def csd_mod_est(self):
print("Fitting CSD model...")
try:
print("Attempting to use spherical harmonic basis first...")
self.mod = ConstrainedSphericalDeconvModel(self.gtab,
None,
sh_order=6)
except:
print("Falling back to estimating recursive response...")
self.response = recursive_response(
self.gtab,
self.data,
mask=self.wm_in_dwi_data,
sh_order=6,
peak_thr=0.01,
init_fa=0.08,
init_trace=0.0021,
iter=8,
convergence=0.001,
parallel=False,
)
print("CSD Reponse: " + str(self.response))
self.mod = ConstrainedSphericalDeconvModel(self.gtab,
self.response,
sh_order=6)
return self.mod
def csd_mod_est(gtab, data, B0_mask, sh_order=8):
'''
Estimate a Constrained Spherical Deconvolution (CSD) model from dwi data.
Parameters
----------
gtab : Obj
DiPy object storing diffusion gradient information.
data : array
4D numpy array of diffusion image data.
B0_mask : str
File path to B0 brain mask.
sh_order : int
The order of the SH model. Default is 8.
Returns
-------
csd_mod : ndarray
Coefficients of the csd reconstruction.
model : obj
Fitted csd model.
References
----------
.. [1] Tournier, J.D., et al. NeuroImage 2007. Robust determination of
the fibre orientation distribution in diffusion MRI:
Non-negativity constrained super-resolved spherical
deconvolution
.. [2] Descoteaux, M., et al. IEEE TMI 2009. Deterministic and
Probabilistic Tractography Based on Complex Fibre Orientation
Distributions
.. [3] Côté, M-A., et al. Medical Image Analysis 2013. Tractometer:
Towards validation of tractography pipelines
.. [4] Tournier, J.D, et al. Imaging Systems and Technology
2012. MRtrix: Diffusion Tractography in Crossing Fiber Regions
'''
from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel, recursive_response
print('Fitting CSD model...')
B0_mask_data = np.asarray(nib.load(B0_mask).dataobj).astype('bool')
print('Reconstructing...')
response = recursive_response(gtab,
data,
mask=B0_mask_data,
sh_order=sh_order,
peak_thr=0.01,
init_fa=0.08,
init_trace=0.0021,
iter=8,
convergence=0.001,
parallel=False)
print('CSD Reponse: ' + str(response))
model = ConstrainedSphericalDeconvModel(gtab, response, sh_order=sh_order)
csd_mod = model.fit(data, B0_mask_data).shm_coeff
del response, B0_mask_data
return csd_mod, model
def csd_mod_est(gtab, data, wm_in_dwi):
from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel, recursive_response
print('Fitting CSD model...')
wm_in_dwi_mask = nib.load(wm_in_dwi).get_fdata().astype('bool')
try:
print('Attempting to use spherical harmonic...')
model = ConstrainedSphericalDeconvModel(gtab, None, sh_order=6)
except:
print('Falling back to recursive response...')
response = recursive_response(gtab, data, mask=wm_in_dwi_mask, sh_order=8,
peak_thr=0.01, init_fa=0.08, init_trace=0.0021, iter=8, convergence=0.001,
parallel=False)
print('CSD Reponse: ' + str(response))
model = ConstrainedSphericalDeconvModel(gtab, response)
mod = model.fit(data, wm_in_dwi_mask)
return mod.shm_coeff
def csd_mod_est(dwi, gtab, wm_mask):
print("Fitting CSD model...")
print("Estimating recursive response...")
response = recursive_response(
gtab,
dwi,
mask=wm_mask,
sh_order=6,
peak_thr=0.01,
init_fa=0.08,
init_trace=0.0021,
iter=8,
convergence=0.001,
parallel=False,
)
mod = ConstrainedSphericalDeconvModel(gtab, response, sh_order=6)
return mod
def csd_response(gtab, data):
""" Estimate response function for given HARDI data.
Unfortunately, does not work for 2d (synthetic) data (why?).
"""
tenmodel = dti.TensorModel(gtab)
tenfit = tenmodel.fit(data, mask=data[..., 0] > 200)
FA = fractional_anisotropy(tenfit.evals)
MD = dti.mean_diffusivity(tenfit.evals)
wm_mask = (np.logical_or(FA >= 0.4,
(np.logical_and(FA >= 0.15, MD >= 0.0011))))
response = recursive_response(gtab,
data,
mask=wm_mask,
sh_order=8,
peak_thr=0.01,
init_fa=0.08,
init_trace=0.0021,
iter=8,
convergence=0.001,
parallel=True)
return response
def csd_mod_est(gtab, data, wm_in_dwi):
'''
Estimate a Constrained Spherical Deconvolution (CSD) model from dwi data.
Parameters
----------
gtab : Obj
DiPy object storing diffusion gradient information.
data : array
4D numpy array of diffusion image data.
wm_in_dwi : str
File path to white-matter tissue segmentation Nifti1Image.
Returns
-------
csd_mod : obj
Spherical harmonics coefficients of the CSD-estimated reconstruction model.
'''
from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel, recursive_response
print('Fitting CSD model...')
wm_in_dwi_mask = nib.load(wm_in_dwi).get_fdata().astype('bool')
try:
print('Reconstructing...')
model = ConstrainedSphericalDeconvModel(gtab, None, sh_order=6)
except:
print('Falling back to recursive response...')
response = recursive_response(gtab,
data,
mask=wm_in_dwi_mask,
sh_order=8,
peak_thr=0.01,
init_fa=0.08,
init_trace=0.0021,
iter=8,
convergence=0.001,
parallel=False)
print('CSD Reponse: ' + str(response))
model = ConstrainedSphericalDeconvModel(gtab, response)
csd_mod = model.fit(data, wm_in_dwi_mask).shm_coeff
return csd_mod
def csd_mod_est(gtab, data, B0_mask, sh_order=8):
'''
Estimate a Constrained Spherical Deconvolution (CSD) model from dwi data.
Parameters
----------
gtab : Obj
DiPy object storing diffusion gradient information.
data : array
4D numpy array of diffusion image data.
B0_mask : str
File path to B0 brain mask.
sh_order : int
The order of the SH model. Default is 8.
Returns
-------
csd_mod : obj
Spherical harmonics coefficients of the CSD-estimated reconstruction model.
'''
from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel, recursive_response
print('Fitting CSD model...')
B0_mask_data = np.asarray(nib.load(B0_mask).dataobj).astype('bool')
print('Reconstructing...')
response = recursive_response(gtab,
data,
mask=B0_mask_data,
sh_order=sh_order,
peak_thr=0.01,
init_fa=0.08,
init_trace=0.0021,
iter=8,
convergence=0.001,
parallel=False)
print('CSD Reponse: ' + str(response))
model = ConstrainedSphericalDeconvModel(gtab, response, sh_order=sh_order)
csd_mod = model.fit(data, B0_mask_data).shm_coeff
del model, response, B0_mask_data
return csd_mod
def test_recursive_response_calibration():
"""
Test the recursive response calibration method.
"""
SNR = 100
S0 = 1
sh_order = 8
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
sphere = get_sphere('symmetric724')
gtab = gradient_table(bvals, bvecs)
evals = np.array([0.0015, 0.0003, 0.0003])
evecs = np.array([[0, 1, 0], [0, 0, 1], [1, 0, 0]]).T
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (90, 0)]
where_dwi = lazy_index(~gtab.b0s_mask)
S_cross, sticks_cross = multi_tensor(gtab, mevals, S0, angles=angles,
fractions=[50, 50], snr=SNR)
S_single = single_tensor(gtab, S0, evals, evecs, snr=SNR)
data = np.concatenate((np.tile(S_cross, (8, 1)),
np.tile(S_single, (2, 1))),
axis=0)
odf_gt_cross = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
odf_gt_single = single_tensor_odf(sphere.vertices, evals, evecs)
response = recursive_response(gtab, data, mask=None, sh_order=8,
peak_thr=0.01, init_fa=0.05,
init_trace=0.0021, iter=8, convergence=0.001,
parallel=False)
csd = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit = csd.fit(data)
assert_equal(np.all(csd_fit.shm_coeff[:, 0] >= 0), True)
fodf = csd_fit.odf(sphere)
directions_gt_single, _, _ = peak_directions(odf_gt_single, sphere)
directions_gt_cross, _, _ = peak_directions(odf_gt_cross, sphere)
directions_single, _, _ = peak_directions(fodf[8, :], sphere)
directions_cross, _, _ = peak_directions(fodf[0, :], sphere)
ang_sim = angular_similarity(directions_cross, directions_gt_cross)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions_cross.shape[0], 2)
assert_equal(directions_gt_cross.shape[0], 2)
ang_sim = angular_similarity(directions_single, directions_gt_single)
assert_equal(ang_sim > 0.9, True)
assert_equal(directions_single.shape[0], 1)
assert_equal(directions_gt_single.shape[0], 1)
sphere = Sphere(xyz=gtab.gradients[where_dwi])
sf = response.on_sphere(sphere)
S = np.concatenate(([response.S0], sf))
tenmodel = dti.TensorModel(gtab, min_signal=0.001)
tenfit = tenmodel.fit(S)
FA = fractional_anisotropy(tenfit.evals)
FA_gt = fractional_anisotropy(evals)
assert_almost_equal(FA, FA_gt, 1)
def test_recursive_response_calibration():
"""
Test the recursive response calibration method.
"""
SNR = 100
S0 = 1
sh_order = 8
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
sphere = get_sphere('symmetric724')
gtab = gradient_table(bvals, bvecs)
evals = np.array([0.0015, 0.0003, 0.0003])
evecs = np.array([[0, 1, 0], [0, 0, 1], [1, 0, 0]]).T
mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (90, 0)]
where_dwi = lazy_index(~gtab.b0s_mask)
S_cross, sticks_cross = multi_tensor(gtab,
mevals,
S0,
angles=angles,
fractions=[50, 50],
snr=SNR)
S_single = single_tensor(gtab, S0, evals, evecs, snr=SNR)
data = np.concatenate((np.tile(S_cross, (8, 1)), np.tile(S_single,
(2, 1))),
axis=0)
odf_gt_cross = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
odf_gt_single = single_tensor_odf(sphere.vertices, evals, evecs)
response = recursive_response(gtab,
data,
mask=None,
sh_order=8,
peak_thr=0.01,
init_fa=0.05,
init_trace=0.0021,
iter=8,
convergence=0.001,
parallel=False)
csd = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit = csd.fit(data)
assert_equal(np.all(csd_fit.shm_coeff[:, 0] >= 0), True)
fodf = csd_fit.odf(sphere)
directions_gt_single, _, _ = peak_directions(odf_gt_single, sphere)
directions_gt_cross, _, _ = peak_directions(odf_gt_cross, sphere)
directions_single, _, _ = peak_directions(fodf[8, :], sphere)
directions_cross, _, _ = peak_directions(fodf[0, :], sphere)
ang_sim = angular_similarity(directions_cross, directions_gt_cross)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions_cross.shape[0], 2)
assert_equal(directions_gt_cross.shape[0], 2)
ang_sim = angular_similarity(directions_single, directions_gt_single)
assert_equal(ang_sim > 0.9, True)
assert_equal(directions_single.shape[0], 1)
assert_equal(directions_gt_single.shape[0], 1)
sphere = Sphere(xyz=gtab.gradients[where_dwi])
sf = response.on_sphere(sphere)
S = np.concatenate(([response.S0], sf))
tenmodel = dti.TensorModel(gtab, min_signal=0.001)
tenfit = tenmodel.fit(S)
FA = fractional_anisotropy(tenfit.evals)
FA_gt = fractional_anisotropy(evals)
assert_almost_equal(FA, FA_gt, 1)
def _run_interface(self, runtime):
from dipy.core.gradients import GradientTable
from dipy.reconst.dti import fractional_anisotropy, mean_diffusivity
from dipy.reconst.csdeconv import recursive_response, auto_response
img = nb.load(self.inputs.in_file)
imref = nb.four_to_three(img)[0]
affine = img.affine
if isdefined(self.inputs.in_mask):
msk = nb.load(self.inputs.in_mask).get_data()
msk[msk > 0] = 1
msk[msk < 0] = 0
else:
msk = np.ones(imref.shape)
data = img.get_data().astype(np.float32)
gtab = self._get_gradient_table()
evals = np.nan_to_num(nb.load(self.inputs.in_evals).get_data())
FA = np.nan_to_num(fractional_anisotropy(evals)) * msk
indices = np.where(FA > self.inputs.fa_thresh)
S0s = data[indices][:, np.nonzero(gtab.b0s_mask)[0]]
S0 = np.mean(S0s)
if self.inputs.auto:
response, ratio = auto_response(gtab,
data,
roi_radius=self.inputs.roi_radius,
fa_thr=self.inputs.fa_thresh)
response = response[0].tolist() + [S0]
elif self.inputs.recursive:
MD = np.nan_to_num(mean_diffusivity(evals)) * msk
indices = np.logical_or(FA >= 0.4,
(np.logical_and(FA >= 0.15, MD >= 0.0011)))
data = nb.load(self.inputs.in_file).get_data()
response = recursive_response(gtab,
data,
mask=indices,
sh_order=8,
peak_thr=0.01,
init_fa=0.08,
init_trace=0.0021,
iter=8,
convergence=0.001,
parallel=True)
ratio = abs(response[1] / response[0])
else:
lambdas = evals[indices]
l01 = np.sort(np.mean(lambdas, axis=0))
response = np.array([l01[-1], l01[-2], l01[-2], S0])
ratio = abs(response[1] / response[0])
if ratio > 0.25:
IFLOGGER.warn(
'Estimated response is not prolate enough. '
'Ratio=%0.3f.', ratio)
elif ratio < 1.e-5 or np.any(np.isnan(response)):
response = np.array([1.8e-3, 3.6e-4, 3.6e-4, S0])
IFLOGGER.warn(
'Estimated response is not valid, using a default one')
else:
IFLOGGER.info('Estimated response: %s', str(response[:3]))
np.savetxt(op.abspath(self.inputs.response), response)
wm_mask = np.zeros_like(FA)
wm_mask[indices] = 1
nb.Nifti1Image(wm_mask.astype(np.uint8), affine,
None).to_filename(op.abspath(self.inputs.out_mask))
return runtime
def dodata(f_name,data_path):
dipy_home = pjoin(os.path.expanduser('~'), 'dipy_data')
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()
affine = img.get_affine()
label_img = nib.load(flabels)
labels=label_img.get_data()
lap=through_label_sl.label_position(labels, labelValue=1)
dataslice = data[40:80, 20:80, lap[2][2] / 2]
#print lap[2][2]/2
#get_csd_gfa(f_name,data,gtab,dataslice)
maskdata, mask = median_otsu(data, 2, 1, False, vol_idx=range(10, 50), dilate=2) #不去背景
""" get fa and tensor evecs and ODF"""
from dipy.reconst.dti import TensorModel,mean_diffusivity
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data, mask)
sphere = get_sphere('symmetric724')
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
np.save(os.getcwd()+'\zhibiao'+f_name+'_FA.npy',FA)
fa_img = nib.Nifti1Image(FA.astype(np.float32), affine)
nib.save(fa_img,os.getcwd()+'\zhibiao'+f_name+'_FA.nii.gz')
print('Saving "DTI_tensor_fa.nii.gz" sucessful.')
evecs_img = nib.Nifti1Image(tenfit.evecs.astype(np.float32), affine)
nib.save(evecs_img, os.getcwd()+'\zhibiao'+f_name+'_DTI_tensor_evecs.nii.gz')
print('Saving "DTI_tensor_evecs.nii.gz" sucessful.')
MD1 = mean_diffusivity(tenfit.evals)
nib.save(nib.Nifti1Image(MD1.astype(np.float32), img.get_affine()), os.getcwd()+'\zhibiao'+f_name+'_MD.nii.gz')
#tensor_odfs = tenmodel.fit(data[20:50, 55:85, 38:39]).odf(sphere)
#from dipy.reconst.odf import gfa
#dti_gfa=gfa(tensor_odfs)
wm_mask = (np.logical_or(FA >= 0.4, (np.logical_and(FA >= 0.15, MD >= 0.0011))))
response = recursive_response(gtab, data, mask=wm_mask, sh_order=8,
peak_thr=0.01, init_fa=0.08,
init_trace=0.0021, iter=8, convergence=0.001,
parallel=False)
from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
#csd_fit = csd_model.fit(data)
from dipy.direction import peaks_from_model
csd_peaks = peaks_from_model(model=csd_model,
data=data,
sphere=sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=False)
GFA = csd_peaks.gfa
nib.save(GFA, os.getcwd()+'\zhibiao'+f_name+'_MSD.nii.gz')
print('Saving "GFA.nii.gz" sucessful.')
from dipy.reconst.shore import ShoreModel
asm = ShoreModel(gtab)
print('Calculating...SHORE msd')
asmfit = asm.fit(data,mask)
msd = asmfit.msd()
msd[np.isnan(msd)] = 0
#print GFA[:,:,slice].T
print('Saving msd_img.png')
nib.save(msd, os.getcwd()+'\zhibiao'+f_name+'_GFA.nii.gz')
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 csd_mod_est(gtab, data, B0_mask, sh_order=8):
"""
Estimate a Constrained Spherical Deconvolution (CSD) model from dwi data.
Parameters
----------
gtab : Obj
DiPy object storing diffusion gradient information.
data : array
4D numpy array of diffusion image data.
B0_mask : str
File path to B0 brain mask.
sh_order : int
The order of the SH model. Default is 8.
Returns
-------
csd_mod : ndarray
Coefficients of the csd reconstruction.
model : obj
Fitted csd model.
References
----------
.. [1] Tournier, J.D., et al. NeuroImage 2007. Robust determination of
the fibre orientation distribution in diffusion MRI:
Non-negativity constrained super-resolved spherical
deconvolution
.. [2] Descoteaux, M., et al. IEEE TMI 2009. Deterministic and
Probabilistic Tractography Based on Complex Fibre Orientation
Distributions
.. [3] Côté, M-A., et al. Medical Image Analysis 2013. Tractometer:
Towards validation of tractography pipelines
.. [4] Tournier, J.D, et al. Imaging Systems and Technology
2012. MRtrix: Diffusion Tractography in Crossing Fiber Regions
"""
from dipy.reconst.csdeconv import (
ConstrainedSphericalDeconvModel,
recursive_response,
)
import pkg_resources
import yaml
with open(pkg_resources.resource_filename("pynets", "runconfig.yaml"),
"r") as stream:
hardcoded_params = yaml.load(stream)
nthreads = hardcoded_params["nthreads"][0]
stream.close()
print("Fitting CSD model...")
B0_mask_data = np.nan_to_num(np.asarray(
nib.load(B0_mask).dataobj)).astype("bool")
print("Reconstructing...")
response = recursive_response(gtab,
data,
mask=B0_mask_data,
sh_order=sh_order,
peak_thr=0.01,
init_fa=0.08,
init_trace=0.0021,
iter=8,
convergence=0.001,
parallel=False,
nbr_processes=nthreads)
print("CSD Reponse: " + str(response))
model = ConstrainedSphericalDeconvModel(gtab, response, sh_order=sh_order)
csd_mod = model.fit(data, B0_mask_data).shm_coeff
del response, B0_mask_data
return csd_mod, model
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
import dipy.reconst.dti as dti
tenmodel = dti.TensorModel(gtab)
tenfit = tenmodel.fit(data, mask=data[..., 0] > 200)
from dipy.reconst.dti import fractional_anisotropy
FA = fractional_anisotropy(tenfit.evals)
MD = dti.mean_diffusivity(tenfit.evals)
wm_mask = (np.logical_or(FA >= 0.4,
(np.logical_and(FA >= 0.15, MD >= 0.0011))))
response = recursive_response(gtab,
data,
mask=wm_mask,
sh_order=8,
peak_thr=0.01,
init_fa=0.08,
init_trace=0.0021,
iter=8,
convergence=0.001,
parallel=True)
"""
We can check the shape of the signal of the response function, which should be
like a pancake:
"""
response_signal = response.on_sphere(default_sphere)
# transform our data from 1D to 4D
response_signal = response_signal[None, None, None, :]
response_actor = actor.odf_slicer(response_signal,
sphere=default_sphere,
colormap='plasma')
"""
A WM mask can shorten computation time for the whole dataset. Here it is created
based on the DTI fit.
"""
import dipy.reconst.dti as dti
tenmodel = dti.TensorModel(gtab)
tenfit = tenmodel.fit(data, mask=data[..., 0] > 200)
from dipy.reconst.dti import fractional_anisotropy
FA = fractional_anisotropy(tenfit.evals)
MD = dti.mean_diffusivity(tenfit.evals)
wm_mask = (np.logical_or(FA >= 0.4, (np.logical_and(FA >= 0.15, MD >= 0.0011))))
response = recursive_response(gtab, data, mask=wm_mask, sh_order=8,
peak_thr=0.01, init_fa=0.08,
init_trace=0.0021, iter=8, convergence=0.001,
parallel=True)
"""
We can check the shape of the signal of the response function, which should be
like a pancake:
"""
response_signal = response.on_sphere(sphere)
response_actor = fvtk.sphere_funcs(response_signal, sphere)
ren = fvtk.ren()
fvtk.add(ren, response_actor)
print('Saving illustration as csd_recursive_response.png')
def test_recursive_response_calibration():
"""
Test the recursive response calibration method.
"""
SNR = 100
S0 = 1
_, fbvals, fbvecs = get_fnames('small_64D')
bvals, bvecs = read_bvals_bvecs(fbvals, fbvecs)
sphere = default_sphere
gtab = gradient_table(bvals, bvecs)
evals = np.array([0.0015, 0.0003, 0.0003])
evecs = np.array([[0, 1, 0], [0, 0, 1], [1, 0, 0]]).T
mevals = np.array(([0.0015, 0.0003, 0.0003], [0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (90, 0)]
where_dwi = lazy_index(~gtab.b0s_mask)
S_cross, _ = multi_tensor(gtab,
mevals,
S0,
angles=angles,
fractions=[50, 50],
snr=SNR)
S_single = single_tensor(gtab, S0, evals, evecs, snr=SNR)
data = np.concatenate((np.tile(S_cross, (8, 1)), np.tile(S_single,
(2, 1))),
axis=0)
odf_gt_cross = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
odf_gt_single = single_tensor_odf(sphere.vertices, evals, evecs)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
response = recursive_response(gtab,
data,
mask=None,
sh_order=8,
peak_thr=0.01,
init_fa=0.05,
init_trace=0.0021,
iter=8,
convergence=0.001,
parallel=False)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
csd = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit = csd.fit(data)
assert_equal(np.all(csd_fit.shm_coeff[:, 0] >= 0), True)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
fodf = csd_fit.odf(sphere)
directions_gt_single, _, _ = peak_directions(odf_gt_single, sphere)
directions_gt_cross, _, _ = peak_directions(odf_gt_cross, sphere)
directions_single, _, _ = peak_directions(fodf[8, :], sphere)
directions_cross, _, _ = peak_directions(fodf[0, :], sphere)
ang_sim = angular_similarity(directions_cross, directions_gt_cross)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions_cross.shape[0], 2)
assert_equal(directions_gt_cross.shape[0], 2)
ang_sim = angular_similarity(directions_single, directions_gt_single)
assert_equal(ang_sim > 0.9, True)
assert_equal(directions_single.shape[0], 1)
assert_equal(directions_gt_single.shape[0], 1)
with warnings.catch_warnings(record=True) as w:
sphere = Sphere(xyz=gtab.gradients[where_dwi])
npt.assert_equal(len(w), 1)
npt.assert_(issubclass(w[0].category, UserWarning))
npt.assert_("Vertices are not on the unit sphere" in str(w[0].message))
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
sf = response.on_sphere(sphere)
S = np.concatenate(([response.S0], sf))
tenmodel = TensorModel(gtab, min_signal=0.001)
tenfit = tenmodel.fit(S)
FA = fractional_anisotropy(tenfit.evals)
FA_gt = fractional_anisotropy(evals)
assert_almost_equal(FA, FA_gt, 1)
def _run_interface(self, runtime):
from dipy.core.gradients import GradientTable
from dipy.reconst.dti import fractional_anisotropy, mean_diffusivity
from dipy.reconst.csdeconv import recursive_response, auto_response
img = nb.load(self.inputs.in_file)
affine = img.get_affine()
if isdefined(self.inputs.in_mask):
msk = nb.load(self.inputs.in_mask).get_data()
msk[msk > 0] = 1
msk[msk < 0] = 0
else:
msk = np.ones(imref.get_shape())
data = img.get_data().astype(np.float32)
gtab = self._get_gradient_table()
evals = np.nan_to_num(nb.load(self.inputs.in_evals).get_data())
FA = np.nan_to_num(fractional_anisotropy(evals)) * msk
indices = np.where(FA > self.inputs.fa_thresh)
S0s = data[indices][:, np.nonzero(gtab.b0s_mask)[0]]
S0 = np.mean(S0s)
if self.inputs.auto:
response, ratio = auto_response(gtab, data,
roi_radius=self.inputs.roi_radius,
fa_thr=self.inputs.fa_thresh)
response = response[0].tolist() + [S0]
elif self.inputs.recursive:
MD = np.nan_to_num(mean_diffusivity(evals)) * msk
indices = np.logical_or(
FA >= 0.4, (np.logical_and(FA >= 0.15, MD >= 0.0011)))
data = nb.load(self.inputs.in_file).get_data()
response = recursive_response(gtab, data, mask=indices, sh_order=8,
peak_thr=0.01, init_fa=0.08,
init_trace=0.0021, iter=8,
convergence=0.001,
parallel=True)
ratio = abs(response[1] / response[0])
else:
lambdas = evals[indices]
l01 = np.sort(np.mean(lambdas, axis=0))
response = np.array([l01[-1], l01[-2], l01[-2], S0])
ratio = abs(response[1] / response[0])
if ratio > 0.25:
IFLOGGER.warn(('Estimated response is not prolate enough. '
'Ratio=%0.3f.') % ratio)
elif ratio < 1.e-5 or np.any(np.isnan(response)):
response = np.array([1.8e-3, 3.6e-4, 3.6e-4, S0])
IFLOGGER.warn(
('Estimated response is not valid, using a default one'))
else:
IFLOGGER.info(('Estimated response: %s') % str(response[:3]))
np.savetxt(op.abspath(self.inputs.response), response)
wm_mask = np.zeros_like(FA)
wm_mask[indices] = 1
nb.Nifti1Image(
wm_mask.astype(np.uint8), affine,
None).to_filename(op.abspath(self.inputs.out_mask))
return runtime
def dodata(f_name, data_path):
dipy_home = pjoin(os.path.expanduser('~'), 'dipy_data')
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()
affine = img.get_affine()
label_img = nib.load(flabels)
labels = label_img.get_data()
lap = through_label_sl.label_position(labels, labelValue=1)
dataslice = data[40:80, 20:80, lap[2][2] / 2]
#print lap[2][2]/2
#get_csd_gfa(f_name,data,gtab,dataslice)
maskdata, mask = median_otsu(data,
2,
1,
False,
vol_idx=range(10, 50),
dilate=2) #不去背景
""" get fa and tensor evecs and ODF"""
from dipy.reconst.dti import TensorModel, mean_diffusivity
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data, mask)
sphere = get_sphere('symmetric724')
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
np.save(os.getcwd() + '\zhibiao' + f_name + '_FA.npy', FA)
fa_img = nib.Nifti1Image(FA.astype(np.float32), affine)
nib.save(fa_img, os.getcwd() + '\zhibiao' + f_name + '_FA.nii.gz')
print('Saving "DTI_tensor_fa.nii.gz" sucessful.')
evecs_img = nib.Nifti1Image(tenfit.evecs.astype(np.float32), affine)
nib.save(evecs_img,
os.getcwd() + '\zhibiao' + f_name + '_DTI_tensor_evecs.nii.gz')
print('Saving "DTI_tensor_evecs.nii.gz" sucessful.')
MD1 = mean_diffusivity(tenfit.evals)
nib.save(nib.Nifti1Image(MD1.astype(np.float32), img.get_affine()),
os.getcwd() + '\zhibiao' + f_name + '_MD.nii.gz')
#tensor_odfs = tenmodel.fit(data[20:50, 55:85, 38:39]).odf(sphere)
#from dipy.reconst.odf import gfa
#dti_gfa=gfa(tensor_odfs)
wm_mask = (np.logical_or(FA >= 0.4,
(np.logical_and(FA >= 0.15, MD >= 0.0011))))
response = recursive_response(gtab,
data,
mask=wm_mask,
sh_order=8,
peak_thr=0.01,
init_fa=0.08,
init_trace=0.0021,
iter=8,
convergence=0.001,
parallel=False)
from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
#csd_fit = csd_model.fit(data)
from dipy.direction import peaks_from_model
csd_peaks = peaks_from_model(model=csd_model,
data=data,
sphere=sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=False)
GFA = csd_peaks.gfa
nib.save(GFA, os.getcwd() + '\zhibiao' + f_name + '_MSD.nii.gz')
print('Saving "GFA.nii.gz" sucessful.')
from dipy.reconst.shore import ShoreModel
asm = ShoreModel(gtab)
print('Calculating...SHORE msd')
asmfit = asm.fit(data, mask)
msd = asmfit.msd()
msd[np.isnan(msd)] = 0
#print GFA[:,:,slice].T
print('Saving msd_img.png')
nib.save(msd, os.getcwd() + '\zhibiao' + f_name + '_GFA.nii.gz')