def test_eudx_further():
""" Cause we love testin.. ;-)
"""
fimg,fbvals,fbvecs=get_data('small_101D')
img=ni.load(fimg)
affine=img.get_affine()
data=img.get_data()
gtab = gradient_table(fbvals, fbvecs)
tensor_model = TensorModel(gtab)
ten = tensor_model.fit(data)
x,y,z=data.shape[:3]
seeds=np.zeros((10**4,3))
for i in range(10**4):
rx=(x-1)*np.random.rand()
ry=(y-1)*np.random.rand()
rz=(z-1)*np.random.rand()
seeds[i]=np.ascontiguousarray(np.array([rx,ry,rz]),dtype=np.float64)
#print seeds
#"""
ind = quantize_evecs(ten.evecs)
eu=EuDX(a=ten.fa, ind=ind, seeds=seeds, a_low=.2)
T=[e for e in eu]
#check that there are no negative elements
for t in T:
assert_equal(np.sum(t.ravel()<0),0)
def test_eudx_further():
""" Cause we love testin.. ;-)
"""
fimg,fbvals,fbvecs=get_data('small_101D')
img=ni.load(fimg)
affine=img.get_affine()
data=img.get_data()
gtab = gradient_table(fbvals, fbvecs)
tensor_model = TensorModel(gtab)
ten = tensor_model.fit(data)
x,y,z=data.shape[:3]
seeds=np.zeros((10**4,3))
for i in range(10**4):
rx=(x-1)*np.random.rand()
ry=(y-1)*np.random.rand()
rz=(z-1)*np.random.rand()
seeds[i]=np.ascontiguousarray(np.array([rx,ry,rz]),dtype=np.float64)
ind = quantize_evecs(ten.evecs)
eu=EuDX(a=ten.fa, ind=ind, seeds=seeds, a_low=.2)
T=[e for e in eu]
#check that there are no negative elements
for t in T:
assert_equal(np.sum(t.ravel()<0),0)
def test_eudx_bad_seed():
"""Test passing a bad seed to eudx"""
fimg, fbvals, fbvecs = get_data('small_101D')
img = ni.load(fimg)
affine = img.get_affine()
data = img.get_data()
gtab = gradient_table(fbvals, fbvecs)
tensor_model = TensorModel(gtab)
ten = tensor_model.fit(data)
ind = quantize_evecs(ten.evecs)
seed = [1000000., 1000000., 1000000.]
eu = EuDX(a=ten.fa, ind=ind, seeds=[seed], a_low=.2)
try:
track = list(eu)
except ValueError as ve:
if ve.args[0] == 'Seed outside boundaries':
print(ve)
print(data.shape)
seed = [1., 5., 8.]
eu = EuDX(a=ten.fa, ind=ind, seeds=[seed], a_low=.2)
track = list(eu)
seed = [-1., 1000000., 1000000.]
eu = EuDX(a=ten.fa, ind=ind, seeds=[seed], a_low=.2)
try:
track = list(eu)
except ValueError as ve:
if ve.args[0] == 'Seed outside boundaries':
print(ve)
def tracking_eudx(dir_src, dir_out, verbose=False):
# Loading FA and evecs data
fa_name = 'data_' + par_b_tag + '_' + par_dim_tag + '_FA.nii.gz'
FA, affine = load_nifti(pjoin(dir_src, fa_name), verbose)
evecs_name = 'data_' + par_b_tag + '_' + par_dim_tag + '_EV.nii.gz'
evecs, _ = load_nifti(pjoin(dir_src, evecs_name), verbose)
# Computation of streamlines
sphere = get_sphere('symmetric724')
peak_indices = quantize_evecs(evecs, sphere.vertices)
streamlines = EuDX(FA.astype('f8'),
ind=peak_indices,
seeds=par_eudx_seeds,
odf_vertices=sphere.vertices,
a_low=par_eudx_threshold)
# Saving tractography
voxel_size = (par_dim_vox, ) * 3
dims = FA.shape[:3]
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = voxel_size
hdr['voxel_order'] = 'LAS'
hdr['dim'] = dims
hdr['vox_to_ras'] = affine
strm = ((sl, None, None) for sl in streamlines)
trk_name = 'tractogram_' + par_b_tag + '_' + par_dim_tag + '_' + par_rec_tag + '_' + par_eudx_tag + '.trk'
trk_out = os.path.join(dir_out, trk_name)
nib.trackvis.write(trk_out, strm, hdr, points_space='voxel')
def test_eudx_bad_seed():
"""Test passing a bad seed to eudx"""
fimg, fbvals, fbvecs = get_data('small_101D')
img = ni.load(fimg)
affine = img.get_affine()
data = img.get_data()
gtab = gradient_table(fbvals, fbvecs)
tensor_model = TensorModel(gtab)
ten = tensor_model.fit(data)
ind = quantize_evecs(ten.evecs)
sphere = get_sphere('symmetric724')
seed = [1000000., 1000000., 1000000.]
eu = EuDX(a=ten.fa, ind=ind, seeds=[seed],
odf_vertices=sphere.vertices, a_low=.2)
assert_raises(ValueError, list, eu)
print(data.shape)
seed = [1., 5., 8.]
eu = EuDX(a=ten.fa, ind=ind, seeds=[seed],
odf_vertices=sphere.vertices, a_low=.2)
track = list(eu)
seed = [-1., 1000000., 1000000.]
eu = EuDX(a=ten.fa, ind=ind, seeds=[seed],
odf_vertices=sphere.vertices, a_low=.2)
assert_raises(ValueError, list, eu)
def tensorTractography(tenfit, img, falow=0.3):
fa = tenfit.fa
fa[np.isnan(fa)] = 0
evecs = tenfit.evecs
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = img.get_header().get_zooms()[:3]
hdr['voxel_order'] = 'LAS'
hdr['dim'] = fa.shape
from dipy.data import get_sphere #EuDX needs to map voxel dirs on a sphere
sphere = get_sphere('symmetric724')
from dipy.reconst.dti import quantize_evecs
peak_indices = quantize_evecs(evecs, sphere.vertices)
from dipy.tracking.eudx import EuDX
eu = EuDX(fa,
peak_indices,
odf_vertices=sphere.vertices,
a_low=falow,
ang_thr=30.0)
tensor_streamlines = [streamline for streamline in eu]
tensor_streamlines_trk = ((sl, None, None) for sl in tensor_streamlines)
ten_sl_fname = 'tensor_streamlines_tests.trk'
nib.trackvis.write(ten_sl_fname,
tensor_streamlines_trk,
hdr,
points_space='voxel')
def tracking_eudx(dir_src, dir_out, verbose=False):
# Loading FA and evecs data
fa_name = 'data_' + par_b_tag + '_' + par_dim_tag + '_FA.nii.gz'
FA, affine = load_nifti(pjoin(dir_src, fa_name), verbose)
evecs_name = 'data_' + par_b_tag + '_' + par_dim_tag + '_EV.nii.gz'
evecs, _ = load_nifti(pjoin(dir_src, evecs_name), verbose)
# Computation of streamlines
sphere = get_sphere('symmetric724')
peak_indices = quantize_evecs(evecs, sphere.vertices)
streamlines = EuDX(FA.astype('f8'),
ind=peak_indices,
seeds=par_eudx_seeds,
odf_vertices= sphere.vertices,
a_low=par_eudx_threshold)
# Saving tractography
voxel_size = (par_dim_vox,) * 3
dims = FA.shape[:3]
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = voxel_size
hdr['voxel_order'] = 'LAS'
hdr['dim'] = dims
hdr['vox_to_ras'] = affine
strm = ((sl, None, None) for sl in streamlines)
trk_name = 'tractogram_' + par_b_tag + '_' + par_dim_tag + '_' + par_rec_tag + '_' + par_eudx_tag + '.trk'
trk_out = os.path.join(dir_out, trk_name)
nib.trackvis.write(trk_out, strm, hdr, points_space='voxel')
dpy_out = trk_out.replace('.trk', '.dpy')
dpy = Dpy(dpy_out, 'w')
dpy.write_tracks(streamlines)
dpy.close()
def test_eudx_bad_seed():
"""Test passing a bad seed to eudx"""
fimg, fbvals, fbvecs = get_data('small_101D')
img = ni.load(fimg)
affine = img.affine
data = img.get_data()
gtab = gradient_table(fbvals, fbvecs)
tensor_model = TensorModel(gtab)
ten = tensor_model.fit(data)
ind = quantize_evecs(ten.evecs)
sphere = get_sphere('symmetric724')
seed = [1000000., 1000000., 1000000.]
eu = EuDX(a=ten.fa, ind=ind, seeds=[seed],
odf_vertices=sphere.vertices, a_low=.2)
assert_raises(ValueError, list, eu)
print(data.shape)
seed = [1., 5., 8.]
eu = EuDX(a=ten.fa, ind=ind, seeds=[seed],
odf_vertices=sphere.vertices, a_low=.2)
track = list(eu)
seed = [-1., 1000000., 1000000.]
eu = EuDX(a=ten.fa, ind=ind, seeds=[seed],
odf_vertices=sphere.vertices, a_low=.2)
assert_raises(ValueError, list, eu)
def DIPY_nii2streamlines(imgfile, maskfile, bvals, bvecs, output_prefix):
import numpy as np
import nibabel as nib
import os
from dipy.reconst.dti import TensorModel
print "nii2streamlines"
img = nib.load(imgfile)
bvals = np.genfromtxt(bvals)
bvecs = np.genfromtxt(bvecs)
if bvecs.shape[1] != 3:
bvecs = bvecs.T
print bvecs.shape
from nipype.utils.filemanip import split_filename
_, prefix, _ = split_filename(imgfile)
from dipy.data import gradient_table
gtab = gradient_table(bvals, bvecs)
data = img.get_data()
affine = img.get_affine()
zooms = img.get_header().get_zooms()[:3]
new_zooms = (2., 2., 2.)
data2, affine2 = data, affine
mask = nib.load(maskfile).get_data().astype(np.bool)
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data2, mask)
from dipy.reconst.dti import fractional_anisotropy
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
fa_img = nib.Nifti1Image(FA, img.get_affine())
nib.save(fa_img, experiment_dir + '/' + ('%s_tensor_fa.nii.gz' % prefix))
evecs = tenfit.evecs
evec_img = nib.Nifti1Image(evecs, img.get_affine())
nib.save(evec_img, experiment_dir + '/' + ('%s_tensor_evec.nii.gz' % prefix))
from dipy.data import get_sphere
sphere = get_sphere('symmetric724')
from dipy.reconst.dti import quantize_evecs
peak_indices = quantize_evecs(tenfit.evecs, sphere.vertices)
from dipy.tracking.eudx import EuDX
eu = EuDX(FA, peak_indices, odf_vertices = sphere.vertices, a_low=0.2, seeds=10**6, ang_thr=35)
tensor_streamlines = [streamline for streamline in eu]
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = new_zooms
hdr['voxel_order'] = 'LPS'
hdr['dim'] = data2.shape[:3]
import dipy.tracking.metrics as dmetrics
tensor_streamlines = ((sl, None, None) for sl in tensor_streamlines if dmetrics.length(sl) > 15)
ten_sl_fname = experiment_dir + '/' + ('%s_streamline.trk' % prefix)
nib.trackvis.write(ten_sl_fname, tensor_streamlines, hdr, points_space='voxel')
return ten_sl_fname
def test_eudx_further():
""" Cause we love testin.. ;-)
"""
fimg, fbvals, fbvecs = get_data('small_101D')
img = ni.load(fimg)
affine = img.affine
data = img.get_data()
gtab = gradient_table(fbvals, fbvecs)
tensor_model = TensorModel(gtab)
ten = tensor_model.fit(data)
x, y, z = data.shape[:3]
seeds = np.zeros((10**4, 3))
for i in range(10**4):
rx = (x-1)*np.random.rand()
ry = (y-1)*np.random.rand()
rz = (z-1)*np.random.rand()
seeds[i] = np.ascontiguousarray(np.array([rx, ry, rz]),
dtype=np.float64)
sphere = get_sphere('symmetric724')
ind = quantize_evecs(ten.evecs)
eu = EuDX(a=ten.fa, ind=ind, seeds=seeds,
odf_vertices=sphere.vertices, a_low=.2)
T = [e for e in eu]
# check that there are no negative elements
for t in T:
assert_equal(np.sum(t.ravel() < 0), 0)
# Test eudx with affine
def random_affine(seeds):
affine = np.eye(4)
affine[:3, :] = np.random.random((3, 4))
seeds = np.dot(seeds, affine[:3, :3].T)
seeds += affine[:3, 3]
return affine, seeds
# Make two random affines and move seeds
affine1, seeds1 = random_affine(seeds)
affine2, seeds2 = random_affine(seeds)
# Make tracks using different affines
eu1 = EuDX(a=ten.fa, ind=ind, odf_vertices=sphere.vertices,
seeds=seeds1, a_low=.2, affine=affine1)
eu2 = EuDX(a=ten.fa, ind=ind, odf_vertices=sphere.vertices,
seeds=seeds2, a_low=.2, affine=affine2)
# Move from eu2 affine2 to affine1
eu2_to_eu1 = utils.move_streamlines(eu2, output_space=affine1,
input_space=affine2)
# Check that the tracks are the same
for sl1, sl2 in zip(eu1, eu2_to_eu1):
assert_array_almost_equal(sl1, sl2)
def tens_mod_est(self):
print("Fitting tensor model...")
self.model = TensorModel(self.gtab)
self.ten = self.model.fit(self.data, self.wm_in_dwi_data)
self.fa = self.ten.fa
self.fa[np.isnan(self.fa)] = 0
self.sphere = get_sphere("repulsion724")
self.ind = quantize_evecs(self.ten.evecs, self.sphere.vertices)
return self.ten
def test_eudx_further():
""" Cause we love testin.. ;-)
"""
fimg,fbvals,fbvecs=get_data('small_101D')
img=ni.load(fimg)
affine=img.get_affine()
data=img.get_data()
gtab = gradient_table(fbvals, fbvecs)
tensor_model = TensorModel(gtab)
ten = tensor_model.fit(data)
x,y,z=data.shape[:3]
seeds=np.zeros((10**4,3))
for i in range(10**4):
rx=(x-1)*np.random.rand()
ry=(y-1)*np.random.rand()
rz=(z-1)*np.random.rand()
seeds[i]=np.ascontiguousarray(np.array([rx,ry,rz]),dtype=np.float64)
sphere = get_sphere('symmetric724')
ind = quantize_evecs(ten.evecs)
eu=EuDX(a=ten.fa, ind=ind, seeds=seeds,
odf_vertices=sphere.vertices, a_low=.2)
T=[e for e in eu]
#check that there are no negative elements
for t in T:
assert_equal(np.sum(t.ravel()<0),0)
# Test eudx with affine
def random_affine(seeds):
affine = np.eye(4)
affine[:3, :] = np.random.random((3, 4))
seeds = np.dot(seeds, affine[:3, :3].T)
seeds += affine[:3, 3]
return affine, seeds
# Make two random affines and move seeds
affine1, seeds1 = random_affine(seeds)
affine2, seeds2 = random_affine(seeds)
# Make tracks using different affines
eu1 = EuDX(a=ten.fa, ind=ind, odf_vertices=sphere.vertices,
seeds=seeds1, a_low=.2, affine=affine1)
eu2 = EuDX(a=ten.fa, ind=ind, odf_vertices=sphere.vertices,
seeds=seeds2, a_low=.2, affine=affine2)
# Move from eu2 affine2 to affine1
eu2_to_eu1 = utils.move_streamlines(eu2, output_space=affine1,
input_space=affine2)
# Check that the tracks are the same
for sl1, sl2 in zip(eu1, eu2_to_eu1):
assert_array_almost_equal(sl1, sl2)
def streamlines( fa_file, evecs_file, fibers_file ):
'''
Generate streamlines from FA and EVECS maps and store a TrackVis file.
fa_file
the fa map file path
evecs_file
the evecs map file path
fibers_file
the TrackVis output file path
'''
# load the inputs
fa_image = nibabel.load( fa_file )
evecs_image = nibabel.load( evecs_file )
fa_map = fa_image.get_data()
evecs_map = evecs_image.get_data()
# clean-up FA map
fa_map[numpy.isnan( fa_map )] = 0
# load evenly distributed sphere of 724 points
sphere = dipy.data.get_sphere( 'symmetric724' )
# apply the sphere for discretization
peak_indices = reconstructer.quantize_evecs( evecs_map, sphere.vertices )
# perform tracking
print 'start tracking'
tracking_results = tracker.EuDX( fa_map, peak_indices, seeds=1000000, odf_vertices=sphere.vertices, a_low=0.2 )
streamlines = [streamline for streamline in tracking_results]
print 'end tracking'
# save as .TRK file
trk_header = nibabel.trackvis.empty_header()
# trk_header['voxel_size'] = fa_image.get_header().get_zooms()[:3]
# trk_header['voxel_order'] = 'LPS'
trk_header['dim'] = fa_map.shape
trk_header['n_count'] = len(streamlines)
# adjust trackvis header according to affine from FA
nibabel.trackvis.aff_to_hdr( fa_image.get_affine(), trk_header, True, True )
trk_tracks = ( ( sl, None, None ) for sl in streamlines )
nibabel.trackvis.write( fibers_file, trk_tracks, trk_header, points_space='voxel' )
def DTImaps(ImgPath, Bvalpath, Bvecpath, tracto=True):
data, affine = resli(ImgPath)
data = Nonlocal(data, affine)
b0_mask, mask = otsu(data, affine) #maask binary
evals, evecs = DTImodel(b0_mask, affine, mask, gtab(Bvalpath, Bvecpath))
print('--> Calculando el mapa de anisotropia fraccional')
FA = fractional_anisotropy(evals)
FA[np.isnan(FA)] = 0
nib.save(nib.Nifti1Image(FA.astype(np.float32), affine),
"Mapa_anisotropia_fraccional")
print('--> Calculando el mapa de anisotropia fraccional RGB')
FA2 = np.clip(FA, 0, 1)
RGB = color_fa(FA2, evecs)
nib.save(nib.Nifti1Image(np.array(255 * RGB, 'uint8'), affine),
"Mapa_anisotropia_fraccional RGB")
print('--> Calculando el mapa de difusividad media')
MD1 = dti.mean_diffusivity(evals)
nib.save(nib.Nifti1Image(MD1.astype(np.float32), affine),
"Mapa_difusividad_media")
if tracto:
sphere = get_sphere('symmetric724')
peak_indices = quantize_evecs(evecs, sphere.vertices)
eu = EuDX(FA.astype('f8'),
peak_indices,
seeds=500000,
odf_vertices=sphere.vertices,
a_low=0.15)
tensor_streamlines = [streamline for streamline in eu]
new_vox_sz = (2., 2., 2.)
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = new_vox_sz
hdr['voxel_order'] = 'LAS'
hdr['dim'] = FA.shape
tensor_streamlines_trk = ((sl, None, None)
for sl in tensor_streamlines)
ten_sl_fname = "Tracto.trk"
nib.trackvis.write(ten_sl_fname,
tensor_streamlines_trk,
hdr,
points_space='voxel')
return FA
def compute_tracking(src_dti_dir, out_trk_dir, subj_name):
# Loading FA and evecs data
src_fa_file = os.path.join(src_dti_dir, subj_name + par_fa_suffix)
fa_img = nib.load(src_fa_file)
FA = fa_img.get_data()
affine = fa_img.get_affine()
src_evecs_file = os.path.join(src_dti_dir, subj_name + par_evecs_suffix)
evecs_img = nib.load(src_evecs_file)
evecs = evecs_img.get_data()
# Computation of streamlines
sphere = get_sphere('symmetric724')
peak_indices = dti.quantize_evecs(evecs, sphere.vertices)
streamlines = EuDX(FA.astype('f8'),
ind=peak_indices,
seeds=par_eudx_seeds,
odf_vertices= sphere.vertices,
a_low=par_eudx_threshold)
# Saving tractography
voxel_size = fa_img.get_header().get_zooms()[:3]
dims = FA.shape[:3]
seed = par_eudx_seeds
seed = "_%d%s" % (seed/10**6 if seed>10**5 else seed/10**3,
'K' if seed < 1000000 else 'M')
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = voxel_size
hdr['voxel_order'] = 'LAS'
hdr['dim'] = dims
hdr['vox_to_ras'] = affine
strm = ((sl, None, None) for sl in streamlines
if length(sl) > par_trk_min and length(sl) < par_trk_max)
out_trk_file = os.path.join(out_trk_dir, subj_name + seed + par_trk_suffix)
nib.trackvis.write(out_trk_file, strm, hdr, points_space='voxel')
tracks = [track for track in streamlines]
out_dipy_file = os.path.join(out_trk_dir,subj_name + seed + par_dipy_suffix)
dpw = Dpy(out_dipy_file, 'w')
dpw.write_tracks(tracks)
dpw.close()
def compute_tracking(src_dti_dir, out_trk_dir, subj_name):
# Loading FA and evecs data
src_fa_file = os.path.join(src_dti_dir, subj_name + par_fa_suffix)
fa_img = nib.load(src_fa_file)
FA = fa_img.get_data()
src_evecs_file = os.path.join(src_dti_dir, subj_name + par_evecs_suffix)
evecs_img = nib.load(src_evecs_file)
evecs = evecs_img.get_data()
# Computation of streamlines
sphere = get_sphere('symmetric724')
peak_indices = dti.quantize_evecs(evecs, sphere.vertices)
streamlines = EuDX(FA.astype('f8'),
ind=peak_indices,
seeds=par_eudx_seeds,
odf_vertices=sphere.vertices,
a_low=par_eudx_threshold)
# Saving tractography
voxel_size = fa_img.get_header().get_zooms()[:3]
dims = FA.shape[:3]
seed = par_eudx_seeds
seed = "_%d%s" % (seed / 10**6 if seed > 10**5 else seed / 10**3,
'K' if seed < 1000000 else 'M')
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = voxel_size
hdr['voxel_order'] = 'LAS'
hdr['dim'] = dims
strm = ((sl, None, None) for sl in streamlines
if length(sl) > par_trk_min and length(sl) < par_trk_max)
out_trk_file = os.path.join(out_trk_dir, subj_name + seed + par_trk_suffix)
nib.trackvis.write(out_trk_file, strm, hdr, points_space='voxel')
tracks = [track for track in streamlines]
out_dipy_file = os.path.join(out_trk_dir,
subj_name + seed + par_dipy_suffix)
dpw = Dpy(out_dipy_file, 'w')
dpw.write_tracks(tracks)
dpw.close()
def track_gen_model(brain_mask_file, dwi_data_file, dwi_bval_file,
dwi_bvec_file):
img_mask = nib.load(brain_mask_file)
img, gtab = dwi.get_dwi_img_gtab(dwi_data_file, dwi_bval_file,
dwi_bvec_file)
data_mask = img_mask.get_data()
affine = img.get_affine()
data = img.get_data()
model = TensorModel(gtab)
ten = model.fit(data, mask=data_mask)
sphere = get_sphere('symmetric724')
ind = quantize_evecs(ten.evecs, sphere.vertices)
p = Struct()
p.affine = affine
p.sphere = sphere
p.ten = ten
p.ind = ind
return p
def tractography_rec(imag, bvals, bvecs, seed, threshold):
''' Script to generate tractography. Uses the EuDX function from dipy. Returns tractography and FA.
Parameters
----------
imag: NiftiImage object
bvals: bvals array
bvecs: bvecs array
seed: int or ndarray (Parameter for the EuDX function)
threshold : float (Parameter for the EuDX function)
'''
print "Retrieving data and affine"
data = img.get_data()
affine = img.get_affine()
#new version of dipy
print "Computing tensor model"
gradients = gradient_table(bvals,bvecs)
tensor_model = dti.TensorModel(gradients)
tensors = tensor_model.fit(data)
print "Computing FA"
FA = dti.fractional_anisotropy(tensors.evals)
FA[np.isnan(FA)] = 0
print "Computing evecs"
evecs_img = nib.Nifti1Image(tensors.evecs.astype(np.float32), affine)
evecs = evecs_img.get_data()
sphere = get_sphere('symmetric724')
peak_indices = dti.quantize_evecs(evecs, sphere.vertices)
print "Computing EuDX reconstruction."
streamlines = EuDX(FA.astype('f8'),
ind=peak_indices,
seeds=seed,
odf_vertices= sphere.vertices,
a_low=threshold)
return streamlines, FA
def eudx_basic(self, dti_file, mask_file, gtab, stop_val=0.1):
"""
Tracking with basic tensors and basic eudx - experimental
We now force seeding at every voxel in the provided mask for
simplicity. Future functionality will extend these options.
**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:**
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()
# use all points in mask
seedIdx = np.where(mask > 0) # seed everywhere not equal to zero
seedIdx = np.transpose(seedIdx)
model = TensorModel(gtab)
ten = model.fit(data, mask)
sphere = get_sphere('symmetric724')
ind = quantize_evecs(ten.evecs, sphere.vertices)
eu = EuDX(a=ten.fa,
ind=ind,
seeds=seedIdx,
odf_vertices=sphere.vertices,
a_low=stop_val)
tracks = [e for e in eu]
return (ten, tracks)
def test_fit_data():
fdata, fbval, fbvec = dpd.get_data('small_25')
gtab = grad.gradient_table(fbval, fbvec)
ni_data = nib.load(fdata)
data = ni_data.get_data()
dtmodel = dti.TensorModel(gtab)
dtfit = dtmodel.fit(data)
sphere = dpd.get_sphere()
peak_idx = dti.quantize_evecs(dtfit.evecs, sphere.vertices)
eu = edx.EuDX(dtfit.fa.astype('f8'), peak_idx,
seeds=list(nd.ndindex(data.shape[:-1])),
odf_vertices=sphere.vertices, a_low=0)
tensor_streamlines = [streamline for streamline in eu]
life_model = life.FiberModel(gtab)
life_fit = life_model.fit(data, tensor_streamlines)
model_error = life_fit.predict() - life_fit.data
model_rmse = np.sqrt(np.mean(model_error ** 2, -1))
matlab_rmse, matlab_weights = dpd.matlab_life_results()
# Lower error than the matlab implementation for these data:
npt.assert_(np.median(model_rmse) < np.median(matlab_rmse))
# And a moderate correlation with the Matlab implementation weights:
npt.assert_(np.corrcoef(matlab_weights, life_fit.beta)[0, 1] > 0.6)
def eudx_basic(self, dti_file, mask_file, gtab, stop_val=0.1):
"""
Tracking with basic tensors and basic eudx - experimental
We now force seeding at every voxel in the provided mask for
simplicity. Future functionality will extend these options.
**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:**
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()
# use all points in mask
seedIdx = np.where(mask > 0) # seed everywhere not equal to zero
seedIdx = np.transpose(seedIdx)
model = TensorModel(gtab)
ten = model.fit(data, mask)
sphere = get_sphere('symmetric724')
ind = quantize_evecs(ten.evecs, sphere.vertices)
eu = EuDX(a=ten.fa, ind=ind, seeds=seedIdx,
odf_vertices=sphere.vertices, a_low=stop_val)
tracks = [e for e in eu]
return (ten, tracks)
def test_fit_data():
fdata, fbval, fbvec = dpd.get_data('small_25')
gtab = grad.gradient_table(fbval, fbvec)
ni_data = nib.load(fdata)
data = ni_data.get_data()
dtmodel = dti.TensorModel(gtab)
dtfit = dtmodel.fit(data)
sphere = dpd.get_sphere()
peak_idx = dti.quantize_evecs(dtfit.evecs, sphere.vertices)
eu = edx.EuDX(dtfit.fa.astype('f8'),
peak_idx,
seeds=list(nd.ndindex(data.shape[:-1])),
odf_vertices=sphere.vertices,
a_low=0)
tensor_streamlines = [streamline for streamline in eu]
life_model = life.FiberModel(gtab)
life_fit = life_model.fit(data, tensor_streamlines)
model_error = life_fit.predict() - life_fit.data
model_rmse = np.sqrt(np.mean(model_error**2, -1))
matlab_rmse, matlab_weights = dpd.matlab_life_results()
# Lower error than the matlab implementation for these data:
npt.assert_(np.median(model_rmse) < np.median(matlab_rmse))
# And a moderate correlation with the Matlab implementation weights:
npt.assert_(np.corrcoef(matlab_weights, life_fit.beta)[0, 1] > 0.68)
def tracking(input_filename_fa, input_filename_evecs, num_seeds=10000, low_thresh=0.2, output_filename=None):
# print 'Tracking ...'
# print 'Loading data ...'
fa_img = nib.load(input_filename_fa)
FA = fa_img.get_data()
evecs_img = nib.load(input_filename_evecs)
evecs = evecs_img.get_data()
FA[np.isnan(FA)] = 0
sphere = get_sphere("symmetric724")
peak_indices = quantize_evecs(evecs, sphere.vertices)
eu = EuDX(FA, peak_indices, seeds=num_seeds, odf_vertices=sphere.vertices, a_low=low_thresh)
tensor_tracks_old = [streamline for streamline in eu]
# remove one point tracks
tracks = [track for track in tensor_tracks_old if track.shape[0] > 1]
# tracks = create_tracks(FA, peak_indices,sphere.vertices, num_seeds, low_thresh)
if output_filename == None:
filename_save = "tracks_dti.dpy"
else:
filename_save = os.path.abspath(output_filename)
# print 'Saving tracks to:', filename_save
dpw = Dpy(filename_save, "w")
dpw.write_tracks(tracks)
dpw.close()
return filename_save
gtab = gradient_table(bvals, bvecs)
# Create tensor model
tenmodel = TensorModel(gtab)
# Fit the data using created tensor model
tenfit = tenmodel.fit(data)
# Compute FA
fa = fractional_anisotropy(tenfit.evals)
# Compute spherical coordinates
sphere = get_sphere('symmetric724') # Symmetric Sphere with 724 vertices
# Get quantize vectors
ind = quantize_evecs(tenfit.evecs, sphere.vertices)
print('ind.shape (%d, %d, %d)' % ind.shape)
# Compute Eular Delta Crossing with FA
eu = EuDX(a=fa, ind=ind, seeds=100000, odf_vertices=sphere.vertices,
a_low=0.2) # FA uses a_low = 0.2
streamlines = [line for line in eu]
print('Number of streamlines %i' % len(streamlines))
'''
for line in streamlines:
print(line.shape)
'''
# Do steamline clustering using QuickBundles (QB) using Eular's Method
# dist_thr (distance threshold) which affects number of clusters and their size
# pts (number of points in each streamline) which will be used for downsampling before clustering
# Default values : dist_thr = 4 & pts = 12
For the discretization procedure we use an evenly distributed sphere of 724
points which we can access using the get_sphere function.
"""
from dipy.data import get_sphere
sphere = get_sphere('symmetric724')
"""
We use quantize_evecs (evecs here stands for eigen vectors) to apply the
discretization.
"""
from dipy.reconst.dti import quantize_evecs
peak_indices = quantize_evecs(tenfit.evecs, sphere.vertices)
"""
EuDX is the fiber tracking algorithm that we use in this example.
The most important parameters are the first one which represents the
magnitude of the peak of a scalar anisotropic function, the
second which represents the indices of the discretized directions of
the peaks and odf_vertices are the vertices of the input sphere.
"""
from dipy.tracking.eudx import EuDX
eu = EuDX(FA, peak_indices, odf_vertices = sphere.vertices, a_low=0.2)
tensor_streamlines = [streamline for streamline in eu]
def dwi_dipy_run(dwi_dir,
node_size,
dir_path,
conn_model,
parc,
atlas_select,
network,
wm_mask=None):
import os
import glob
import re
import nipype.interfaces.fsl as fsl
from dipy.reconst.dti import TensorModel, quantize_evecs
from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel, recursive_response
from dipy.tracking.local import LocalTracking, ThresholdTissueClassifier
from dipy.tracking import utils
from dipy.direction import peaks_from_model
from dipy.tracking.eudx import EuDX
from dipy.data import get_sphere
from dipy.core.gradients import gradient_table
from dipy.io import read_bvals_bvecs
def atoi(text):
return int(text) if text.isdigit() else text
def natural_keys(text):
return [atoi(c) for c in re.split('(\d+)', text)]
dwi_img = "%s%s" % (dwi_dir, '/dwi.nii.gz')
nodif_brain_mask_path = "%s%s" % (dwi_dir, '/nodif_brain_mask.nii.gz')
bvals = "%s%s" % (dwi_dir, '/bval')
bvecs = "%s%s" % (dwi_dir, '/bvec')
img = nib.load(dwi_img)
data = img.get_data()
# Loads mask and ensures it's a true binary mask
img = nib.load(nodif_brain_mask_path)
mask = img.get_data()
mask = mask > 0
[bvals, bvecs] = read_bvals_bvecs(bvals, bvecs)
gtab = gradient_table(bvals, bvecs)
# Estimates some tensors
model = TensorModel(gtab)
ten = model.fit(data, mask)
sphere = get_sphere('symmetric724')
ind = quantize_evecs(ten.evecs, sphere.vertices)
# Tractography
if conn_model == 'csd':
trac_mod = 'csd'
else:
conn_model = 'tensor'
trac_mod = ten.fa
affine = img.affine
print('Tracking with tensor model...')
if wm_mask is None:
mask = nib.load(mask).get_data()
mask[0, :, :] = False
mask[:, 0, :] = False
mask[:, :, 0] = False
seeds = utils.seeds_from_mask(mask, density=2)
else:
wm_mask_data = nib.load(wm_mask).get_data()
wm_mask_data[0, :, :] = False
wm_mask_data[:, 0, :] = False
wm_mask_data[:, :, 0] = False
seeds = utils.seeds_from_mask(wm_mask_data, density=2)
#seeds = random_seeds_from_mask(ten.fa > 0.3, seeds_count=num_total_samples)
if conn_model == 'tensor':
eu = EuDX(a=trac_mod,
ind=ind,
seeds=seeds,
odf_vertices=sphere.vertices,
a_low=0.05,
step_sz=.5)
tracks = [e for e in eu]
elif conn_model == 'csd':
print('Tracking with CSD model...')
if wm_mask is None:
response = recursive_response(gtab,
data,
mask=mask.astype('bool'),
sh_order=8,
peak_thr=0.01,
init_fa=0.08,
init_trace=0.0021,
iter=8,
convergence=0.001,
parallel=True)
else:
response = recursive_response(gtab,
data,
mask=wm_mask_data.astype('bool'),
sh_order=8,
peak_thr=0.01,
init_fa=0.08,
init_trace=0.0021,
iter=8,
convergence=0.001,
parallel=True)
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
csd_peaks = peaks_from_model(model=csd_model,
data=data,
sphere=sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=True)
tissue_classifier = ThresholdTissueClassifier(ten.fa, 0.1)
streamline_generator = LocalTracking(csd_peaks,
tissue_classifier,
seeds,
affine=affine,
step_size=0.5)
tracks = [e for e in streamline_generator]
if parc is True:
node_size = 'parc'
if network:
seeds_dir = "%s%s%s%s%s%s%s" % (dir_path, '/seeds_', network, '_',
atlas_select, '_', str(node_size))
else:
seeds_dir = "%s%s%s%s%s" % (dir_path, '/seeds_', atlas_select, '_',
str(node_size))
seed_files = glob.glob("%s%s" % (seeds_dir, '/*diff.nii.gz'))
seed_files.sort(key=natural_keys)
# Binarize ROIs
print('\nBinarizing seed masks...')
j = 1
for i in seed_files:
args = ' -bin '
out_file = "%s%s" % (i.split('.nii.gz')[0], '_bin.nii.gz')
maths = fsl.ImageMaths(in_file=i, op_string=args, out_file=out_file)
os.system(maths.cmdline)
args = ' -mul ' + str(j)
maths = fsl.ImageMaths(in_file=out_file,
op_string=args,
out_file=out_file)
os.system(maths.cmdline)
j = j + 1
# Create atlas from ROIs
seed_files = glob.glob("%s%s" % (seeds_dir, '/*diff_bin.nii.gz'))
seed_files.sort(key=natural_keys)
print('\nMerging seed masks into single labels image...')
label_sum = "%s%s" % (seeds_dir, '/all_rois.nii.gz')
args = ' -add ' + i
maths = fsl.ImageMaths(in_file=seed_files[0],
op_string=args,
out_file=label_sum)
os.system(maths.cmdline)
for i in seed_files:
args = ' -add ' + i
maths = fsl.ImageMaths(in_file=label_sum,
op_string=args,
out_file=label_sum)
os.system(maths.cmdline)
labels_im = nib.load(label_sum)
labels_data = labels_im.get_data().astype('int')
conn_matrix, grouping = utils.connectivity_matrix(
tracks,
labels_data,
affine=affine,
return_mapping=True,
mapping_as_streamlines=True)
conn_matrix[:3, :] = 0
conn_matrix[:, :3] = 0
return conn_matrix
def run_to_estimate_dti_maps(path_input,
path_output,
file_tensor_fitevals="",
file_tensor_fitevecs="",
fbval="",
fbvec=""):
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_tensor_fitevals == "" or file_tensor_fitevecs == "":
for i in os.listdir(folder):
if "DTIEvals" in i:
file_tensor_fitevals = os.path.join(folder, i)
for i in os.listdir(folder):
if "DTIEvecs" in i:
file_tensor_fitevecs = os.path.join(folder, i)
if not os.path.exists(os.path.join(folder, "list_maps.txt")):
# def to_estimate_dti_maps(path_dwi_input, path_output, file_tensor_fitevecs, file_tensor_fitevals):
ref_name_only = utils.to_extract_filename(file_tensor_fitevecs)
ref_name_only = ref_name_only[:-9]
list_maps = []
img_tensorFitevecs = nib.load(file_tensor_fitevecs)
img_tensorFitevals = nib.load(file_tensor_fitevals)
evecs = img_tensorFitevecs.get_data()
evals = img_tensorFitevals.get_data()
affine = img_tensorFitevecs.affine
print(d.separador + d.separador + 'computing of FA map')
FA = fractional_anisotropy(evals)
FA[np.isnan(FA)] = 0
nib.save(
nib.Nifti1Image(FA.astype(np.float32), affine),
os.path.join(path_output, ref_name_only + '_FA' + d.extension))
list_maps.append(
os.path.join(path_output, ref_name_only + '_FA' + d.extension))
print(d.separador + d.separador + 'computing of Color FA map')
FA2 = np.clip(FA, 0, 1)
RGB = color_fa(FA2, evecs)
nib.save(
nib.Nifti1Image(np.array(255 * RGB, 'uint8'), affine),
os.path.join(path_output, ref_name_only + '_FA_RGB' + d.extension))
print(d.separador + d.separador + 'computing of MD map')
MD = dti.mean_diffusivity(evals)
nib.save(
nib.Nifti1Image(MD.astype(np.float32), affine),
os.path.join(path_output, ref_name_only + '_MD' + d.extension))
list_maps.append(
os.path.join(path_output, ref_name_only + '_MD' + d.extension))
print(d.separador + d.separador + 'computing of AD map')
AD = dti.axial_diffusivity(evals)
nib.save(
nib.Nifti1Image(AD.astype(np.float32), affine),
os.path.join(path_output, ref_name_only + '_AD' + d.extension))
list_maps.append(
os.path.join(path_output, ref_name_only + '_AD' + d.extension))
print(d.separador + d.separador + 'computing of RD map')
RD = dti.radial_diffusivity(evals)
nib.save(
nib.Nifti1Image(RD.astype(np.float32), affine),
os.path.join(path_output, ref_name_only + '_RD' + d.extension))
list_maps.append(
os.path.join(path_output, ref_name_only + '_RD' + d.extension))
sphere = get_sphere('symmetric724')
peak_indices = quantize_evecs(evecs, sphere.vertices)
eu = EuDX(FA.astype('f8'),
peak_indices,
seeds=300000,
odf_vertices=sphere.vertices,
a_low=0.15)
tensor_streamlines = [streamline for streamline in eu]
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = nib.load(path_input).header.get_zooms()[:3]
hdr['voxel_order'] = 'LAS'
hdr['dim'] = FA.shape
tensor_streamlines_trk = ((sl, None, None)
for sl in tensor_streamlines)
nib.trackvis.write(os.path.join(
path_output, ref_name_only + '_tractography_EuDx.trk'),
tensor_streamlines_trk,
hdr,
points_space='voxel')
print(list_maps)
with open(os.path.join(path_output, "list_maps.txt"), "w") as f:
for s in list_maps:
f.write(str(s) + "\n")
return path_input
def experiment1(f_name, data_path):
"OUTPUT"
f = open(f_name + '_out.txt', 'w')
"""""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """"""
"""Read Data"""
dipy_home = pjoin(os.path.expanduser('~'), '.dipy')
folder = pjoin(dipy_home, data_path)
fraw = pjoin(folder, f_name + '.nii.gz')
fbval = pjoin(folder, f_name + '.bval')
fbvec = pjoin(folder, f_name + '.bvec')
flabels = pjoin(folder, f_name + '.nii-label.nii.gz')
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
gtab = gradient_table(bvals, bvecs)
img = nib.load(fraw)
data = img.get_data()
print('data.shape (%d, %d, %d, %d)' % data.shape)
print('Building DTI Model Data......')
"""Load label"""
label_img = nib.load(flabels)
labels = label_img.get_data()
labelpo1 = label_position(labels, 1)
print labelpo1
"""""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """"""
maskdata, mask = median_otsu(data,
3,
1,
False,
vol_idx=range(10, 50),
dilate=2)
from dipy.reconst.dti import TensorModel
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data, mask)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
np.save(f_name + '_FA', FA)
fa_img = nib.Nifti1Image(FA.astype(np.float32), img.get_affine())
#nib.save(fa_img,f_name+'_DTI_tensor_fa.nii.gz')
#print('Saving "DTI_tensor_fa.nii.gz" sucessful.')
evecs_img = nib.Nifti1Image(tenfit.evecs.astype(np.float32),
img.get_affine())
#nib.save(evecs_img, f_name+'_DTI_tensor_evecs.nii.gz')
#print('Saving "DTI_tensor_evecs.nii.gz" sucessful.')
"""""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""" """""
""Fiber Tracking"""
print('Fiber Tracking......')
from dipy.tracking.eudx import EuDX
from dipy.data import get_sphere
from dipy.tracking import utils
seeds = utils.seeds_from_mask(labels, density=3)
print('The number of seeds is %d.' % len(seeds))
print >> f, ('The number of seeds is %d.' % len(seeds))
sphere = get_sphere('symmetric724')
from dipy.reconst.dti import quantize_evecs
evecs = evecs_img.get_data()
peak_indices = quantize_evecs(evecs, sphere.vertices)
"""""" """""" """""" """""" """""" """""" """""" """""" """""" """"""
streamline_generator = EuDX(FA.astype('f8'),
peak_indices,
seeds=10**5,
odf_vertices=sphere.vertices,
a_low=0.2,
step_sz=0.5,
ang_thr=60.0,
total_weight=.5,
max_points=10**5)
streamlines_all = [
streamlines_all for streamlines_all in streamline_generator
]
"""""" """""" """""" """""" """"""
"""Select length bigger than 10"""
from dipy.tracking.utils import length
lengths = list(length(streamlines_all))
select_length = 0
length = len(streamlines_all)
j = 0
for i in range(length):
if ((lengths[i]) > select_length):
streamlines_all[j] = streamlines_all[i]
j = j + 1
j = j - 1
streamlines = streamlines_all[0:j]
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = img.get_header().get_zooms()[:3]
hdr['voxel_order'] = 'LAS'
hdr['dim'] = FA.shape[:3]
"""划出roi streamlines"""
affine = streamline_generator.affine
cc_streamlines = label_streamlines(streamlines, labels, 1, affine, hdr,
f_name, data_path)
#M,grouping = connective_label(cc_streamlines, labels, affine, hdr, f_name, data_path)
label_streamlines_density(cc_streamlines, labels, affine, f_name, img,
label_img)
"""两个label的问题"""
flabels2 = pjoin(folder, f_name + '22.nii-label.nii.gz')
label_img2 = nib.load(flabels2)
labels2 = label_img2.get_data()
cc22_streamlines = label_streamlines(streamlines, labels2, 3, affine, hdr,
f_name, data_path)
labels3 = labels[:]
for i in range(len(labels)):
for j in range(len(labels[i])):
for k in range(len(labels[i][j])):
if (labels[i][j][k] == 0 and labels2[i][j][k] != 0):
labels3[i][j][k] = labels2[i][j][k]
M, grouping = connective_label(streamlines, labels3, affine, hdr, f_name,
data_path)
print M
print grouping[0, 3]
#experiment1('zhu long ping','patient_data')
def nii2streamlines(imgfile, maskfile, bvals, bvecs):
import numpy as np
import nibabel as nib
import os
from dipy.reconst.dti import TensorModel
img = nib.load(imgfile)
bvals = np.genfromtxt(bvals)
bvecs = np.genfromtxt(bvecs)
if bvecs.shape[1] != 3:
bvecs = bvecs.T
from nipype.utils.filemanip import split_filename
_, prefix, _ = split_filename(imgfile)
from dipy.data import gradient_table
gtab = gradient_table(bvals, bvecs)
data = img.get_data()
affine = img.get_affine()
zooms = img.get_header().get_zooms()[:3]
new_zooms = (2., 2., 2.)
data2, affine2 = data, affine
mask = nib.load(maskfile).get_data().astype(np.bool)
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data2, mask)
from dipy.reconst.dti import fractional_anisotropy
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
fa_img = nib.Nifti1Image(FA, img.get_affine())
nib.save(fa_img, '%s_tensor_fa.nii.gz' % prefix)
evecs = tenfit.evecs
evec_img = nib.Nifti1Image(evecs, img.get_affine())
nib.save(evec_img, '%s_tensor_evec.nii.gz' % prefix)
from dipy.data import get_sphere
sphere = get_sphere('symmetric724')
from dipy.reconst.dti import quantize_evecs
peak_indices = quantize_evecs(tenfit.evecs, sphere.vertices)
from dipy.tracking.eudx import EuDX
eu = EuDX(FA,
peak_indices,
odf_vertices=sphere.vertices,
a_low=0.2,
seeds=10**6,
ang_thr=35)
tensor_streamlines = [streamline for streamline in eu]
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = new_zooms
hdr['voxel_order'] = 'LPS'
hdr['dim'] = data2.shape[:3]
import dipy.tracking.metrics as dmetrics
tensor_streamlines = ((sl, None, None) for sl in tensor_streamlines
if dmetrics.length(sl) > 15)
ten_sl_fname = '%s_streamline.trk' % prefix
nib.trackvis.write(ten_sl_fname,
tensor_streamlines,
hdr,
points_space='voxel')
return ten_sl_fname
def dwi_dipy_run(dwi_dir,
node_size,
dir_path,
conn_model,
parc,
atlas_select,
network,
wm_mask=None):
from dipy.reconst.dti import TensorModel, quantize_evecs
from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel, recursive_response
from dipy.tracking.local import LocalTracking, ActTissueClassifier
from dipy.tracking import utils
from dipy.direction import peaks_from_model
from dipy.tracking.eudx import EuDX
from dipy.data import get_sphere, default_sphere
from dipy.core.gradients import gradient_table
from dipy.io import read_bvals_bvecs
from dipy.tracking.streamline import Streamlines
from dipy.direction import ProbabilisticDirectionGetter, ClosestPeakDirectionGetter, BootDirectionGetter
from nibabel.streamlines import save as save_trk
from nibabel.streamlines import Tractogram
##
dwi_dir = '/Users/PSYC-dap3463/Downloads/bedpostx_s002'
img_pve_csf = nib.load(
'/Users/PSYC-dap3463/Downloads/002_all/tmp/reg_a/t1w_vent_csf_diff_dwi.nii.gz'
)
img_pve_wm = nib.load(
'/Users/PSYC-dap3463/Downloads/002_all/tmp/reg_a/t1w_wm_in_dwi_bin.nii.gz'
)
img_pve_gm = nib.load(
'/Users/PSYC-dap3463/Downloads/002_all/tmp/reg_a/t1w_gm_mask_dwi.nii.gz'
)
labels_img = nib.load(
'/Users/PSYC-dap3463/Downloads/002_all/tmp/reg_a/dwi_aligned_atlas.nii.gz'
)
num_total_samples = 10000
tracking_method = 'boot' # Options are 'boot', 'prob', 'peaks', 'closest'
procmem = [2, 4]
##
if parc is True:
node_size = 'parc'
dwi_img = "%s%s" % (dwi_dir, '/dwi.nii.gz')
nodif_brain_mask_path = "%s%s" % (dwi_dir, '/nodif_brain_mask.nii.gz')
bvals = "%s%s" % (dwi_dir, '/bval')
bvecs = "%s%s" % (dwi_dir, '/bvec')
dwi_img = nib.load(dwi_img)
data = dwi_img.get_data()
[bvals, bvecs] = read_bvals_bvecs(bvals, bvecs)
gtab = gradient_table(bvals, bvecs)
gtab.b0_threshold = min(bvals)
sphere = get_sphere('symmetric724')
# Loads mask and ensures it's a true binary mask
mask_img = nib.load(nodif_brain_mask_path)
mask = mask_img.get_data()
mask = mask > 0
# Fit a basic tensor model first
model = TensorModel(gtab)
ten = model.fit(data, mask)
fa = ten.fa
# Tractography
if conn_model == 'csd':
print('Tracking with csd model...')
elif conn_model == 'tensor':
print('Tracking with tensor model...')
else:
raise RuntimeError("%s%s" % (conn_model, ' is not a valid model.'))
# Combine seed counts from voxel with seed counts total
wm_mask_data = img_pve_wm.get_data()
wm_mask_data[0, :, :] = False
wm_mask_data[:, 0, :] = False
wm_mask_data[:, :, 0] = False
seeds = utils.seeds_from_mask(wm_mask_data,
density=1,
affine=dwi_img.get_affine())
seeds_rnd = utils.random_seeds_from_mask(ten.fa > 0.02,
seeds_count=num_total_samples,
seed_count_per_voxel=True)
seeds_all = np.vstack([seeds, seeds_rnd])
# Load tissue maps and prepare tissue classifier (Anatomically-Constrained Tractography (ACT))
background = np.ones(img_pve_gm.shape)
background[(img_pve_gm.get_data() + img_pve_wm.get_data() +
img_pve_csf.get_data()) > 0] = 0
include_map = img_pve_gm.get_data()
include_map[background > 0] = 1
exclude_map = img_pve_csf.get_data()
act_classifier = ActTissueClassifier(include_map, exclude_map)
if conn_model == 'tensor':
ind = quantize_evecs(ten.evecs, sphere.vertices)
streamline_generator = EuDX(a=fa,
ind=ind,
seeds=seeds_all,
odf_vertices=sphere.vertices,
a_low=0.05,
step_sz=.5)
elif conn_model == 'csd':
print('Tracking with CSD model...')
response = recursive_response(
gtab,
data,
mask=img_pve_wm.get_data().astype('bool'),
sh_order=8,
peak_thr=0.01,
init_fa=0.05,
init_trace=0.0021,
iter=8,
convergence=0.001,
parallel=True)
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
if tracking_method == 'boot':
dg = BootDirectionGetter.from_data(data,
csd_model,
max_angle=30.,
sphere=default_sphere)
elif tracking_method == 'prob':
try:
print(
'First attempting to build the direction getter directly from the spherical harmonic representation of the FOD...'
)
csd_fit = csd_model.fit(
data, mask=img_pve_wm.get_data().astype('bool'))
dg = ProbabilisticDirectionGetter.from_shcoeff(
csd_fit.shm_coeff, max_angle=30., sphere=default_sphere)
except:
print(
'Sphereical harmonic not available for this model. Using peaks_from_model to represent the ODF of the model on a spherical harmonic basis instead...'
)
peaks = peaks_from_model(
csd_model,
data,
default_sphere,
.5,
25,
mask=img_pve_wm.get_data().astype('bool'),
return_sh=True,
parallel=True,
nbr_processes=procmem[0])
dg = ProbabilisticDirectionGetter.from_shcoeff(
peaks.shm_coeff, max_angle=30., sphere=default_sphere)
elif tracking_method == 'peaks':
dg = peaks_from_model(model=csd_model,
data=data,
sphere=default_sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
mask=img_pve_wm.get_data().astype('bool'),
parallel=True,
nbr_processes=procmem[0])
elif tracking_method == 'closest':
csd_fit = csd_model.fit(data,
mask=img_pve_wm.get_data().astype('bool'))
pmf = csd_fit.odf(default_sphere).clip(min=0)
dg = ClosestPeakDirectionGetter.from_pmf(pmf,
max_angle=30.,
sphere=default_sphere)
streamline_generator = LocalTracking(dg,
act_classifier,
seeds_all,
affine=dwi_img.affine,
step_size=0.5)
del dg
try:
del csd_fit
except:
pass
try:
del response
except:
pass
try:
del csd_model
except:
pass
streamlines = Streamlines(streamline_generator, buffer_size=512)
save_trk(Tractogram(streamlines, affine_to_rasmm=dwi_img.affine),
'prob_streamlines.trk')
tracks = [sl for sl in streamlines if len(sl) > 1]
labels_data = labels_img.get_data().astype('int')
labels_affine = labels_img.affine
conn_matrix, grouping = utils.connectivity_matrix(
tracks,
labels_data,
affine=labels_affine,
return_mapping=True,
mapping_as_streamlines=True,
symmetric=True)
conn_matrix[:3, :] = 0
conn_matrix[:, :3] = 0
return conn_matrix
def to_estimate_dti_maps(path_dwi_input, path_output, file_tensor_fitevecs,
file_tensor_fitevals):
ref_name_only = utils.to_extract_filename(file_tensor_fitevecs)
ref_name_only = ref_name_only[:-9]
list_maps = []
img_tensorFitevecs = nib.load(file_tensor_fitevecs)
img_tensorFitevals = nib.load(file_tensor_fitevals)
evecs = img_tensorFitevecs.get_data()
evals = img_tensorFitevals.get_data()
affine = img_tensorFitevecs.affine
print(d.separador + d.separador + 'computing of FA map')
FA = fractional_anisotropy(evals)
FA[np.isnan(FA)] = 0
nib.save(nib.Nifti1Image(FA.astype(np.float32), affine),
path_output + ref_name_only + '_FA' + d.extension)
list_maps.append(path_output + ref_name_only + '_FA' + d.extension)
print(d.separador + d.separador + 'computing of Color FA map')
FA2 = np.clip(FA, 0, 1)
RGB = color_fa(FA2, evecs)
nib.save(nib.Nifti1Image(np.array(255 * RGB, 'uint8'), affine),
path_output + ref_name_only + '_FA_RGB' + d.extension)
print(d.separador + d.separador + 'computing of MD map')
MD = dti.mean_diffusivity(evals)
nib.save(nib.Nifti1Image(MD.astype(np.float32), affine),
path_output + ref_name_only + '_MD' + d.extension)
list_maps.append(path_output + ref_name_only + '_MD' + d.extension)
print(d.separador + d.separador + 'computing of AD map')
AD = dti.axial_diffusivity(evals)
nib.save(nib.Nifti1Image(AD.astype(np.float32), affine),
path_output + ref_name_only + '_AD' + d.extension)
list_maps.append(path_output + ref_name_only + '_AD' + d.extension)
print(d.separador + d.separador + 'computing of RD map')
RD = dti.radial_diffusivity(evals)
nib.save(nib.Nifti1Image(RD.astype(np.float32), affine),
path_output + ref_name_only + '_RD' + d.extension)
list_maps.append(path_output + ref_name_only + '_RD' + d.extension)
sphere = get_sphere('symmetric724')
peak_indices = quantize_evecs(evecs, sphere.vertices)
eu = EuDX(FA.astype('f8'),
peak_indices,
seeds=300000,
odf_vertices=sphere.vertices,
a_low=0.15)
tensor_streamlines = [streamline for streamline in eu]
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = nib.load(path_dwi_input).get_header().get_zooms()[:3]
hdr['voxel_order'] = 'LAS'
hdr['dim'] = FA.shape
tensor_streamlines_trk = ((sl, None, None) for sl in tensor_streamlines)
nib.trackvis.write(path_output + ref_name_only + '_tractography_EuDx.trk',
tensor_streamlines_trk,
hdr,
points_space='voxel')
return list_maps
For the discretization procedure we use an evenly distributed sphere of 724
points which we can access using the get_sphere function.
"""
from dipy.data import get_sphere
sphere = get_sphere('symmetric724')
"""
We use quantize_evecs (evecs here stands for eigen vectors) to apply the
discretization.
"""
from dipy.reconst.dti import quantize_evecs
peak_indices = quantize_evecs(evecs, sphere.vertices)
"""
EuDX is the fiber tracking algorithm that we use in this example.
The most important parameters are the first one which represents the
magnitude of the peak of a scalar anisotropic function, the
second which represents the indices of the discretized directions of
the peaks and odf_vertices are the vertices of the input sphere.
"""
from dipy.tracking.eudx import EuDX
from dipy.tracking.streamline import Streamlines
eu = EuDX(FA.astype('f8'), peak_indices, seeds=50000, odf_vertices = sphere.vertices, a_low=0.2)
tensor_streamlines = Streamlines(eu)
For the discretization procedure we use an evenly distributed sphere of 724
points which we can access using the get_sphere function.
"""
from dipy.data import get_sphere
sphere = get_sphere('symmetric724')
"""
We use quantize_evecs (evecs here stands for eigen vectors) to apply the
discretization.
"""
from dipy.reconst.dti import quantize_evecs
peak_indices = quantize_evecs(evecs, sphere.vertices)
"""
EuDX is the fiber tracking algorithm that we use in this example.
The most important parameters are the first one which represents the
magnitude of the peak of a scalar anisotropic function, the
second which represents the indices of the discretized directions of
the peaks and odf_vertices are the vertices of the input sphere.
"""
from dipy.tracking.eudx import EuDX
eu = EuDX(FA.astype('f8'),
peak_indices,
seeds=50000,
odf_vertices=sphere.vertices,
a_low=0.2)
def experiment1(f_name,data_path):
"OUTPUT"
f=open(f_name+'_out.txt','w')
""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
"""Read Data"""
dipy_home = pjoin(os.path.expanduser('~'), '.dipy')
folder = pjoin(dipy_home, data_path)
fraw = pjoin(folder, f_name+'.nii.gz')
fbval = pjoin(folder, f_name+'.bval')
fbvec = pjoin(folder, f_name+'.bvec')
flabels = pjoin(folder, f_name+'.nii-label.nii.gz')
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
gtab = gradient_table(bvals, bvecs)
img = nib.load(fraw)
data = img.get_data()
print('data.shape (%d, %d, %d, %d)' % data.shape)
print('Building DTI Model Data......')
"""Load label"""
label_img = nib.load(flabels)
labels=label_img.get_data()
labelpo1=label_position(labels,1)
print labelpo1
""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
maskdata, mask = median_otsu(data, 3, 1, False, vol_idx=range(10, 50), dilate=2)
from dipy.reconst.dti import TensorModel
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data, mask)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
np.save(f_name+'_FA',FA)
fa_img = nib.Nifti1Image(FA.astype(np.float32), img.get_affine())
#nib.save(fa_img,f_name+'_DTI_tensor_fa.nii.gz')
#print('Saving "DTI_tensor_fa.nii.gz" sucessful.')
evecs_img = nib.Nifti1Image(tenfit.evecs.astype(np.float32), img.get_affine())
#nib.save(evecs_img, f_name+'_DTI_tensor_evecs.nii.gz')
#print('Saving "DTI_tensor_evecs.nii.gz" sucessful.')
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
""Fiber Tracking"""
print('Fiber Tracking......')
from dipy.tracking.eudx import EuDX
from dipy.data import get_sphere
from dipy.tracking import utils
seeds = utils.seeds_from_mask(labels, density=3)
print('The number of seeds is %d.' % len(seeds))
print >>f,('The number of seeds is %d.' % len(seeds))
sphere = get_sphere('symmetric724')
from dipy.reconst.dti import quantize_evecs
evecs = evecs_img.get_data()
peak_indices = quantize_evecs(evecs, sphere.vertices)
""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""
streamline_generator = EuDX(FA.astype('f8'),
peak_indices,
seeds=10**5,
odf_vertices=sphere.vertices,
a_low=0.2,
step_sz=0.5,
ang_thr=60.0,
total_weight=.5,
max_points=10**5)
streamlines_all = [streamlines_all for streamlines_all in streamline_generator]
""""""""""""""""""""""""""""""
"""Select length bigger than 10"""
from dipy.tracking.utils import length
lengths = list(length(streamlines_all))
select_length = 0
length=len(streamlines_all)
j=0
for i in range(length):
if ((lengths[i]) > select_length):
streamlines_all[j] = streamlines_all[i]
j=j+1
j=j-1
streamlines = streamlines_all[0:j]
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = img.get_header().get_zooms()[:3]
hdr['voxel_order'] = 'LAS'
hdr['dim'] = FA.shape[:3]
"""划出roi streamlines"""
affine = streamline_generator.affine
cc_streamlines=label_streamlines(streamlines, labels,1, affine, hdr, f_name, data_path)
#M,grouping = connective_label(cc_streamlines, labels, affine, hdr, f_name, data_path)
label_streamlines_density(cc_streamlines, labels, affine,f_name,img, label_img)
"""两个label的问题"""
flabels2 = pjoin(folder, f_name+'22.nii-label.nii.gz')
label_img2 = nib.load(flabels2)
labels2=label_img2.get_data()
cc22_streamlines=label_streamlines(streamlines, labels2,3, affine, hdr, f_name, data_path)
labels3 = labels[:]
for i in range(len(labels)):
for j in range(len(labels[i])):
for k in range(len(labels[i][j])) :
if (labels[i][j][k]==0 and labels2[i][j][k]!=0):
labels3[i][j][k] = labels2[i][j][k]
M,grouping = connective_label(streamlines, labels3, affine, hdr, f_name, data_path)
print M
print grouping[0,3]
#experiment1('zhu long ping','patient_data')