def test_MaximumDeterministicTracker():
"""This tests that the Maximum Deterministic Direction Getter plays nice
LocalTracking and produces reasonable streamlines in a simple example.
"""
sphere = HemiSphere.from_sphere(unit_octahedron)
# A simple image with three possible configurations, a vertical tract,
# a horizontal tract and a crossing
pmf_lookup = np.array([[0., 0., 1.],
[1., 0., 0.],
[0., 1., 0.],
[.4, .6, 0.]])
simple_image = np.array([[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0],
[0, 3, 2, 2, 2, 0],
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0],
])
simple_image = simple_image[..., None]
pmf = pmf_lookup[simple_image]
seeds = [np.array([1., 1., 0.])] * 30
mask = (simple_image > 0).astype(float)
tc = ThresholdTissueClassifier(mask, .5)
dg = DeterministicMaximumDirectionGetter.from_pmf(pmf, 90, sphere)
streamlines = LocalTracking(dg, tc, seeds, np.eye(4), 1.)
expected = [np.array([[ 0., 1., 0.],
[ 1., 1., 0.],
[ 2., 1., 0.],
[ 2., 2., 0.],
[ 2., 3., 0.],
[ 2., 4., 0.],
[ 2., 5., 0.]]),
np.array([[ 0., 1., 0.],
[ 1., 1., 0.],
[ 2., 1., 0.],
[ 3., 1., 0.],
[ 4., 1., 0.]])
]
def allclose(x, y):
return x.shape == y.shape and np.allclose(x, y)
for sl in streamlines:
dir = ( -sphere.vertices[0] ).copy()
if not allclose(sl, expected[0]):
raise AssertionError()
# The first path is not possible if 90 degree turns are excluded
dg = DeterministicMaximumDirectionGetter.from_pmf(pmf, 80, sphere)
streamlines = LocalTracking(dg, tc, seeds, np.eye(4), 1.)
for sl in streamlines:
npt.assert_(np.allclose(sl, expected[1]))
def test_MaximumDeterministicTracker():
"""This tests that the Maximum Deterministic Direction Getter plays nice
LocalTracking and produces reasonable streamlines in a simple example.
"""
sphere = HemiSphere.from_sphere(unit_octahedron)
# A simple image with three possible configurations, a vertical tract,
# a horizontal tract and a crossing
pmf_lookup = np.array([[0., 0., 1.],
[1., 0., 0.],
[0., 1., 0.],
[.4, .6, 0.]])
simple_image = np.array([[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0],
[0, 3, 2, 2, 2, 0],
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0],
])
simple_image = simple_image[..., None]
pmf = pmf_lookup[simple_image]
seeds = [np.array([1., 1., 0.])] * 30
mask = (simple_image > 0).astype(float)
tc = ThresholdTissueClassifier(mask, .5)
dg = DeterministicMaximumDirectionGetter.from_pmf(pmf, 90, sphere)
streamlines = LocalTracking(dg, tc, seeds, np.eye(4), 1.)
expected = [np.array([[0., 1., 0.],
[1., 1., 0.],
[2., 1., 0.],
[2., 2., 0.],
[2., 3., 0.],
[2., 4., 0.],
[2., 5., 0.]]),
np.array([[0., 1., 0.],
[1., 1., 0.],
[2., 1., 0.],
[3., 1., 0.],
[4., 1., 0.]])]
def allclose(x, y):
return x.shape == y.shape and np.allclose(x, y)
for sl in streamlines:
if not allclose(sl, expected[0]):
raise AssertionError()
# The first path is not possible if 90 degree turns are excluded
dg = DeterministicMaximumDirectionGetter.from_pmf(pmf, 80, sphere)
streamlines = LocalTracking(dg, tc, seeds, np.eye(4), 1.)
for sl in streamlines:
npt.assert_(np.allclose(sl, expected[1]))
def _get_direction_getter(self,
strategy_name,
pam,
pmf_threshold=0.1,
max_angle=30.):
"""Get Tracking Direction Getter object.
Parameters
----------
strategy_name: str
string representing direction getter name
Returns
-------
direction_getter : instance of DirectionGetter
Used to get directions for fiber tracking.
"""
dg, msg = None, ''
if strategy_name.lower() in ["deterministic", "det"]:
msg = "Deterministic"
dg = DeterministicMaximumDirectionGetter.from_shcoeff(
pam.shm_coeff,
sphere=pam.sphere,
max_angle=max_angle,
pmf_threshold=pmf_threshold)
elif strategy_name.lower() in ["probabilistic", "prob"]:
msg = "Probabilistic"
dg = ProbabilisticDirectionGetter.from_shcoeff(
pam.shm_coeff,
sphere=pam.sphere,
max_angle=max_angle,
pmf_threshold=pmf_threshold)
elif strategy_name.lower() in ["closestpeaks", "cp"]:
msg = "ClosestPeaks"
dg = ClosestPeakDirectionGetter.from_shcoeff(
pam.shm_coeff,
sphere=pam.sphere,
max_angle=max_angle,
pmf_threshold=pmf_threshold)
elif strategy_name.lower() in [
"eudx",
]:
msg = "Eudx"
dg = pam
else:
msg = "No direction getter defined. Deterministic"
dg = DeterministicMaximumDirectionGetter.from_shcoeff(
pam.shm_coeff,
sphere=pam.sphere,
max_angle=max_angle,
pmf_threshold=pmf_threshold)
logging.info('{0} direction getter strategy selected'.format(msg))
return dg
def _get_direction_getter(self, strategy_name, pam, pmf_threshold=0.1,
max_angle=30.):
"""Get Tracking Direction Getter object.
Parameters
----------
strategy_name: str
string representing direction getter name
Returns
-------
direction_getter : instance of DirectionGetter
Used to get directions for fiber tracking.
"""
dg, msg = None, ''
if strategy_name.lower() in ["deterministic", "det"]:
msg = "Deterministic"
dg = DeterministicMaximumDirectionGetter.from_shcoeff(
pam.shm_coeff,
sphere=pam.sphere,
max_angle=max_angle,
pmf_threshold=pmf_threshold)
elif strategy_name.lower() in ["probabilistic", "prob"]:
msg = "Probabilistic"
dg = ProbabilisticDirectionGetter.from_shcoeff(
pam.shm_coeff,
sphere=pam.sphere,
max_angle=max_angle,
pmf_threshold=pmf_threshold)
elif strategy_name.lower() in ["closestpeaks", "cp"]:
msg = "ClosestPeaks"
dg = ClosestPeakDirectionGetter.from_shcoeff(
pam.shm_coeff,
sphere=pam.sphere,
max_angle=max_angle,
pmf_threshold=pmf_threshold)
elif strategy_name.lower() in ["eudx", ]:
msg = "Eudx"
dg = pam
else:
msg = "No direction getter defined. Deterministic"
dg = DeterministicMaximumDirectionGetter.from_shcoeff(
pam.shm_coeff,
sphere=pam.sphere,
max_angle=max_angle,
pmf_threshold=pmf_threshold)
logging.info('{0} direction getter strategy selected'.format(msg))
return dg
def fiber_tracking(self):
from dipy.data import default_sphere
from dipy.direction import DeterministicMaximumDirectionGetter
from dipy.tracking.streamline import Streamlines
from dipy.tracking.local_tracking import ParticleFilteringTracking
from Tractography.files_saving import save_ft
self.create_model_fit()
detmax_dg = DeterministicMaximumDirectionGetter.from_shcoeff(
self.model_fit.shm_coeff,
max_angle=self.parameters_dict['max_ang'],
sphere=default_sphere)
print(f"Tractography using PFT and {self.sc_method} clasifier")
self.create_classifier()
print('Starting to compute streamlines')
self.streamlines = Streamlines(
ParticleFilteringTracking(
detmax_dg,
self.classifier,
self.seeds,
self.affine,
step_size=self.parameters_dict['step_size'],
maxlen=self.parameters_dict['length_margins'][1],
pft_back_tracking_dist=2,
pft_front_tracking_dist=1,
particle_count=15,
return_all=False))
self._remove_streamlines_outliers()
file_name = f'wb_{self.ft_method}_{self.sc_method}.tck'
save_ft(self.subj_folder, self.streamlines, self.nii_ref, file_name)
def tracking_maxodf(dir_src, dir_out, verbose=False):
wm_name = 'wm_mask_' + par_b_tag + '_' + par_dim_tag + '.nii.gz'
wm_mask, affine = load_nifti(pjoin(dir_src, wm_name), verbose)
sh_name = 'sh_' + par_b_tag + '_' + par_dim_tag + '.nii.gz'
sh, _ = load_nifti(pjoin(dir_src, sh_name), verbose)
sphere = get_sphere('symmetric724')
classifier = ThresholdTissueClassifier(wm_mask.astype('f8'), .5)
classifier = BinaryTissueClassifier(wm_mask)
max_dg = DeterministicMaximumDirectionGetter.from_shcoeff(sh, max_angle=par_trk_max_angle, sphere=sphere)
seeds = utils.seeds_from_mask(wm_mask, density=2, affine=affine)
streamlines = LocalTracking(max_dg, classifier, seeds, affine, step_size=par_trk_step_size)
streamlines = list(streamlines)
trk_name = 'tractogram_' + par_b_tag + '_' + par_dim_tag + '_' + par_trk_odf_tag + '.trk'
trk_out = os.path.join(dir_out, trk_name)
save_trk(trk_out, streamlines, affine, wm_mask.shape)
dpy_out = trk_out.replace('.trk', '.dpy')
dpy = Dpy(dpy_out, 'w')
dpy.write_tracks(streamlines)
dpy.close()
def _core_run(self, stopping_path, stopping_thr, seeding_path,
seed_density, use_sh, pam, out_tract):
stop, affine = load_nifti(stopping_path)
classifier = ThresholdTissueClassifier(stop, stopping_thr)
logging.info('classifier done')
seed_mask, _ = load_nifti(seeding_path)
seeds = \
utils.seeds_from_mask(
seed_mask,
density=[seed_density, seed_density, seed_density],
affine=affine)
logging.info('seeds done')
direction_getter = pam
if use_sh:
direction_getter = \
DeterministicMaximumDirectionGetter.from_shcoeff(
pam.shm_coeff,
max_angle=30.,
sphere=pam.sphere)
streamlines = LocalTracking(direction_getter, classifier,
seeds, affine, step_size=.5)
logging.info('LocalTracking initiated')
tractogram = Tractogram(streamlines, affine_to_rasmm=np.eye(4))
save(tractogram, out_tract)
logging.info('Saved {0}'.format(out_tract))
def create_streamlines(model_fit,
seeds,
affine,
gtab=None,
data=None,
white_matter=None,
classifier_type="fa"):
from dipy.data import default_sphere
from dipy.direction import DeterministicMaximumDirectionGetter
from dipy.tracking.streamline import Streamlines
from dipy.tracking.local_tracking import LocalTracking
detmax_dg = DeterministicMaximumDirectionGetter.from_shcoeff(
model_fit.shm_coeff, max_angle=30., sphere=default_sphere)
if classifier_type == "fa":
print("Tractography using local tracking and FA clasifier")
classifier = create_fa_classifier(gtab, data, white_matter)[1]
print('Starting to compute streamlines')
streamlines = Streamlines(
LocalTracking(detmax_dg,
classifier,
seeds,
affine,
step_size=1,
return_all=False))
long_streamlines = np.ones((len(streamlines)), bool)
for i in range(0, len(streamlines)):
if streamlines[i].shape[0] < 100:
long_streamlines[i] = False
streamlines = streamlines[long_streamlines]
return streamlines
def _core_run(self, stopping_path, stopping_thr, seeding_path,
seed_density, use_sh, pam, out_tract):
stop, affine = load_nifti(stopping_path)
classifier = ThresholdTissueClassifier(stop, stopping_thr)
logging.info('classifier done')
seed_mask, _ = load_nifti(seeding_path)
seeds = \
utils.seeds_from_mask(
seed_mask,
density=[seed_density, seed_density, seed_density],
affine=affine)
logging.info('seeds done')
direction_getter = pam
if use_sh:
direction_getter = \
DeterministicMaximumDirectionGetter.from_shcoeff(
pam.shm_coeff,
max_angle=30.,
sphere=pam.sphere)
streamlines = LocalTracking(direction_getter,
classifier,
seeds,
affine,
step_size=.5)
logging.info('LocalTracking initiated')
tractogram = Tractogram(streamlines, affine_to_rasmm=np.eye(4))
save(tractogram, out_tract)
logging.info('Saved {0}'.format(out_tract))
def test_DeterministicMaximumDirectionGetter():
# Test the DeterministicMaximumDirectionGetter
dir = unit_octahedron.vertices[-1].copy()
point = np.zeros(3)
N = unit_octahedron.theta.shape[0]
# No valid direction
pmf = np.zeros((3, 3, 3, N))
dg = DeterministicMaximumDirectionGetter.from_pmf(pmf, 90, unit_octahedron)
state = dg.get_direction(point, dir)
npt.assert_equal(state, 1)
# Test BF #1566 - bad condition in DeterministicMaximumDirectionGetter
pmf = np.zeros((3, 3, 3, N))
pmf[0, 0, 0, 0] = 1
dg = DeterministicMaximumDirectionGetter.from_pmf(pmf, 0, unit_octahedron)
state = dg.get_direction(point, dir)
npt.assert_equal(state, 1)
def _get_direction_getter(self, strategy_name, pam, pmf_threshold,
max_angle):
"""Get Tracking Direction Getter object.
Parameters
----------
strategy_name : str
String representing direction getter name.
pam : instance of PeaksAndMetrics
An object with ``gfa``, ``peak_directions``, ``peak_values``,
``peak_indices``, ``odf``, ``shm_coeffs`` as attributes.
pmf_threshold : float
Threshold for ODF functions.
max_angle : float
Maximum angle between streamline segments.
Returns
-------
direction_getter : instance of DirectionGetter
Used to get directions for fiber tracking.
"""
dg, msg = None, ''
if strategy_name.lower() in ["deterministic", "det"]:
msg = "Deterministic"
dg = DeterministicMaximumDirectionGetter.from_shcoeff(
pam.shm_coeff,
sphere=pam.sphere,
max_angle=max_angle,
pmf_threshold=pmf_threshold)
elif strategy_name.lower() in ["probabilistic", "prob"]:
msg = "Probabilistic"
dg = ProbabilisticDirectionGetter.from_shcoeff(
pam.shm_coeff,
sphere=pam.sphere,
max_angle=max_angle,
pmf_threshold=pmf_threshold)
elif strategy_name.lower() in ["closestpeaks", "cp"]:
msg = "ClosestPeaks"
dg = ClosestPeakDirectionGetter.from_shcoeff(
pam.shm_coeff,
sphere=pam.sphere,
max_angle=max_angle,
pmf_threshold=pmf_threshold)
elif strategy_name.lower() in [
"eudx",
]:
msg = "Eudx"
dg = pam
else:
msg = "No direction getter defined. Eudx"
dg = pam
logging.info('{0} direction getter strategy selected'.format(msg))
return dg
def _get_direction_getter(self, strategy_name, pam, pmf_threshold,
max_angle):
"""Get Tracking Direction Getter object.
Parameters
----------
strategy_name: str
String representing direction getter name.
pam: instance of PeaksAndMetrics
An object with ``gfa``, ``peak_directions``, ``peak_values``,
``peak_indices``, ``odf``, ``shm_coeffs`` as attributes.
pmf_threshold : float
Threshold for ODF functions.
max_angle : float
Maximum angle between streamline segments.
Returns
-------
direction_getter : instance of DirectionGetter
Used to get directions for fiber tracking.
"""
dg, msg = None, ''
if strategy_name.lower() in ["deterministic", "det"]:
msg = "Deterministic"
dg = DeterministicMaximumDirectionGetter.from_shcoeff(
pam.shm_coeff,
sphere=pam.sphere,
max_angle=max_angle,
pmf_threshold=pmf_threshold)
elif strategy_name.lower() in ["probabilistic", "prob"]:
msg = "Probabilistic"
dg = ProbabilisticDirectionGetter.from_shcoeff(
pam.shm_coeff,
sphere=pam.sphere,
max_angle=max_angle,
pmf_threshold=pmf_threshold)
elif strategy_name.lower() in ["closestpeaks", "cp"]:
msg = "ClosestPeaks"
dg = ClosestPeakDirectionGetter.from_shcoeff(
pam.shm_coeff,
sphere=pam.sphere,
max_angle=max_angle,
pmf_threshold=pmf_threshold)
elif strategy_name.lower() in ["eudx", ]:
msg = "Eudx"
dg = pam
else:
msg = "No direction getter defined. Eudx"
dg = pam
logging.info('{0} direction getter strategy selected'.format(msg))
return dg
def test_DeterministicMaximumDirectionGetter():
# Test the DeterministicMaximumDirectionGetter
dir = unit_octahedron.vertices[-1].copy()
point = np.zeros(3)
N = unit_octahedron.theta.shape[0]
# No valid direction
pmf = np.zeros((3, 3, 3, N))
dg = DeterministicMaximumDirectionGetter.from_pmf(pmf, 90,
unit_octahedron)
state = dg.get_direction(point, dir)
npt.assert_equal(state, 1)
# Test BF #1566 - bad condition in DeterministicMaximumDirectionGetter
pmf = np.zeros((3, 3, 3, N))
pmf[0, 0, 0, 0] = 1
dg = DeterministicMaximumDirectionGetter.from_pmf(pmf, 0,
unit_octahedron)
state = dg.get_direction(point, dir)
npt.assert_equal(state, 1)
def get_dti_streamlines(data_container,
random_seeds=False,
seeds_count=30000,
seeds_per_voxel=False,
step_width=1.0,
max_angle=30.0,
fa_threshold=0.15):
"""
Tracks and returns CSD Streamlines for the given DataContainer.
Parameters
----------
data_container
The DataContainer we would like to track streamlines on
random_seeds
A boolean indicating whether we would like to use random seeds
seeds_count
If we use random seeds, this specifies the seed count
seeds_per_voxel
If True, the seed count is specified per voxel
step_width
The step width used while tracking
fa_threshold
The FA threshold to use to stop tracking
max_angle
The maximum allowed angle between incoming and outgoing angle, float between 0.0 and 90.0 deg
Returns
-------
Streamlines
A list of Streamlines
"""
seeds = _get_seeds(data_container, random_seeds, seeds_count,
seeds_per_voxel)
dti_fit = TensorModel(data_container.gtab).fit(
data_container.dwi, mask=data_container.binary_mask)
dti_fit_odf = dti_fit.odf(sphere=default_sphere)
direction_getter = DeterministicMaximumDirectionGetter.from_pmf(
dti_fit_odf, max_angle=max_angle, sphere=default_sphere)
classifier = ThresholdStoppingCriterion(dti_fit.fa, fa_threshold)
streamlines_generator = LocalTracking(direction_getter,
classifier,
seeds,
data_container.aff,
step_size=step_width)
streamlines = Streamlines(streamlines_generator)
return streamlines
def track(self):
SeedBasedTracker.track(self)
if self.streamlines is not None:
return
dti_model = TensorModel(self.data.gtab)
dti_fit = dti_model.fit(self.data.dwi, mask=self.data.binarymask)
dti_fit_odf = dti_fit.odf(sphere=default_sphere)
max_angle = Config.get_config().getfloat("DTITracking", "maxAngle", fallback="30.0")
direction_getter = DeterministicMaximumDirectionGetter.from_pmf(dti_fit_odf,
max_angle=max_angle,
sphere=default_sphere)
self._track(ThresholdStoppingCriterion(dti_fit.fa, self.options.fa_threshold),
direction_getter)
Cache.get_cache().set(self.id, self.streamlines)
def tracking_maxodf(dir_src, dir_out, verbose=False):
wm_name = 'wm_mask_' + par_b_tag + '_' + par_dim_tag + '.nii.gz'
wm_mask, affine = load_nifti(pjoin(dir_src, wm_name), verbose)
sh_name = 'sh_' + par_b_tag + '_' + par_dim_tag + '.nii.gz'
sh, _ = load_nifti(pjoin(dir_src, sh_name), verbose)
sphere = get_sphere('symmetric724')
classifier = ThresholdTissueClassifier(wm_mask.astype('f8'), .5)
classifier = BinaryTissueClassifier(wm_mask)
max_dg = DeterministicMaximumDirectionGetter.from_shcoeff(sh, max_angle=par_trk_max_angle, sphere=sphere)
seeds = utils.seeds_from_mask(wm_mask, density=2, affine=affine)
streamlines = LocalTracking(max_dg, classifier, seeds, affine, step_size=par_trk_step_size)
streamlines = list(streamlines)
trk_name = 'tractogram_' + par_b_tag + '_' + par_dim_tag + '_' + par_trk_odf_tag + '.trk'
save_trk(pjoin(dir_out, trk_name), streamlines, affine, wm_mask.shape)
def test_num_sls():
toydict = uniform_toy_data()
csd_model = ConstrainedSphericalDeconvModel(toydict['gtab'], None, sh_order=6)
csd_fit = csd_model.fit(toydict['toy_data'])
sltest_list = [('toy_roi_long_plane', 121),
('toy_roi_radial_plane', 121),
('toy_roi_center_vox', 1)]
classifier = ThresholdTissueClassifier(toydict['toy_tissue_classifier'], .1)
detmax_dg = DeterministicMaximumDirectionGetter.from_shcoeff(csd_fit.shm_coeff,
max_angle=30.,
sphere=default_sphere)
expected_sl_length = 11
for roi, num_sl in sltest_list:
seed = utils.seeds_from_mask(toydict[roi])
streamlines = LocalTracking(detmax_dg, classifier, seed, toydict['toy_affine'], step_size=1)
streamlines = list(streamlines)
npt.assert_equal(len(streamlines), num_sl)
for sl in streamlines:
npt.assert_equal(len(sl), expected_sl_length)
def create_streamlines(csd_fit, classifier, seeds, affine):
from dipy.data import default_sphere
from dipy.direction import DeterministicMaximumDirectionGetter
from dipy.tracking.streamline import Streamlines
detmax_dg = DeterministicMaximumDirectionGetter.from_shcoeff(
csd_fit.shm_coeff, max_angle=30.0, sphere=default_sphere)
streamlines = Streamlines(
LocalTracking(detmax_dg,
classifier,
seeds,
affine,
step_size=1,
return_all=False))
long_streamlines = np.ones((len(streamlines)), bool)
for i in range(0, len(streamlines)):
if streamlines[i].shape[0] < 100:
long_streamlines[i] = False
streamlines = streamlines[long_streamlines]
return streamlines
def test_maximum_deterministic_tracker():
"""This tests that the Maximum Deterministic Direction Getter plays nice
LocalTracking and produces reasonable streamlines in a simple example.
"""
sphere = HemiSphere.from_sphere(unit_octahedron)
# A simple image with three possible configurations, a vertical tract,
# a horizontal tract and a crossing
pmf_lookup = np.array([[0., 0., 1.], [1., 0., 0.], [0., 1., 0.],
[.4, .6, 0.]])
simple_image = np.array([
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0],
[0, 3, 2, 2, 2, 0],
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0],
])
simple_image = simple_image[..., None]
pmf = pmf_lookup[simple_image]
seeds = [np.array([1., 1., 0.])] * 30
mask = (simple_image > 0).astype(float)
sc = ThresholdStoppingCriterion(mask, .5)
dg = DeterministicMaximumDirectionGetter.from_pmf(pmf,
90,
sphere,
pmf_threshold=0.1)
streamlines = LocalTracking(dg, sc, seeds, np.eye(4), 1.)
expected = [
np.array([[0., 1., 0.], [1., 1., 0.], [2., 1., 0.], [2., 2., 0.],
[2., 3., 0.], [2., 4., 0.]]),
np.array([[0., 1., 0.], [1., 1., 0.], [2., 1., 0.], [3., 1., 0.],
[4., 1., 0.]]),
np.array([[0., 1., 0.], [1., 1., 0.], [2., 1., 0.]])
]
def allclose(x, y):
return x.shape == y.shape and np.allclose(x, y)
for sl in streamlines:
if not allclose(sl, expected[0]):
raise AssertionError()
# The first path is not possible if 90 degree turns are excluded
dg = DeterministicMaximumDirectionGetter.from_pmf(pmf,
80,
sphere,
pmf_threshold=0.1)
streamlines = LocalTracking(dg, sc, seeds, np.eye(4), 1.)
for sl in streamlines:
npt.assert_(np.allclose(sl, expected[1]))
# Both path are not possible if 90 degree turns are exclude and
# if pmf_threshold is larger than 0.67. Streamlines should stop at
# the crossing.
# 0.4/0.6 < 2/3, multiplying the pmf should not change the ratio
dg = DeterministicMaximumDirectionGetter.from_pmf(10 * pmf,
80,
sphere,
pmf_threshold=0.67)
streamlines = LocalTracking(dg, sc, seeds, np.eye(4), 1.)
for sl in streamlines:
npt.assert_(np.allclose(sl, expected[2]))
_, _, img_pve_wm = read_stanford_pve_maps()
data = hardi_img.get_data()
labels = labels_img.get_data()
affine = hardi_img.get_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 [:, :, :]
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
def test_maximum_deterministic_tracker():
"""This tests that the Maximum Deterministic Direction Getter plays nice
LocalTracking and produces reasonable streamlines in a simple example.
"""
sphere = HemiSphere.from_sphere(unit_octahedron)
# A simple image with three possible configurations, a vertical tract,
# a horizontal tract and a crossing
pmf_lookup = np.array([[0., 0., 1.],
[1., 0., 0.],
[0., 1., 0.],
[.4, .6, 0.]])
simple_image = np.array([[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0],
[0, 3, 2, 2, 2, 0],
[0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0],
])
simple_image = simple_image[..., None]
pmf = pmf_lookup[simple_image]
seeds = [np.array([1., 1., 0.])] * 30
mask = (simple_image > 0).astype(float)
tc = ThresholdTissueClassifier(mask, .5)
dg = DeterministicMaximumDirectionGetter.from_pmf(pmf, 90, sphere,
pmf_threshold=0.1)
streamlines = LocalTracking(dg, tc, seeds, np.eye(4), 1.)
expected = [np.array([[0., 1., 0.],
[1., 1., 0.],
[2., 1., 0.],
[2., 2., 0.],
[2., 3., 0.],
[2., 4., 0.]]),
np.array([[0., 1., 0.],
[1., 1., 0.],
[2., 1., 0.],
[3., 1., 0.],
[4., 1., 0.]]),
np.array([[0., 1., 0.],
[1., 1., 0.],
[2., 1., 0.]])]
def allclose(x, y):
return x.shape == y.shape and np.allclose(x, y)
for sl in streamlines:
if not allclose(sl, expected[0]):
raise AssertionError()
# The first path is not possible if 90 degree turns are excluded
dg = DeterministicMaximumDirectionGetter.from_pmf(pmf, 80, sphere,
pmf_threshold=0.1)
streamlines = LocalTracking(dg, tc, seeds, np.eye(4), 1.)
for sl in streamlines:
npt.assert_(np.allclose(sl, expected[1]))
# Both path are not possible if 90 degree turns are exclude and
# if pmf_threshold is larger than 0.67. Streamlines should stop at
# the crossing.
# 0.4/0.6 < 2/3, multiplying the pmf should not change the ratio
dg = DeterministicMaximumDirectionGetter.from_pmf(10*pmf, 80, sphere,
pmf_threshold=0.67)
streamlines = LocalTracking(dg, tc, seeds, np.eye(4), 1.)
for sl in streamlines:
npt.assert_(np.allclose(sl, expected[2]))
def determine(name=None,
data_path=None,
output_path='.',
Threshold=.20,
data_list=None,
seed='.',
minus_ROI_mask='.',
one_node=False,
two_node=False):
time0 = time.time()
print("begin loading data, time:", time.time() - time0)
if data_list == None:
data, affine, img, labels, gtab, head_mask = get_data(name, data_path)
else:
data = data_list['DWI']
affine = data_list['affine']
img = data_list['img']
labels = data_list['labels']
gtab = data_list['gtab']
head_mask = data_list['head_mask']
print(type(seed))
if type(seed) != str:
seed_mask = seed
else:
seed_mask = (labels == 2) * (head_mask == 1)
white_matter = (labels == 2) * (head_mask == 1)
seeds = utils.seeds_from_mask(seed_mask, affine, density=1)
print("begin reconstruction, time:", time.time() - time0)
response, ratio = auto_response_ssst(gtab, data, roi_radii=10, fa_thr=0.7)
csd_model = ConstrainedSphericalDeconvModel(gtab, response, sh_order=6)
csd_fit = csd_model.fit(data, mask=white_matter)
csa_model = CsaOdfModel(gtab, sh_order=6)
gfa = csa_model.fit(data, mask=white_matter).gfa
stopping_criterion = ThresholdStoppingCriterion(gfa, Threshold)
#from dipy.data import small_sphere
print("begin tracking, time:", time.time() - time0)
detmax_dg = DeterministicMaximumDirectionGetter.from_shcoeff(
csd_fit.shm_coeff, max_angle=30., sphere=default_sphere)
streamline_generator = LocalTracking(detmax_dg,
stopping_criterion,
seeds,
affine,
step_size=.5)
streamlines = Streamlines(streamline_generator)
sft = StatefulTractogram(streamlines, img, Space.RASMM)
if one_node or two_node:
sft.to_vox()
streamlines = reduct_seed_ROI(sft.streamlines, seed_mask, one_node,
two_node)
if type(minus_ROI_mask) != str:
streamlines = minus_ROI(streamlines=streamlines,
ROI=minus_ROI_mask)
sft = StatefulTractogram(streamlines, img, Space.VOX)
sft._vox_to_rasmm()
print("begin saving, time:", time.time() - time0)
output = output_path + '/tractogram_deterministic_' + name + '.trk'
save_trk(sft, output)
print("finished, time:", time.time() - time0)
def _run_interface(self, runtime): import numpy as np import nibabel as nib from dipy.io import read_bvals_bvecs from dipy.core.gradients import gradient_table from nipype.utils.filemanip import split_filename # Loading the data fname = self.inputs.in_file img = nib.load(fname) data = img.get_data() affine = img.get_affine() FA_fname = self.inputs.FA_file FA_img = nib.load(FA_fname) fa = FA_img.get_data() affine = FA_img.get_affine() affine = np.matrix.round(affine) mask_fname = self.inputs.brain_mask mask_img = nib.load(mask_fname) mask = mask_img.get_data() bval_fname = self.inputs.bval bvals = np.loadtxt(bval_fname) bvec_fname = self.inputs.bvec bvecs = np.loadtxt(bvec_fname) bvecs = np.vstack([bvecs[0,:],bvecs[1,:],bvecs[2,:]]).T gtab = gradient_table(bvals, bvecs) # Creating a white matter mask fa = fa*mask white_matter = fa >= 0.2 # Creating a seed mask from dipy.tracking import utils seeds = utils.seeds_from_mask(white_matter, density=[2, 2, 2], affine=affine) # Fitting the CSA model from dipy.reconst.shm import CsaOdfModel from dipy.data import default_sphere from dipy.direction import peaks_from_model csa_model = CsaOdfModel(gtab, sh_order=8) csa_peaks = peaks_from_model(csa_model, data, default_sphere, relative_peak_threshold=.8, min_separation_angle=45, mask=white_matter) from dipy.tracking.local import ThresholdTissueClassifier classifier = ThresholdTissueClassifier(csa_peaks.gfa, .25) # CSD model from dipy.reconst.csdeconv import (ConstrainedSphericalDeconvModel, auto_response) response, ratio = auto_response(gtab, data, roi_radius=10, fa_thr=0.7) csd_model = ConstrainedSphericalDeconvModel(gtab, response, sh_order=8) csd_fit = csd_model.fit(data, mask=white_matter) from dipy.direction import DeterministicMaximumDirectionGetter det_dg = DeterministicMaximumDirectionGetter.from_shcoeff(csd_fit.shm_coeff, max_angle=45., sphere=default_sphere) # Tracking from dipy.tracking.local import LocalTracking streamlines = LocalTracking(det_dg, classifier, seeds, affine, step_size=.5, maxlen=200, max_cross=1) # Compute streamlines and store as a list. streamlines = list(streamlines) # Saving the trackfile from dipy.io.trackvis import save_trk _, base, _ = split_filename(fname) save_trk(base + '_CSDdet.trk', streamlines, affine, fa.shape) return runtime
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 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(dwi_file, bval, bvec, mask_file, stop_val=0.1):
# """
# Tracking with basic tensors and basic eudx - experimental
# We now force seeding at every voxel in the provided mask for
# simplicity. Future functionality will extend these options.
# **Positional Arguments:**
# dwi_file:
# - File (registered) to use for tensor/fiber tracking
# mask_file:
# - Brain mask to keep tensors inside the brain
# gtab:
# - dipy formatted bval/bvec Structure
# **Optional Arguments:**
# stop_val:
# - Value to cutoff fiber track
# """
#img = nb.load(dwi_file)
#data = img.get_data()
dwi = dipy.data.load(dwi_file)
data = dwi.get_data()
#img = nb.load(mask_file)
#mask = img.get_data()
dwi_mask = dipy.data.load(mask_file)
mask = dwi_mask.get_data()
gtab = gradient_table(bval, bvec)
affine = dwi.affine
seed_mask = mask
seeds = utils.seeds_from_mask(seed_mask, density=1, affine=affine)
# use all points in mask
seedIdx = np.where(mask > 0) # seed everywhere not equal to zero
seedIdx = np.transpose(seedIdx)
sphere = get_sphere('symmetric724')
csd_model = ConstrainedSphericalDeconvModel(gtab, None, sh_order=6)
csd_fit = csd_model.fit(data, mask=mask)
tensor_model = dti.TensorModel(gtab)
tenfit = tensor_model.fit(data, mask=mask)
FA = fractional_anisotropy(tenfit.evals)
classifier = ThresholdTissueClassifier(FA, 0.1)
dg = DeterministicMaximumDirectionGetter.from_shcoeff(csd_fit.shm_coeff,
max_angle=80.,
sphere=sphere)
streamlines_generator = LocalTracking(dg,
classifier,
seeds,
affine,
step_size=0.5)
streamlines = Streamlines(streamlines_generator)
trk_file = save_trk("deterministic_threshold_DDG_samp_data.trk",
streamlines,
affine=affine,
shape=mask.shape)
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
mask=mask)
#csd_peaks = peaks_from_model(model=csd_model,
# data=data,
# sphere=default_sphere,
# relative_peak_threshold=relative_peak_threshold,
# min_separation_angle=min_separation_angle,
# mask=mask)
streamline_eudx = EuDX(csa_peaks.peak_values, csa_peaks.peak_indices,
odf_vertices=default_sphere.vertices,
a_low=threshold_tissue_classifier, step_sz=step_size, seeds=seeds)
save(streamline_eudx, streamline_eudx.affine, mask.shape, '1.trk', lenght_threshold)
detmax_dg = DeterministicMaximumDirectionGetter.from_shcoeff(csa_peaks.shm_coeff, max_angle=max_angle, sphere=default_sphere)
tensor_model = dti.TensorModel(gtab)
dti_fit = tensor_model.fit(data, mask=mask)
FA = fractional_anisotropy(dti_fit.evals)
classifier = ThresholdTissueClassifier(FA, threshold_tissue_classifier)
streamlines_dmdg = LocalTracking(detmax_dg, classifier, seeds, affine, step_size=step_size)
save(streamlines_dmdg, streamline_eudx.affine, mask.shape, '1.trk', lenght_threshold)
classifier = ThresholdTissueClassifier(csa_peaks.gfa, threshold_tissue_classifier)
prob_dg = ProbabilisticDirectionGetter.from_shcoeff(csa_peaks.shm_coeff, max_angle=max_angle, sphere=default_sphere)
streamlines_pdg = LocalTracking(prob_dg, classifier, seeds, affine, step_size=step_size)
save(streamlines_pdg, streamline_eudx.affine, mask.shape, '1.trk', lenght_threshold)
#M, grouping = connectivity_matrix(streamlines, labels, affine=s_affine, symmetric=True, return_mapping=True, mapping_as_streamlines=True)
def test_affine_transformations():
"""This tests that the input affine is properly handled by
LocalTracking and produces reasonable streamlines in a simple example.
"""
sphere = HemiSphere.from_sphere(unit_octahedron)
# A simple image with three possible configurations, a vertical tract,
# a horizontal tract and a crossing
pmf_lookup = np.array([[0., 0., 1.],
[1., 0., 0.],
[0., 1., 0.],
[.4, .6, 0.]])
simple_image = np.array([[0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0],
[0, 3, 2, 2, 2, 0],
[0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
])
simple_image = simple_image[..., None]
pmf = pmf_lookup[simple_image]
seeds = [np.array([1., 1., 0.]),
np.array([2., 4., 0.])]
expected = [np.array([[0., 1., 0.],
[1., 1., 0.],
[2., 1., 0.],
[3., 1., 0.],
[4., 1., 0.]]),
np.array([[2., 0., 0.],
[2., 1., 0.],
[2., 2., 0.],
[2., 3., 0.],
[2., 4., 0.],
[2., 5., 0.]])]
mask = (simple_image > 0).astype(float)
tc = BinaryTissueClassifier(mask)
dg = DeterministicMaximumDirectionGetter.from_pmf(pmf, 60, sphere,
pmf_threshold=0.1)
# TST- bad affine wrong shape
bad_affine = np.eye(3)
npt.assert_raises(ValueError, LocalTracking, dg, tc, seeds, bad_affine, 1.)
# TST - bad affine with shearing
bad_affine = np.eye(4)
bad_affine[0, 1] = 1.
npt.assert_raises(ValueError, LocalTracking, dg, tc, seeds, bad_affine, 1.)
# TST - identity
a0 = np.eye(4)
# TST - affines with positive/negative offsets
a1 = np.eye(4)
a1[:3, 3] = [1, 2, 3]
a2 = np.eye(4)
a2[:3, 3] = [-2, 0, -1]
# TST - affine with scaling
a3 = np.eye(4)
a3[0, 0] = a3[1, 1] = a3[2, 2] = 8
# TST - affine with axes inverting (negative value)
a4 = np.eye(4)
a4[1, 1] = a4[2, 2] = -1
# TST - combined affines
a5 = a1 + a2 + a3
a5[3, 3] = 1
# TST - in vivo affine exemple
# Sometimes data have affines with tiny shear components.
# For example, the small_101D data-set has some of that:
fdata, _, _ = get_data('small_101D')
a6 = nib.load(fdata).affine
for affine in [a0, a1, a2, a3, a4, a5, a6]:
lin = affine[:3, :3]
offset = affine[:3, 3]
seeds_trans = [np.dot(lin, s) + offset for s in seeds]
# We compute the voxel size to ajust the step size to one voxel
voxel_size = np.mean(np.sqrt(np.dot(lin, lin).diagonal()))
streamlines = LocalTracking(direction_getter=dg,
tissue_classifier=tc,
seeds=seeds_trans,
affine=affine,
step_size=voxel_size,
return_all=True)
# We apply the inverse affine transformation to the generated
# streamlines. It should be equals to the expected streamlines
# (generated with the identity affine matrix).
affine_inv = np.linalg.inv(affine)
lin = affine_inv[:3, :3]
offset = affine_inv[:3, 3]
streamlines_inv = []
for line in streamlines:
streamlines_inv.append([np.dot(pts, lin) + offset for pts in line])
npt.assert_equal(len(streamlines_inv[0]), len(expected[0]))
npt.assert_(np.allclose(streamlines_inv[0], expected[0], atol=0.3))
npt.assert_equal(len(streamlines_inv[1]), len(expected[1]))
npt.assert_(np.allclose(streamlines_inv[1], expected[1], atol=0.3))
def test_affine_transformations():
"""This tests that the input affine is properly handled by
LocalTracking and produces reasonable streamlines in a simple example.
"""
sphere = HemiSphere.from_sphere(unit_octahedron)
# A simple image with three possible configurations, a vertical tract,
# a horizontal tract and a crossing
pmf_lookup = np.array([[0., 0., 1.], [1., 0., 0.], [0., 1., 0.],
[.4, .6, 0.]])
simple_image = np.array([
[0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0],
[0, 3, 2, 2, 2, 0],
[0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
])
simple_image = simple_image[..., None]
pmf = pmf_lookup[simple_image]
seeds = [np.array([1., 1., 0.]), np.array([2., 4., 0.])]
expected = [
np.array([[1., 1., 0.], [2., 1., 0.], [3., 1., 0.]]),
np.array([[2., 1., 0.], [2., 2., 0.], [2., 3., 0.], [2., 4., 0.]])
]
mask = (simple_image > 0).astype(float)
sc = BinaryStoppingCriterion(mask)
dg = DeterministicMaximumDirectionGetter.from_pmf(pmf,
60,
sphere,
pmf_threshold=0.1)
# TST- bad affine wrong shape
bad_affine = np.eye(3)
npt.assert_raises(ValueError, LocalTracking, dg, sc, seeds, bad_affine, 1.)
# TST - bad affine with shearing
bad_affine = np.eye(4)
bad_affine[0, 1] = 1.
npt.assert_raises(ValueError, LocalTracking, dg, sc, seeds, bad_affine, 1.)
# TST - bad seeds
bad_seeds = 1000
npt.assert_raises(ValueError, LocalTracking, dg, sc, bad_seeds, np.eye(4),
1.)
# TST - identity
a0 = np.eye(4)
# TST - affines with positive/negative offsets
a1 = np.eye(4)
a1[:3, 3] = [1, 2, 3]
a2 = np.eye(4)
a2[:3, 3] = [-2, 0, -1]
# TST - affine with scaling
a3 = np.eye(4)
a3[0, 0] = a3[1, 1] = a3[2, 2] = 8
# TST - affine with axes inverting (negative value)
a4 = np.eye(4)
a4[1, 1] = a4[2, 2] = -1
# TST - combined affines
a5 = a1 + a2 + a3
a5[3, 3] = 1
# TST - in vivo affine example
# Sometimes data have affines with tiny shear components.
# For example, the small_101D data-set has some of that:
fdata, _, _ = get_fnames('small_101D')
a6 = nib.load(fdata).affine
for affine in [a0, a1, a2, a3, a4, a5, a6]:
lin = affine[:3, :3]
offset = affine[:3, 3]
seeds_trans = [np.dot(lin, s) + offset for s in seeds]
# We compute the voxel size to adjust the step size to one voxel
voxel_size = np.mean(np.sqrt(np.dot(lin, lin).diagonal()))
streamlines = LocalTracking(direction_getter=dg,
stopping_criterion=sc,
seeds=seeds_trans,
affine=affine,
step_size=voxel_size,
return_all=True)
# We apply the inverse affine transformation to the generated
# streamlines. It should be equals to the expected streamlines
# (generated with the identity affine matrix).
affine_inv = np.linalg.inv(affine)
lin = affine_inv[:3, :3]
offset = affine_inv[:3, 3]
streamlines_inv = []
for line in streamlines:
streamlines_inv.append([np.dot(pts, lin) + offset for pts in line])
npt.assert_equal(len(streamlines_inv[0]), len(expected[0]))
npt.assert_(np.allclose(streamlines_inv[0], expected[0], atol=0.3))
npt.assert_equal(len(streamlines_inv[1]), len(expected[1]))
npt.assert_(np.allclose(streamlines_inv[1], expected[1], atol=0.3))
_, _, 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 = (labels == 2)
seed_mask[img_pve_wm.get_data() < 0.5] = 0
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 Stopping Criterion
============================
A scalar map can be used to define where the tracking stops. The threshold
stopping criterion 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 stopping criterion uses a trilinear interpolation at the
tracking position.
**Parameters**
- metric_map: numpy array [:, :, :]
- threshold: float
if fodf_pred_sum[i,j,k]>0:
pmf[i,j,k,:]/= fodf_pred_sum[i,j,k]
pmf_sfm_40= pmf.copy()
print('Finished estimating FODF with sfm, time elapsed (minutes): ' + str(int( (time.time()-t1)/60 ) ) )
del odf_sfm, pmf
'''
if run_dipy_tractography:
print('Running tractography; CSD')
t1 = time.time()
det_dg = DeterministicMaximumDirectionGetter.from_pmf(pmf_dipy,
max_angle=30.,
sphere=sphere_fod)
streamline_generator = LocalTracking(det_dg,
stopping_criterion,
seeds,
affine=affine,
step_size=.5)
streamlines = Streamlines(streamline_generator)
sft = StatefulTractogram(streamlines, FA_img_nii, Space.RASMM)
save_trk(sft,
dav_dir + "tractogram_dipy_deterministic.trk",
bbox_valid_check=False)
print('Finished tractography with DIPY, time elapsed (minutes): ' +
