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 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)
