def tracking_prob(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 = ProbabilisticDirectionGetter.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_prob_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 runStream(csd_peaks, roi_file, roi_label=1, ang_thr=45., a_low=0.2, step_size=0.1, seeds_per_voxel=30, out_name=None):
img = nib.load(roi_file)
affine = img.get_affine()
mask_data = img.get_data()
p = np.asarray(np.where(mask_data == roi_label))
p = p.transpose()
# seed_points = None
# for i in p:
# points = np.random.uniform(size=[seeds_per_voxel,3]) + (i-0.5)
# if seed_points is None:
# seed_points = points
# else:
# seed_points = np.concatenate([seed_points, points], axis=0)
import dipy.tracking.utils as utils
seeds = utils.seeds_from_mask(mask_data==1, density=seeds_per_voxel)
print '# of seeds: ',len(seeds)
sphere = get_sphere('symmetric724')
print "seed eudx tractography"
eu = EuDX(csd_peaks.peak_values,
csd_peaks.peak_indices,
odf_vertices=sphere.vertices,
step_sz=step_size,
seeds=seeds,
ang_thr=ang_thr,
a_low=a_low)
csa_streamlines_mult_peaks = [streamline for streamline in eu]
out_file = 'tracts.dipy'
if out_name:
out_file = out_name+'_'+out_file
from dipy.io.trackvis import save_trk
save_trk(out_file, csa_streamlines_mult_peaks, affine,
mask.shape)
dpw = Dpy(out_file, 'w')
dpw.write_tracks(csa_streamlines_mult_peaks)
print 'write tracts to %s' % out_file
return (csa_streamlines_mult_peaks, out_file)
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)
"""
.. figure:: deterministic.png
:align: center
**Corpus Callosum Deterministic**
We've created a deterministic set of streamlines, so called because if you
repeat the fiber tracking (keeping all the inputs the same) you will get
exactly the same set of streamlines. We can save the streamlines as a Trackvis
file so it can be loaded into other software for visualization or further
analysis.
"""
from dipy.io.trackvis import save_trk
save_trk("CSA_detr.trk", streamlines, affine, labels.shape)
"""
Next let's try some probabilistic fiber tracking. For this, we'll be using the
Constrained Spherical Deconvolution (CSD) Model. This model represents each
voxel in the data set as a collection of small white matter fibers with
different orientations. The density of fibers along each orientation is known
as the Fiber Orientation Distribution (FOD). In order to perform probabilistic
fiber tracking, we pick a fiber from the FOD at random at each new location
along the streamline. Note: one could use this model to perform deterministic
fiber tracking by always tracking along the directions that have the most
fibers.
Let's begin probabilistic fiber tracking by fitting the data to the CSD model.
"""
renderer.add(actor.line(streamlines, line_colors(streamlines)))
window.record(renderer, out_path='bootstrap_dg_CSD.png', size=(600, 600))
"""
.. figure:: bootstrap_dg_CSD.png
:align: center
**Corpus Callosum Bootstrap Probabilistic Direction Getter**
We have created a bootstrapped probabilistic set of streamlines. If you repeat
the fiber tracking (keeping all inputs the same) you will NOT get exactly the
same set of streamlines. We can save the streamlines as a Trackvis file so it
can be loaded into other software for visualization or further analysis.
"""
save_trk("bootstrap_dg_CSD.trk", streamlines, affine, labels.shape)
"""
Example #2: Closest peak direction getter with CSD Model
"""
from dipy.direction import ClosestPeakDirectionGetter
pmf = csd_fit.odf(small_sphere).clip(min=0)
peak_dg = ClosestPeakDirectionGetter.from_pmf(pmf, max_angle=30.,
sphere=small_sphere)
peak_streamline_generator = LocalTracking(peak_dg, classifier, seeds, affine,
step_size=.5)
streamlines = Streamlines(peak_streamline_generator)
renderer.clear()
import dipy.reconst.dti as dti
from dipy.reconst.dti import fractional_anisotropy
tensor_model = dti.TensorModel(gtab)
tenfit = tensor_model.fit(data, mask=white_matter)
FA = fractional_anisotropy(tenfit.evals)
classifier = ThresholdTissueClassifier(FA, .2)
"""
The Fiber Orientation Distribution (FOD) of the CSD model estimates the
distribution of small fiber bundles within each voxel. This distribution
can be used for deterministic fiber tracking. As for probabilistic tracking,
there are many ways to provide those distributions to the deterministic maximum
direction getter. Here, the spherical harmonic representation of the FOD
is used.
"""
from dipy.data import default_sphere
from dipy.direction import DeterministicMaximumDirectionGetter
from dipy.io.trackvis import save_trk
detmax_dg = DeterministicMaximumDirectionGetter.from_shcoeff(csd_fit.shm_coeff,
max_angle=30.,
sphere=default_sphere)
streamlines = LocalTracking(detmax_dg, classifier, seeds, affine, step_size=.5)
save_trk("deterministic_maximum_shm_coeff.trk", streamlines, affine,
labels.shape)
# Particle Filtering Tractography
pft_streamline_generator = ParticleFilteringTracking(dg,
cmc_classifier,
seeds,
affine,
max_cross=1,
step_size=step_size,
maxlen=1000,
pft_back_tracking_dist=2,
pft_front_tracking_dist=1,
particle_count=15,
return_all=False)
#streamlines = list(pft_streamline_generator)
streamlines = Streamlines(pft_streamline_generator)
save_trk("pft_streamline.trk", streamlines, affine, shape)
renderer.clear()
renderer.add(actor.line(streamlines, line_colors(streamlines)))
window.record(renderer, out_path='pft_streamlines.png', size=(600, 600))
"""
.. figure:: pft_streamlines.png
:align: center
**Particle Filtering Tractography**
"""
# Local Probabilistic Tractography
prob_streamline_generator = LocalTracking(dg,
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 ProbabilisticDirectionGetter prob_dg = ProbabilisticDirectionGetter.from_shcoeff(csd_fit.shm_coeff, max_angle=45., sphere=default_sphere) # Tracking from dipy.tracking.local import LocalTracking streamlines = LocalTracking(prob_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 + '_CSDprob.trk', streamlines, affine, fa.shape) return runtime
classifier.
"""
import dipy.reconst.dti as dti
from dipy.reconst.dti import fractional_anisotropy
tensor_model = dti.TensorModel(gtab)
tenfit = tensor_model.fit(data, mask=white_matter)
FA = fractional_anisotropy(tenfit.evals)
classifier = ThresholdTissueClassifier(FA, .2)
"""
The fiber orientation distribution (FOD) of the CSD model estimates the
distribution of small fiber bundles within each voxel. This distribution
can be used for deterministic fiber tracking. As for probabilistic tracking,
there are many ways to provide those distributions to the deterministic maximum
direction getter. Here, the spherical harmonic representation of the FOD
is used.
"""
from dipy.data import default_sphere
from dipy.direction import DeterministicMaximumDirectionGetter
from dipy.io.trackvis import save_trk
detmax_dg = DeterministicMaximumDirectionGetter.from_shcoeff(
csd_fit.shm_coeff, max_angle=30., sphere=default_sphere)
streamlines = LocalTracking(detmax_dg, classifier, seeds, affine, step_size=.5)
save_trk("deterministic_maximum_shm_coeff.trk", streamlines, affine,
labels.shape)
need to clip the FOD to use it as a PMF because the latter cannot have negative
values. (Ideally the FOD should be strictly positive, but because of noise
and/or model failures sometimes it can have negative values).
"""
from dipy.direction import ProbabilisticDirectionGetter
from dipy.data import small_sphere
from dipy.io.trackvis import save_trk
fod = csd_fit.odf(small_sphere)
pmf = fod.clip(min=0)
prob_dg = ProbabilisticDirectionGetter.from_pmf(pmf,
max_angle=30.,
sphere=small_sphere)
streamlines = LocalTracking(prob_dg, classifier, seeds, affine, step_size=.5)
save_trk("probabilistic_small_sphere.trk", streamlines, affine, labels.shape)
"""
One disadvantage of using a discrete PMF to represent possible tracking
directions is that it tends to take up a lot of memory (RAM). The size of the
PMF, the FOD in this case, must be equal to the number of possible tracking
directions on the hemisphere, and every voxel has a unique PMF. In this case
the data is ``(81, 106, 76)`` and ``small_sphere`` has 181 directions so the
FOD is ``(81, 106, 76, 181)``. One way to avoid sampling the PMF and holding it
in memory is to build the direction getter directly from the spherical harmonic
representation of the FOD. By using this approach, we can also use a larger
sphere, like ``default_sphere`` which has 362 directions on the hemisphere,
without having to worry about memory limitations.
"""
from dipy.data import default_sphere
renderer.clear()
renderer.add(actor.line(streamlines, cmap.line_colors(streamlines)))
window.record(renderer, out_path='bootstrap_dg_CSD.png', size=(600, 600))
"""
.. figure:: bootstrap_dg_CSD.png
:align: center
**Corpus Callosum Bootstrap Probabilistic Direction Getter**
We have created a bootstrapped probabilistic set of streamlines. If you repeat
the fiber tracking (keeping all inputs the same) you will NOT get exactly the
same set of streamlines. We can save the streamlines as a Trackvis file so it
can be loaded into other software for visualization or further analysis.
"""
save_trk("bootstrap_dg_CSD.trk", streamlines, affine, labels.shape)
"""
Example #2: Closest peak direction getter with CSD Model
"""
from dipy.direction import ClosestPeakDirectionGetter
pmf = csd_fit.odf(small_sphere).clip(min=0)
peak_dg = ClosestPeakDirectionGetter.from_pmf(pmf,
max_angle=30.,
sphere=small_sphere)
peak_streamline_generator = LocalTracking(peak_dg,
classifier,
seeds,
affine,
step_size=.5)
# In[ ]:
print len(streamlines)
print len(results)
print len(streamlines[0])
print len(results[0])
for j in range(0, len(streamlines)):
for i in range(0,len(streamlines[j])):
print streamlines[j][i]==results[j][i]
# In[ ]:
from dipy.io.trackvis import save_trk
save_trk("CSA_detr.trk", streamlines, affine, labels.shape)
# In[ ]:
# In[ ]:
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=6)
csd_fit = csd_model.fit(data, mask=white_matter)
cmc_classifier = CmcTissueClassifier.from_pve(img_pve_wm.get_data(),
img_pve_gm.get_data(),
img_pve_csf.get_data(),
step_size=step_size,
average_voxel_size=voxel_size)
# seeds are place in voxel of the corpus callosum containing only white matter
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)
# Particle Filtering Tractography
pft_streamline_generator = ParticleFilteringTracking(dg,
cmc_classifier,
seeds,
affine,
max_cross=1,
step_size=step_size,
maxlen=1000,
pft_back_tracking_dist=2,
pft_front_tracking_dist=1,
particle_count=15,
return_all=False)
# streamlines = list(pft_streamline_generator)
streamlines = Streamlines(pft_streamline_generator)
save_trk("pft_streamline.trk", streamlines, affine, shape)
renderer.clear()
renderer.add(actor.line(streamlines, cmap.line_colors(streamlines)))
window.record(renderer, out_path='pft_streamlines.png', size=(600, 600))
csa_model = CsaOdfModel(g_tab, sh_order=6)
csa_peaks = peaks_from_model(csa_model,
dwi_data,
default_sphere,
sh_order=6,
relative_peak_threshold=.85,
min_separation_angle=35,
mask=dwi_mask_data.astype(bool))
classifier = ThresholdTissueClassifier(csa_peaks.gfa, .2)
seeds = utils.seeds_from_mask(dwi_mask_data.astype(bool),
density=[1, 1, 1],
affine=dwi_affine)
streamlines = LocalTracking(csa_peaks,
classifier,
seeds,
dwi_affine,
step_size=3)
streamlines = [s for s in streamlines if s.shape[0] > 30]
streamlines = list(streamlines)
save_trk(os.path.join(path_output, '_tractography_CsaOdf' + '.trk'),
streamlines, dwi_affine, dwi_mask_data.shape)
print(' - Ending reconstruction of Tractography...')
print(path_input)
ren.add(vol_actor)
ren.add(vol_actor2)
window.record(ren, out_path='sfm_streamlines.png', size=(800, 800))
if interactive:
window.show(ren)
"""
.. figure:: sfm_streamlines.png
:align: center
**Sparse Fascicle Model tracks**
Finally, we can save these streamlines to a 'trk' file, for use in other
software, or for further analysis.
"""
from dipy.io.trackvis import save_trk
save_trk("sfm_detr.trk", streamlines, affine, labels.shape)
"""
References
----------
.. [Rokem2015] Ariel Rokem, Jason D. Yeatman, Franco Pestilli, Kendrick
N. Kay, Aviv Mezer, Stefan van der Walt, Brian A. Wandell (2015). Evaluating
the accuracy of diffusion MRI models in white matter. PLoS ONE 10(4):
e0123272. doi:10.1371/journal.pone.0123272
"""
Probabilistic Direction Getter as a PMF for sampling tracking directions. We
need to clip the FOD to use it as a PMF because the latter cannot have negative
values. (Ideally the FOD should be strictly positive, but because of noise
and/or model failures sometimes it can have negative values).
"""
from dipy.direction import ProbabilisticDirectionGetter
from dipy.data import small_sphere
from dipy.io.trackvis import save_trk
fod = csd_fit.odf(small_sphere)
pmf = fod.clip(min=0)
prob_dg = ProbabilisticDirectionGetter.from_pmf(pmf, max_angle=30.,
sphere=small_sphere)
streamlines = LocalTracking(prob_dg, classifier, seeds, affine, step_size=.5)
save_trk("probabilistic_small_sphere.trk", streamlines, affine, labels.shape)
"""
One disadvantage of using a discrete PMF to represent possible tracking
directions is that it tends to take up a lot of memory (RAM). The size of the
PMF, the FOD in this case, must be equal to the number of possible tracking
directions on the hemisphere, and every voxel has a unique PMF. In this case
the data is ``(81, 106, 76)`` and ``small_sphere`` has 181 directions so the
FOD is ``(81, 106, 76, 181)``. One way to avoid sampling the PMF and holding it
in memory is to build the direction getter directly from the spherical harmonic
representation of the FOD. By using this approach, we can also use a larger
sphere, like ``default_sphere`` which has 362 directions on the hemisphere,
without having to worry about memory limitations.
"""
from dipy.data import default_sphere
ren = window.Renderer()
ren.add(streamlines_actor)
ren.add(vol_actor)
ren.add(vol_actor2)
window.record(ren, out_path='sfm_streamlines.png', size=(800, 800))
if interactive:
window.show(ren)
"""
.. figure:: sfm_streamlines.png
:align: center
**Sparse Fascicle Model tracks**
Finally, we can save these streamlines to a 'trk' file, for use in other
software, or for further analysis.
"""
from dipy.io.trackvis import save_trk
save_trk("sfm_detr.trk", streamlines, affine, labels.shape)
"""
References
----------
.. [Rokem2015] Ariel Rokem, Jason D. Yeatman, Franco Pestilli, Kendrick
N. Kay, Aviv Mezer, Stefan van der Walt, Brian A. Wandell (2015). Evaluating
the accuracy of diffusion MRI models in white matter. PLoS ONE 10(4):
e0123272. doi:10.1371/journal.pone.0123272
"""
"""
.. figure:: threshold_fa.png
:align: center
**Thresholded fractional anisotropy map.**
"""
all_streamlines_threshold_classifier = LocalTracking(dg,
threshold_classifier,
seeds,
affine,
step_size=.5,
return_all=True)
save_trk("deterministic_threshold_classifier_all.trk",
all_streamlines_threshold_classifier,
affine,
labels.shape)
streamlines = [sl for sl in all_streamlines_threshold_classifier]
fvtk.clear(ren)
fvtk.add(ren, fvtk.line(streamlines, line_colors(streamlines)))
fvtk.record(ren, out_path='all_streamlines_threshold_classifier.png',
size=(600, 600))
"""
.. figure:: all_streamlines_threshold_classifier.png
:align: center
**Deterministic tractography using a thresholded fractional anisotropy.**
"""
fig.savefig('threshold_fa.png')
"""
.. figure:: threshold_fa.png
:align: center
**Thresholded fractional anisotropy map.**
"""
all_streamlines_threshold_classifier = LocalTracking(dg,
threshold_classifier,
seeds,
affine,
step_size=.5,
return_all=True)
save_trk("deterministic_threshold_classifier_all.trk",
all_streamlines_threshold_classifier, affine, labels.shape)
streamlines = [sl for sl in all_streamlines_threshold_classifier]
fvtk.clear(ren)
fvtk.add(ren, fvtk.line(streamlines, line_colors(streamlines)))
fvtk.record(ren,
out_path='all_streamlines_threshold_classifier.png',
size=(600, 600))
"""
.. figure:: all_streamlines_threshold_classifier.png
:align: center
**Deterministic tractography using a thresholded fractional anisotropy.**
"""
"""