np.linalg.inv(img.affine))
]
if reg_template is None:
reg_template = dpd.read_mni_template()
if mapping is None:
mapping = reg.syn_register_dwi(fdata,
gtab,
template=reg_template,
**reg_kwargs)
if isinstance(mapping, str) or isinstance(mapping, nib.Nifti1Image):
mapping = reg.read_mapping(mapping, img, reg_template)
fiber_groups = {}
for bundle in bundles:
select_sl = xform_sl
for ROI, rule in zip(bundles[bundle]['ROIs'],
bundles[bundle]['rules']):
data = ROI.get_data()
warped_ROI = patch_up_roi(
mapping.transform_inverse(data, interpolation='nearest'))
# This function requires lists as inputs:
select_sl = dts.select_by_rois(select_sl,
[warped_ROI.astype(bool)], [rule])
# Next, we reorient each streamline according to an ARBITRARY, but
# CONSISTENT order. To do this, we use the first ROI for which the rule
# is True as the first one to pass through, and the last ROI for which
# the rule is True as the last one to pass through:
# Indices where the 'rule' is True:
idx = np.where(bundles[bundle]['rules'])
orient_ROIs = [
bundles[bundle]['ROIs'][idx[0][0]],
bundles[bundle]['ROIs'][idx[0][-1]]
]
select_sl = dts.orient_by_rois(select_sl,
orient_ROIs[0].get_data(),
orient_ROIs[1].get_data(),
as_generator=True)
# XXX Implement clipping to the ROIs
# if clip_to_roi:
# dts.clip()
if as_generator:
fiber_groups[bundle] = select_sl
else:
fiber_groups[bundle] = list(select_sl)
return fiber_groups
def run_tracking(step_curv_combinations, recon_path, n_seeds_per_iter,
directget, maxcrossing, max_length, pft_back_tracking_dist,
pft_front_tracking_dist, particle_count, roi_neighborhood_tol,
waymask, min_length, track_type, min_separation_angle, sphere,
tiss_class, tissues4d, cache_dir):
import gc
import os
import h5py
from dipy.tracking import utils
from dipy.tracking.streamline import select_by_rois
from dipy.tracking.local_tracking import LocalTracking, \
ParticleFilteringTracking
from dipy.direction import (ProbabilisticDirectionGetter,
ClosestPeakDirectionGetter,
DeterministicMaximumDirectionGetter)
from nilearn.image import index_img
from pynets.dmri.track import prep_tissues
from nibabel.streamlines.array_sequence import ArraySequence
from nipype.utils.filemanip import copyfile, fname_presuffix
recon_path_tmp_path = fname_presuffix(recon_path,
suffix=f"_{step_curv_combinations}",
newpath=cache_dir)
copyfile(recon_path, recon_path_tmp_path, copy=True, use_hardlink=False)
if waymask is not None:
waymask_tmp_path = fname_presuffix(waymask,
suffix=f"_{step_curv_combinations}",
newpath=cache_dir)
copyfile(waymask, waymask_tmp_path, copy=True, use_hardlink=False)
else:
waymask_tmp_path = None
tissue_img = nib.load(tissues4d)
# Order:
B0_mask = index_img(tissue_img, 0)
atlas_img = index_img(tissue_img, 1)
atlas_data_wm_gm_int = index_img(tissue_img, 2)
t1w2dwi = index_img(tissue_img, 3)
gm_in_dwi = index_img(tissue_img, 4)
vent_csf_in_dwi = index_img(tissue_img, 5)
wm_in_dwi = index_img(tissue_img, 6)
tiss_classifier = prep_tissues(t1w2dwi, gm_in_dwi, vent_csf_in_dwi,
wm_in_dwi, tiss_class, B0_mask)
B0_mask_data = np.asarray(B0_mask.dataobj).astype("bool")
atlas_data = np.array(atlas_img.dataobj).astype("uint16")
atlas_data_wm_gm_int_data = np.asarray(
atlas_data_wm_gm_int.dataobj).astype("bool").astype("int16")
# Build mask vector from atlas for later roi filtering
parcels = []
i = 0
intensities = [i for i in np.unique(atlas_data) if i != 0]
for roi_val in intensities:
parcels.append(atlas_data == roi_val)
i += 1
del atlas_data
parcel_vec = list(np.ones(len(parcels)).astype("bool"))
with h5py.File(recon_path_tmp_path, 'r+') as hf:
mod_fit = hf['reconstruction'][:].astype('float32')
hf.close()
print("%s%s" % ("Curvature: ", step_curv_combinations[1]))
# Instantiate DirectionGetter
if directget == "prob" or directget == "probabilistic":
dg = ProbabilisticDirectionGetter.from_shcoeff(
mod_fit,
max_angle=float(step_curv_combinations[1]),
sphere=sphere,
min_separation_angle=min_separation_angle,
)
elif directget == "clos" or directget == "closest":
dg = ClosestPeakDirectionGetter.from_shcoeff(
mod_fit,
max_angle=float(step_curv_combinations[1]),
sphere=sphere,
min_separation_angle=min_separation_angle,
)
elif directget == "det" or directget == "deterministic":
maxcrossing = 1
dg = DeterministicMaximumDirectionGetter.from_shcoeff(
mod_fit,
max_angle=float(step_curv_combinations[1]),
sphere=sphere,
min_separation_angle=min_separation_angle,
)
else:
raise ValueError("ERROR: No valid direction getter(s) specified.")
print("%s%s" % ("Step: ", step_curv_combinations[0]))
# Perform wm-gm interface seeding, using n_seeds at a time
seeds = utils.random_seeds_from_mask(
atlas_data_wm_gm_int_data > 0,
seeds_count=n_seeds_per_iter,
seed_count_per_voxel=False,
affine=np.eye(4),
)
if len(seeds) == 0:
print(
UserWarning("No valid seed points found in wm-gm "
"interface..."))
return None
# print(seeds)
# Perform tracking
if track_type == "local":
streamline_generator = LocalTracking(
dg,
tiss_classifier,
seeds,
np.eye(4),
max_cross=int(maxcrossing),
maxlen=int(max_length),
step_size=float(step_curv_combinations[0]),
fixedstep=False,
return_all=True,
)
elif track_type == "particle":
streamline_generator = ParticleFilteringTracking(
dg,
tiss_classifier,
seeds,
np.eye(4),
max_cross=int(maxcrossing),
step_size=float(step_curv_combinations[0]),
maxlen=int(max_length),
pft_back_tracking_dist=pft_back_tracking_dist,
pft_front_tracking_dist=pft_front_tracking_dist,
particle_count=particle_count,
return_all=True,
)
else:
try:
raise ValueError("ERROR: No valid tracking method(s) specified.")
except ValueError:
import sys
sys.exit(0)
# Filter resulting streamlines by those that stay entirely
# inside the brain
try:
roi_proximal_streamlines = utils.target(streamline_generator,
np.eye(4),
B0_mask_data,
include=True)
except BaseException:
print('No streamlines found inside the brain! ' 'Check registrations.')
return None
# Filter resulting streamlines by roi-intersection
# characteristics
try:
roi_proximal_streamlines = \
nib.streamlines.array_sequence.ArraySequence(
select_by_rois(
roi_proximal_streamlines,
affine=np.eye(4),
rois=parcels,
include=parcel_vec,
mode="%s" % ("any" if waymask is not None else
"both_end"),
tol=roi_neighborhood_tol,
)
)
print("%s%s" % ("Filtering by: \nNode intersection: ",
len(roi_proximal_streamlines)))
except BaseException:
print('No streamlines found to connect any parcels! '
'Check registrations.')
return None
try:
roi_proximal_streamlines = nib.streamlines. \
array_sequence.ArraySequence(
[
s for s in roi_proximal_streamlines
if len(s) >= float(min_length)
]
)
print(f"Minimum fiber length >{min_length}mm: "
f"{len(roi_proximal_streamlines)}")
except BaseException:
print('No streamlines remaining after minimal length criterion.')
return None
if waymask is not None and os.path.isfile(waymask_tmp_path):
from nilearn.image import math_img
mask = math_img("img > 0.0075", img=nib.load(waymask_tmp_path))
waymask_data = np.asarray(mask.dataobj).astype("bool")
try:
roi_proximal_streamlines = roi_proximal_streamlines[utils.near_roi(
roi_proximal_streamlines,
np.eye(4),
waymask_data,
tol=roi_neighborhood_tol,
mode="all")]
print("%s%s" %
("Waymask proximity: ", len(roi_proximal_streamlines)))
except BaseException:
print('No streamlines remaining in waymask\'s vacinity.')
return None
out_streams = [s.astype("float32") for s in roi_proximal_streamlines]
del dg, seeds, roi_proximal_streamlines, streamline_generator, \
atlas_data_wm_gm_int_data, mod_fit, B0_mask_data
os.remove(recon_path_tmp_path)
gc.collect()
try:
return ArraySequence(out_streams)
except BaseException:
return None
# obtain that data array as bool
sphereNifti = WMA_pyFuncs.createSphere(testRadius, testCentroid,
testT1)
# add that and a True to the list vector for each
roisData.append(sphereNifti.get_fdata().astype(bool))
roisNifti.append(sphereNifti)
# randomly select include or exclude
include.append(bool(random.getrandbits(1)))
operations.append('any')
# start timing
t1_start = time.process_time()
# specify segmentation
dipySegmented = select_by_rois(testTractogram.streamlines,
testT1.affine,
roisData,
include,
mode='any')
# actually perform segmentation and get count (cant do indexes here for whatever reason)
dipyCount = len(list(dipySegmented))
# stop time
t1_stop = time.process_time()
# get the elapsed time
dipyTime = t1_stop - t1_start
#restart time
t1_start = time.process_time()
#perform segmentation again, but with the modified version
#for a valid comparison between these methods we have to split into two operations
#since select_by_rois implicitly treats multiple operations in a fairly
#specific modal fashion (https://github.com/dipy/dipy/blob/8898fc962d5aaf7f7cdbf82b027054070fcef49d/dipy/tracking/streamline.py#L240-L243)
def segment(fdata, fbval, fbvec, streamlines, bundles,
reg_template=None, mapping=None, as_generator=True, **reg_kwargs):
"""
generate : bool
Whether to generate the streamlines here, or return generators.
reg_template : template to use for registration (defaults to the MNI T2)
bundles: dict
The format is something like::
{'name': {'ROIs':[img, img], 'rules':[True, True]}}
"""
img, data, gtab, mask = ut.prepare_data(fdata, fbval, fbvec)
xform_sl = [s for s in dtu.move_streamlines(streamlines,
np.linalg.inv(img.affine))]
if reg_template is None:
reg_template = dpd.read_mni_template()
if mapping is None:
mapping = reg.syn_register_dwi(fdata, gtab, template=reg_template,
**reg_kwargs)
if isinstance(mapping, str) or isinstance(mapping, nib.Nifti1Image):
mapping = reg.read_mapping(mapping, img, reg_template)
fiber_groups = {}
for bundle in bundles:
select_sl = xform_sl
for ROI, rule in zip(bundles[bundle]['ROIs'],
bundles[bundle]['rules']):
data = ROI.get_data()
warped_ROI = patch_up_roi(mapping.transform_inverse(
data,
interpolation='nearest'))
# This function requires lists as inputs:
select_sl = dts.select_by_rois(select_sl,
[warped_ROI.astype(bool)],
[rule])
# Next, we reorient each streamline according to an ARBITRARY, but
# CONSISTENT order. To do this, we use the first ROI for which the rule
# is True as the first one to pass through, and the last ROI for which
# the rule is True as the last one to pass through:
# Indices where the 'rule' is True:
idx = np.where(bundles[bundle]['rules'])
orient_ROIs = [bundles[bundle]['ROIs'][idx[0][0]],
bundles[bundle]['ROIs'][idx[0][-1]]]
select_sl = dts.orient_by_rois(select_sl,
orient_ROIs[0].get_data(),
orient_ROIs[1].get_data(),
in_place=True)
if as_generator:
fiber_groups[bundle] = select_sl
else:
fiber_groups[bundle] = list(select_sl)
return fiber_groups
def track_ensemble(target_samples, atlas_data_wm_gm_int, parcels, parcel_vec, mod_fit,
tiss_classifier, sphere, directget, curv_thr_list, step_list, track_type, maxcrossing, max_length,
n_seeds_per_iter=200):
from colorama import Fore, Style
from dipy.tracking import utils
from dipy.tracking.streamline import Streamlines, select_by_rois
from dipy.tracking.local import LocalTracking, ParticleFilteringTracking
from dipy.direction import ProbabilisticDirectionGetter, BootDirectionGetter, ClosestPeakDirectionGetter, DeterministicMaximumDirectionGetter
# Commence Ensemble Tractography
streamlines = nib.streamlines.array_sequence.ArraySequence()
ix = 0
circuit_ix = 0
stream_counter = 0
while int(stream_counter) < int(target_samples):
for curv_thr in curv_thr_list:
print("%s%s" % ('Curvature: ', curv_thr))
# Instantiate DirectionGetter
if directget == 'prob':
dg = ProbabilisticDirectionGetter.from_shcoeff(mod_fit, max_angle=float(curv_thr),
sphere=sphere)
elif directget == 'boot':
dg = BootDirectionGetter.from_shcoeff(mod_fit, max_angle=float(curv_thr),
sphere=sphere)
elif directget == 'closest':
dg = ClosestPeakDirectionGetter.from_shcoeff(mod_fit, max_angle=float(curv_thr),
sphere=sphere)
elif directget == 'det':
dg = DeterministicMaximumDirectionGetter.from_shcoeff(mod_fit, max_angle=float(curv_thr),
sphere=sphere)
else:
raise ValueError('ERROR: No valid direction getter(s) specified.')
for step in step_list:
print("%s%s" % ('Step: ', step))
# Perform wm-gm interface seeding, using n_seeds at a time
seeds = utils.random_seeds_from_mask(atlas_data_wm_gm_int > 0, seeds_count=n_seeds_per_iter,
seed_count_per_voxel=False, affine=np.eye(4))
if len(seeds) == 0:
raise RuntimeWarning('Warning: No valid seed points found in wm-gm interface...')
print(seeds)
# Perform tracking
if track_type == 'local':
streamline_generator = LocalTracking(dg, tiss_classifier, seeds, np.eye(4),
max_cross=int(maxcrossing), maxlen=int(max_length),
step_size=float(step), return_all=True)
elif track_type == 'particle':
streamline_generator = ParticleFilteringTracking(dg, tiss_classifier, seeds, np.eye(4),
max_cross=int(maxcrossing),
step_size=float(step),
maxlen=int(max_length),
pft_back_tracking_dist=2,
pft_front_tracking_dist=1,
particle_count=15, return_all=True)
else:
raise ValueError('ERROR: No valid tracking method(s) specified.')
# Filter resulting streamlines by roi-intersection characteristics
streamlines_more = Streamlines(select_by_rois(streamline_generator, parcels, parcel_vec.astype('bool'),
mode='any', affine=np.eye(4), tol=8))
# Repeat process until target samples condition is met
ix = ix + 1
for s in streamlines_more:
stream_counter = stream_counter + len(s)
streamlines.append(s)
if int(stream_counter) >= int(target_samples):
break
else:
continue
circuit_ix = circuit_ix + 1
print("%s%s%s%s%s" % ('Completed hyperparameter circuit: ', circuit_ix, '...\nCumulative Streamline Count: ',
Fore.CYAN, stream_counter))
print(Style.RESET_ALL)
print('\n')
return streamlines