def probal(Threshold=.2,
data_list=None,
seed='.',
one_node=False,
two_node=False):
time0 = time.time()
print("begin loading data, time:", time.time() - time0)
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']
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)
print("begin tracking, time:", time.time() - time0)
fod = csd_fit.odf(small_sphere)
pmf = fod.clip(min=0)
prob_dg = ProbabilisticDirectionGetter.from_pmf(pmf,
max_angle=30.,
sphere=small_sphere)
streamline_generator = LocalTracking(prob_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)
sft = StatefulTractogram(streamlines, img, Space.VOX)
sft._vox_to_rasmm()
print("begin saving, time:", time.time() - time0)
output = 'tractogram_probabilistic.trk'
save_trk(sft, output)
print("finished, time:", time.time() - time0)
def track(self):
SeedBasedTracker.track(self)
if self.streamlines is not None:
return
roi_r = Config.get_config().getint("CSDTracking", "autoResponseRoiRadius",
fallback="10")
fa_thr = Config.get_config().getfloat("CSDTracking", "autoResponseFaThreshold",
fallback="0.7")
response, _ = auto_response_ssst(self.data.gtab, self.data.dwi, roi_radii=roi_r, fa_thr=fa_thr)
csd_model = ConstrainedSphericalDeconvModel(self.data.gtab, response)
relative_peak_thr = Config.get_config().getfloat("CSDTracking", "relativePeakTreshold",
fallback="0.5")
min_separation_angle = Config.get_config().getfloat("CSDTracking", "minimumSeparationAngle",
fallback="25")
direction_getter = peaks_from_model(model=csd_model,
data=self.data.dwi,
sphere=get_sphere('symmetric724'),
mask=self.data.binarymask,
relative_peak_threshold=relative_peak_thr,
min_separation_angle=min_separation_angle,
parallel=False)
dti_fit = dti.TensorModel(self.data.gtab, fit_method='LS')
dti_fit = dti_fit.fit(self.data.dwi, mask=self.data.binarymask)
self._track(ThresholdStoppingCriterion(dti_fit.fa, self.options.fa_threshold),
direction_getter)
Cache.get_cache().set(self.id, self.streamlines)
def _model(gtab, data, response=None, sh_order=None, msmt=False):
"""
Helper function that defines a CSD model.
"""
if sh_order is None:
ndata = np.sum(~gtab.b0s_mask)
# See dipy.reconst.shm.calculate_max_order
L1 = (-3 + np.sqrt(1 + 8 * ndata)) / 2.0
sh_order = int(L1)
if np.mod(sh_order, 2) != 0:
sh_order = sh_order - 1
if sh_order > 8:
sh_order = 8
if msmt:
my_model = mcsd.MultiShellDeconvModel
if response is None:
mask_wm, mask_gm, mask_csf =\
mcsd.mask_for_response_msmt(gtab, data)
response_wm, response_gm, response_csf =\
mcsd.response_from_mask_msmt(gtab, data,
mask_wm, mask_gm, mask_csf)
response = np.array([response_wm, response_gm, response_csf])
else:
my_model = csd.ConstrainedSphericalDeconvModel
if response is None:
response, _ = csd.auto_response_ssst(gtab,
data,
roi_radii=10,
fa_thr=0.7)
csdmodel = my_model(gtab, response, sh_order=sh_order)
return csdmodel
def basic_tracking(name=None,
data_path=None,
output_path='.',
Threshold=.20,
data_list=None):
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']
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)
from dipy.reconst.csdeconv import auto_response_ssst
from dipy.reconst.shm import CsaOdfModel
from dipy.data import default_sphere
from dipy.direction import peaks_from_model
response, ratio = auto_response_ssst(gtab, data, roi_radii=10, fa_thr=0.7)
csa_model = CsaOdfModel(gtab, sh_order=6)
csa_peaks = peaks_from_model(csa_model,
data,
default_sphere,
relative_peak_threshold=.8,
min_separation_angle=45,
mask=white_matter)
stopping_criterion = ThresholdStoppingCriterion(csa_peaks.gfa, Threshold)
print("begin tracking, time:", time.time() - time0)
# Initialization of LocalTracking. The computation happens in the next step.
streamlines_generator = LocalTracking(csa_peaks,
stopping_criterion,
seeds,
affine=affine,
step_size=.5)
# Generate streamlines object
streamlines = Streamlines(streamlines_generator)
print('begin saving, time:', time.time() - time0)
from dipy.io.stateful_tractogram import Space, StatefulTractogram
from dipy.io.streamline import save_trk
sft = StatefulTractogram(streamlines, img, Space.RASMM)
output = output_path + '/tractogram_EuDX_' + name + '.trk'
save_trk(sft, output, streamlines)
def create_csd_model(data, gtab, white_matter, sh_order=6):
from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel, auto_response_ssst
response, ratio = auto_response_ssst(gtab, data, roi_radii=10, fa_thr=0.7)
csd_model = ConstrainedSphericalDeconvModel(gtab,
response,
sh_order=sh_order)
csd_fit = csd_model.fit(data, mask=white_matter)
return csd_fit
def _csd_ft(self):
from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel, auto_response_ssst
response, ratio = auto_response_ssst(self.gtab,
self.data,
roi_radii=10,
fa_thr=0.7)
csd_model = ConstrainedSphericalDeconvModel(
self.gtab, response, sh_order=self.parameters_dict['sh_order'])
csd_fit = csd_model.fit(self.data)
self.model_fit = csd_fit
def PFT_tracking(name=None, data_path=None, output_path='.', Threshold=.20):
time0 = time.time()
print("begin loading data, time:", time.time() - time0)
data, affine, img, labels, gtab, head_mask = get_data(name, data_path)
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)
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)
dg = ProbabilisticDirectionGetter.from_shcoeff(csd_fit.shm_coeff,
max_angle=20.,
sphere=default_sphere)
#seed_mask = (labels == 2)
#seed_mask[pve_wm_data < 0.5] = 0
seeds = utils.seeds_from_mask(seed_mask, affine, density=1)
#voxel_size = np.average(voxel_size[1:4])
step_size = 0.2
#cmc_criterion = CmcStoppingCriterion.from_pve(pve_wm_data,
# pve_gm_data,
# pve_csf_data,
# step_size=step_size,
# average_voxel_size=voxel_size)
# Particle Filtering Tractography
pft_streamline_generator = ParticleFilteringTracking(
dg,
stopping_criterion,
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 = Streamlines(pft_streamline_generator)
sft = StatefulTractogram(streamlines, img, Space.RASMM)
output = output_path + '/tractogram_pft_' + name + '.trk'
def ClosestPeak(name=None, data_path=None, output_path='.', Threshold=.20):
time0 = time.time()
print("begin loading data, time:", time.time() - time0)
data, affine, img, labels, gtab, head_mask = get_data(name, data_path)
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)
#pmf = csd_fit.odf(small_sphere).clip(min=0)
peak_dg = BootDirectionGetter.from_data(data,
csd_model,
max_angle=30.,
sphere=small_sphere)
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(peak_dg,
stopping_criterion,
seeds,
affine,
step_size=.5)
streamlines = Streamlines(streamline_generator)
sft = StatefulTractogram(streamlines, img, Space.RASMM)
print("begin saving, time:", time.time() - time0)
output = output_path + '/tractogram_ClosestPeak_' + name + '.trk'
save_trk(sft, output)
print("finished, time:", time.time() - time0)
def test_auto_response_ssst():
gtab, data, _, _, _ = get_test_data()
response_auto, ratio_auto = auto_response_ssst(gtab,
data,
roi_center=None,
roi_radii=(1, 1, 0),
fa_thr=0.7)
mask = mask_for_response_ssst(gtab, data,
roi_center=None,
roi_radii=(1, 1, 0),
fa_thr=0.7)
response_from_mask, ratio_from_mask = response_from_mask_ssst(gtab,
data,
mask)
assert_array_equal(response_auto[0], response_from_mask[0])
assert_equal(response_auto[1], response_from_mask[1])
assert_array_equal(ratio_auto, ratio_from_mask)
different strategies are presented.
**Strategy 1 - response function estimates from a local brain region**
One simple way to estimate the fiber response function is to look for regions
of the brain where it is known that there are single coherent fiber
populations. For example, if we use a ROI at the center of the brain, we will
find single fibers from the corpus callosum. The ``auto_response_ssst``
function will calculate FA for a cuboid ROI of radii equal to ``roi_radii`` in
the center of the volume and return the response function estimated in that
region for the voxels with FA higher than 0.7.
"""
from dipy.reconst.csdeconv import (auto_response_ssst, mask_for_response_ssst,
response_from_mask_ssst)
response, ratio = auto_response_ssst(gtab, data, roi_radii=10, fa_thr=0.7)
"""
Note that the ``auto_response_ssst`` function calls two functions that can be
used separately. First, the function ``mask_for_response_ssst`` creates a mask
of voxels within the cuboid ROI that meet the FA threshold constraint. This
mask can be used to calculate the number of voxels that were kept, or to also
apply an external mask (a WM mask for example). Second, the function
``response_from_mask_ssst`` takes the mask and returns the response function
calculated within the mask. If no changes are made to the mask between the two
calls, the resulting responses should be identical.
"""
mask = mask_for_response_ssst(gtab, data, roi_radii=10, fa_thr=0.7)
nvoxels = np.sum(mask)
print(nvoxels)
def run(self,
input_files,
bvalues_files,
bvectors_files,
mask_files,
b0_threshold=50.0,
bvecs_tol=0.01,
roi_center=None,
roi_radii=10,
fa_thr=0.7,
frf=None,
extract_pam_values=False,
sh_order=8,
odf_to_sh_order=8,
parallel=False,
nbr_processes=None,
out_dir='',
out_pam='peaks.pam5',
out_shm='shm.nii.gz',
out_peaks_dir='peaks_dirs.nii.gz',
out_peaks_values='peaks_values.nii.gz',
out_peaks_indices='peaks_indices.nii.gz',
out_gfa='gfa.nii.gz'):
""" Constrained spherical deconvolution
Parameters
----------
input_files : string
Path to the input volumes. This path may contain wildcards to
process multiple inputs at once.
bvalues_files : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
bvectors_files : string
Path to the bvectors files. This path may contain wildcards to use
multiple bvectors files at once.
mask_files : string
Path to the input masks. This path may contain wildcards to use
multiple masks at once. (default: No mask used)
b0_threshold : float, optional
Threshold used to find b0 volumes.
bvecs_tol : float, optional
Bvecs should be unit vectors.
roi_center : variable int, optional
Center of ROI in data. If center is None, it is assumed that it is
the center of the volume with shape `data.shape[:3]`.
roi_radii : int or array-like, optional
radii of cuboid ROI in voxels.
fa_thr : float, optional
FA threshold for calculating the response function.
frf : variable float, optional
Fiber response function can be for example inputed as 15 4 4
(from the command line) or [15, 4, 4] from a Python script to be
converted to float and multiplied by 10**-4 . If None
the fiber response function will be computed automatically.
extract_pam_values : bool, optional
Save or not to save pam volumes as single nifti files.
sh_order : int, optional
Spherical harmonics order used in the CSA fit.
odf_to_sh_order : int, optional
Spherical harmonics order used for peak_from_model to compress
the ODF to spherical harmonics coefficients.
parallel : bool, optional
Whether to use parallelization in peak-finding during the
calibration procedure.
nbr_processes : int, optional
If `parallel` is True, the number of subprocesses to use
(default multiprocessing.cpu_count()).
out_dir : string, optional
Output directory. (default current directory)
out_pam : string, optional
Name of the peaks volume to be saved.
out_shm : string, optional
Name of the spherical harmonics volume to be saved.
out_peaks_dir : string, optional
Name of the peaks directions volume to be saved.
out_peaks_values : string, optional
Name of the peaks values volume to be saved.
out_peaks_indices : string, optional
Name of the peaks indices volume to be saved.
out_gfa : string, optional
Name of the generalized FA volume to be saved.
References
----------
.. [1] Tournier, J.D., et al. NeuroImage 2007. Robust determination of
the fibre orientation distribution in diffusion MRI: Non-negativity
constrained super-resolved spherical deconvolution.
"""
io_it = self.get_io_iterator()
for (dwi, bval, bvec, maskfile, opam, oshm, opeaks_dir, opeaks_values,
opeaks_indices, ogfa) in io_it:
logging.info('Loading {0}'.format(dwi))
data, affine = load_nifti(dwi)
bvals, bvecs = read_bvals_bvecs(bval, bvec)
print(b0_threshold, bvals.min())
if b0_threshold < bvals.min():
warn(
"b0_threshold (value: {0}) is too low, increase your "
"b0_threshold. It should be higher than the first b0 value "
"({1}).".format(b0_threshold, bvals.min()))
gtab = gradient_table(bvals,
bvecs,
b0_threshold=b0_threshold,
atol=bvecs_tol)
mask_vol = load_nifti_data(maskfile).astype(bool)
n_params = ((sh_order + 1) * (sh_order + 2)) / 2
if data.shape[-1] < n_params:
raise ValueError('You need at least {0} unique DWI volumes to '
'compute fiber odfs. You currently have: {1}'
' DWI volumes.'.format(
n_params, data.shape[-1]))
if frf is None:
logging.info('Computing response function')
if roi_center is not None:
logging.info(
'Response ROI center:\n{0}'.format(roi_center))
logging.info('Response ROI radii:\n{0}'.format(roi_radii))
response, ratio = auto_response_ssst(gtab,
data,
roi_center=roi_center,
roi_radii=roi_radii,
fa_thr=fa_thr)
response = list(response)
else:
logging.info('Using response function')
if isinstance(frf, str):
l01 = np.array(literal_eval(frf), dtype=np.float64)
else:
l01 = np.array(frf, dtype=np.float64)
l01 *= 10**-4
response = np.array([l01[0], l01[1], l01[1]])
ratio = l01[1] / l01[0]
response = (response, ratio)
logging.info("Eigenvalues for the frf of the input"
" data are :{0}".format(response[0]))
logging.info(
'Ratio for smallest to largest eigen value is {0}'.format(
ratio))
peaks_sphere = default_sphere
logging.info('CSD computation started.')
csd_model = ConstrainedSphericalDeconvModel(gtab,
response,
sh_order=sh_order)
peaks_csd = peaks_from_model(model=csd_model,
data=data,
sphere=peaks_sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
mask=mask_vol,
return_sh=True,
sh_order=sh_order,
normalize_peaks=True,
parallel=parallel,
nbr_processes=nbr_processes)
peaks_csd.affine = affine
save_peaks(opam, peaks_csd)
logging.info('CSD computation completed.')
if extract_pam_values:
peaks_to_niftis(peaks_csd,
oshm,
opeaks_dir,
opeaks_values,
opeaks_indices,
ogfa,
reshape_dirs=True)
dname_ = os.path.dirname(opam)
if dname_ == '':
logging.info('Pam5 file saved in current directory')
else:
logging.info('Pam5 file saved in {0}'.format(dname_))
return io_it
def run(context):
####################################################
# Get the path to input files and other parameter #
####################################################
analysis_data = context.fetch_analysis_data()
settings = analysis_data['settings']
postprocessing = settings['postprocessing']
dataset = settings['dataset']
if dataset == "HCPL":
dwi_file_handle = context.get_files('input', modality='HARDI')[0]
dwi_file_path = dwi_file_handle.download('/root/')
bvalues_file_handle = context.get_files(
'input', reg_expression='.*prep.bvalues.hcpl.txt')[0]
bvalues_file_path = bvalues_file_handle.download('/root/')
bvecs_file_handle = context.get_files(
'input', reg_expression='.*prep.gradients.hcpl.txt')[0]
bvecs_file_path = bvecs_file_handle.download('/root/')
elif dataset == "DSI":
dwi_file_handle = context.get_files('input', modality='DSI')[0]
dwi_file_path = dwi_file_handle.download('/root/')
bvalues_file_handle = context.get_files(
'input', reg_expression='.*prep.bvalues.txt')[0]
bvalues_file_path = bvalues_file_handle.download('/root/')
bvecs_file_handle = context.get_files(
'input', reg_expression='.*prep.gradients.txt')[0]
bvecs_file_path = bvecs_file_handle.download('/root/')
else:
context.set_progress(message='Wrong dataset parameter')
inject_file_handle = context.get_files(
'input', reg_expression='.*prep.inject.nii.gz')[0]
inject_file_path = inject_file_handle.download('/root/')
VUMC_ROIs_file_handle = context.get_files(
'input', reg_expression='.*VUMC_ROIs.nii.gz')[0]
VUMC_ROIs_file_path = VUMC_ROIs_file_handle.download('/root/')
###############################
# _____ _____ _______ __ #
# | __ \_ _| __ \ \ / / #
# | | | || | | |__) \ \_/ / #
# | | | || | | ___/ \ / #
# | |__| || |_| | | | #
# |_____/_____|_| |_| #
# #
###############################
########################################################################################
# _______ _ __ __ _______ _ __ #
# |__ __| | | | \/ | |__ __| | | / _| #
# | |_ __ __ _ ___| | ___ _| \ / | ___| |_ __ __ _ ___| | _| |_ __ _ ___ ___ #
# | | '__/ _` |/ __| |/ / | | | |\/| |/ __| | '__/ _` |/ __| |/ / _/ _` |/ __/ _ \ #
# | | | | (_| | (__| <| |_| | | | | (__| | | | (_| | (__| <| || (_| | (_| __/ #
# |_|_| \__,_|\___|_|\_\\__, |_| |_|\___|_|_| \__,_|\___|_|\_\_| \__,_|\___\___| #
# __/ | #
# |___/ #
# #
# #
# IronTract Team #
########################################################################################
#################
# Load the data #
#################
dwi_img = nib.load(dwi_file_path)
bvals, bvecs = read_bvals_bvecs(bvalues_file_path,
bvecs_file_path)
gtab = gradient_table(bvals, bvecs)
############################################
# Extract the brain mask from the b0 image #
############################################
_, brain_mask = median_otsu(dwi_img.get_data()[:, :, :, 0],
median_radius=2, numpass=1)
##################################################################
# Fit the tensor model and compute the fractional anisotropy map #
##################################################################
context.set_progress(message='Processing voxel-wise DTI metrics.')
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(dwi_img.get_data(), mask=brain_mask)
FA = fractional_anisotropy(tenfit.evals)
stopping_criterion = ThresholdStoppingCriterion(FA, 0.2)
sphere = get_sphere("repulsion724")
seed_mask_img = nib.load(inject_file_path)
affine = seed_mask_img.affine
seeds = utils.random_seeds_from_mask(seed_mask_img.get_data(),
affine,
seed_count_per_voxel=True,
seeds_count=5000)
if dataset == "HCPL":
################################################
# Compute Fiber Orientation Distribution (CSD) #
################################################
context.set_progress(message='Processing voxel-wise FOD estimation.')
response, _ = auto_response_ssst(gtab, dwi_img.get_data(),
roi_radii=10, fa_thr=0.7)
csd_model = ConstrainedSphericalDeconvModel(gtab, response, sh_order=8)
csd_fit = csd_model.fit(dwi_img.get_data(), mask=brain_mask)
shm = csd_fit.shm_coeff
prob_dg = ProbabilisticDirectionGetter.from_shcoeff(shm,
max_angle=20.,
sphere=sphere,
pmf_threshold=0.1)
elif dataset == "DSI":
context.set_progress(message='Processing voxel-wise DSI estimation.')
dsmodel = DiffusionSpectrumModel(gtab)
dsfit = dsmodel.fit(dwi_img.get_data())
ODFs = dsfit.odf(sphere)
prob_dg = ProbabilisticDirectionGetter.from_pmf(ODFs,
max_angle=20.,
sphere=sphere,
pmf_threshold=0.01)
###########################################
# Compute DIPY Probabilistic Tractography #
###########################################
context.set_progress(message='Processing tractography.')
streamline_generator = LocalTracking(prob_dg, stopping_criterion, seeds,
affine, step_size=.2, max_cross=1)
streamlines = Streamlines(streamline_generator)
# sft = StatefulTractogram(streamlines, seed_mask_img, Space.RASMM)
# streamlines_file_path = "/root/streamlines.trk"
# save_trk(sft, streamlines_file_path)
###########################################################################
# Compute 3D volumes for the IronTract Challenge. For 'EPFL', we only #
# keep streamlines with length > 1mm. We compute the visitation count #
# image and apply a small gaussian smoothing. The gaussian smoothing #
# is especially usefull to increase voxel coverage of deterministic #
# algorithms. The log of the smoothed visitation count map is then #
# iteratively thresholded producing 200 volumes/operation points. #
# For VUMC, additional streamline filtering is done using anatomical #
# priors (keeping only streamlines that intersect with at least one ROI). #
###########################################################################
if postprocessing in ["EPFL", "ALL"]:
context.set_progress(message='Processing density map (EPFL)')
volume_folder = "/root/vol_epfl"
output_epfl_zip_file_path = "/root/TrackyMcTrackface_EPFL_example.zip"
os.mkdir(volume_folder)
lengths = length(streamlines)
streamlines = streamlines[lengths > 1]
density = utils.density_map(streamlines, affine, seed_mask_img.shape)
density = scipy.ndimage.gaussian_filter(density.astype("float32"), 0.5)
log_density = np.log10(density + 1)
max_density = np.max(log_density)
for i, t in enumerate(np.arange(0, max_density, max_density / 200)):
nbr = str(i)
nbr = nbr.zfill(3)
mask = log_density >= t
vol_filename = os.path.join(volume_folder,
"vol" + nbr + "_t" + str(t) + ".nii.gz")
nib.Nifti1Image(mask.astype("int32"), affine,
seed_mask_img.header).to_filename(vol_filename)
shutil.make_archive(output_epfl_zip_file_path[:-4], 'zip', volume_folder)
if postprocessing in ["VUMC", "ALL"]:
context.set_progress(message='Processing density map (VUMC)')
ROIs_img = nib.load(VUMC_ROIs_file_path)
volume_folder = "/root/vol_vumc"
output_vumc_zip_file_path = "/root/TrackyMcTrackface_VUMC_example.zip"
os.mkdir(volume_folder)
lengths = length(streamlines)
streamlines = streamlines[lengths > 1]
rois = ROIs_img.get_fdata().astype(int)
_, grouping = utils.connectivity_matrix(streamlines, affine, rois,
inclusive=True,
return_mapping=True,
mapping_as_streamlines=False)
streamlines = streamlines[grouping[(0, 1)]]
density = utils.density_map(streamlines, affine, seed_mask_img.shape)
density = scipy.ndimage.gaussian_filter(density.astype("float32"), 0.5)
log_density = np.log10(density + 1)
max_density = np.max(log_density)
for i, t in enumerate(np.arange(0, max_density, max_density / 200)):
nbr = str(i)
nbr = nbr.zfill(3)
mask = log_density >= t
vol_filename = os.path.join(volume_folder,
"vol" + nbr + "_t" + str(t) + ".nii.gz")
nib.Nifti1Image(mask.astype("int32"), affine,
seed_mask_img.header).to_filename(vol_filename)
shutil.make_archive(output_vumc_zip_file_path[:-4], 'zip', volume_folder)
###################
# Upload the data #
###################
context.set_progress(message='Uploading results...')
#context.upload_file(fa_file_path, 'fa.nii.gz')
# context.upload_file(fod_file_path, 'fod.nii.gz')
# context.upload_file(streamlines_file_path, 'streamlines.trk')
if postprocessing in ["EPFL", "ALL"]:
context.upload_file(output_epfl_zip_file_path,
'TrackyMcTrackface_' + dataset +'_EPFL.zip')
if postprocessing in ["VUMC", "ALL"]:
context.upload_file(output_vumc_zip_file_path,
'TrackyMcTrackface_' + dataset +'_VUMC.zip')
def test_csdeconv():
SNR = 100
S0 = 1
_, fbvals, fbvecs = get_fnames('small_64D')
bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs)
gtab = gradient_table(bvals, bvecs, b0_threshold=0)
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (60, 0)]
S, sticks = multi_tensor(gtab, mevals, S0, angles=angles,
fractions=[50, 50], snr=SNR)
sphere = get_sphere('symmetric362')
odf_gt = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
response = (np.array([0.0015, 0.0003, 0.0003]), S0)
csd = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit = csd.fit(S)
assert_equal(csd_fit.shm_coeff[0] > 0, True)
fodf = csd_fit.odf(sphere)
directions, _, _ = peak_directions(odf_gt, sphere)
directions2, _, _ = peak_directions(fodf, sphere)
ang_sim = angular_similarity(directions, directions2)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions.shape[0], 2)
assert_equal(directions2.shape[0], 2)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", category=UserWarning)
_ = ConstrainedSphericalDeconvModel(gtab, response, sh_order=10)
assert_greater(len([lw for lw in w if issubclass(lw.category,
UserWarning)]), 0)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", category=UserWarning)
ConstrainedSphericalDeconvModel(gtab, response, sh_order=8)
assert_equal(len([lw for lw in w if issubclass(lw.category,
UserWarning)]), 0)
mevecs = []
for s in sticks:
mevecs += [all_tensor_evecs(s).T]
S2 = single_tensor(gtab, 100, mevals[0], mevecs[0], snr=None)
big_S = np.zeros((10, 10, 10, len(S2)))
big_S[:] = S2
aresponse, aratio = auto_response_ssst(gtab, big_S, roi_center=(5, 5, 4),
roi_radii=3, fa_thr=0.5)
assert_array_almost_equal(aresponse[0], response[0])
assert_almost_equal(aresponse[1], 100)
assert_almost_equal(aratio, response[0][1] / response[0][0])
auto_response_ssst(gtab, big_S, roi_radii=3, fa_thr=0.5)
assert_array_almost_equal(aresponse[0], response[0])
def get_csd_streamlines(data_container,
random_seeds=False,
seeds_count=30000,
seeds_per_voxel=False,
step_width=1.0,
roi_r=10,
auto_response_fa_threshold=0.7,
fa_threshold=0.15,
relative_peak_threshold=0.5,
min_separation_angle=25):
"""
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
roi_r
The radii of the cuboid roi for the automatic estimation of single-shell single-tissue response function using FA.
auto_response_fa_threshold
The FA threshold for the automatic estimation of single-shell single-tissue response function using FA.
fa_threshold
The FA threshold to use to stop tracking
relative_peak_threshold
The relative peak threshold to use to get peaks from the CSDModel
min_separation_angle
The minimal separation angle of peaks
Returns
-------
Streamlines
A list of Streamlines
"""
seeds = _get_seeds(data_container, random_seeds, seeds_count,
seeds_per_voxel)
response, _ = auto_response_ssst(data_container.gtab,
data_container.dwi,
roi_radii=roi_r,
fa_thr=auto_response_fa_threshold)
csd_model = ConstrainedSphericalDeconvModel(data_container.gtab, response)
direction_getter = peaks_from_model(
model=csd_model,
data=data_container.dwi,
sphere=get_sphere('symmetric724'),
mask=data_container.binary_mask,
relative_peak_threshold=relative_peak_threshold,
min_separation_angle=min_separation_angle,
parallel=False)
dti_fit = dti.TensorModel(data_container.gtab, fit_method='LS').fit(
data_container.dwi, mask=data_container.binary_mask)
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 sfm_tracking(name=None,
data_path=None,
output_path='.',
Threshold=.20,
data_list=None,
return_streamlines=False,
save_track=True,
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']
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)
from dipy.reconst.csdeconv import auto_response_ssst
from dipy.reconst.shm import CsaOdfModel
from dipy.data import default_sphere
from dipy.direction import peaks_from_model
response, ratio = auto_response_ssst(gtab, data, roi_radii=10, fa_thr=0.7)
sphere = get_sphere()
sf_model = sfm.SparseFascicleModel(gtab,
sphere=sphere,
l1_ratio=0.5,
alpha=0.001,
response=response[0])
pnm = peaks_from_model(sf_model,
data,
sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
mask=white_matter,
parallel=True)
stopping_criterion = ThresholdStoppingCriterion(pnm.gfa, Threshold)
#seeds = utils.seeds_from_mask(white_matter, affine, density=1)
print('begin tracking, time:', time.time() - time0)
streamline_generator = LocalTracking(pnm,
stopping_criterion,
seeds,
affine,
step_size=.5)
streamlines = Streamlines(streamline_generator)
print('begin saving, time:', time.time() - time0)
from dipy.io.stateful_tractogram import Space, StatefulTractogram
from dipy.io.streamline import save_trk
if save_track:
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()
output = output_path + '/tractogram_sfm_' + name + '.trk'
save_trk(sft, output, streamlines)
if return_streamlines:
return streamlines
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)
