def tractography(brain, affine_brain, labels, diff, affine_diff, gtab, img):
# Tractography reconstruction based on EuDX determinist algorithm
labels = segmentation(brain, affine_brain, diff, affine_diff)
white_matter = (labels == 3)
csa_model = CsaOdfModel(gtab, sh_order=2)
csa_peaks = peaks_from_model(csa_model,
diff,
default_sphere,
relative_peak_threshold=.8,
min_separation_angle=45,
mask=white_matter)
stopping_criterion = dipy.tracking.stopping_criterion.ThresholdStoppingCriterion(
csa_peaks.gfa, .25)
seeds = utils.seeds_from_mask(white_matter, affine_diff, density=1)
streamlines_generator = LocalTracking(csa_peaks,
stopping_criterion,
seeds,
affine=affine_diff,
step_size=.5)
streamlines = Streamlines(streamlines_generator)
if has_fury:
color = colormap.line_colors(streamlines)
streamlines_actor = actor.line(streamlines, color)
r = window.Renderer()
r.add(streamlines_actor)
window.record(r, out_path='tractogram.png', size=(800, 800))
window.show(r)
sft = StatefulTractogram(streamlines, img, Space.RASMM)
save_trk(sft, "tractogram.trk", streamlines)
return streamlines
def reconstruct_shore_fodf(bval_path,
bvec_path,
data_path,
data_var,
zeta_val=700.0):
bval_path = os.path.normpath(bval_path)
bvec_path = os.path.normpath(bvec_path)
data_path = os.path.normpath(data_path)
bvals, bvecs = read_bvals_bvecs(bval_path, bvec_path)
gtab = gradient_table(bvals, bvecs)
data = loadmat(data_path)
data = data[data_var]
shore_model = ShoreModel(gtab, radial_order=6, zeta=zeta_val)
shore_peaks = peaks_from_model(shore_model,
data,
default_sphere,
relative_peak_threshold=.1,
min_separation_angle=45)
print('Debug Here')
return shore_peaks.shm_coeff
def anisotropic_power_map(data,
mask,
gtab,
power=2,
nbr_processes=None,
verbose=False):
# compute response
if verbose:
print(' - computing response')
response, _ = auto_response(gtab, data, roi_radius=10, fa_thr=0.7)
# compute spherical harmonics from spherical deconvoluten
if verbose:
print(' - preparing spherical deconvolution model')
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
sphere = get_sphere('symmetric724')
if verbose:
print(' - computing spherical harmonics from spherical deconvolution')
csd_peaks = peaks_from_model(model=csd_model,
data=data,
mask=mask,
sphere=sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=True,
nbr_processes=nbr_processes)
# compute anisotropic power map
if verbose:
print(' - computing anisotropic power map')
return anisotropic_power(csd_peaks.shm_coeff,
norm_factor=1e-05,
power=power,
non_negative=True)
def reconst_gqi_fodf_return_sh_coeffs(bval_path,bvec_path,data_path,data_var):
bval_path = os.path.normpath(bval_path)
bvec_path = os.path.normpath(bvec_path)
data_path = os.path.normpath(data_path)
bvals, bvecs = read_bvals_bvecs(bval_path, bvec_path)
gtab = gradient_table(bvals, bvecs)
data = loadmat(data_path)
data = data[data_var]
gqmodel = GeneralizedQSamplingModel(gtab, sampling_length=1.2)
#gqfit = gqmodel.fit(dataslice, mask=mask)
sphere = get_sphere('symmetric724')
#ODF = gqfit.odf(sphere)
#odf = gqmodel.fit(data).odf(sphere)
gqpeaks = peaks_from_model(model=gqmodel,
data=data,
sphere=sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
return_odf=True,
normalize_peaks=True)
print('Debug here')
fodfs_shm = gqpeaks.shm_coeff
return fodfs_shm
def loc_track(path, default_sphere):
data, affine = load_nifti(path + 'data.nii.gz')
data[np.isnan(data) == 1] = 0
mask, affine = load_nifti(path + 'nodif_brain_mask.nii.gz')
mask[np.isnan(mask) == 1] = 0
mask[:, :, 2:] = 0
gtab = gradient_table(path + 'bvals', path + 'bvecs')
if os.path.exists(path + 'grad_dev.nii.gz'):
gd, affine_g = load_nifti(path + 'grad_dev.nii.gz')
else:
gd = None
csa_model = CsaOdfModel(gtab, smooth=1, sh_order=12)
peaks = peaks_from_model(csa_model,
data,
default_sphere,
relative_peak_threshold=0.99,
min_separation_angle=25,
mask=mask)
seedss = copy.deepcopy(mask)
seeds = utils.seeds_from_mask(seedss, affine, [2, 2, 2])
stopping_criterion = ThresholdStoppingCriterion(mask, 0)
tracked = tracking(peaks,
stopping_criterion,
seeds,
affine,
graddev=gd,
sphere=default_sphere)
tracked.localTracking()
return tracked
def get_csd_peaks(DWI_image_noisy, sch_mat_b0, num_fasc):
'''Get peak orientations using constrained spherical deconvolution (CSD)
'''
S0mean = np.mean(DW_image_noisy[bvals == 0])
# mean on dictionary is array([0.00215257, 0.00022733, 0.00022463])
# median is array([0.00213064, 0.00017771, 0.00017323])
sing_fasc_response = (np.array([0.0020, 0.00024, 0.00024]), S0mean)
csd_mod = ConstrainedSphericalDeconvModel(gtab,
sing_fasc_response,
sh_order=sh_max_order)
# we cheat a bit by using groundtruth information on the angle to set the
# minimum separation angle:
min_sep_angle = 0.9 * crossangle_min * 180 / np.pi # in degrees !
peaks = peaks_from_model(model=csd_mod,
data=DW_image_noisy,
sphere=odf_sphere,
relative_peak_threshold=0.9 * nu_min / nu_max,
min_separation_angle=min_sep_angle,
sh_order=sh_max_order,
npeaks=num_fasc)
# direction of peaks, shape (npeaks, 3). pdirs[0] is main peak,
# peak_dirs[1] secondary peak. peak_dirs[1] could be [0, 0, 0] if no peak
# was detected.
return peaks.peak_dirs
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 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 loc_track(path,
default_sphere,
coords=None,
npath=None,
UParams=None,
ang_thr=None):
data, affine = load_nifti(path + 'data.nii.gz')
data[np.isnan(data) == 1] = 0
mask, affine = load_nifti(path + 'nodif_brain_mask.nii.gz')
mask[np.isnan(mask) == 1] = 0
mask[:, :, 1:] = 0
stopper = copy.deepcopy(mask)
#stopper[:, :, :] = 1
gtab = gradient_table(path + 'bvals', path + 'bvecs')
csa_model = CsaOdfModel(gtab, smooth=1, sh_order=12)
peaks = peaks_from_model(csa_model,
data,
default_sphere,
relative_peak_threshold=0.99,
min_separation_angle=25,
mask=mask)
if ang_thr is not None:
peaks.ang_thr = ang_thr
if os.path.exists(path + 'grad_dev.nii.gz'):
gd, affine_g = load_nifti(path + 'grad_dev.nii.gz')
nmask, naffine = load_nifti(npath + 'nodif_brain_mask.nii.gz')
nmask[np.isnan(nmask) == 1] = 0
nmask[:, :, 1:] = 0
seedss = copy.deepcopy(nmask)
seedss = utils.seeds_from_mask(seedss, naffine, [2, 2, 2])
useed = []
UParams = coords.Uparams
for seed in seedss:
us = coords.rFUa_xyz(seed[0], seed[1], seed[2])
vs = coords.rFVa_xyz(seed[0], seed[1], seed[2])
ws = coords.rFWa_xyz(seed[0], seed[1], seed[2])
condition = us >= UParams.min_a and us <= UParams.max_a and vs >= UParams.min_b and vs <= UParams.max_b \
and ws >= UParams.min_c and ws <= UParams.max_c
if condition == True:
useed.append([float(us), float(vs), float(ws)])
seeds = np.asarray(useed)
else:
gd = None
seedss = copy.deepcopy(mask)
seeds = utils.seeds_from_mask(seedss, affine, [2, 2, 2])
stopping_criterion = BinaryStoppingCriterion(stopper)
tracked = tracking(peaks,
stopping_criterion,
seeds,
affine,
graddev=gd,
sphere=default_sphere)
tracked.localTracking()
return tracked
def generate_peaks(self):
csa_peaks = peaks_from_model(
self.csa_model,
self.data,
default_sphere,
relative_peak_threshold=self.relative_peak_threshold,
min_separation_angle=self.min_angle,
mask=self.white_matter,
)
return csa_peaks
def tractography(img, gtab, mask, dwi_dir, do_viz=True):
data = img.get_data()
# dirty imputation
data[np.isnan(data)] = 0
# Diffusion model
csd_model = ConstrainedSphericalDeconvModel(gtab, response=None)
sphere = get_sphere('symmetric724')
csd_peaks = peaks_from_model(model=csd_model,
data=data,
sphere=sphere,
mask=mask,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=False)
# FA values to stop the tractography
tensor_model = TensorModel(gtab, fit_method='WLS')
tensor_fit = tensor_model.fit(data, mask)
fa = fractional_anisotropy(tensor_fit.evals)
stopping_values = np.zeros(csd_peaks.peak_values.shape)
stopping_values[:] = fa[..., None]
# tractography
streamline_generator = EuDX(stopping_values,
csd_peaks.peak_indices,
seeds=10**6,
odf_vertices=sphere.vertices,
a_low=0.1)
streamlines = [streamline for streamline in streamline_generator]
streamlines = filter_according_to_length(streamlines)
np.savez(os.path.join(dwi_dir, 'streamlines.npz'), streamlines)
# write the result as images
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = img.header.get_zooms()[:3]
hdr['voxel_order'] = 'LAS'
hdr['dim'] = fa.shape[:3]
csd_streamlines_trk = ((sl, None, None) for sl in streamlines)
csd_sl_fname = os.path.join(dwi_dir, 'csd_streamline.trk')
nib.trackvis.write(csd_sl_fname,
csd_streamlines_trk,
hdr,
points_space='voxel')
fa_image = os.path.join(dwi_dir, 'fa_map.nii.gz')
nib.save(nib.Nifti1Image(fa, img.affine), fa_image)
if 1:
visualization(os.path.join(dwi_dir, 'streamlines.npz'))
return streamlines
def to_generate_tractography(path_dwi_input, path_binary_mask, path_out,
path_bval, path_bvec):
from dipy.reconst.shm import CsaOdfModel
from dipy.data import default_sphere
from dipy.direction import peaks_from_model
from dipy.tracking.local import LocalTracking
from dipy.tracking import utils
from dipy.tracking.local import ThresholdTissueClassifier
print(' - Starting reconstruction of Tractography...')
if not os.path.exists(path_out + '_tractography_CsaOdf' + '.trk'):
dwi_img = nib.load(path_dwi_input)
dwi_data = dwi_img.get_data()
dwi_affine = dwi_img.affine
dwi_mask_data = nib.load(path_binary_mask).get_data()
g_tab = gradient_table(path_bval, path_bvec)
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(path_out + '_tractography_CsaOdf' + '.trk', streamlines,
dwi_affine, dwi_mask_data.shape)
print(' - Ending reconstruction of Tractography...')
def main():
start = time.time()
with open('config.json') as config_json:
config = json.load(config_json)
# Load the data
dmri_image = nib.load(config['data_file'])
dmri = dmri_image.get_data()
print('Dmri' + str(dmri.shape))
#aparc_im = nib.load(config['freesurfer'])
aparc_im = nib.load("volume.nii.gz")
aparc = aparc_im.get_data()
print('Aparc' + str(aparc.shape))
end = time.time()
print('Loaded Files: ' + str((end - start)))
# Create the white matter mask
start = time.time()
wm_regions = [
2, 41, 16, 17, 28, 60, 51, 53, 12, 52, 12, 52, 13, 18, 54, 50, 11, 251,
252, 253, 254, 255, 10, 49, 46, 7
]
wm_mask = np.zeros(aparc.shape)
for l in wm_regions:
wm_mask[aparc == l] = 1
print('Created white matter mask: ' + str(time.time() - start))
# Create the gradient table from the bvals and bvecs
start = time.time()
bvals, bvecs = read_bvals_bvecs(config['data_bval'], config['data_bvec'])
gtab = gradient_table(bvals, bvecs, b0_threshold=100)
end = time.time()
print('Created Gradient Table: ' + str((end - start)))
# Create the csa model and calculate peaks
start = time.time()
csa_model = CsaOdfModel(gtab, sh_order=6)
print('Created CSA Model: ' + str(time.time() - start))
csa_peaks = peaks_from_model(csa_model,
dmri,
default_sphere,
relative_peak_threshold=.8,
min_separation_angle=45,
mask=wm_mask)
print('Generated peaks: ' + str(time.time() - start))
save_peaks('peaks.pam5', csa_peaks)
def _init_shm_coefficient(self, sh_basis_type=None):
print("Initialising spherical harmonics")
self.dti_model = dti.TensorModel(self.dataset.gtab, fit_method='LS')
peaks = peaks_from_model(model=self.dti_model,
data=self.dataset.dwi,
sphere=self.sphere,
relative_peak_threshold=.2,
min_separation_angle=25,
mask=self.dataset.binary_mask,
npeaks=2)
self.sh_coefficient = peaks.shm_coeff
sh_order = order_from_ncoef(self.sh_coefficient.shape[-1])
try:
basis = sph_harm_lookup[sh_basis_type]
except KeyError:
raise ValueError("%s is not a known basis type." % sh_basis_type)
self._B, m, n = basis(sh_order, self.sphere.theta, self.sphere.phi)
model = GeneralizedQSamplingModel(gtab,
sampling_length=1.2)
if rec_model == 'SFM':
# Ariel please add here your best SFM calls and parameters
pass
if rec_model == 'MAPL':
# Brent look at Aman's PR and call here
pass
if rec_model == 'CSD':
# Elef add CSD version and add MTMSCSD when is ready.
pass
pam = peaks_from_model(model, data, sphere,
relative_peak_threshold=.8,
min_separation_angle=45,
mask=mask, parallel=parallel)
ten_model = TensorModel(gtab)
fa = ten_model.fit(data, mask).fa
save_nifti(ffa, fa, affine)
save_peaks(fpam5, pam, affine)
show_odfs_and_fa(fa, pam, mask, None, sphere, ftmp='odf.mmap',
basis_type=None)
logic = np.logical_or(logic1, logic2)
logic_ = np.logical_or(labels_==150, labels_==316)
wm = np.where(logic, True, np.where(logic_, True, False))
mask = wm
radius = 15
response, ratio, nvl = auto_response(gtab, data, roi_radius=radius, return_number_of_voxels=True)
print 'We use the roi_radius={},\nand the response is {},\nthe ratio is {},\nusing {} of voxels'.format(radius, response, ratio, nvl)
print 'fitting CSD model'
st2 = time.time()
sphere = get_sphere('symmetric724')
csd_model = ConstrainedSphericalDeconvModel(gtab, response, sh_order=8)
csd_peaks = peaks_from_model(csd_model, data, sphere = sphere, relative_peak_threshold=0.1, min_separation_angle=25,mask=mask, return_sh=True, parallel=True, normalize_peaks=True)
et2 = time.time() - st2
print 'fitting CSD model finished, running time is {}'.format(et2)
print 'seeding begins, using np.random.seed(123)'
st3 = time.time()
np.random.seed(123)
seeds = utils.random_seeds_from_mask(mask, 4)
for i in range(len(seeds)):
if seeds[i][0]>199.:
seeds[i][0]=398-seeds[i][0]
if seeds[i][1]>399.:
seeds[i][1]=798-seeds[i][1]
if seeds[i][2]>199.:
seeds[i][2]=398-seeds[i][2]
def eudx_advanced(self, dti_file, mask_file, gtab,
seed_num=100000, stop_val=0.1):
"""
Tracking with more complex tensors - experimental
Initializes the graph with nodes corresponding to the number of ROIs
**Positional Arguments:**
dti_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:**
seed_num:
- Number of seeds to use for fiber tracking
stop_val:
- Value to cutoff fiber track
"""
img = nb.load(dti_file)
data = img.get_data()
img = nb.load(mask_file)
mask = img.get_data()
mask = mask > 0 # to ensure binary mask
"""
For the constrained spherical deconvolution we need to estimate the
response function (see :ref:`example_reconst_csd`) and create a model.
"""
response, ratio = auto_response(gtab, data, roi_radius=10,
fa_thr=0.7)
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
"""
Next, we use ``peaks_from_model`` to fit the data and calculated
the fiber directions in all voxels.
"""
sphere = get_sphere('symmetric724')
csd_peaks = peaks_from_model(model=csd_model,
data=data,
sphere=sphere,
mask=mask,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=True)
"""
For the tracking part, we will use ``csd_model`` fiber directions
but stop tracking where fractional anisotropy (FA) is low (< 0.1).
To derive the FA, used as a stopping criterion, we need to fit a
tensor model first. Here, we use weighted least squares (WLS).
"""
print 'tensors...'
tensor_model = TensorModel(gtab, fit_method='WLS')
tensor_fit = tensor_model.fit(data, mask)
FA = fractional_anisotropy(tensor_fit.evals)
"""
In order for the stopping values to be used with our tracking
algorithm we need to have the same dimensions as the
``csd_peaks.peak_values``. For this reason, we can assign the
same FA value to every peak direction in the same voxel in
the following way.
"""
stopping_values = np.zeros(csd_peaks.peak_values.shape)
stopping_values[:] = FA[..., None]
streamline_generator = EuDX(stopping_values,
csd_peaks.peak_indices,
seeds=seed_num,
odf_vertices=sphere.vertices,
a_low=stop_val)
streamlines = [streamline for streamline in streamline_generator]
return streamlines
# Compute odfs in Brain Mask
t2 = time()
radius = 20
response, ratio, nvl = auto_response(gtab,
data,
roi_radius=radius,
return_number_of_voxels=True)
print(
'We use the roi_radius={},\nand the response is {},\nthe ratio is {},\nusing {} of voxels'
.format(radius, response, ratio, nvl))
csd_model = ConstrainedSphericalDeconvModel(gtab, response, sh_order=6)
csd_peaks = peaks_from_model(csd_model,
data,
sphere=peaks.default_sphere,
relative_peak_threshold=0.5,
min_separation_angle=60,
mask=mask,
return_odf=True,
return_sh=False,
parallel=True,
nbr_processes=multiprocessing.cpu_count())
duration2 = time() - t2
print(runno + ' CSD duration %.3f' % (duration2, ))
#mask and classifier
seeds = utils.seeds_from_mask(mask, density=1, affine=np.eye(4))
classifier = BinaryTissueClassifier(bm)
#local tracking
streamlines_generator = LocalTracking(csd_peaks,
classifier,
seeds,
affine=np.eye(4),
"""
ODF = gqfit.odf(sphere)
print('ODF.shape (%d, %d, %d)' % ODF.shape)
"""
ODF.shape ``(96, 96, 724)``
Using ``peaks_from_model`` we can find the main peaks of the ODFs and other
properties.
"""
gqpeaks = peaks_from_model(model=gqmodel,
data=dataslice,
sphere=sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
mask=mask,
return_odf=False,
normalize_peaks=True)
gqpeak_values = gqpeaks.peak_values
"""
``gqpeak_indices`` show which sphere points have the maximum values.
"""
gqpeak_indices = gqpeaks.peak_indices
"""
It is also possible to calculate GFA.
"""
GFA = gqpeaks.gfa
parallel. If ``num_processes`` is None it will figure out automatically the
number of CPUs available in your system. Alternatively, you can set
``num_processes`` manually. Here, we show an example where we compare the
duration of execution with or without parallelism.
"""
import time
from dipy.direction import peaks_from_model
start_time = time.time()
csd_peaks_parallel = peaks_from_model(model=csd_model,
data=data,
sphere=default_sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
mask=mask,
return_sh=True,
return_odf=False,
normalize_peaks=True,
npeaks=5,
parallel=True,
num_processes=None)
time_parallel = time.time() - start_time
print("peaks_from_model using " + str(multiprocessing.cpu_count()) +
" process ran in :" + str(time_parallel) + " seconds")
"""
``peaks_from_model`` using 8 processes ran in 114.425682068 seconds
"""
start_time = time.time()
csd_peaks = peaks_from_model(model=csd_model,
"""
The data is first fitted to Constant Solid Angle (CDA) ODF Model. CSA is a
good choice to estimate general fractional anisotropy (GFA), which the tissue
classifier can use to restrict fiber tracking to those areas where the ODF
shows significant restricted diffusion, thus creating a region-of-interest in
which the computations are done.
"""
# Perform CSA
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=6)
csa_peaks = peaks_from_model(csa_model, data, default_sphere,
relative_peak_threshold=.6,
min_separation_angle=45,
mask=selectionmask)
# Tissue classifier
from dipy.tracking.local import ThresholdTissueClassifier
classifier = ThresholdTissueClassifier(csa_peaks.gfa, 0.25)
"""
In order to perform probabilistic fiber tracking we first fit the data to the
Constrained Spherical Deconvolution (CSD) model in DIPY. 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), used in the fiber tracking.
"""
"""
The data is first fitted to Constant Solid Angle (CDA) ODF Model. CSA is a
good choice to estimate general fractional anisotropy (GFA), which the tissue
classifier can use to restrict fiber tracking to those areas where the ODF
shows significant restricted diffusion, thus creating a region-of-interest in
which the computations are done.
"""
# Perform CSA
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=6)
csa_peaks = peaks_from_model(csa_model, data, default_sphere,
relative_peak_threshold=.6,
min_separation_angle=45,
mask=selectionmask)
# Tissue classifier
from dipy.tracking.local import ThresholdTissueClassifier
classifier = ThresholdTissueClassifier(csa_peaks.gfa, 0.25)
"""
In order to perform probabilistic fiber tracking we first fit the data to the
Constrained Spherical Deconvolution (CSD) model in DIPY. 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), used in the fiber tracking.
"""
response, ratio = auto_response(gtab, data, roi_radius=10, fa_thr=0.7)
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
"""
Next, we use ``peaks_from_model`` to fit the data and calculated the fiber
directions in all voxels.
"""
sphere = get_sphere('symmetric724')
csd_peaks = peaks_from_model(model=csd_model,
data=data,
sphere=sphere,
mask=mask,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=True)
"""
For the tracking part, we will use the fiber directions from the ``csd_model``
but stop tracking in areas where fractional anisotropy is low (< 0.1).
To derive the FA, used here as a stopping criterion, we would need to fit a
tensor model first. Here, we fit the tensor using weighted least squares (WLS).
"""
tensor_model = TensorModel(gtab, fit_method='WLS')
tensor_fit = tensor_model.fit(data, mask)
fa = tensor_fit.fa
ODF = gqfit.odf(sphere)
print('ODF.shape (%d, %d, %d)' % ODF.shape)
"""
ODF.shape ``(96, 96, 724)``
Using peaks_from_model we can find the main peaks of the ODFs and other
properties.
"""
gqpeaks = peaks_from_model(model=gqmodel,
data=dataslice,
sphere=sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
mask=mask,
return_odf=False,
normalize_peaks=True)
gqpeak_values = gqpeaks.peak_values
"""
gqpeak_indices show which sphere points have the maximum values.
"""
gqpeak_indices = gqpeaks.peak_indices
"""
It is also possible to calculate GFA.
"""
fa = tensor_fit.fa
duration1 = time() - t1
#wenlin make this change-adress name to each animal
# print('DTI duration %.3f' % (duration1,))
print(runno + ' DTI duration %.3f' % (duration1, ))
# Compute odfs in Brain Mask
t2 = time()
csa_model = CsaOdfModel(gtab, 6)
csa_peaks = peaks_from_model(
model=csa_model,
data=data,
sphere=peaks.default_sphere, # issue with complete sphere
mask=mask,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=True,
nbr_processes=4)
duration2 = time() - t2
print(duration2)\
#wenlin make this change-adress name to each animal
# print('CSA duration %.3f' % (duration2,))
print(runno + ' CSA duration %.3f' % (duration2, ))
t3 = time()
# from dipy.tracking.local import ThresholdTissueClassifier
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
prob_dg = ProbabilisticDirectionGetter.from_shcoeff(csd_fit.shm_coeff,
max_angle=30.,
sphere=default_sphere)
streamlines = LocalTracking(prob_dg, classifier, seeds, affine, step_size=.5)
save_trk("probabilistic_shm_coeff.trk", streamlines, affine, labels.shape)
"""
Not all model fits have the ``shm_coeff`` attribute because not all models use
this basis to represent the data internally. However we can fit the ODF of any
model to the spherical harmonic basis using the ``peaks_from_model`` function.
"""
from dipy.direction import peaks_from_model
peaks = peaks_from_model(csd_model, data, default_sphere, .5, 25,
mask=white_matter, return_sh=True, parallel=True)
fod_coeff = peaks.shm_coeff
prob_dg = ProbabilisticDirectionGetter.from_shcoeff(fod_coeff, max_angle=30.,
sphere=default_sphere)
streamlines = LocalTracking(prob_dg, classifier, seeds, affine, step_size=.5)
save_trk("probabilistic_peaks_from_model.trk", streamlines, affine,
labels.shape)
def main():
start = time.time()
with open('config.json') as config_json:
config = json.load(config_json)
# Load the data
dmri_image = nib.load(config['data_file'])
dmri = dmri_image.get_data()
affine = dmri_image.affine
#aparc_im = nib.load(config['freesurfer'])
aparc_im = nib.load('volume.nii.gz')
aparc = aparc_im.get_data()
end = time.time()
print('Loaded Files: ' + str((end - start)))
print(dmri.shape)
print(aparc.shape)
# Create the white matter and callosal masks
start = time.time()
wm_regions = [
2, 41, 16, 17, 28, 60, 51, 53, 12, 52, 12, 52, 13, 18, 54, 50, 11, 251,
252, 253, 254, 255, 10, 49, 46, 7
]
wm_mask = np.zeros(aparc.shape)
for l in wm_regions:
wm_mask[aparc == l] = 1
#np.save('wm_mask',wm_mask)
#p = os.getcwd()+'wm.json'
#json.dump(wm_mask, codecs.open(p, 'w', encoding='utf-8'), separators=(',', ':'), sort_keys=True, indent=4)
#with open('wm_mask.txt', 'wb') as wm:
#np.savetxt('wm.txt', wm_mask, fmt='%5s')
#print(wm_mask)
# Create the gradient table from the bvals and bvecs
bvals, bvecs = read_bvals_bvecs(config['data_bval'], config['data_bvec'])
gtab = gradient_table(bvals, bvecs, b0_threshold=100)
end = time.time()
print('Created Gradient Table: ' + str((end - start)))
##The probabilistic model##
"""
# Use the Constant Solid Angle (CSA) to find the Orientation Dist. Function
# Helps orient the wm tracts
start = time.time()
csa_model = CsaOdfModel(gtab, sh_order=6)
csa_peaks = peaks_from_model(csa_model, dmri, default_sphere,
relative_peak_threshold=.8,
min_separation_angle=45,
mask=wm_mask)
print('Creating CSA Model: ' + str(time.time() - start))
"""
# Use the SHORE model to find Orientation Dist. Function
start = time.time()
shore_model = ShoreModel(gtab)
shore_peaks = peaks_from_model(shore_model,
dmri,
default_sphere,
relative_peak_threshold=.8,
min_separation_angle=45,
mask=wm_mask)
print('Creating Shore Model: ' + str(time.time() - start))
# Begins the seed in the wm tracts
seeds = utils.seeds_from_mask(wm_mask, density=[1, 1, 1], affine=affine)
print('Created White Matter seeds: ' + str(time.time() - start))
# Create a CSD model to measure Fiber Orientation Dist
print('Begin the probabilistic model')
response, ratio = auto_response(gtab, dmri, roi_radius=10, fa_thr=0.7)
csd_model = ConstrainedSphericalDeconvModel(gtab, response, sh_order=6)
csd_fit = csd_model.fit(data=dmri, mask=wm_mask)
print('Created the CSD model: ' + str(time.time() - start))
# Set the Direction Getter to randomly choose directions
prob_dg = ProbabilisticDirectionGetter.from_shcoeff(csd_fit.shm_coeff,
max_angle=30.,
sphere=default_sphere)
print('Created the Direction Getter: ' + str(time.time() - start))
# Restrict the white matter tracking
classifier = ThresholdTissueClassifier(shore_peaks.gfa, .25)
print('Created the Tissue Classifier: ' + str(time.time() - start))
# Create the probabilistic model
streamlines = LocalTracking(prob_dg,
tissue_classifier=classifier,
seeds=seeds,
step_size=.5,
max_cross=1,
affine=affine)
print('Created the probabilistic model: ' + str(time.time() - start))
# Compute streamlines and store as a list.
streamlines = list(streamlines)
print('Computed streamlines: ' + str(time.time() - start))
#from dipy.tracking.streamline import transform_streamlines
#streamlines = transform_streamlines(streamlines, np.linalg.inv(affine))
# Create a tractogram from the streamlines and save it
tractogram = Tractogram(streamlines, affine_to_rasmm=affine)
save(tractogram, 'track.tck')
end = time.time()
print("Created the tck file: " + str((end - start)))
1. The first thing we need to begin fiber tracking is a way of getting
directions from this diffusion data set. In order to do that, we can fit the
data to a Constant Solid Angle ODF Model. This model will estimate the
Orientation Distribution Function (ODF) at each voxel. The ODF is the
distribution of water diffusion as a function of direction. The peaks of an ODF
are good estimates for the orientation of tract segments at a point in the
image.
"""
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=6)
csa_peaks = peaks_from_model(csa_model, data, default_sphere,
relative_peak_threshold=.8,
min_separation_angle=45,
mask=white_matter)
"""
2. Next we need some way of restricting the fiber tracking to areas with good
directionality information. We've already created the white matter mask,
but we can go a step further and restrict fiber tracking to those areas where
the ODF shows significant restricted diffusion by thresholding on
the general fractional anisotropy (GFA).
"""
from dipy.tracking.local import ThresholdTissueClassifier
classifier = ThresholdTissueClassifier(csa_peaks.gfa, .25)
"""
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
def test_contour_from_roi():
# Render volume
renderer = window.renderer()
data = np.zeros((50, 50, 50))
data[20:30, 25, 25] = 1.
data[25, 20:30, 25] = 1.
affine = np.eye(4)
surface = actor.contour_from_roi(data, affine,
color=np.array([1, 0, 1]),
opacity=.5)
renderer.add(surface)
renderer.reset_camera()
renderer.reset_clipping_range()
# window.show(renderer)
# Test binarization
renderer2 = window.renderer()
data2 = np.zeros((50, 50, 50))
data2[20:30, 25, 25] = 1.
data2[35:40, 25, 25] = 1.
affine = np.eye(4)
surface2 = actor.contour_from_roi(data2, affine,
color=np.array([0, 1, 1]),
opacity=.5)
renderer2.add(surface2)
renderer2.reset_camera()
renderer2.reset_clipping_range()
# window.show(renderer2)
arr = window.snapshot(renderer, 'test_surface.png', offscreen=True)
arr2 = window.snapshot(renderer2, 'test_surface2.png', offscreen=True)
report = window.analyze_snapshot(arr, find_objects=True)
report2 = window.analyze_snapshot(arr2, find_objects=True)
npt.assert_equal(report.objects, 1)
npt.assert_equal(report2.objects, 2)
# test on real streamlines using tracking example
from dipy.data import read_stanford_labels
from dipy.reconst.shm import CsaOdfModel
from dipy.data import default_sphere
from dipy.direction import peaks_from_model
from dipy.tracking.local import ThresholdTissueClassifier
from dipy.tracking import utils
from dipy.tracking.local import LocalTracking
from dipy.viz.colormap import line_colors
hardi_img, gtab, labels_img = read_stanford_labels()
data = hardi_img.get_data()
labels = labels_img.get_data()
affine = hardi_img.get_affine()
white_matter = (labels == 1) | (labels == 2)
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)
classifier = ThresholdTissueClassifier(csa_peaks.gfa, .25)
seed_mask = labels == 2
seeds = utils.seeds_from_mask(seed_mask, density=[1, 1, 1], affine=affine)
# Initialization of LocalTracking.
# The computation happens in the next step.
streamlines = LocalTracking(csa_peaks, classifier, seeds, affine,
step_size=2)
# Compute streamlines and store as a list.
streamlines = list(streamlines)
# Prepare the display objects.
streamlines_actor = actor.line(streamlines, line_colors(streamlines))
seedroi_actor = actor.contour_from_roi(seed_mask, affine, [0, 1, 1], 0.5)
# Create the 3d display.
r = window.ren()
r2 = window.ren()
r.add(streamlines_actor)
arr3 = window.snapshot(r, 'test_surface3.png', offscreen=True)
report3 = window.analyze_snapshot(arr3, find_objects=True)
r2.add(streamlines_actor)
r2.add(seedroi_actor)
arr4 = window.snapshot(r2, 'test_surface4.png', offscreen=True)
report4 = window.analyze_snapshot(arr4, find_objects=True)
# assert that the seed ROI rendering is not far
# away from the streamlines (affine error)
npt.assert_equal(report3.objects, report4.objects)
"""
`Peaks_from_model` is used to calculate properties of the ODFs (Orientation
Distribution Function) and return for
example the peaks and their indices, or GFA which is similar to FA but for ODF
based models. This function mainly needs a reconstruction model, the data and a
sphere as input. The sphere is an object that represents the spherical discrete
grid where the ODF values will be evaluated.
"""
sphere = get_sphere('symmetric724')
csapeaks = peaks_from_model(model=csamodel,
data=maskdata,
sphere=sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
mask=mask,
return_odf=False,
normalize_peaks=True)
GFA = csapeaks.gfa
print('GFA.shape (%d, %d, %d)' % GFA.shape)
"""
GFA.shape ``(81, 106, 76)``
Apart from GFA, csapeaks also has the attributes peak_values, peak_indices and
ODF. peak_values shows the maxima values of the ODF and peak_indices gives us
their position on the discrete sphere that was used to do the reconstruction of
the ODF. In order to obtain the full ODF, return_odf should be True. Before
parallel. If ``nbr_processes`` is None it will figure out automatically the
number of CPUs available in your system. Alternatively, you can set
``nbr_processes`` manually. Here, we show an example where we compare the
duration of execution with or without parallelism.
"""
import time
from dipy.direction import peaks_from_model
start_time = time.time()
csd_peaks_parallel = peaks_from_model(model=csd_model,
data=data,
sphere=sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
mask=mask,
return_sh=True,
return_odf=False,
normalize_peaks=True,
npeaks=5,
parallel=True,
nbr_processes=None)
time_parallel = time.time() - start_time
print("peaks_from_model using " + str(multiprocessing.cpu_count())
+ " process ran in :" + str(time_parallel) + " seconds")
"""
``peaks_from_model`` using 8 processes ran in 114.425682068 seconds
"""
start_time = time.time()
def test_contour_from_roi():
# Render volume
renderer = window.renderer()
data = np.zeros((50, 50, 50))
data[20:30, 25, 25] = 1.
data[25, 20:30, 25] = 1.
affine = np.eye(4)
surface = actor.contour_from_roi(data, affine,
color=np.array([1, 0, 1]),
opacity=.5)
renderer.add(surface)
renderer.reset_camera()
renderer.reset_clipping_range()
# window.show(renderer)
# Test binarization
renderer2 = window.renderer()
data2 = np.zeros((50, 50, 50))
data2[20:30, 25, 25] = 1.
data2[35:40, 25, 25] = 1.
affine = np.eye(4)
surface2 = actor.contour_from_roi(data2, affine,
color=np.array([0, 1, 1]),
opacity=.5)
renderer2.add(surface2)
renderer2.reset_camera()
renderer2.reset_clipping_range()
# window.show(renderer2)
arr = window.snapshot(renderer, 'test_surface.png', offscreen=True)
arr2 = window.snapshot(renderer2, 'test_surface2.png', offscreen=True)
report = window.analyze_snapshot(arr, find_objects=True)
report2 = window.analyze_snapshot(arr2, find_objects=True)
npt.assert_equal(report.objects, 1)
npt.assert_equal(report2.objects, 2)
# test on real streamlines using tracking example
from dipy.data import read_stanford_labels
from dipy.reconst.shm import CsaOdfModel
from dipy.data import default_sphere
from dipy.direction import peaks_from_model
from dipy.tracking.local import ThresholdTissueClassifier
from dipy.tracking import utils
from dipy.tracking.local import LocalTracking
from fury.colormap import line_colors
hardi_img, gtab, labels_img = read_stanford_labels()
data = hardi_img.get_data()
labels = labels_img.get_data()
affine = hardi_img.affine
white_matter = (labels == 1) | (labels == 2)
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)
classifier = ThresholdTissueClassifier(csa_peaks.gfa, .25)
seed_mask = labels == 2
seeds = utils.seeds_from_mask(seed_mask, density=[1, 1, 1], affine=affine)
# Initialization of LocalTracking.
# The computation happens in the next step.
streamlines = LocalTracking(csa_peaks, classifier, seeds, affine,
step_size=2)
# Compute streamlines and store as a list.
streamlines = list(streamlines)
# Prepare the display objects.
streamlines_actor = actor.line(streamlines, line_colors(streamlines))
seedroi_actor = actor.contour_from_roi(seed_mask, affine, [0, 1, 1], 0.5)
# Create the 3d display.
r = window.Renderer()
r2 = window.Renderer()
r.add(streamlines_actor)
arr3 = window.snapshot(r, 'test_surface3.png', offscreen=True)
report3 = window.analyze_snapshot(arr3, find_objects=True)
r2.add(streamlines_actor)
r2.add(seedroi_actor)
arr4 = window.snapshot(r2, 'test_surface4.png', offscreen=True)
report4 = window.analyze_snapshot(arr4, find_objects=True)
# assert that the seed ROI rendering is not far
# away from the streamlines (affine error)
npt.assert_equal(report3.objects, report4.objects)
def run_to_generate_tractography(path_input,
path_output,
file_inMask="",
fbval="",
fbvec=""):
print(' - Starting reconstruction of Tractography...')
folder = os.path.dirname(path_input)
if fbval == "" or fbvec == "":
folder_sujeto = path_output
for l in os.listdir(folder_sujeto):
if "TENSOR" in l and "bval" in l:
fbval = os.path.join(folder_sujeto, l)
if "TENSOR" in l and "bvec" in l:
fbvec = os.path.join(folder_sujeto, l)
if file_inMask == "":
for i in os.listdir(folder):
if "masked_mask" in i:
file_inMask = os.path.join(folder, i)
if not os.path.exists(
os.path.join(path_output, '_tractography_CsaOdf' + '.trk')):
dwi_img = nib.load(path_input)
dwi_data = dwi_img.get_data()
dwi_affine = dwi_img.affine
dwi_mask_data = nib.load(file_inMask).get_data()
g_tab = gradient_table(fbval, fbvec)
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...')
return path_input
window.show(scene)
"""
.. figure:: csd_odfs.png
:align: center
CSD ODFs.
In DIPY we also provide tools for finding the peak directions (maxima) of the
ODFs. For this purpose we recommend using ``peaks_from_model``.
"""
from dipy.direction import peaks_from_model
csd_peaks = peaks_from_model(model=csd_model,
data=data_small,
sphere=default_sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=True)
scene.clear()
fodf_peaks = actor.peak_slicer(csd_peaks.peak_dirs, csd_peaks.peak_values)
scene.add(fodf_peaks)
print('Saving illustration as csd_peaks.png')
window.record(scene, out_path='csd_peaks.png', size=(600, 600))
if interactive:
window.show(scene)
"""
.. figure:: csd_peaks.png
:align: center
def QCSA_tractmake(data, affine, vox_size, gtab, mask, masktype, header, step_size, peak_processes, outpathtrk, subject='NA',
ratio=1, overwrite=False, get_params=False, doprune=False, figspath=None, verbose=None):
# Compute odfs in Brain Mask
t2 = time()
if os.path.isfile(outpathtrk) and not overwrite:
txt = "Subject already saved at "+outpathtrk
print(txt)
streamlines_generator = None
params = None
return outpathtrk, streamlines_generator, params
csa_model = CsaOdfModel(gtab, 6)
if peak_processes == 1:
parallel = False
else:
parallel = True
if verbose:
send_mail("Starting calculation of Constant solid angle model for subject " + subject,subject="CSA model start")
wholemask = np.where(mask == 0, False, True)
print(f"There are {peak_processes} and {parallel} here")
csa_peaks = peaks_from_model(model=csa_model,
data=data,
sphere=peaks.default_sphere, # issue with complete sphere
mask=wholemask,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=parallel,
nbr_processes=peak_processes)
duration = time() - t2
if verbose:
print(subject + ' CSA duration %.3f' % (duration,))
t3 = time()
if verbose:
send_mail('Computing classifier for local tracking for subject ' + subject +
',it has been ' + str(round(duration)) + 'seconds since the start of tractmaker',subject="Seed computation" )
print('Computing classifier for local tracking for subject ' + subject)
if masktype == "FA":
#tensor_model = dti.TensorModel(gtab)
#tenfit = tensor_model.fit(data, mask=labels > 0)
#FA = fractional_anisotropy(tenfit.evals)
FA_threshold = 0.05
classifier = ThresholdStoppingCriterion(mask, FA_threshold)
if figspath is not None:
fig = plt.figure()
mask_fa = mask.copy()
mask_fa[mask_fa < FA_threshold] = 0
plt.xticks([])
plt.yticks([])
plt.imshow(mask_fa[:, :, data.shape[2] // 2].T, cmap='gray', origin='lower',
interpolation='nearest')
fig.tight_layout()
fig.savefig(figspath + 'threshold_fa.png')
else:
classifier = BinaryStoppingCriterion(wholemask)
# generates about 2 seeds per voxel
# seeds = utils.random_seeds_from_mask(fa > .2, seeds_count=2,
# affine=np.eye(4))
# generates about 2 million streamlines
# seeds = utils.seeds_from_mask(fa > .2, density=1,
# affine=np.eye(4))
if verbose:
print('Computing seeds')
seeds = utils.seeds_from_mask(wholemask, density=1,
affine=np.eye(4))
#streamlines_generator = local_tracking.local_tracker(csa_peaks,classifier,seeds,affine=np.eye(4),step_size=step_size)
if verbose:
print('Computing the local tracking')
duration = time() - t2
send_mail('Start of the local tracking ' + ',it has been ' + str(round(duration)) +
'seconds since the start of tractmaker', subject="Seed computation")
#stepsize = 2 #(by default)
stringstep = str(step_size)
stringstep = stringstep.replace(".", "_")
if verbose:
print("stringstep is "+stringstep)
streamlines_generator = LocalTracking(csa_peaks, classifier,
seeds, affine=np.eye(4), step_size=step_size)
if verbose:
duration = time() - t2
txt = 'About to save streamlines at ' + outpathtrk + ',it has been ' + str(round(duration)) + \
'seconds since the start of tractmaker',
send_mail(txt,subject="Tract saving" )
cutoff = 2
if doprune:
streamlines_generator = prune_streamlines(list(streamlines_generator), data[:, :, :, 0], cutoff=cutoff,
verbose=verbose)
myheader = create_tractogram_header(outpathtrk, *header)
sg = lambda: (s for i, s in enumerate(streamlines_generator) if i % ratio == 0)
save_trk_heavy_duty(outpathtrk, streamlines=sg,
affine=affine, header=myheader,
shape=mask.shape, vox_size=vox_size)
else:
sg = lambda: (s for i, s in enumerate(streamlines_generator) if i % ratio == 0)
myheader = create_tractogram_header(outpathtrk, *header)
save_trk_heavy_duty(outpathtrk, streamlines=sg,
affine=affine, header=myheader,
shape=mask.shape, vox_size=vox_size)
if verbose:
duration = time() - t2
txt = "Tract files were saved at "+outpathtrk + ',it has been ' + str(round(duration)) + \
'seconds since the start of tractmaker'
print(txt)
send_mail(txt,subject="Tract saving" )
# save everything - will generate a 20+ GBytes of data - hard to manipulate
# possibly add parameter in csv file or other to decide whether to save large tractogram file
# outpathfile=outpath+subject+"bmCSA_detr"+stringstep+".trk"
# myheader=create_tractogram_header(outpathfile,*get_reference_info(fdwi))
duration3 = time() - t2
if verbose:
print(duration3)
print(subject + ' Tracking duration %.3f' % (duration3,))
send_mail("Finished file save at "+outpathtrk+" with tracking duration of " + str(duration3) + "seconds",
subject="file save update" )
if get_params:
numtracts, minlength, maxlength, meanlength, stdlength = get_trk_params(streamlines_generator, verbose)
params = [numtracts, minlength, maxlength, meanlength, stdlength]
if verbose:
print("For subject " + str(subject) + " the number of tracts is " + str(numtracts) + ", the minimum length is " +
str(minlength) + ", the maximum length is " + str(maxlength) + ", the mean length is " +
str(meanlength) + ", the std is " + str(stdlength))
else:
params = None
return outpathtrk, streamlines_generator, params
"""
.. figure:: csd_odfs.png
:align: center
**CSD ODFs**.
In Dipy we also provide tools for finding the peak directions (maxima) of the
ODFs. For this purpose we recommend using ``peaks_from_model``.
"""
from dipy.direction import peaks_from_model
csd_peaks = peaks_from_model(model=csd_model,
data=data_small,
sphere=sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=True)
fvtk.clear(ren)
fodf_peaks = fvtk.peaks(csd_peaks.peak_dirs, csd_peaks.peak_values, scale=1.3)
fvtk.add(ren, fodf_peaks)
print('Saving illustration as csd_peaks.png')
fvtk.record(ren, out_path='csd_peaks.png', size=(600, 600))
"""
.. figure:: csd_peaks.png
:align: center
**CSD Peaks**.
def particle_tracking(self):
self.sphere = get_sphere("repulsion724")
if self.mod_type == "det":
maxcrossing = 1
print("Obtaining peaks from model...")
self.mod_peaks = peaks_from_model(
self.mod,
self.data,
self.sphere,
relative_peak_threshold=0.5,
min_separation_angle=25,
mask=self.wm_in_dwi_data,
npeaks=5,
normalize_peaks=True,
)
qa_tensor.create_qa_figure(self.mod_peaks.peak_dirs,
self.mod_peaks.peak_values,
self.qa_tensor_out, self.mod_func)
self.streamline_generator = ParticleFilteringTracking(
self.mod_peaks,
self.tiss_classifier,
self.seeds,
self.stream_affine,
max_cross=maxcrossing,
step_size=0.5,
maxlen=1000,
pft_back_tracking_dist=2,
pft_front_tracking_dist=1,
particle_count=15,
return_all=True,
)
elif self.mod_type == "prob":
maxcrossing = 2
print("Preparing probabilistic tracking...")
print("Fitting model to data...")
self.mod_fit = self.mod.fit(self.data, self.wm_in_dwi_data)
print("Building direction-getter...")
self.mod_peaks = peaks_from_model(
self.mod,
self.data,
self.sphere,
relative_peak_threshold=0.5,
min_separation_angle=25,
mask=self.wm_in_dwi_data,
npeaks=5,
normalize_peaks=True,
)
qa_tensor.create_qa_figure(self.mod_peaks.peak_dirs,
self.mod_peaks.peak_values,
self.qa_tensor_out, self.mod_func)
try:
print(
"Proceeding using spherical harmonic coefficient from model estimation..."
)
self.pdg = ProbabilisticDirectionGetter.from_shcoeff(
self.mod_fit.shm_coeff, max_angle=60.0, sphere=self.sphere)
except:
print("Proceeding using FOD PMF from model estimation...")
self.fod = self.mod_fit.odf(self.sphere)
self.pmf = self.fod.clip(min=0)
self.pdg = ProbabilisticDirectionGetter.from_pmf(
self.pmf, max_angle=60.0, sphere=self.sphere)
self.streamline_generator = ParticleFilteringTracking(
self.pdg,
self.tiss_classifier,
self.seeds,
self.stream_affine,
max_cross=maxcrossing,
step_size=0.5,
maxlen=1000,
pft_back_tracking_dist=2,
pft_front_tracking_dist=1,
particle_count=15,
return_all=True,
)
print("Reconstructing tractogram streamlines...")
self.streamlines = Streamlines(self.streamline_generator)
return self.streamlines
First, we need to generate some streamlines. For a more complete
description of these steps, please refer to the CSA Probabilistic Tracking
Tutorial.
"""
hardi_img, gtab, labels_img = read_stanford_labels()
data = hardi_img.get_data()
labels = labels_img.get_data()
affine = hardi_img.affine
white_matter = (labels == 1) | (labels == 2)
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, .25)
seed_mask = labels == 2
seeds = utils.seeds_from_mask(seed_mask, affine, density=[1, 1, 1])
# Initialization of LocalTracking. The computation happens in the next step.
streamlines = LocalTracking(csa_peaks,
stopping_criterion,
seeds,
affine,
step_size=2)
"""
csamodel = CsaOdfModel(gtab, 4)
"""
`Peaks_from_model` is used to calculate properties of the ODFs (Orientation
Distribution Function) and return for
example the peaks and their indices, or GFA which is similar to FA but for ODF
based models. This function mainly needs a reconstruction model, the data and a
sphere as input. The sphere is an object that represents the spherical discrete
grid where the ODF values will be evaluated.
"""
csapeaks = peaks_from_model(model=csamodel,
data=maskdata,
sphere=default_sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
mask=mask,
return_odf=False,
normalize_peaks=True)
GFA = csapeaks.gfa
print('GFA.shape (%d, %d, %d)' % GFA.shape)
"""
GFA.shape ``(81, 106, 76)``
Apart from GFA, csapeaks also has the attributes peak_values, peak_indices and
ODF. peak_values shows the maxima values of the ODF and peak_indices gives us
their position on the discrete sphere that was used to do the reconstruction of
the ODF. In order to obtain the full ODF, return_odf should be True. Before
enabling this option, make sure that you have enough memory.
:align: center
**Corpus Callosum using probabilistic direction getter from SH**
"""
"""
Not all model fits have the ``shm_coeff`` attribute because not all models use
this basis to represent the data internally. However we can fit the ODF of any
model to the spherical harmonic basis using the ``peaks_from_model`` function.
"""
from dipy.direction import peaks_from_model
peaks = peaks_from_model(csd_model,
data,
default_sphere,
.5,
25,
mask=white_matter,
return_sh=True,
parallel=True)
fod_coeff = peaks.shm_coeff
prob_dg = ProbabilisticDirectionGetter.from_shcoeff(fod_coeff,
max_angle=30.,
sphere=default_sphere)
streamline_generator = LocalTracking(prob_dg,
stopping_criterion,
seeds,
affine,
step_size=.5)
streamlines = Streamlines(streamline_generator)
sft = StatefulTractogram(streamlines, hardi_img, Space.RASMM)
def compute_tensors(self, dti_vol, atlas_file, gtab):
# WGR:TODO figure out how to organize tensor options and formats
# WGR:TODO figure out how to deal with files on disk vs. in workspace
"""
Takes registered DTI image and produces tensors
**Positional Arguments:**
dti_vol:
- Registered DTI volume, from workspace.
atlas_file:
- File containing an atlas (or brain mask).
gtab:
- Structure containing dipy formatted bval/bvec information
"""
labeldata = nib.load(atlas_file)
label = labeldata.get_data()
"""
Create a brain mask. Here we just threshold labels.
"""
mask = (label > 0)
gtab.info
print data.shape
"""
For the constrained spherical deconvolution we need to estimate the
response function (see :ref:`example_reconst_csd`) and create a model.
"""
response, ratio = auto_response(gtab, dti_vol, roi_radius=10,
fa_thr=0.7)
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
"""
Next, we use ``peaks_from_model`` to fit the data and calculated
the fiber directions in all voxels.
"""
sphere = get_sphere('symmetric724')
csd_peaks = peaks_from_model(model=csd_model,
data=data,
sphere=sphere,
mask=mask,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=True)
"""
For the tracking part, we will use ``csd_model`` fiber directions
but stop tracking where fractional anisotropy (FA) is low (< 0.1).
To derive the FA, used as a stopping criterion, we need to fit a
tensor model first. Here, we use weighted least squares (WLS).
"""
print 'tensors...'
tensor_model = TensorModel(gtab, fit_method='WLS')
tensor_fit = tensor_model.fit(data, mask)
FA = fractional_anisotropy(tensor_fit.evals)
"""
In order for the stopping values to be used with our tracking
algorithm we need to have the same dimensions as the
``csd_peaks.peak_values``. For this reason, we can assign the
same FA value to every peak direction in the same voxel in
the following way.
"""
stopping_values = np.zeros(csd_peaks.peak_values.shape)
stopping_values[:] = FA[..., None]
print datetime.now() - startTime
pass