def filterlength(dname, fdwi, ffa, ftrk, thr_length, show=False):
fa_img = nib.load(ffa)
fa = fa_img.get_data()
affine = fa_img.get_affine()
img = nib.load(fdwi)
data = img.get_data()
from nibabel import trackvis
streams, hdr = trackvis.read(ftrk)
streamlines = [s[0] for s in streams]
# threshold on streamline length
from dipy.tracking.utils import length
lengths = list(length(streamlines))
new_streamlines = [
s for s, l in zip(streamlines, lengths) if l > thr_length
] #3.5
# info length streamlines
print(len(streamlines))
print(len(new_streamlines))
print(max(length(streamlines)))
print(min(length(streamlines)))
print(max(length(new_streamlines)))
print(min(length(new_streamlines)))
# show new tracto
new_streamlines = list(new_streamlines)
new_lengths = list(length(new_streamlines))
fnew_tractogram = dname + 'filteredtractogram.trk'
save_trk_old_style(fnew_tractogram, new_streamlines, affine, fa.shape)
if show:
show_results(data, new_streamlines, fa, affine, opacity=0.6)
def filterlength(dname, fdwi, ffa, ftrk, thr_length, show=False):
fa_img = nib.load(ffa)
fa = fa_img.get_data()
affine = fa_img.get_affine()
img = nib.load(fdwi)
data = img.get_data()
from nibabel import trackvis
streams, hdr = trackvis.read(ftrk)
streamlines = [s[0] for s in streams]
# threshold on streamline length
from dipy.tracking.utils import length
lengths = list(length(streamlines))
new_streamlines = [ s for s, l in zip(streamlines, lengths) if l > thr_length ] #3.5
# info length streamlines
print(len(streamlines))
print(len(new_streamlines))
print(max(length(streamlines)))
print(min(length(streamlines)))
print(max(length(new_streamlines)))
print(min(length(new_streamlines)))
# show new tracto
new_streamlines = list(new_streamlines)
new_lengths = list(length(new_streamlines))
fnew_tractogram = dname + 'filteredtractogram.trk'
save_trk_old_style(fnew_tractogram, new_streamlines, affine, fa.shape)
if show:
show_results(data, new_streamlines, fa, affine, opacity=0.6)
def track(dname, fdwi, fbval, fbvec, fmask=None, seed_density=1, show=False):
data, affine = load_nifti(fdwi)
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
gtab = gradient_table(bvals, bvecs, b0_threshold=50)
if fmask is None:
from dipy.segment.mask import median_otsu
b0_mask, mask = median_otsu(
data) # TODO: check parameters to improve the mask
else:
mask, mask_affine = load_nifti(fmask)
mask = np.squeeze(mask) #fix mask dimensions
# compute DTI model
from dipy.reconst.dti import TensorModel
tenmodel = TensorModel(gtab) #, fit_method='OLS') #, min_signal=5000)
# fit the dti model
tenfit = tenmodel.fit(data, mask=mask)
# save fa
ffa = dname + 'tensor_fa.nii.gz'
fa_img = nib.Nifti1Image(tenfit.fa.astype(np.float32), affine)
nib.save(fa_img, ffa)
sh_order = 8 #TODO: check what that does
if data.shape[-1] < 15:
raise ValueError('You need at least 15 unique DWI volumes to '
'compute fiber ODFs. You currently have: {0}'
' DWI volumes.'.format(data.shape[-1]))
elif data.shape[-1] < 30:
sh_order = 6
# compute the response equation ?
from dipy.reconst.csdeconv import auto_response
response, ratio = auto_response(gtab, data)
response = list(response)
peaks_sphere = get_sphere('symmetric362')
#TODO: check what that does
peaks_csd = peaks_from_model(
model=tenmodel,
data=data,
sphere=peaks_sphere,
relative_peak_threshold=.5, #.5
min_separation_angle=25,
mask=mask,
return_sh=True,
sh_order=sh_order,
normalize_peaks=True,
parallel=False)
peaks_csd.affine = affine
fpeaks = dname + 'peaks.npz'
save_peaks(fpeaks, peaks_csd)
from dipy.io.trackvis import save_trk
from dipy.tracking import utils
from dipy.tracking.local import (ThresholdTissueClassifier, LocalTracking)
stopping_thr = 0.25 #0.25
pam = load_peaks(fpeaks)
#ffa = dname + 'tensor_fa_nomask.nii.gz'
fa, fa_affine = load_nifti(ffa)
classifier = ThresholdTissueClassifier(fa, stopping_thr)
# seeds
seed_mask = fa > 0.4 #0.4 #TODO: check this parameter
seeds = utils.seeds_from_mask(seed_mask,
density=seed_density,
affine=affine)
# tractography, if affine then in world coordinates
streamlines = LocalTracking(pam,
classifier,
seeds,
affine=affine,
step_size=.5)
# Compute streamlines and store as a list.
streamlines = list(streamlines)
ftractogram = dname + 'tractogram.trk'
#save .trk
save_trk_old_style(ftractogram, streamlines, affine, fa.shape)
if show:
#render
show_results(data, streamlines, fa, fa_affine)
print(max(length(streamlines)))
print(min(length(streamlines)))
print(max(length(new_streamlines)))
print(min(length(new_streamlines)))
# In[20]:
# show new tracto
new_streamlines = list(new_streamlines)
new_lengths = list(length(new_streamlines))
fnew_tractogram = dname + 'filteredtractogram.trk'
save_trk_old_style(fnew_tractogram, new_streamlines, affine, fa.shape)
show_results(new_streamlines, fa, fa_affine, opacity=0.6)
# In[161]:
# In[43]:
import matplotlib.pyplot as plt
fig_hist, ax = plt.subplots(1)
ax.hist(lengths, color='burlywood')
ax.set_xlabel('Length')
ax.set_ylabel('Count')
plt.show()
plt.legend()
def track(dname, fdwi, fbval, fbvec, fmask=None, seed_density = 1, show=False):
data, affine = load_nifti(fdwi)
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
gtab = gradient_table(bvals, bvecs, b0_threshold=50)
if fmask is None:
from dipy.segment.mask import median_otsu
b0_mask, mask = median_otsu(data) # TODO: check parameters to improve the mask
else:
mask, mask_affine = load_nifti(fmask)
mask = np.squeeze(mask) #fix mask dimensions
# compute DTI model
from dipy.reconst.dti import TensorModel
tenmodel = TensorModel(gtab)#, fit_method='OLS') #, min_signal=5000)
# fit the dti model
tenfit = tenmodel.fit(data, mask=mask)
# save fa
ffa = dname + 'tensor_fa.nii.gz'
fa_img = nib.Nifti1Image(tenfit.fa.astype(np.float32), affine)
nib.save(fa_img, ffa)
sh_order = 8 #TODO: check what that does
if data.shape[-1] < 15:
raise ValueError('You need at least 15 unique DWI volumes to '
'compute fiber ODFs. You currently have: {0}'
' DWI volumes.'.format(data.shape[-1]))
elif data.shape[-1] < 30:
sh_order = 6
# compute the response equation ?
from dipy.reconst.csdeconv import auto_response
response, ratio = auto_response(gtab, data)
response = list(response)
peaks_sphere = get_sphere('symmetric362')
#TODO: check what that does
peaks_csd = peaks_from_model(model=tenmodel,
data=data,
sphere=peaks_sphere,
relative_peak_threshold=.5, #.5
min_separation_angle=25,
mask=mask,
return_sh=True,
sh_order=sh_order,
normalize_peaks=True,
parallel=False)
peaks_csd.affine = affine
fpeaks = dname + 'peaks.npz'
save_peaks(fpeaks, peaks_csd)
from dipy.io.trackvis import save_trk
from dipy.tracking import utils
from dipy.tracking.local import (ThresholdTissueClassifier,
LocalTracking)
stopping_thr = 0.25 #0.25
pam = load_peaks(fpeaks)
#ffa = dname + 'tensor_fa_nomask.nii.gz'
fa, fa_affine = load_nifti(ffa)
classifier = ThresholdTissueClassifier(fa,
stopping_thr)
# seeds
seed_mask = fa > 0.4 #0.4 #TODO: check this parameter
seeds = utils.seeds_from_mask(
seed_mask,
density=seed_density,
affine=affine)
# tractography, if affine then in world coordinates
streamlines = LocalTracking(pam, classifier,
seeds, affine=affine, step_size=.5)
# Compute streamlines and store as a list.
streamlines = list(streamlines)
ftractogram = dname + 'tractogram.trk'
#save .trk
save_trk_old_style(ftractogram, streamlines, affine, fa.shape)
if show:
#render
show_results(data,streamlines, fa, fa_affine)
print(max(length(streamlines))) print(min(length(streamlines))) print(max(length(new_streamlines))) print(min(length(new_streamlines))) # In[20]: # show new tracto new_streamlines = list(new_streamlines) new_lengths = list(length(new_streamlines)) fnew_tractogram = dname + 'filteredtractogram.trk' save_trk_old_style(fnew_tractogram, new_streamlines, affine, fa.shape) show_results(new_streamlines, fa, fa_affine, opacity=0.6) # In[161]: # In[43]: import matplotlib.pyplot as plt fig_hist, ax = plt.subplots(1) ax.hist(lengths, color='burlywood')