def test_diffusivities():
psphere = get_sphere('symmetric362')
bvecs = np.concatenate(([[0, 0, 0]], psphere.vertices))
bvals = np.zeros(len(bvecs)) + 1000
bvals[0] = 0
gtab = grad.gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0001], [0.0015, 0.0003, 0.0003]))
mevecs = [np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]),
np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]])]
S = single_tensor(gtab, 100, mevals[0], mevecs[0], snr=None)
dm = dti.TensorModel(gtab, 'LS')
dmfit = dm.fit(S)
md = mean_diffusivity(dmfit.evals)
Trace = trace(dmfit.evals)
rd = radial_diffusivity(dmfit.evals)
ad = axial_diffusivity(dmfit.evals)
lin = linearity(dmfit.evals)
plan = planarity(dmfit.evals)
spher = sphericity(dmfit.evals)
assert_almost_equal(md, (0.0015 + 0.0003 + 0.0001) / 3)
assert_almost_equal(Trace, (0.0015 + 0.0003 + 0.0001))
assert_almost_equal(ad, 0.0015)
assert_almost_equal(rd, (0.0003 + 0.0001) / 2)
assert_almost_equal(lin, (0.0015 - 0.0003)/Trace)
assert_almost_equal(plan, 2 * (0.0003 - 0.0001)/Trace)
assert_almost_equal(spher, (3 * 0.0001)/Trace)
def test_diffusivities():
psphere = get_sphere('symmetric362')
bvecs = np.concatenate(([[0, 0, 0]], psphere.vertices))
bvals = np.zeros(len(bvecs)) + 1000
bvals[0] = 0
gtab = grad.gradient_table(bvals, bvecs)
mevals = np.array(([0.0015, 0.0003, 0.0001], [0.0015, 0.0003, 0.0003]))
mevecs = [np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]),
np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]])]
S = single_tensor(gtab, 100, mevals[0], mevecs[0], snr=None)
dm = dti.TensorModel(gtab, 'LS')
dmfit = dm.fit(S)
md = mean_diffusivity(dmfit.evals)
Trace = trace(dmfit.evals)
rd = radial_diffusivity(dmfit.evals)
ad = axial_diffusivity(dmfit.evals)
lin = linearity(dmfit.evals)
plan = planarity(dmfit.evals)
spher = sphericity(dmfit.evals)
assert_almost_equal(md, (0.0015 + 0.0003 + 0.0001) / 3)
assert_almost_equal(Trace, (0.0015 + 0.0003 + 0.0001))
assert_almost_equal(ad, 0.0015)
assert_almost_equal(rd, (0.0003 + 0.0001) / 2)
assert_almost_equal(lin, (0.0015 - 0.0003)/Trace)
assert_almost_equal(plan, 2 * (0.0003 - 0.0001)/Trace)
assert_almost_equal(spher, (3 * 0.0001)/Trace)
def compute_dti(fname_in, fname_bvals, fname_bvecs, prefix):
"""
Compute DTI.
:param fname_in: input 4d file.
:param bvals: bvals txt file
:param bvecs: bvecs txt file
:param prefix: output prefix. Example: "dti_"
:return: True/False
"""
# Open file.
from msct_image import Image
nii = Image(fname_in)
data = nii.data
print('data.shape (%d, %d, %d, %d)' % data.shape)
# open bvecs/bvals
from dipy.io import read_bvals_bvecs
bvals, bvecs = read_bvals_bvecs(fname_bvals, fname_bvecs)
from dipy.core.gradients import gradient_table
gtab = gradient_table(bvals, bvecs)
# # mask and crop the data. This is a quick way to avoid calculating Tensors on the background of the image.
# from dipy.segment.mask import median_otsu
# maskdata, mask = median_otsu(data, 3, 1, True, vol_idx=range(10, 50), dilate=2)
# print('maskdata.shape (%d, %d, %d, %d)' % maskdata.shape)
# fit tensor model
import dipy.reconst.dti as dti
tenmodel = dti.TensorModel(gtab)
tenfit = tenmodel.fit(data)
# Compute metrics
printv('Computing metrics...', param.verbose)
# FA
from dipy.reconst.dti import fractional_anisotropy
nii.data = fractional_anisotropy(tenfit.evals)
nii.setFileName(prefix + 'FA.nii.gz')
nii.save('float32')
# MD
from dipy.reconst.dti import mean_diffusivity
nii.data = mean_diffusivity(tenfit.evals)
nii.setFileName(prefix + 'MD.nii.gz')
nii.save('float32')
# RD
from dipy.reconst.dti import radial_diffusivity
nii.data = radial_diffusivity(tenfit.evals)
nii.setFileName(prefix + 'RD.nii.gz')
nii.save('float32')
# AD
from dipy.reconst.dti import axial_diffusivity
nii.data = axial_diffusivity(tenfit.evals)
nii.setFileName(prefix + 'AD.nii.gz')
nii.save('float32')
return True
def compute_dti(fname_in, fname_bvals, fname_bvecs, prefix):
"""
Compute DTI.
:param fname_in: input 4d file.
:param bvals: bvals txt file
:param bvecs: bvecs txt file
:param prefix: output prefix. Example: "dti_"
:return: True/False
"""
# Open file.
from msct_image import Image
nii = Image(fname_in)
data = nii.data
print('data.shape (%d, %d, %d, %d)' % data.shape)
# open bvecs/bvals
from dipy.io import read_bvals_bvecs
bvals, bvecs = read_bvals_bvecs(fname_bvals, fname_bvecs)
from dipy.core.gradients import gradient_table
gtab = gradient_table(bvals, bvecs)
# # mask and crop the data. This is a quick way to avoid calculating Tensors on the background of the image.
# from dipy.segment.mask import median_otsu
# maskdata, mask = median_otsu(data, 3, 1, True, vol_idx=range(10, 50), dilate=2)
# print('maskdata.shape (%d, %d, %d, %d)' % maskdata.shape)
# fit tensor model
import dipy.reconst.dti as dti
tenmodel = dti.TensorModel(gtab)
tenfit = tenmodel.fit(data)
# Compute metrics
printv('Computing metrics...', param.verbose)
# FA
from dipy.reconst.dti import fractional_anisotropy
nii.data = fractional_anisotropy(tenfit.evals)
nii.setFileName(prefix+'FA.nii.gz')
nii.save('float32')
# MD
from dipy.reconst.dti import mean_diffusivity
nii.data = mean_diffusivity(tenfit.evals)
nii.setFileName(prefix+'MD.nii.gz')
nii.save('float32')
# RD
from dipy.reconst.dti import radial_diffusivity
nii.data = radial_diffusivity(tenfit.evals)
nii.setFileName(prefix+'RD.nii.gz')
nii.save('float32')
# AD
from dipy.reconst.dti import axial_diffusivity
nii.data = axial_diffusivity(tenfit.evals)
nii.setFileName(prefix+'AD.nii.gz')
nii.save('float32')
return True
def test_single_voxel_DKI_stats():
# tests if AK and RK are equal to expected values for a single fiber
# simulate randomly oriented
ADi = 0.00099
ADe = 0.00226
RDi = 0
RDe = 0.00087
# Reference values
AD = fie * ADi + (1 - fie) * ADe
AK = 3 * fie * (1 - fie) * ((ADi - ADe) / AD)**2
RD = fie * RDi + (1 - fie) * RDe
RK = 3 * fie * (1 - fie) * ((RDi - RDe) / RD)**2
ref_vals = np.array([AD, AK, RD, RK])
# simulate fiber randomly oriented
theta = random.uniform(0, 180)
phi = random.uniform(0, 320)
angles = [(theta, phi), (theta, phi)]
mevals = np.array([[ADi, RDi, RDi], [ADe, RDe, RDe]])
frac = [fie * 100, (1 - fie) * 100]
signal, dt, kt = multi_tensor_dki(gtab_2s,
mevals,
S0=100,
angles=angles,
fractions=frac,
snr=None)
evals, evecs = decompose_tensor(from_lower_triangular(dt))
dki_par = np.concatenate((evals, evecs[0], evecs[1], evecs[2], kt), axis=0)
# Estimates using dki functions
ADe1 = dti.axial_diffusivity(evals)
RDe1 = dti.radial_diffusivity(evals)
AKe1 = axial_kurtosis(dki_par)
RKe1 = radial_kurtosis(dki_par)
e1_vals = np.array([ADe1, AKe1, RDe1, RKe1])
assert_array_almost_equal(e1_vals, ref_vals)
# Estimates using the kurtosis class object
dkiM = dki.DiffusionKurtosisModel(gtab_2s)
dkiF = dkiM.fit(signal)
e2_vals = np.array([dkiF.ad, dkiF.ak(), dkiF.rd, dkiF.rk()])
assert_array_almost_equal(e2_vals, ref_vals)
# test MK (note this test correspond to the MK singularity L2==L3)
MK_as = dkiF.mk()
sph = Sphere(xyz=gtab.bvecs[gtab.bvals > 0])
MK_nm = np.mean(dkiF.akc(sph))
assert_array_almost_equal(MK_as, MK_nm, decimal=1)
def hindered_rd(self):
""" Returns the radial diffusivity of the hindered compartment.
Note
-----
The hindered diffusion tensor can be seem as the tissue's
extra-cellular diffusion compartment [1]_.
References
----------
.. [1] Fieremans, E., Jensen, J.H., Helpern, J.A., 2011. White Matter
Characterization with Diffusion Kurtosis Imaging. Neuroimage
58(1): 177-188. doi:10.1016/j.neuroimage.2011.06.006
"""
return radial_diffusivity(self.hindered_evals)
def hindered_rd(self):
""" Returns the radial diffusivity of the hindered compartment.
Notes
-----
The hindered diffusion tensor can be seem as the tissue's
extra-cellular diffusion compartment [1]_.
References
----------
.. [1] Fieremans, E., Jensen, J.H., Helpern, J.A., 2011. White Matter
Characterization with Diffusion Kurtosis Imaging. Neuroimage
58(1): 177-188. doi:10.1016/j.neuroimage.2011.06.006
"""
return radial_diffusivity(self.hindered_evals)
def test_single_voxel_DKI_stats():
# tests if AK and RK are equal to expected values for a single fiber
# simulate randomly oriented
ADi = 0.00099
ADe = 0.00226
RDi = 0
RDe = 0.00087
# Reference values
AD = fie * ADi + (1 - fie) * ADe
AK = 3 * fie * (1 - fie) * ((ADi-ADe) / AD) ** 2
RD = fie * RDi + (1 - fie) * RDe
RK = 3 * fie * (1 - fie) * ((RDi-RDe) / RD) ** 2
ref_vals = np.array([AD, AK, RD, RK])
# simulate fiber randomly oriented
theta = random.uniform(0, 180)
phi = random.uniform(0, 320)
angles = [(theta, phi), (theta, phi)]
mevals = np.array([[ADi, RDi, RDi], [ADe, RDe, RDe]])
frac = [fie * 100, (1 - fie) * 100]
signal, dt, kt = multi_tensor_dki(gtab_2s, mevals, S0=100, angles=angles,
fractions=frac, snr=None)
evals, evecs = decompose_tensor(from_lower_triangular(dt))
dki_par = np.concatenate((evals, evecs[0], evecs[1], evecs[2], kt), axis=0)
# Estimates using dki functions
ADe1 = dti.axial_diffusivity(evals)
RDe1 = dti.radial_diffusivity(evals)
AKe1 = axial_kurtosis(dki_par)
RKe1 = radial_kurtosis(dki_par)
e1_vals = np.array([ADe1, AKe1, RDe1, RKe1])
assert_array_almost_equal(e1_vals, ref_vals)
# Estimates using the kurtosis class object
dkiM = dki.DiffusionKurtosisModel(gtab_2s)
dkiF = dkiM.fit(signal)
e2_vals = np.array([dkiF.ad, dkiF.ak(), dkiF.rd, dkiF.rk()])
assert_array_almost_equal(e2_vals, ref_vals)
# test MK (note this test correspond to the MK singularity L2==L3)
MK_as = dkiF.mk()
sph = Sphere(xyz=gtab.bvecs[gtab.bvals > 0])
MK_nm = np.mean(dkiF.akc(sph))
assert_array_almost_equal(MK_as, MK_nm, decimal=1)
def calculate_scalars(tensor_data, mask):
'''
Calculate the scalar images from the tensor
returns: FA, MD, TR, AX, RAD
'''
mask = np.asarray(mask, dtype=np.bool)
shape = mask.shape
data = dti.from_lower_triangular(tensor_data[mask])
w, v = dti.decompose_tensor(data)
w = np.squeeze(w)
v = np.squeeze(v)
md = np.zeros(shape)
md[mask] = dti.mean_diffusivity(w, axis=-1)
fa = np.zeros(shape)
fa[mask] = dti.fractional_anisotropy(w, axis=-1)
tr = np.zeros(shape)
tr[mask] = dti.trace(w, axis=-1)
ax = np.zeros(shape)
ax[mask] = dti.axial_diffusivity(w, axis=-1)
rad = np.zeros(shape)
rad[mask] = dti.radial_diffusivity(w, axis=-1)
return fa, md, tr, ax, rad
def compute_dti(fname_in, fname_bvals, fname_bvecs, prefix, method, file_mask):
"""
Compute DTI.
:param fname_in: input 4d file.
:param bvals: bvals txt file
:param bvecs: bvecs txt file
:param prefix: output prefix. Example: "dti_"
:param method: algo for computing dti
:return: True/False
"""
# Open file.
from msct_image import Image
nii = Image(fname_in)
data = nii.data
print('data.shape (%d, %d, %d, %d)' % data.shape)
# open bvecs/bvals
from dipy.io import read_bvals_bvecs
bvals, bvecs = read_bvals_bvecs(fname_bvals, fname_bvecs)
from dipy.core.gradients import gradient_table
gtab = gradient_table(bvals, bvecs)
# mask and crop the data. This is a quick way to avoid calculating Tensors on the background of the image.
if not file_mask == '':
printv('Open mask file...', param.verbose)
# open mask file
nii_mask = Image(file_mask)
mask = nii_mask.data
# fit tensor model
printv('Computing tensor using "'+method+'" method...', param.verbose)
import dipy.reconst.dti as dti
if method == 'standard':
tenmodel = dti.TensorModel(gtab)
if file_mask == '':
tenfit = tenmodel.fit(data)
else:
tenfit = tenmodel.fit(data, mask)
elif method == 'restore':
import dipy.denoise.noise_estimate as ne
sigma = ne.estimate_sigma(data)
dti_restore = dti.TensorModel(gtab, fit_method='RESTORE', sigma=sigma)
if file_mask == '':
tenfit = dti_restore.fit(data)
else:
tenfit = dti_restore.fit(data, mask)
# Compute metrics
printv('Computing metrics...', param.verbose)
# FA
from dipy.reconst.dti import fractional_anisotropy
nii.data = fractional_anisotropy(tenfit.evals)
nii.setFileName(prefix+'FA.nii.gz')
nii.save('float32')
# MD
from dipy.reconst.dti import mean_diffusivity
nii.data = mean_diffusivity(tenfit.evals)
nii.setFileName(prefix+'MD.nii.gz')
nii.save('float32')
# RD
from dipy.reconst.dti import radial_diffusivity
nii.data = radial_diffusivity(tenfit.evals)
nii.setFileName(prefix+'RD.nii.gz')
nii.save('float32')
# AD
from dipy.reconst.dti import axial_diffusivity
nii.data = axial_diffusivity(tenfit.evals)
nii.setFileName(prefix+'AD.nii.gz')
nii.save('float32')
return True
def compute_dti(fname_in, fname_bvals, fname_bvecs, prefix, method, file_mask):
"""
Compute DTI.
:param fname_in: input 4d file.
:param bvals: bvals txt file
:param bvecs: bvecs txt file
:param prefix: output prefix. Example: "dti_"
:param method: algo for computing dti
:return: True/False
"""
# Open file.
from msct_image import Image
nii = Image(fname_in)
data = nii.data
sct.printv('data.shape (%d, %d, %d, %d)' % data.shape)
# open bvecs/bvals
from dipy.io import read_bvals_bvecs
bvals, bvecs = read_bvals_bvecs(fname_bvals, fname_bvecs)
from dipy.core.gradients import gradient_table
gtab = gradient_table(bvals, bvecs)
# mask and crop the data. This is a quick way to avoid calculating Tensors on the background of the image.
if not file_mask == '':
sct.printv('Open mask file...', param.verbose)
# open mask file
nii_mask = Image(file_mask)
mask = nii_mask.data
# fit tensor model
sct.printv('Computing tensor using "' + method + '" method...',
param.verbose)
import dipy.reconst.dti as dti
if method == 'standard':
tenmodel = dti.TensorModel(gtab)
if file_mask == '':
tenfit = tenmodel.fit(data)
else:
tenfit = tenmodel.fit(data, mask)
elif method == 'restore':
import dipy.denoise.noise_estimate as ne
sigma = ne.estimate_sigma(data)
dti_restore = dti.TensorModel(gtab, fit_method='RESTORE', sigma=sigma)
if file_mask == '':
tenfit = dti_restore.fit(data)
else:
tenfit = dti_restore.fit(data, mask)
# Compute metrics
sct.printv('Computing metrics...', param.verbose)
# FA
from dipy.reconst.dti import fractional_anisotropy
nii.data = fractional_anisotropy(tenfit.evals)
nii.setFileName(prefix + 'FA.nii.gz')
nii.save('float32')
# MD
from dipy.reconst.dti import mean_diffusivity
nii.data = mean_diffusivity(tenfit.evals)
nii.setFileName(prefix + 'MD.nii.gz')
nii.save('float32')
# RD
from dipy.reconst.dti import radial_diffusivity
nii.data = radial_diffusivity(tenfit.evals)
nii.setFileName(prefix + 'RD.nii.gz')
nii.save('float32')
# AD
from dipy.reconst.dti import axial_diffusivity
nii.data = axial_diffusivity(tenfit.evals)
nii.setFileName(prefix + 'AD.nii.gz')
nii.save('float32')
return True
def main():
parser = _build_args_parser()
args = parser.parse_args()
if not args.not_all:
args.fa = args.fa or 'fa.nii.gz'
args.ga = args.ga or 'ga.nii.gz'
args.rgb = args.rgb or 'rgb.nii.gz'
args.md = args.md or 'md.nii.gz'
args.ad = args.ad or 'ad.nii.gz'
args.rd = args.rd or 'rd.nii.gz'
args.mode = args.mode or 'mode.nii.gz'
args.norm = args.norm or 'tensor_norm.nii.gz'
args.tensor = args.tensor or 'tensor.nii.gz'
args.evecs = args.evecs or 'tensor_evecs.nii.gz'
args.evals = args.evals or 'tensor_evals.nii.gz'
args.residual = args.residual or 'dti_residual.nii.gz'
args.p_i_signal =\
args.p_i_signal or 'physically_implausible_signals_mask.nii.gz'
args.pulsation = args.pulsation or 'pulsation_and_misalignment.nii.gz'
outputs = [args.fa, args.ga, args.rgb, args.md, args.ad, args.rd,
args.mode, args.norm, args.tensor, args.evecs, args.evals,
args.residual, args.p_i_signal, args.pulsation]
if args.not_all and not any(outputs):
parser.error('When using --not_all, you need to specify at least ' +
'one metric to output.')
assert_inputs_exist(
parser, [args.input, args.bvals, args.bvecs], args.mask)
assert_outputs_exist(parser, args, outputs)
img = nib.load(args.input)
data = img.get_data()
affine = img.get_affine()
if args.mask is None:
mask = None
else:
mask = nib.load(args.mask).get_data().astype(np.bool)
# Validate bvals and bvecs
logging.info('Tensor estimation with the %s method...', args.method)
bvals, bvecs = read_bvals_bvecs(args.bvals, args.bvecs)
if not is_normalized_bvecs(bvecs):
logging.warning('Your b-vectors do not seem normalized...')
bvecs = normalize_bvecs(bvecs)
check_b0_threshold(args, bvals.min())
gtab = gradient_table(bvals, bvecs, b0_threshold=bvals.min())
# Get tensors
if args.method == 'restore':
sigma = ne.estimate_sigma(data)
tenmodel = TensorModel(gtab, fit_method=args.method, sigma=sigma,
min_signal=_get_min_nonzero_signal(data))
else:
tenmodel = TensorModel(gtab, fit_method=args.method,
min_signal=_get_min_nonzero_signal(data))
tenfit = tenmodel.fit(data, mask)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
if args.tensor:
# Get the Tensor values and format them for visualisation
# in the Fibernavigator.
tensor_vals = lower_triangular(tenfit.quadratic_form)
correct_order = [0, 1, 3, 2, 4, 5]
tensor_vals_reordered = tensor_vals[..., correct_order]
fiber_tensors = nib.Nifti1Image(
tensor_vals_reordered.astype(np.float32), affine)
nib.save(fiber_tensors, args.tensor)
if args.fa:
fa_img = nib.Nifti1Image(FA.astype(np.float32), affine)
nib.save(fa_img, args.fa)
if args.ga:
GA = geodesic_anisotropy(tenfit.evals)
GA[np.isnan(GA)] = 0
ga_img = nib.Nifti1Image(GA.astype(np.float32), affine)
nib.save(ga_img, args.ga)
if args.rgb:
RGB = color_fa(FA, tenfit.evecs)
rgb_img = nib.Nifti1Image(np.array(255 * RGB, 'uint8'), affine)
nib.save(rgb_img, args.rgb)
if args.md:
MD = mean_diffusivity(tenfit.evals)
md_img = nib.Nifti1Image(MD.astype(np.float32), affine)
nib.save(md_img, args.md)
if args.ad:
AD = axial_diffusivity(tenfit.evals)
ad_img = nib.Nifti1Image(AD.astype(np.float32), affine)
nib.save(ad_img, args.ad)
if args.rd:
RD = radial_diffusivity(tenfit.evals)
rd_img = nib.Nifti1Image(RD.astype(np.float32), affine)
nib.save(rd_img, args.rd)
if args.mode:
# Compute tensor mode
inter_mode = dipy_mode(tenfit.quadratic_form)
# Since the mode computation can generate NANs when not masked,
# we need to remove them.
non_nan_indices = np.isfinite(inter_mode)
mode = np.zeros(inter_mode.shape)
mode[non_nan_indices] = inter_mode[non_nan_indices]
mode_img = nib.Nifti1Image(mode.astype(np.float32), affine)
nib.save(mode_img, args.mode)
if args.norm:
NORM = norm(tenfit.quadratic_form)
norm_img = nib.Nifti1Image(NORM.astype(np.float32), affine)
nib.save(norm_img, args.norm)
if args.evecs:
evecs = tenfit.evecs.astype(np.float32)
evecs_img = nib.Nifti1Image(evecs, affine)
nib.save(evecs_img, args.evecs)
# save individual e-vectors also
e1_img = nib.Nifti1Image(evecs[..., 0], affine)
e2_img = nib.Nifti1Image(evecs[..., 1], affine)
e3_img = nib.Nifti1Image(evecs[..., 2], affine)
nib.save(e1_img, add_filename_suffix(args.evecs, '_v1'))
nib.save(e2_img, add_filename_suffix(args.evecs, '_v2'))
nib.save(e3_img, add_filename_suffix(args.evecs, '_v3'))
if args.evals:
evals = tenfit.evals.astype(np.float32)
evals_img = nib.Nifti1Image(evals, affine)
nib.save(evals_img, args.evals)
# save individual e-values also
e1_img = nib.Nifti1Image(evals[..., 0], affine)
e2_img = nib.Nifti1Image(evals[..., 1], affine)
e3_img = nib.Nifti1Image(evals[..., 2], affine)
nib.save(e1_img, add_filename_suffix(args.evals, '_e1'))
nib.save(e2_img, add_filename_suffix(args.evals, '_e2'))
nib.save(e3_img, add_filename_suffix(args.evals, '_e3'))
if args.p_i_signal:
S0 = np.mean(data[..., gtab.b0s_mask], axis=-1, keepdims=True)
DWI = data[..., ~gtab.b0s_mask]
pis_mask = np.max(S0 < DWI, axis=-1)
if args.mask is not None:
pis_mask *= mask
pis_img = nib.Nifti1Image(pis_mask.astype(np.int16), affine)
nib.save(pis_img, args.p_i_signal)
if args.pulsation:
STD = np.std(data[..., ~gtab.b0s_mask], axis=-1)
if args.mask is not None:
STD *= mask
std_img = nib.Nifti1Image(STD.astype(np.float32), affine)
nib.save(std_img, add_filename_suffix(args.pulsation, '_std_dwi'))
if np.sum(gtab.b0s_mask) <= 1:
logger.info('Not enough b=0 images to output standard '
'deviation map')
else:
if len(np.where(gtab.b0s_mask)) == 2:
logger.info('Only two b=0 images. Be careful with the '
'interpretation of this std map')
STD = np.std(data[..., gtab.b0s_mask], axis=-1)
if args.mask is not None:
STD *= mask
std_img = nib.Nifti1Image(STD.astype(np.float32), affine)
nib.save(std_img, add_filename_suffix(args.pulsation, '_std_b0'))
if args.residual:
# Mean residual image
S0 = np.mean(data[..., gtab.b0s_mask], axis=-1)
data_p = tenfit.predict(gtab, S0)
R = np.mean(np.abs(data_p[..., ~gtab.b0s_mask] -
data[..., ~gtab.b0s_mask]), axis=-1)
if args.mask is not None:
R *= mask
R_img = nib.Nifti1Image(R.astype(np.float32), affine)
nib.save(R_img, args.residual)
# Each volume's residual statistics
if args.mask is None:
logger.info("Outlier detection will not be performed, since no "
"mask was provided.")
stats = [dict.fromkeys(['label', 'mean', 'iqr', 'cilo', 'cihi', 'whishi',
'whislo', 'fliers', 'q1', 'med', 'q3'], [])
for i in range(data.shape[-1])] # stats with format for boxplots
# Note that stats will be computed manually and plotted using bxp
# but could be computed using stats = cbook.boxplot_stats
# or pyplot.boxplot(x)
R_k = np.zeros(data.shape[-1]) # mean residual per DWI
std = np.zeros(data.shape[-1]) # std residual per DWI
q1 = np.zeros(data.shape[-1]) # first quartile per DWI
q3 = np.zeros(data.shape[-1]) # third quartile per DWI
iqr = np.zeros(data.shape[-1]) # interquartile per DWI
percent_outliers = np.zeros(data.shape[-1])
nb_voxels = np.count_nonzero(mask)
for k in range(data.shape[-1]):
x = np.abs(data_p[..., k] - data[..., k])[mask]
R_k[k] = np.mean(x)
std[k] = np.std(x)
q3[k], q1[k] = np.percentile(x, [75, 25])
iqr[k] = q3[k] - q1[k]
stats[k]['med'] = (q1[k] + q3[k]) / 2
stats[k]['mean'] = R_k[k]
stats[k]['q1'] = q1[k]
stats[k]['q3'] = q3[k]
stats[k]['whislo'] = q1[k] - 1.5 * iqr[k]
stats[k]['whishi'] = q3[k] + 1.5 * iqr[k]
stats[k]['label'] = k
# Outliers are observations that fall below Q1 - 1.5(IQR) or
# above Q3 + 1.5(IQR) We check if a voxel is an outlier only if
# we have a mask, else we are biased.
if args.mask is not None:
outliers = (x < stats[k]['whislo']) | (x > stats[k]['whishi'])
percent_outliers[k] = np.sum(outliers)/nb_voxels*100
# What would be our definition of too many outliers?
# Maybe mean(all_means)+-3SD?
# Or we let people choose based on the figure.
# if percent_outliers[k] > ???? :
# logger.warning(' Careful! Diffusion-Weighted Image'
# ' i=%s has %s %% outlier voxels',
# k, percent_outliers[k])
# Saving all statistics as npy values
residual_basename, _ = split_name_with_nii(args.residual)
res_stats_basename = residual_basename + ".npy"
np.save(add_filename_suffix(
res_stats_basename, "_mean_residuals"), R_k)
np.save(add_filename_suffix(res_stats_basename, "_q1_residuals"), q1)
np.save(add_filename_suffix(res_stats_basename, "_q3_residuals"), q3)
np.save(add_filename_suffix(res_stats_basename, "_iqr_residuals"), iqr)
np.save(add_filename_suffix(res_stats_basename, "_std_residuals"), std)
# Showing results in graph
if args.mask is None:
fig, axe = plt.subplots(nrows=1, ncols=1, squeeze=False)
else:
fig, axe = plt.subplots(nrows=1, ncols=2, squeeze=False,
figsize=[10, 4.8])
# Default is [6.4, 4.8]. Increasing width to see better.
medianprops = dict(linestyle='-', linewidth=2.5, color='firebrick')
meanprops = dict(linestyle='-', linewidth=2.5, color='green')
axe[0, 0].bxp(stats, showmeans=True, meanline=True, showfliers=False,
medianprops=medianprops, meanprops=meanprops)
axe[0, 0].set_xlabel('DW image')
axe[0, 0].set_ylabel('Residuals per DWI volume. Red is median,\n'
'green is mean. Whiskers are 1.5*interquartile')
axe[0, 0].set_title('Residuals')
axe[0, 0].set_xticks(range(0, q1.shape[0], 5))
axe[0, 0].set_xticklabels(range(0, q1.shape[0], 5))
if args.mask is not None:
axe[0, 1].plot(range(data.shape[-1]), percent_outliers)
axe[0, 1].set_xticks(range(0, q1.shape[0], 5))
axe[0, 1].set_xticklabels(range(0, q1.shape[0], 5))
axe[0, 1].set_xlabel('DW image')
axe[0, 1].set_ylabel('Percentage of outlier voxels')
axe[0, 1].set_title('Outliers')
plt.savefig(residual_basename + '_residuals_stats.png')
def run(self,
input_files,
bvalues_files,
bvectors_files,
mask_files,
b0_threshold=50,
bvecs_tol=0.01,
save_metrics=[],
out_dir='',
out_tensor='tensors.nii.gz',
out_fa='fa.nii.gz',
out_ga='ga.nii.gz',
out_rgb='rgb.nii.gz',
out_md='md.nii.gz',
out_ad='ad.nii.gz',
out_rd='rd.nii.gz',
out_mode='mode.nii.gz',
out_evec='evecs.nii.gz',
out_eval='evals.nii.gz',
nifti_tensor=True):
""" Workflow for tensor reconstruction and for computing DTI metrics.
using Weighted Least-Squares.
Performs a tensor reconstruction on the files by 'globing'
``input_files`` and saves the DTI metrics in a directory specified by
``out_dir``.
Parameters
----------
input_files : string
Path to the input volumes. This path may contain wildcards to
process multiple inputs at once.
bvalues_files : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
bvectors_files : string
Path to the bvectors files. This path may contain wildcards to use
multiple bvectors files at once.
mask_files : string
Path to the input masks. This path may contain wildcards to use
multiple masks at once.
b0_threshold : float, optional
Threshold used to find b0 volumes.
bvecs_tol : float, optional
Threshold used to check that norm(bvec) = 1 +/- bvecs_tol
b-vectors are unit vectors.
save_metrics : variable string, optional
List of metrics to save.
Possible values: fa, ga, rgb, md, ad, rd, mode, tensor, evec, eval
out_dir : string, optional
Output directory. (default current directory)
out_tensor : string, optional
Name of the tensors volume to be saved.
Per default, this will be saved following the nifti standard:
with the tensor elements as Dxx, Dxy, Dyy, Dxz, Dyz, Dzz on the
last (5th) dimension of the volume (shape: (i, j, k, 1, 6)). If
`nifti_tensor` is False, this will be saved in an alternate format
that is used by other software (e.g., FSL): a
4-dimensional volume (shape (i, j, k, 6)) with Dxx, Dxy, Dxz, Dyy,
Dyz, Dzz on the last dimension.
out_fa : string, optional
Name of the fractional anisotropy volume to be saved.
out_ga : string, optional
Name of the geodesic anisotropy volume to be saved.
out_rgb : string, optional
Name of the color fa volume to be saved.
out_md : string, optional
Name of the mean diffusivity volume to be saved.
out_ad : string, optional
Name of the axial diffusivity volume to be saved.
out_rd : string, optional
Name of the radial diffusivity volume to be saved.
out_mode : string, optional
Name of the mode volume to be saved.
out_evec : string, optional
Name of the eigenvectors volume to be saved.
out_eval : string, optional
Name of the eigenvalues to be saved.
nifti_tensor : bool, optional
Whether the tensor is saved in the standard Nifti format or in an
alternate format
that is used by other software (e.g., FSL): a
4-dimensional volume (shape (i, j, k, 6)) with
Dxx, Dxy, Dxz, Dyy, Dyz, Dzz on the last dimension.
References
----------
.. [1] Basser, P.J., Mattiello, J., LeBihan, D., 1994. Estimation of
the effective self-diffusion tensor from the NMR spin echo. J Magn
Reson B 103, 247-254.
.. [2] Basser, P., Pierpaoli, C., 1996. Microstructural and
physiological features of tissues elucidated by quantitative
diffusion-tensor MRI. Journal of Magnetic Resonance 111, 209-219.
.. [3] Lin-Ching C., Jones D.K., Pierpaoli, C. 2005. RESTORE: Robust
estimation of tensors by outlier rejection. MRM 53: 1088-1095
.. [4] hung, SW., Lu, Y., Henry, R.G., 2006. Comparison of bootstrap
approaches for estimation of uncertainties of DTI parameters.
NeuroImage 33, 531-541.
"""
io_it = self.get_io_iterator()
for dwi, bval, bvec, mask, otensor, ofa, oga, orgb, omd, oad, orad, \
omode, oevecs, oevals in io_it:
logging.info('Computing DTI metrics for {0}'.format(dwi))
data, affine = load_nifti(dwi)
if mask is not None:
mask = load_nifti_data(mask).astype(bool)
tenfit, _ = self.get_fitted_tensor(data, mask, bval, bvec,
b0_threshold, bvecs_tol)
if not save_metrics:
save_metrics = [
'fa', 'md', 'rd', 'ad', 'ga', 'rgb', 'mode', 'evec',
'eval', 'tensor'
]
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
if 'tensor' in save_metrics:
tensor_vals = lower_triangular(tenfit.quadratic_form)
if nifti_tensor:
ten_img = nifti1_symmat(tensor_vals, affine=affine)
else:
alt_order = [0, 1, 3, 2, 4, 5]
ten_img = nib.Nifti1Image(
tensor_vals[..., alt_order].astype(np.float32), affine)
nib.save(ten_img, otensor)
if 'fa' in save_metrics:
save_nifti(ofa, FA.astype(np.float32), affine)
if 'ga' in save_metrics:
GA = geodesic_anisotropy(tenfit.evals)
save_nifti(oga, GA.astype(np.float32), affine)
if 'rgb' in save_metrics:
RGB = color_fa(FA, tenfit.evecs)
save_nifti(orgb, np.array(255 * RGB, 'uint8'), affine)
if 'md' in save_metrics:
MD = mean_diffusivity(tenfit.evals)
save_nifti(omd, MD.astype(np.float32), affine)
if 'ad' in save_metrics:
AD = axial_diffusivity(tenfit.evals)
save_nifti(oad, AD.astype(np.float32), affine)
if 'rd' in save_metrics:
RD = radial_diffusivity(tenfit.evals)
save_nifti(orad, RD.astype(np.float32), affine)
if 'mode' in save_metrics:
MODE = get_mode(tenfit.quadratic_form)
save_nifti(omode, MODE.astype(np.float32), affine)
if 'evec' in save_metrics:
save_nifti(oevecs, tenfit.evecs.astype(np.float32), affine)
if 'eval' in save_metrics:
save_nifti(oevals, tenfit.evals.astype(np.float32), affine)
dname_ = os.path.dirname(oevals)
if dname_ == '':
logging.info('DTI metrics saved in current directory')
else:
logging.info('DTI metrics saved in {0}'.format(dname_))
def main():
parser = buildArgsParser()
args = parser.parse_args()
# Load data
img = nib.load(args.input)
data = img.get_data()
affine = img.get_affine()
# Setting suffix savename
if args.savename is None:
filename = ""
else:
filename = args.savename + "_"
if os.path.exists(filename + 'fa.nii.gz'):
if not args.overwrite:
raise ValueError("File " + filename + "fa.nii.gz"
+ " already exists. Use -f option to overwrite.")
print (filename + "fa.nii.gz", " already exists and will be overwritten.")
if args.mask is not None:
mask = nib.load(args.mask).get_data()
else:
print("No mask specified. Computing mask with median_otsu.")
data, mask = median_otsu(data)
mask_img = nib.Nifti1Image(mask.astype(np.float32), affine)
nib.save(mask_img, filename + 'mask.nii.gz')
# Get tensors
print('Tensor estimation...')
b_vals, b_vecs = read_bvals_bvecs(args.bvals, args.bvecs)
gtab = gradient_table_from_bvals_bvecs(b_vals, b_vecs)
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data, mask)
# FA
print('Computing FA...')
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
# RGB
print('Computing RGB...')
FA = np.clip(FA, 0, 1)
RGB = color_fa(FA, tenfit.evecs)
if args.all :
print('Computing Diffusivities...')
# diffusivities
MD = mean_diffusivity(tenfit.evals)
AD = axial_diffusivity(tenfit.evals)
RD = radial_diffusivity(tenfit.evals)
print('Computing Mode...')
MODE = mode(tenfit.quadratic_form)
print('Saving tensor coefficients and metrics...')
# Get the Tensor values and format them for visualisation in the Fibernavigator.
tensor_vals = lower_triangular(tenfit.quadratic_form)
correct_order = [0, 1, 3, 2, 4, 5]
tensor_vals_reordered = tensor_vals[..., correct_order]
fiber_tensors = nib.Nifti1Image(tensor_vals_reordered.astype(np.float32), affine)
nib.save(fiber_tensors, filename + 'tensors.nii.gz')
# Save - for some reason this is not read properly by the FiberNav
md_img = nib.Nifti1Image(MD.astype(np.float32), affine)
nib.save(md_img, filename + 'md.nii.gz')
ad_img = nib.Nifti1Image(AD.astype(np.float32), affine)
nib.save(ad_img, filename + 'ad.nii.gz')
rd_img = nib.Nifti1Image(RD.astype(np.float32), affine)
nib.save(rd_img, filename + 'rd.nii.gz')
mode_img = nib.Nifti1Image(MODE.astype(np.float32), affine)
nib.save(mode_img, filename + 'mode.nii.gz')
fa_img = nib.Nifti1Image(FA.astype(np.float32), affine)
nib.save(fa_img, filename + 'fa.nii.gz')
rgb_img = nib.Nifti1Image(np.array(255 * RGB, 'uint8'), affine)
nib.save(rgb_img, filename + 'rgb.nii.gz')
def run(self, input_files, bvalues_files, bvectors_files, mask_files,
b0_threshold=50, bvecs_tol=0.01, save_metrics=[],
out_dir='', out_tensor='tensors.nii.gz', out_fa='fa.nii.gz',
out_ga='ga.nii.gz', out_rgb='rgb.nii.gz', out_md='md.nii.gz',
out_ad='ad.nii.gz', out_rd='rd.nii.gz', out_mode='mode.nii.gz',
out_evec='evecs.nii.gz', out_eval='evals.nii.gz'):
""" Workflow for tensor reconstruction and for computing DTI metrics.
using Weighted Least-Squares.
Performs a tensor reconstruction on the files by 'globing'
``input_files`` and saves the DTI metrics in a directory specified by
``out_dir``.
Parameters
----------
input_files : string
Path to the input volumes. This path may contain wildcards to
process multiple inputs at once.
bvalues_files : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
bvectors_files : string
Path to the bvectors files. This path may contain wildcards to use
multiple bvectors files at once.
mask_files : string
Path to the input masks. This path may contain wildcards to use
multiple masks at once. (default: No mask used)
b0_threshold : float, optional
Threshold used to find b=0 directions (default 0.0)
bvecs_tol : float, optional
Threshold used to check that norm(bvec) = 1 +/- bvecs_tol
b-vectors are unit vectors (default 0.01)
save_metrics : variable string, optional
List of metrics to save.
Possible values: fa, ga, rgb, md, ad, rd, mode, tensor, evec, eval
(default [] (all))
out_dir : string, optional
Output directory (default input file directory)
out_tensor : string, optional
Name of the tensors volume to be saved (default 'tensors.nii.gz')
out_fa : string, optional
Name of the fractional anisotropy volume to be saved
(default 'fa.nii.gz')
out_ga : string, optional
Name of the geodesic anisotropy volume to be saved
(default 'ga.nii.gz')
out_rgb : string, optional
Name of the color fa volume to be saved (default 'rgb.nii.gz')
out_md : string, optional
Name of the mean diffusivity volume to be saved
(default 'md.nii.gz')
out_ad : string, optional
Name of the axial diffusivity volume to be saved
(default 'ad.nii.gz')
out_rd : string, optional
Name of the radial diffusivity volume to be saved
(default 'rd.nii.gz')
out_mode : string, optional
Name of the mode volume to be saved (default 'mode.nii.gz')
out_evec : string, optional
Name of the eigenvectors volume to be saved
(default 'evecs.nii.gz')
out_eval : string, optional
Name of the eigenvalues to be saved (default 'evals.nii.gz')
References
----------
.. [1] Basser, P.J., Mattiello, J., LeBihan, D., 1994. Estimation of
the effective self-diffusion tensor from the NMR spin echo. J Magn
Reson B 103, 247-254.
.. [2] Basser, P., Pierpaoli, C., 1996. Microstructural and
physiological features of tissues elucidated by quantitative
diffusion-tensor MRI. Journal of Magnetic Resonance 111, 209-219.
.. [3] Lin-Ching C., Jones D.K., Pierpaoli, C. 2005. RESTORE: Robust
estimation of tensors by outlier rejection. MRM 53: 1088-1095
.. [4] hung, SW., Lu, Y., Henry, R.G., 2006. Comparison of bootstrap
approaches for estimation of uncertainties of DTI parameters.
NeuroImage 33, 531-541.
"""
io_it = self.get_io_iterator()
for dwi, bval, bvec, mask, otensor, ofa, oga, orgb, omd, oad, orad, \
omode, oevecs, oevals in io_it:
logging.info('Computing DTI metrics for {0}'.format(dwi))
data, affine = load_nifti(dwi)
if mask is not None:
mask = nib.load(mask).get_data().astype(np.bool)
tenfit, _ = self.get_fitted_tensor(data, mask, bval, bvec,
b0_threshold, bvecs_tol)
if not save_metrics:
save_metrics = ['fa', 'md', 'rd', 'ad', 'ga', 'rgb', 'mode',
'evec', 'eval', 'tensor']
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
if 'tensor' in save_metrics:
tensor_vals = lower_triangular(tenfit.quadratic_form)
correct_order = [0, 1, 3, 2, 4, 5]
tensor_vals_reordered = tensor_vals[..., correct_order]
save_nifti(otensor, tensor_vals_reordered.astype(np.float32),
affine)
if 'fa' in save_metrics:
save_nifti(ofa, FA.astype(np.float32), affine)
if 'ga' in save_metrics:
GA = geodesic_anisotropy(tenfit.evals)
save_nifti(oga, GA.astype(np.float32), affine)
if 'rgb' in save_metrics:
RGB = color_fa(FA, tenfit.evecs)
save_nifti(orgb, np.array(255 * RGB, 'uint8'), affine)
if 'md' in save_metrics:
MD = mean_diffusivity(tenfit.evals)
save_nifti(omd, MD.astype(np.float32), affine)
if 'ad' in save_metrics:
AD = axial_diffusivity(tenfit.evals)
save_nifti(oad, AD.astype(np.float32), affine)
if 'rd' in save_metrics:
RD = radial_diffusivity(tenfit.evals)
save_nifti(orad, RD.astype(np.float32), affine)
if 'mode' in save_metrics:
MODE = get_mode(tenfit.quadratic_form)
save_nifti(omode, MODE.astype(np.float32), affine)
if 'evec' in save_metrics:
save_nifti(oevecs, tenfit.evecs.astype(np.float32), affine)
if 'eval' in save_metrics:
save_nifti(oevals, tenfit.evals.astype(np.float32), affine)
dname_ = os.path.dirname(oevals)
if dname_ == '':
logging.info('DTI metrics saved in current directory')
else:
logging.info(
'DTI metrics saved in {0}'.format(dname_))
gtab = cg.gradient_table(bvals, bvecs) # noise estimation from the b=0 sigma = ne.estimate_sigma(dataraw[:,:,:,bvals==0]) sigmamean=np.mean(sigma) # tensor computation using restore tenmodel=dti.TensorModel(gtab,fit_method='RESTORE', sigma=sigmamean) tenfit = tenmodel.fit(data) # Derivated measures from dipy.reconst.dti import fractional_anisotropy, mean_diffusivity, radial_diffusivity, axial_diffusivity,mode FA = fractional_anisotropy(tenfit.evals) MD = mean_diffusivity(tenfit.evals) AD = axial_diffusivity(tenfit.evals) RD = radial_diffusivity(tenfit.evals) MO = mode(tenfit.evecs) tenfit.evals[np.isnan(tenfit.evals)] = 0 evals1_img = nib.Nifti1Image(tenfit.evals[:,:,:,0].astype(np.float32), img.get_affine()) nib.save(evals1_img, output+'_restore_L1.nii.gz') evals2_img = nib.Nifti1Image(tenfit.evals[:,:,:,1].astype(np.float32), img.get_affine()) nib.save(evals2_img, output+'_restore_L2.nii.gz') evals3_img = nib.Nifti1Image(tenfit.evals[:,:,:,2].astype(np.float32), img.get_affine()) nib.save(evals3_img, output+'_restore_L3.nii.gz') tenfit.evecs[np.isnan(tenfit.evecs)] = 0 evecs_img1 = nib.Nifti1Image(tenfit.evecs[:,:,:,:,0].astype(np.float32), img.get_affine()) nib.save(evecs_img1, output+'_restore_V1.nii.gz') evecs_img2 = nib.Nifti1Image(tenfit.evecs[:,:,:,:,1].astype(np.float32), img.get_affine()) nib.save(evecs_img2, output+'_restore_V2.nii.gz')
def dmri_recon(sid,
data_dir,
out_dir,
resolution,
recon='csd',
dirs='',
num_threads=2):
import tempfile
#tempfile.tempdir = '/om/scratch/Fri/ksitek/'
import os
oldval = None
if 'MKL_NUM_THREADS' in os.environ:
oldval = os.environ['MKL_NUM_THREADS']
os.environ['MKL_NUM_THREADS'] = '%d' % num_threads
ompoldval = None
if 'OMP_NUM_THREADS' in os.environ:
ompoldval = os.environ['OMP_NUM_THREADS']
os.environ['OMP_NUM_THREADS'] = '%d' % num_threads
import nibabel as nib
import numpy as np
from glob import glob
if resolution == '0.2mm':
filename = 'Reg_S64550_nii4d.nii'
#filename = 'angular_resample/dwi_%s.nii.gz'%dirs
fimg = os.path.abspath(glob(os.path.join(data_dir, filename))[0])
else:
filename = 'Reg_S64550_nii4d_resamp-%s.nii.gz' % (resolution)
fimg = os.path.abspath(
glob(os.path.join(data_dir, 'resample', filename))[0])
print("dwi file = %s" % fimg)
fbval = os.path.abspath(
glob(os.path.join(data_dir, 'bvecs', 'camino_120_RAS.bvals'))[0])
print("bval file = %s" % fbval)
fbvec = os.path.abspath(
glob(os.path.join(data_dir, 'bvecs',
'camino_120_RAS_flipped-xy.bvecs'))[0])
# 'angular_resample',
# 'dwi_%s.bvecs'%dirs))[0])
print("bvec file = %s" % fbvec)
img = nib.load(fimg)
data = img.get_fdata()
affine = img.get_affine()
prefix = sid
from dipy.io import read_bvals_bvecs
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
'''
from dipy.core.gradients import vector_norm
b0idx = []
for idx, val in enumerate(bvals):
if val < 1:
pass
#bvecs[idx] = [1, 0, 0]
else:
b0idx.append(idx)
#print "b0idx=%d"%idx
#print "input bvecs:"
#print bvecs
bvecs[b0idx, :] = bvecs[b0idx, :]/vector_norm(bvecs[b0idx])[:, None]
#print "bvecs after normalization:"
#print bvecs
'''
from dipy.core.gradients import gradient_table
gtab = gradient_table(bvals, bvecs)
gtab.bvecs.shape == bvecs.shape
gtab.bvecs
gtab.bvals.shape == bvals.shape
gtab.bvals
#from dipy.segment.mask import median_otsu
#b0_mask, mask = median_otsu(data[:, :, :, b0idx].mean(axis=3).squeeze(), 4, 4)
if resolution == '0.2mm':
mask_name = 'Reg_S64550_nii_b0-slice_mask.nii.gz'
fmask1 = os.path.join(data_dir, mask_name)
else:
mask_name = 'Reg_S64550_nii_b0-slice_mask_resamp-%s.nii.gz' % (
resolution)
fmask1 = os.path.join(data_dir, 'resample', mask_name)
print("fmask file = %s" % fmask1)
mask = nib.load(fmask1).get_fdata()
''' DTI model & save metrics '''
from dipy.reconst.dti import TensorModel
print("running tensor model")
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data, mask)
from dipy.reconst.dti import fractional_anisotropy
print("running FA")
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
fa_img = nib.Nifti1Image(FA, img.get_affine())
tensor_fa_file = os.path.abspath('%s_tensor_fa.nii.gz' % (prefix))
nib.save(fa_img, tensor_fa_file)
from dipy.reconst.dti import axial_diffusivity
print("running AD")
AD = axial_diffusivity(tenfit.evals)
AD[np.isnan(AD)] = 0
ad_img = nib.Nifti1Image(AD, img.get_affine())
tensor_ad_file = os.path.abspath('%s_tensor_ad.nii.gz' % (prefix))
nib.save(ad_img, tensor_ad_file)
from dipy.reconst.dti import radial_diffusivity
print("running RD")
RD = radial_diffusivity(tenfit.evals)
RD[np.isnan(RD)] = 0
rd_img = nib.Nifti1Image(RD, img.get_affine())
tensor_rd_file = os.path.abspath('%s_tensor_rd.nii.gz' % (prefix))
nib.save(rd_img, tensor_rd_file)
from dipy.reconst.dti import mean_diffusivity
print("running MD")
MD = mean_diffusivity(tenfit.evals)
MD[np.isnan(MD)] = 0
md_img = nib.Nifti1Image(MD, img.get_affine())
tensor_md_file = os.path.abspath('%s_tensor_md.nii.gz' % (prefix))
nib.save(md_img, tensor_md_file)
evecs = tenfit.evecs
evec_img = nib.Nifti1Image(evecs, img.get_affine())
tensor_evec_file = os.path.abspath('%s_tensor_evec.nii.gz' % (prefix))
nib.save(evec_img, tensor_evec_file)
''' ODF model '''
useFA = True
print("creating %s model" % recon)
if recon == 'csd':
from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel
from dipy.reconst.csdeconv import auto_response
response, ratio = auto_response(gtab, data, roi_radius=10,
fa_thr=0.5) # 0.7
model = ConstrainedSphericalDeconvModel(gtab, response)
useFA = True
return_sh = True
elif recon == 'csa':
from dipy.reconst.shm import CsaOdfModel, normalize_data
model = CsaOdfModel(gtab, sh_order=8)
useFA = True
return_sh = True
elif recon == 'gqi':
from dipy.reconst.gqi import GeneralizedQSamplingModel
model = GeneralizedQSamplingModel(gtab)
return_sh = False
else:
raise ValueError('only csd, csa supported currently')
from dipy.reconst.dsi import (DiffusionSpectrumDeconvModel,
DiffusionSpectrumModel)
model = DiffusionSpectrumDeconvModel(gtab)
'''reconstruct ODFs'''
from dipy.data import get_sphere
sphere = get_sphere('symmetric724')
#odfs = fit.odf(sphere)
# with CSD/GQI, uses > 50GB per core; don't get greedy with cores!
from dipy.reconst.peaks import peaks_from_model
print("running peaks_from_model")
peaks = peaks_from_model(
model=model,
data=data,
sphere=sphere,
mask=mask,
return_sh=return_sh,
return_odf=False,
normalize_peaks=True,
npeaks=5,
relative_peak_threshold=.5,
min_separation_angle=10, #25,
parallel=num_threads > 1,
nbr_processes=num_threads)
# save the peaks
from dipy.io.peaks import save_peaks
peaks_file = os.path.abspath('%s_peaks.pam5' % (prefix))
save_peaks(peaks_file, peaks)
# save the spherical harmonics
shm_coeff_file = os.path.abspath('%s_shm_coeff.nii.gz' % (prefix))
if return_sh:
shm_coeff = peaks.shm_coeff
nib.save(nib.Nifti1Image(shm_coeff, img.get_affine()), shm_coeff_file)
else:
# if it's not a spherical model, output it as an essentially null file
np.savetxt(shm_coeff_file, [0])
# save the generalized fractional anisotropy image
gfa_img = nib.Nifti1Image(peaks.gfa, img.get_affine())
model_gfa_file = os.path.abspath('%s_%s_gfa.nii.gz' % (prefix, recon))
nib.save(gfa_img, model_gfa_file)
#from dipy.reconst.dti import quantize_evecs
#peak_indices = quantize_evecs(tenfit.evecs, sphere.vertices)
#eu = EuDX(FA, peak_indices, odf_vertices = sphere.vertices,
#a_low=0.2, seeds=10**6, ang_thr=35)
''' probabilistic tracking '''
'''
from dipy.direction import ProbabilisticDirectionGetter
from dipy.tracking.local import LocalTracking
from dipy.tracking.streamline import Streamlines
from dipy.io.streamline import save_trk
prob_dg = ProbabilisticDirectionGetter.from_shcoeff(shm_coeff,
max_angle=45.,
sphere=sphere)
streamlines_generator = LocalTracking(prob_dg,
affine,
step_size=.5,
max_cross=1)
# Generate streamlines object
streamlines = Streamlines(streamlines_generator)
affine = img.get_affine()
vox_size=fa_img.get_header().get_zooms()[:3]
fname = os.path.abspath('%s_%s_prob_streamline.trk' % (prefix, recon))
save_trk(fname, streamlines, affine, vox_size=vox_size)
'''
''' deterministic tracking with EuDX method'''
from dipy.tracking.eudx import EuDX
print("reconstructing with EuDX")
if useFA:
eu = EuDX(
FA,
peaks.peak_indices[..., 0],
odf_vertices=sphere.vertices,
a_low=0.001, # default is 0.0239
seeds=10**6,
ang_thr=75)
else:
eu = EuDX(
peaks.gfa,
peaks.peak_indices[..., 0],
odf_vertices=sphere.vertices,
#a_low=0.1,
seeds=10**6,
ang_thr=45)
sl_fname = os.path.abspath('%s_%s_det_streamline.trk' % (prefix, recon))
# trying new dipy.io.streamline module, per email to neuroimaging list
# 2018.04.05
from nibabel.streamlines import Field
from nibabel.orientations import aff2axcodes
affine = img.get_affine()
vox_size = fa_img.get_header().get_zooms()[:3]
fov_shape = FA.shape[:3]
if vox_size is not None and fov_shape is not None:
hdr = {}
hdr[Field.VOXEL_TO_RASMM] = affine.copy()
hdr[Field.VOXEL_SIZES] = vox_size
hdr[Field.DIMENSIONS] = fov_shape
hdr[Field.VOXEL_ORDER] = "".join(aff2axcodes(affine))
tractogram = nib.streamlines.Tractogram(eu)
tractogram.affine_to_rasmm = affine
trk_file = nib.streamlines.TrkFile(tractogram, header=hdr)
nib.streamlines.save(trk_file, sl_fname)
if oldval:
os.environ['MKL_NUM_THREADS'] = oldval
else:
del os.environ['MKL_NUM_THREADS']
if ompoldval:
os.environ['OMP_NUM_THREADS'] = ompoldval
else:
del os.environ['OMP_NUM_THREADS']
print('all output files created')
return (tensor_fa_file, tensor_evec_file, model_gfa_file, sl_fname, affine,
tensor_ad_file, tensor_rd_file, tensor_md_file, shm_coeff_file,
peaks_file)
def fwdti_processing(base_dir,
subject_id,
visit_id,
output_dir,
init_method="md",
init_values_extraction="intensity",
csf_roi=None,
wm_roi=None,
template="MNI152_T1_2mm.nii.gz",
denoised_data=True,
bg_wm_bound=1,
csf_bound=75,
write_masks=True,
indexes=["FA", "MD", "FW"],
dof=6,
output_type="NIFTI",
median_radius=3,
numpass=1,
autocrop=False,
dilate=1,
learning_rate=0.0025,
iterations=50):
"""Function to derive dti indexes from dwi data using dipy functions
Parameters
----------
base_dir : absolute path pointing at the root directory of the bids repository
subject_id: string, the tag subject (i.e. sub-sublabel) of the subject to treat
visit_id: the VALUE of the session tag to be treated (i.e. for ses-02, only "02" need to be entered)
output_dir: absolute path of the directories were the indexes will be written
init_method: string. should the Beltrami optimization be initialized using the mean diffusivity or the hybrid method (see BeltramiModel help). Default to md but hybrid is suggested
Accepted values are "md" and "hybrid"
init_values_extraction: string. if hybrid has been chosen as initialization, how the initial values
for wm and csf should be extracted ? If "registration" is chosen, csf and wm roi will be registered to diffusion image
and the median value extracted. If "intensity" than an intensity based threshold is applied to b0 image
to segment wm and csf and median values from these masks are extracted.
csf_roi: absolute path of the roi representative of the csf. It should be in the same space as "template" and binary. Default to None
wm_roi: absolute path of the roi representative of the wm. It should be in in the same space as "template" and binary. Default to None
template: name of the file with extension of the template to be used for inverse registation of the rois.
bg_wm_bound: int. Percentile of the boundary between background and white matter
csf_bound: Percentile of the boundary between wm and csf
denoised_data: boolean, should the denoised (True) or only the eddy current correct and realigned data (False) be used for fitting ?
write_masks: boolean. If intensity threshold method has been choses, should the masks be written ? This is useful for debugging. Default to True
indexes : list, a list of the indexes that will be written, accepted values are ["FA", "MD", "RD", "AD", "FW"]
output_type: ["NIFTI", "NIFTI_GZ"], the type of nifti output desired,
... : other parameters related to the dipy functions used
"""
if output_type == "NIFTI":
ext = ".nii"
elif output_type == "NIFTI_GZ":
ext = ".nii.gz"
else:
raise ValueError(
"Output file type {} not recognized".format(output_type))
diff_dir = "{}/{}/ses-{}/dwi".format(base_dir, subject_id, visit_id)
fbval = glob("{}/*bval".format(diff_dir))[0]
fbvec = glob("{}/*bvec".format(diff_dir))[0]
if (init_method == "hybrid" and init_values_extraction
== "registration") and (csf_roi == None or wm_roi == None):
raise ValueError(
"You chose hybrid initialization method without specifing a csf_roi, a wm_roi or both"
)
dti_preroc_dir = "{}/derivatives/fsl-dipy_dti-preproc/{}/ses-{}".format(
base_dir, subject_id, visit_id)
if denoised_data:
fdwi = glob("{}/*_denoised*".format(dti_preroc_dir))[0]
else:
fdwi = glob("{}/*_ec.nii*".format(dti_preroc_dir))[0]
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
bvals = bvals / 1000
gtab = gradient_table(bvals, bvecs, b0_threshold=0)
img = nb.load(fdwi)
mask = nb.load(glob("{}/*_mask.nii*".format(dti_preroc_dir))[0])
x_ix, y_ix, z_ix, mask_crop, maskdata_crop = crop_and_indexes(mask, img)
if init_method == "md":
print("Fitting tensor")
tenmodel = fwdti.BeltramiModel(gtab,
learning_rate=learning_rate,
iterations=iterations)
elif init_method == "hybrid":
if init_values_extraction == "registration":
print("Registering csf and wm rois")
try:
csf_mean, wm_mean = hybrid_rois_registration(
base_dir=base_dir,
subject_id=subject_id,
visit_id=visit_id,
output_dir=output_dir,
csf_roi=csf_roi,
wm_roi=wm_roi,
template=template,
dof=dof,
output_type=output_type)
except:
raise BaseException(
"Could not perform the necessary registration for hybrid initialization for subject {}_ses-{}"
.format(subject_id, visit_id))
elif init_values_extraction == "intensity":
print("Performing intensity based csf and wm extraction")
try:
csf_mean, wm_mean = hybrid_intensity_threshold_based(
base_dir=base_dir,
subject_id=subject_id,
visit_id=visit_id,
output_dir=output_dir,
bg_wm_bound=bg_wm_bound,
csf_bound=csf_bound,
denoised_data=denoised_data,
write_masks=write_masks,
output_type=output_type,
median_radius=median_radius,
numpass=numpass,
autocrop=autocrop,
dilate=dilate)
except:
raise BaseException(
"Could not perform the intensity based values extraction for hybrid initialization for subject {}_ses-{}"
.format(subject_id, visit_id))
print("Fitting tensor")
tenmodel = fwdti.BeltramiModel(gtab,
init_method="hybrid",
Stissue=wm_mean,
Swater=csf_mean)
else:
raise ValueError(
"{} is an unknwon initialization method".format(init_method))
tenfit = tenmodel.fit(maskdata_crop)
output_base_name = splitext(PurePath(fbval).parts[-1])[0]
if "FA" in indexes:
print("Computing fwFA")
FA = fwdti.fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
opt = np.zeros(mask.shape)
opt[x_ix, y_ix, z_ix] = FA * mask_crop
fa_img = nb.Nifti1Image(opt.astype(np.float32), img.affine)
nb.save(fa_img, "{}/{}_fwFA{}".format(output_dir, output_base_name,
ext))
if "MD" in indexes:
print("Computing fwMD")
MD = fwdti.mean_diffusivity(tenfit.evals)
MD[np.isnan(MD)] = 0
opt = np.zeros(mask.shape)
opt[x_ix, y_ix, z_ix] = MD * mask_crop
MD_img = nb.Nifti1Image(opt.astype(np.float32), img.affine)
nb.save(MD_img, "{}/{}_fwMD{}".format(output_dir, output_base_name,
ext))
if "RD" in indexes:
print("Computing fwRD")
RD = radial_diffusivity(tenfit.evals)
RD[np.isnan(RD)] = 0
opt = np.zeros(mask.shape)
opt[x_ix, y_ix, z_ix] = RD * mask_crop
RD_img = nb.Nifti1Image(opt.astype(np.float32), img.affine)
nb.save(RD_img, "{}/{}_fwRD{}".format(output_dir, output_base_name,
ext))
if "AD" in indexes:
print("Computing fwAD")
AD = axial_diffusivity(tenfit.evals)
AD[np.isnan(AD)] = 0
opt = np.zeros(mask.shape)
opt[x_ix, y_ix, z_ix] = AD * mask_crop
AD_img = nb.Nifti1Image(opt.astype(np.float32), img.affine)
nb.save(AD_img, "{}/{}_fwAD{}".format(output_dir, output_base_name,
ext))
if "FW" in indexes:
print("Computing FW")
FW = tenfit.fw
FW[np.isnan(FW)] = 0
opt = np.zeros(mask.shape)
opt[x_ix, y_ix, z_ix] = FW * mask_crop
fw_image = nb.Nifti1Image(opt.astype(np.float32), img.affine)
nb.save(fw_image, "{}/{}_FW{}".format(output_dir, output_base_name,
ext))
# def diff_direct_registration_crossectional(base_dir,
# subject_id,
# visit_id,
# output_dir,
# tbss_like = True,
# output_type = "NIFTI",
# template = "/usr/local/fsl/data/standard/FMRIB58_FA_1mm.nii.gz",
# config_file = "/usr/local/fsl/etc/flirtsch/FA_2_FMRIB58_1mm.cnf"):
# dti_dir = "{}/derivatives/dipy_dti/{}/ses-{}".format(base_dir, subject_id, visit_id)
# fdwi = glob("{}/*FA.nii*".format(dti_dir))[0]
# output_base_name = get_base_name_all_type(fdwi)
# other_indexes = glob("{}/*nii*".format(dti_dir))
# other_indexes.remove(fdwi)
# if output_type == "NIFTI":
# ext = ".nii"
# elif output_type == "NIFTI_GZ":
# ext = ".nii.gz"
# else:
# raise ValueError("Output file type {} not recognized".format(output_type))
# flirt = FLIRT()
# fnirt = FNIRT()
# apply_warp = ApplyWarp()
# flirt.inputs.reference = template
# flirt.inputs.output_type = output_type
# fnirt.inputs.ref_file = template
# fnirt.inputs.output_type = output_type
# fnirt.inputs.config_file = config_file
# fnirt.inputs.log_file = "{}/{}_fnirt.txt".format(output_dir, output_base_name)
# apply_warp.inputs.ref_file = template
# apply_warp.inputs.output_type = output_type
# if tbss_like:
# img = nb.load(fdwi)
# img_data = img.get_fdata()
# binary_wm = np.zeros_like(img_data)
# binary_wm[img_data != 0] = 1
# eroded_wm_mask = binary_erosion(binary_wm)
# eroded_wm = img_data * (eroded_wm_mask * 1)
# eroded_wm[:,:,0] = 0
# eroded_wm[:,:,-1] = 0
# clean_fa = nb.Nifti1Image(eroded_wm, img.affine, img.header)
# nb.save(clean_fa, "{}/{}_clean{}".format(output_dir, output_base_name, ext))
# flirt.inputs.in_file = "{}/{}_clean{}".format(output_dir, output_base_name, ext)
# flirt.inputs.out_file = "{}/{}_clean_linear{}".format(output_dir, output_base_name, ext)
# flirt.inputs.out_matrix_file = "{}/{}_clean_linear.mat".format(output_dir, output_base_name)
# print("running linear registration")
# flirt.run()
# fnirt.inputs.in_file = "{}/{}_clean_linear{}".format(output_dir, output_base_name, ext)
# fnirt.inputs.warped_file = "{}/{}_clean_nonlinear{}".format(output_dir, output_base_name, ext)
# fnirt.inputs.field_file = "{}/{}_clean_nonlinearfield{}".format(output_dir, output_base_name, ext)
# print("running non linear registration")
# fnirt.run()
# else:
# flirt.inputs.in_file = fdwi
# flirt.inputs.out_file = "{}/{}_linear{}".format(output_dir, output_base_name, ext)
# flirt.inputs.out_matrix_file = "{}/{}_linear.mat".format(output_dir, output_base_name)
# print("running linear registration")
# flirt.run()
# fnirt.inputs.in_file = "{}/{}_linear{}".format(output_dir, output_base_name, ext)
# fnirt.inputs.warped_file = "{}/{}_nonlinear{}".format(output_dir, output_base_name, ext)
# fnirt.inputs.field_file = "{}/{}_nonlinearfield{}".format(output_dir, output_base_name, ext)
# print("running non linear registration")
# fnirt.run()
# apply_warp.inputs.premat = flirt.inputs.out_matrix_file
# apply_warp.inputs.field_file = fnirt.inputs.field_file
# print("applying warp")
# for f in other_indexes:
# output_base_name = get_base_name_all_type(f)
# apply_warp.inputs.in_file = f
# apply_warp.inputs.out_file = "{}/{}_nonlinear{}".format(output_dir, output_base_name, ext)
# apply_warp.run()
# def diff_direct_registration_longitudinal(base_dir,
# subject_id,
# output_dir,
# tbss_like = True,
# output_type = "NIFTI",
# template = "/usr/local/fsl/data/standard/FMRIB58_FA_1mm.nii.gz",
# config_file = "/usr/local/fsl/etc/flirtsch/FA_2_FMRIB58_1mm.cnf"):
# if output_type == "NIFTI":
# ext = ".nii"
# elif output_type == "NIFTI_GZ":
# ext = ".nii.gz"
# else:
# raise ValueError("Output file type {} not recognized".format(output_type))
# dti_dir = "{}/derivatives/dipy_dti/{}".format(base_dir, subject_id)
# fdwi_01 = glob("{}/ses-01/*FA.nii*".format(dti_dir))[0]
# fdwi_02 = glob("{}/ses-02/*FA.nii*".format(dti_dir))[0]
# # output_base_name = get_base_name_all_type(fdwi)
# # other_indexes = glob("{}/*nii*".format(dti_dir))
# # other_indexes.remove(fdwi)
# if tbss_like:
# for el in [fdwi_01, fdwi_02]:
# output_base_name = get_base_name_all_type(el)
# img = nb.load(el)
# img_data = img.get_fdata()
# binary_wm = np.zeros_like(img_data)
# binary_wm[img_data != 0] = 1
# eroded_wm_mask = binary_erosion(binary_wm)
# eroded_wm = img_data * (eroded_wm_mask * 1)
# eroded_wm[:,:,0] = 0
# eroded_wm[:,:,-1] = 0
# clean_fa = nb.Nifti1Image(eroded_wm, img.affine, img.header)
# nb.save(clean_fa, "{}/{}_clean{}".format(output_dir, output_base_name, ext))
# #flirt back and forth
# clean_01 = glob("{}/*ses-01*_clean*".format(output_dir, output_base_name))
# clean_02 = glob("{}/*ses-02*_clean*".format(output_dir, output_base_name))
# flirt = FLIRT()
# flirt.inputs.in_file = clean_01
# flirt.inputs.reference = clean_02
# flirt.inputs.output_type = output_type
# flirt.inputs.out_file = "{}/t1_to_t2{}".format(output_dir, ext)
# flirt.inputs.out_matrix_file = "{}/t1_to_t2.mat".format(output_dir)
# flirt.run()
# flirt.inputs.in_file = clean_02
# flirt.inputs.reference = clean_01
# flirt.inputs.output_type = output_type
# flirt.inputs.out_file = "{}/t2_to_t1{}".format(output_dir, ext)
# flirt.inputs.out_matrix_file = "{}/t2_to_t1.mat".format(output_dir)
# mat_transform = ConvertXFM()
# mat_transform.inputs.in_file = "{}/t2_to_t1.mat".format(output_dir)
# mat_transform.inputs.inverse_xfm = True
# mat_transform.inputs.out_file = "{}/t2_to_t1_inverted.mat".format(output_dir)
# fnirt = FNIRT()
# apply_warp = ApplyWarp()
# flirt.inputs.reference = template
# flirt.inputs.output_type = output_type
# fnirt.inputs.ref_file = template
# fnirt.inputs.output_type = output_type
# fnirt.inputs.config_file = config_file
# fnirt.inputs.log_file = "{}/{}_fnirt.txt".format(output_dir, output_base_name)
# apply_warp.inputs.ref_file = template
# apply_warp.inputs.output_type = output_type
# if tbss_like:
# img = nb.load(fdwi)
# img_data = img.get_fdata()
# binary_wm = np.zeros_like(img_data)
# binary_wm[img_data != 0] = 1
# eroded_wm_mask = binary_erosion(binary_wm)
# eroded_wm = img_data * (eroded_wm_mask * 1)
# eroded_wm[:,:,0] = 0
# eroded_wm[:,:,-1] = 0
# clean_fa = nb.Nifti1Image(eroded_wm, img.affine, img.header)
# nb.save(clean_fa, "{}/{}_clean{}".format(output_dir, output_base_name, ext))
# flirt.inputs.in_file = "{}/{}_clean{}".format(output_dir, output_base_name, ext)
# flirt.inputs.out_file = "{}/{}_clean_linear{}".format(output_dir, output_base_name, ext)
# flirt.inputs.out_matrix_file = "{}/{}_clean_linear.mat".format(output_dir, output_base_name)
# print("running linear registration")
# flirt.run()
# fnirt.inputs.in_file = "{}/{}_clean_linear{}".format(output_dir, output_base_name, ext)
# fnirt.inputs.warped_file = "{}/{}_clean_nonlinear{}".format(output_dir, output_base_name, ext)
# fnirt.inputs.field_file = "{}/{}_clean_nonlinearfield{}".format(output_dir, output_base_name, ext)
# print("running non linear registration")
# fnirt.run()
# else:
# flirt.inputs.in_file = fdwi
# flirt.inputs.out_file = "{}/{}_linear{}".format(output_dir, output_base_name, ext)
# flirt.inputs.out_matrix_file = "{}/{}_linear.mat".format(output_dir, output_base_name)
# print("running linear registration")
# flirt.run()
# fnirt.inputs.in_file = "{}/{}_linear{}".format(output_dir, output_base_name, ext)
# fnirt.inputs.warped_file = "{}/{}_nonlinear{}".format(output_dir, output_base_name, ext)
# fnirt.inputs.field_file = "{}/{}_nonlinearfield{}".format(output_dir, output_base_name, ext)
# print("running non linear registration")
# fnirt.run()
# apply_warp.inputs.premat = flirt.inputs.out_matrix_file
# apply_warp.inputs.field_file = fnirt.inputs.field_file
# print("applying warp")
# for f in other_indexes:
# output_base_name = get_base_name_all_type(f)
# apply_warp.inputs.in_file = f
# apply_warp.inputs.out_file = "{}/{}_nonlinear{}".format(output_dir, output_base_name, ext)
# apply_warp.run()
def dti_processing(base_dir,
subject_id,
visit_id,
output_dir,
denoised_data=True,
indexes=["FA", "MD"],
output_type="NIFTI"):
"""Function to derive dti indexes from dwi data using dipy functions
Parameters
----------
base_dir : absolute path pointing at the root directory of the bids repository
subject_id: string, the tag subject (i.e. sub-sublabel) of the subject to treat
visit_id: the VALUE of the session tag to be treated (i.e. for ses-02, only "02" need to be entered)
output_dir: absolute path of the directories were the indexes will be written
denoised_data: boolean, should the denoised (True) or only the eddy current correct and realigned data (False) be used for fitting ?
indexes : list, a list of the indexes that will be written, accepted values are ["FA", "MD", "RD", "AD"]
... : other parameters related to the dipy functions used
"""
if output_type == "NIFTI":
ext = ".nii"
elif output_type == "NIFTI_GZ":
ext = ".nii.gz"
else:
raise ValueError(
"Output file type {} not recognized".format(output_type))
diff_dir = "{}/{}/ses-{}/dwi".format(base_dir, subject_id, visit_id)
fbval = glob("{}/*bval".format(diff_dir))[0]
fbvec = glob("{}/*bvec".format(diff_dir))[0]
dti_preroc_dir = "{}/derivatives/fsl-dipy_dti-preproc/{}/ses-{}".format(
base_dir, subject_id, visit_id)
if denoised_data:
fdwi = glob("{}/*_denoised*".format(dti_preroc_dir))[0]
else:
fdwi = glob("{}/*_masked.nii*".format(dti_preroc_dir))[0]
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
gtab = gradient_table(bvals, bvecs)
img = nb.load(fdwi)
mask = nb.load(glob("{}/*_mask.nii*".format(dti_preroc_dir))[0])
x_ix, y_ix, z_ix, mask_crop, maskdata_crop = crop_and_indexes(mask, img)
tenmodel = dti.TensorModel(gtab)
tenfit = tenmodel.fit(maskdata_crop)
output_base_name = get_base_name_all_type(fbval)
if "FA" in indexes:
print("Computing FA")
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
opt = np.zeros(mask.shape)
opt[x_ix, y_ix, z_ix] = FA * mask_crop
fa_img = nb.Nifti1Image(opt.astype(np.float32), img.affine)
nb.save(fa_img, "{}/{}_FA{}".format(output_dir, output_base_name, ext))
if "MD" in indexes:
print("Computing MD")
MD = mean_diffusivity(tenfit.evals)
MD[np.isnan(MD)] = 0
opt = np.zeros(mask.shape)
opt[x_ix, y_ix, z_ix] = MD * mask_crop
md_img = nb.Nifti1Image(opt.astype(np.float32), img.affine)
nb.save(md_img, "{}/{}_MD{}".format(output_dir, output_base_name, ext))
if "RD" in indexes:
print("Computing RD")
RD = radial_diffusivity(tenfit.evals)
RD[np.isnan(RD)] = 0
opt = np.zeros(mask.shape)
opt[x_ix, y_ix, z_ix] = RD * mask_crop
rd_img = nb.Nifti1Image(opt.astype(np.float32), img.affine)
nb.save(rd_img, "{}/{}_RD{}".format(output_dir, output_base_name, ext))
if "AD" in indexes:
print("Computing AD")
AD = axial_diffusivity(tenfit.evals)
AD[np.isnan(AD)] = 0
opt = np.zeros(mask.shape)
opt[x_ix, y_ix, z_ix] = AD * mask_crop
ad_img = nb.Nifti1Image(opt.astype(np.float32), img.affine)
nb.save(ad_img, "{}/{}_AD{}".format(output_dir, output_base_name, ext))
def main():
parser = _build_args_parser()
args = parser.parse_args()
if not args.not_all:
args.fa = args.fa or 'fa.nii.gz'
args.ga = args.ga or 'ga.nii.gz'
args.rgb = args.rgb or 'rgb.nii.gz'
args.md = args.md or 'md.nii.gz'
args.ad = args.ad or 'ad.nii.gz'
args.rd = args.rd or 'rd.nii.gz'
args.mode = args.mode or 'mode.nii.gz'
args.norm = args.norm or 'tensor_norm.nii.gz'
args.tensor = args.tensor or 'tensor.nii.gz'
args.evecs = args.evecs or 'tensor_evecs.nii.gz'
args.evals = args.evals or 'tensor_evals.nii.gz'
args.residual = args.residual or 'dti_residual.nii.gz'
args.p_i_signal =\
args.p_i_signal or 'physically_implausible_signals_mask.nii.gz'
args.pulsation = args.pulsation or 'pulsation_and_misalignment.nii.gz'
outputs = [args.fa, args.ga, args.rgb, args.md, args.ad, args.rd,
args.mode, args.norm, args.tensor, args.evecs, args.evals,
args.residual, args.p_i_signal, args.pulsation]
if args.not_all and not any(outputs):
parser.error('When using --not_all, you need to specify at least ' +
'one metric to output.')
assert_inputs_exist(
parser, [args.input, args.bvals, args.bvecs], [args.mask])
assert_outputs_exists(parser, args, outputs)
img = nib.load(args.input)
data = img.get_data()
affine = img.get_affine()
if args.mask is None:
mask = None
else:
mask = nib.load(args.mask).get_data().astype(np.bool)
# Validate bvals and bvecs
logging.info('Tensor estimation with the %s method...', args.method)
bvals, bvecs = read_bvals_bvecs(args.bvals, args.bvecs)
if not is_normalized_bvecs(bvecs):
logging.warning('Your b-vectors do not seem normalized...')
bvecs = normalize_bvecs(bvecs)
check_b0_threshold(args, bvals.min())
gtab = gradient_table(bvals, bvecs, b0_threshold=bvals.min())
# Get tensors
if args.method == 'restore':
sigma = ne.estimate_sigma(data)
tenmodel = TensorModel(gtab, fit_method=args.method, sigma=sigma,
min_signal=_get_min_nonzero_signal(data))
else:
tenmodel = TensorModel(gtab, fit_method=args.method,
min_signal=_get_min_nonzero_signal(data))
tenfit = tenmodel.fit(data, mask)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
if args.tensor:
# Get the Tensor values and format them for visualisation
# in the Fibernavigator.
tensor_vals = lower_triangular(tenfit.quadratic_form)
correct_order = [0, 1, 3, 2, 4, 5]
tensor_vals_reordered = tensor_vals[..., correct_order]
fiber_tensors = nib.Nifti1Image(
tensor_vals_reordered.astype(np.float32), affine)
nib.save(fiber_tensors, args.tensor)
if args.fa:
fa_img = nib.Nifti1Image(FA.astype(np.float32), affine)
nib.save(fa_img, args.fa)
if args.ga:
GA = geodesic_anisotropy(tenfit.evals)
GA[np.isnan(GA)] = 0
ga_img = nib.Nifti1Image(GA.astype(np.float32), affine)
nib.save(ga_img, args.ga)
if args.rgb:
RGB = color_fa(FA, tenfit.evecs)
rgb_img = nib.Nifti1Image(np.array(255 * RGB, 'uint8'), affine)
nib.save(rgb_img, args.rgb)
if args.md:
MD = mean_diffusivity(tenfit.evals)
md_img = nib.Nifti1Image(MD.astype(np.float32), affine)
nib.save(md_img, args.md)
if args.ad:
AD = axial_diffusivity(tenfit.evals)
ad_img = nib.Nifti1Image(AD.astype(np.float32), affine)
nib.save(ad_img, args.ad)
if args.rd:
RD = radial_diffusivity(tenfit.evals)
rd_img = nib.Nifti1Image(RD.astype(np.float32), affine)
nib.save(rd_img, args.rd)
if args.mode:
# Compute tensor mode
inter_mode = dipy_mode(tenfit.quadratic_form)
# Since the mode computation can generate NANs when not masked,
# we need to remove them.
non_nan_indices = np.isfinite(inter_mode)
mode = np.zeros(inter_mode.shape)
mode[non_nan_indices] = inter_mode[non_nan_indices]
mode_img = nib.Nifti1Image(mode.astype(np.float32), affine)
nib.save(mode_img, args.mode)
if args.norm:
NORM = norm(tenfit.quadratic_form)
norm_img = nib.Nifti1Image(NORM.astype(np.float32), affine)
nib.save(norm_img, args.norm)
if args.evecs:
evecs = tenfit.evecs.astype(np.float32)
evecs_img = nib.Nifti1Image(evecs, affine)
nib.save(evecs_img, args.evecs)
# save individual e-vectors also
e1_img = nib.Nifti1Image(evecs[..., 0], affine)
e2_img = nib.Nifti1Image(evecs[..., 1], affine)
e3_img = nib.Nifti1Image(evecs[..., 2], affine)
nib.save(e1_img, add_filename_suffix(args.evecs, '_v1'))
nib.save(e2_img, add_filename_suffix(args.evecs, '_v2'))
nib.save(e3_img, add_filename_suffix(args.evecs, '_v3'))
if args.evals:
evals = tenfit.evals.astype(np.float32)
evals_img = nib.Nifti1Image(evals, affine)
nib.save(evals_img, args.evals)
# save individual e-values also
e1_img = nib.Nifti1Image(evals[..., 0], affine)
e2_img = nib.Nifti1Image(evals[..., 1], affine)
e3_img = nib.Nifti1Image(evals[..., 2], affine)
nib.save(e1_img, add_filename_suffix(args.evals, '_e1'))
nib.save(e2_img, add_filename_suffix(args.evals, '_e2'))
nib.save(e3_img, add_filename_suffix(args.evals, '_e3'))
if args.p_i_signal:
S0 = np.mean(data[..., gtab.b0s_mask], axis=-1, keepdims=True)
DWI = data[..., ~gtab.b0s_mask]
pis_mask = np.max(S0 < DWI, axis=-1)
if args.mask is not None:
pis_mask *= mask
pis_img = nib.Nifti1Image(pis_mask.astype(np.int16), affine)
nib.save(pis_img, args.p_i_signal)
if args.pulsation:
STD = np.std(data[..., ~gtab.b0s_mask], axis=-1)
if args.mask is not None:
STD *= mask
std_img = nib.Nifti1Image(STD.astype(np.float32), affine)
nib.save(std_img, add_filename_suffix(args.pulsation, '_std_dwi'))
if np.sum(gtab.b0s_mask) <= 1:
logger.info('Not enough b=0 images to output standard '
'deviation map')
else:
if len(np.where(gtab.b0s_mask)) == 2:
logger.info('Only two b=0 images. Be careful with the '
'interpretation of this std map')
STD = np.std(data[..., gtab.b0s_mask], axis=-1)
if args.mask is not None:
STD *= mask
std_img = nib.Nifti1Image(STD.astype(np.float32), affine)
nib.save(std_img, add_filename_suffix(args.pulsation, '_std_b0'))
if args.residual:
if args.mask is None:
logger.info("Outlier detection will not be performed, since no "
"mask was provided.")
S0 = np.mean(data[..., gtab.b0s_mask], axis=-1)
data_p = tenfit.predict(gtab, S0)
R = np.mean(np.abs(data_p[..., ~gtab.b0s_mask] -
data[..., ~gtab.b0s_mask]), axis=-1)
if args.mask is not None:
R *= mask
R_img = nib.Nifti1Image(R.astype(np.float32), affine)
nib.save(R_img, args.residual)
R_k = np.zeros(data.shape[-1]) # mean residual per DWI
std = np.zeros(data.shape[-1]) # std residual per DWI
q1 = np.zeros(data.shape[-1]) # first quartile
q3 = np.zeros(data.shape[-1]) # third quartile
iqr = np.zeros(data.shape[-1]) # interquartile
for i in range(data.shape[-1]):
x = np.abs(data_p[..., i] - data[..., i])[mask]
R_k[i] = np.mean(x)
std[i] = np.std(x)
q3[i], q1[i] = np.percentile(x, [75, 25])
iqr[i] = q3[i] - q1[i]
# Outliers are observations that fall below Q1 - 1.5(IQR) or
# above Q3 + 1.5(IQR) We check if a volume is an outlier only if
# we have a mask, else we are biased.
if args.mask is not None and R_k[i] < (q1[i] - 1.5 * iqr[i]) \
or R_k[i] > (q3[i] + 1.5 * iqr[i]):
logger.warning('WARNING: Diffusion-Weighted Image i=%s is an '
'outlier', i)
residual_basename, _ = split_name_with_nii(args.residual)
res_stats_basename = residual_basename + ".npy"
np.save(add_filename_suffix(
res_stats_basename, "_mean_residuals"), R_k)
np.save(add_filename_suffix(res_stats_basename, "_q1_residuals"), q1)
np.save(add_filename_suffix(res_stats_basename, "_q3_residuals"), q3)
np.save(add_filename_suffix(res_stats_basename, "_iqr_residuals"), iqr)
np.save(add_filename_suffix(res_stats_basename, "_std_residuals"), std)
# To do: I would like to have an error bar with q1 and q3.
# Now, q1 acts as a std
dwi = np.arange(R_k[~gtab.b0s_mask].shape[0])
plt.bar(dwi, R_k[~gtab.b0s_mask], 0.75,
color='y', yerr=q1[~gtab.b0s_mask])
plt.xlabel('DW image')
plt.ylabel('Mean residuals +- q1')
plt.title('Residuals')
plt.savefig(residual_basename + '_residuals_stats.png')
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]
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 run(self,
input_files,
bvalues,
bvectors,
mask_files,
b0_threshold=0.0,
save_metrics=[],
out_dir='',
out_tensor='tensors.nii.gz',
out_fa='fa.nii.gz',
out_ga='ga.nii.gz',
out_rgb='rgb.nii.gz',
out_md='md.nii.gz',
out_ad='ad.nii.gz',
out_rd='rd.nii.gz',
out_mode='mode.nii.gz',
out_evec='evecs.nii.gz',
out_eval='evals.nii.gz'):
""" Workflow for tensor reconstruction and for computing DTI metrics.
Performs a tensor reconstruction on the files by 'globing'
``input_files`` and saves the DTI metrics in a directory specified by
``out_dir``.
Parameters
----------
input_files : string
Path to the input volumes. This path may contain wildcards to
process multiple inputs at once.
bvalues : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
bvectors : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
mask_files : string
Path to the input masks. This path may contain wildcards to use
multiple masks at once. (default: No mask used)
b0_threshold : float, optional
Threshold used to find b=0 directions (default 0.0)
save_metrics : variable string, optional
List of metrics to save.
Possible values: fa, ga, rgb, md, ad, rd, mode, tensor, evec, eval
(default [] (all))
out_dir : string, optional
Output directory (default input file directory)
out_tensor : string, optional
Name of the tensors volume to be saved (default 'tensors.nii.gz')
out_fa : string, optional
Name of the fractional anisotropy volume to be saved
(default 'fa.nii.gz')
out_ga : string, optional
Name of the geodesic anisotropy volume to be saved
(default 'ga.nii.gz')
out_rgb : string, optional
Name of the color fa volume to be saved (default 'rgb.nii.gz')
out_md : string, optional
Name of the mean diffusivity volume to be saved
(default 'md.nii.gz')
out_ad : string, optional
Name of the axial diffusivity volume to be saved
(default 'ad.nii.gz')
out_rd : string, optional
Name of the radial diffusivity volume to be saved
(default 'rd.nii.gz')
out_mode : string, optional
Name of the mode volume to be saved (default 'mode.nii.gz')
out_evec : string, optional
Name of the eigenvectors volume to be saved
(default 'evecs.nii.gz')
out_eval : string, optional
Name of the eigenvalues to be saved (default 'evals.nii.gz')
"""
io_it = self.get_io_iterator()
for dwi, bval, bvec, mask, otensor, ofa, oga, orgb, omd, oad, orad, \
omode, oevecs, oevals in io_it:
logging.info('Computing DTI metrics for {0}'.format(dwi))
img = nib.load(dwi)
data = img.get_data()
affine = img.get_affine()
if mask is None:
mask = None
else:
mask = nib.load(mask).get_data().astype(np.bool)
tenfit, _ = self.get_fitted_tensor(data, mask, bval, bvec,
b0_threshold)
if not save_metrics:
save_metrics = [
'fa', 'md', 'rd', 'ad', 'ga', 'rgb', 'mode', 'evec',
'eval', 'tensor'
]
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
if 'tensor' in save_metrics:
tensor_vals = lower_triangular(tenfit.quadratic_form)
correct_order = [0, 1, 3, 2, 4, 5]
tensor_vals_reordered = tensor_vals[..., correct_order]
fiber_tensors = nib.Nifti1Image(
tensor_vals_reordered.astype(np.float32), affine)
nib.save(fiber_tensors, otensor)
if 'fa' in save_metrics:
fa_img = nib.Nifti1Image(FA.astype(np.float32), affine)
nib.save(fa_img, ofa)
if 'ga' in save_metrics:
GA = geodesic_anisotropy(tenfit.evals)
ga_img = nib.Nifti1Image(GA.astype(np.float32), affine)
nib.save(ga_img, oga)
if 'rgb' in save_metrics:
RGB = color_fa(FA, tenfit.evecs)
rgb_img = nib.Nifti1Image(np.array(255 * RGB, 'uint8'), affine)
nib.save(rgb_img, orgb)
if 'md' in save_metrics:
MD = mean_diffusivity(tenfit.evals)
md_img = nib.Nifti1Image(MD.astype(np.float32), affine)
nib.save(md_img, omd)
if 'ad' in save_metrics:
AD = axial_diffusivity(tenfit.evals)
ad_img = nib.Nifti1Image(AD.astype(np.float32), affine)
nib.save(ad_img, oad)
if 'rd' in save_metrics:
RD = radial_diffusivity(tenfit.evals)
rd_img = nib.Nifti1Image(RD.astype(np.float32), affine)
nib.save(rd_img, orad)
if 'mode' in save_metrics:
MODE = get_mode(tenfit.quadratic_form)
mode_img = nib.Nifti1Image(MODE.astype(np.float32), affine)
nib.save(mode_img, omode)
if 'evec' in save_metrics:
evecs_img = nib.Nifti1Image(tenfit.evecs.astype(np.float32),
affine)
nib.save(evecs_img, oevecs)
if 'eval' in save_metrics:
evals_img = nib.Nifti1Image(tenfit.evals.astype(np.float32),
affine)
nib.save(evals_img, oevals)
logging.info('DTI metrics saved in {0}'.format(
os.path.dirname(oevals)))
def dmri_recon(sid, data_dir, out_dir, resolution, recon='csd', num_threads=2):
import tempfile
#tempfile.tempdir = '/om/scratch/Fri/ksitek/'
import os
oldval = None
if 'MKL_NUM_THREADS' in os.environ:
oldval = os.environ['MKL_NUM_THREADS']
os.environ['MKL_NUM_THREADS'] = '%d' % num_threads
ompoldval = None
if 'OMP_NUM_THREADS' in os.environ:
ompoldval = os.environ['OMP_NUM_THREADS']
os.environ['OMP_NUM_THREADS'] = '%d' % num_threads
import nibabel as nib
import numpy as np
from glob import glob
if resolution == '0.2mm':
filename = 'Reg_S64550_nii4d.nii'
fimg = os.path.abspath(glob(os.path.join(data_dir, filename))[0])
else:
filename = 'Reg_S64550_nii4d_resamp-%s.nii.gz'%(resolution)
fimg = os.path.abspath(glob(os.path.join(data_dir, 'resample', filename))[0])
print("dwi file = %s"%fimg)
fbvec = os.path.abspath(glob(os.path.join(data_dir, 'bvecs',
'camino_120_RAS_flipped-xy.bvecs'))[0])
print("bvec file = %s"%fbvec)
fbval = os.path.abspath(glob(os.path.join(data_dir, 'bvecs',
'camino_120_RAS.bvals'))[0])
print("bval file = %s"%fbval)
img = nib.load(fimg)
data = img.get_data()
affine = img.get_affine()
prefix = sid
from dipy.io import read_bvals_bvecs
from dipy.core.gradients import vector_norm
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
b0idx = []
for idx, val in enumerate(bvals):
if val < 1:
pass
#bvecs[idx] = [1, 0, 0]
else:
b0idx.append(idx)
#print "b0idx=%d"%idx
#print "input bvecs:"
#print bvecs
bvecs[b0idx, :] = bvecs[b0idx, :]/vector_norm(bvecs[b0idx])[:, None]
#print "bvecs after normalization:"
#print bvecs
from dipy.core.gradients import gradient_table
gtab = gradient_table(bvals, bvecs)
gtab.bvecs.shape == bvecs.shape
gtab.bvecs
gtab.bvals.shape == bvals.shape
gtab.bvals
from dipy.reconst.csdeconv import auto_response
response, ratio = auto_response(gtab, data, roi_radius=10, fa_thr=0.1) # 0.7
#from dipy.segment.mask import median_otsu
#b0_mask, mask = median_otsu(data[:, :, :, b0idx].mean(axis=3).squeeze(), 4, 4)
if resolution == '0.2mm':
mask_name = 'Reg_S64550_nii_b0-slice_mask.nii.gz'
fmask1 = os.path.join(data_dir, mask_name)
else:
mask_name = 'Reg_S64550_nii_b0-slice_mask_resamp-%s.nii.gz'%(resolution)
fmask1 = os.path.join(data_dir, 'resample', mask_name)
print("fmask file = %s"%fmask1)
mask = nib.load(fmask1).get_data()
useFA = True
print("creating model")
if recon == 'csd':
from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel
model = ConstrainedSphericalDeconvModel(gtab, response)
useFA = True
elif recon == 'csa':
from dipy.reconst.shm import CsaOdfModel, normalize_data
model = CsaOdfModel(gtab, 4)
useFA = False
else:
raise ValueError('only csd, csa supported currently')
from dipy.reconst.dsi import (DiffusionSpectrumDeconvModel,
DiffusionSpectrumModel)
model = DiffusionSpectrumDeconvModel(gtab)
fit = model.fit(data)
from dipy.data import get_sphere
sphere = get_sphere('symmetric724')
#odfs = fit.odf(sphere)
from dipy.reconst.peaks import peaks_from_model
print("running peaks_from_model")
peaks = peaks_from_model(model=model,
data=data,
sphere=sphere,
mask=mask,
return_sh=True,
return_odf=False,
normalize_peaks=True,
npeaks=5,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=num_threads > 1,
nbr_processes=num_threads)
from dipy.reconst.dti import TensorModel
print("running tensor model")
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data, mask)
from dipy.reconst.dti import fractional_anisotropy
print("running FA")
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
fa_img = nib.Nifti1Image(FA, img.get_affine())
tensor_fa_file = os.path.abspath('%s_tensor_fa.nii.gz' % (prefix))
nib.save(fa_img, tensor_fa_file)
from dipy.reconst.dti import axial_diffusivity
print("running AD")
AD = axial_diffusivity(tenfit.evals)
AD[np.isnan(AD)] = 0
ad_img = nib.Nifti1Image(AD, img.get_affine())
tensor_ad_file = os.path.abspath('%s_tensor_ad.nii.gz' % (prefix))
nib.save(ad_img, tensor_ad_file)
from dipy.reconst.dti import radial_diffusivity
print("running RD")
RD = radial_diffusivity(tenfit.evals)
RD[np.isnan(RD)] = 0
rd_img = nib.Nifti1Image(RD, img.get_affine())
tensor_rd_file = os.path.abspath('%s_tensor_rd.nii.gz' % (prefix))
nib.save(rd_img, tensor_rd_file)
from dipy.reconst.dti import mean_diffusivity
print("running MD")
MD = mean_diffusivity(tenfit.evals)
MD[np.isnan(MD)] = 0
md_img = nib.Nifti1Image(MD, img.get_affine())
tensor_md_file = os.path.abspath('%s_tensor_md.nii.gz' % (prefix))
nib.save(md_img, tensor_md_file)
evecs = tenfit.evecs
evec_img = nib.Nifti1Image(evecs, img.get_affine())
tensor_evec_file = os.path.abspath('%s_tensor_evec.nii.gz' % (prefix))
nib.save(evec_img, tensor_evec_file)
shm_coeff = fit.shm_coeff
shm_coeff_file = os.path.abspath('%s_shm_coeff.nii.gz' % (prefix))
nib.save(nib.Nifti1Image(shm_coeff, img.get_affine()), shm_coeff_file)
#from dipy.reconst.dti import quantize_evecs
#peak_indices = quantize_evecs(tenfit.evecs, sphere.vertices)
#eu = EuDX(FA, peak_indices, odf_vertices = sphere.vertices,
#a_low=0.2, seeds=10**6, ang_thr=35)
fa_img = nib.Nifti1Image(peaks.gfa, img.get_affine())
model_gfa_file = os.path.abspath('%s_%s_gfa.nii.gz' % (prefix, recon))
nib.save(fa_img, model_gfa_file)
from dipy.tracking.eudx import EuDX
print("reconstructing with EuDX")
if useFA:
eu = EuDX(FA, peaks.peak_indices[..., 0],
odf_vertices = sphere.vertices,
#a_low=0.1,
seeds=10**6,
ang_thr=45)
else:
eu = EuDX(peaks.gfa, peaks.peak_indices[..., 0],
odf_vertices = sphere.vertices,
#a_low=0.1,
seeds=10**6,
ang_thr=45)
sl_fname = os.path.abspath('%s_%s_streamline.trk' % (prefix, recon))
"""
#import dipy.tracking.metrics as dmetrics
streamlines = ((sl, None, None) for sl in eu) # if dmetrics.length(sl) > 15)
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = fa_img.get_header().get_zooms()[:3]
hdr['voxel_order'] = 'RAS' #LAS
hdr['dim'] = FA.shape[:3]
nib.trackvis.write(sl_fname, streamlines, hdr, points_space='voxel')
"""
# trying new dipy.io.streamline module, per email to neuroimaging list
# 2018.04.05
from nibabel.streamlines import Field
from nibabel.orientations import aff2axcodes
affine = img.get_affine()
vox_size=fa_img.get_header().get_zooms()[:3]
fov_shape=FA.shape[:3]
if vox_size is not None and fov_shape is not None:
hdr = {}
hdr[Field.VOXEL_TO_RASMM] = affine.copy()
hdr[Field.VOXEL_SIZES] = vox_size
hdr[Field.DIMENSIONS] = fov_shape
hdr[Field.VOXEL_ORDER] = "".join(aff2axcodes(affine))
tractogram = nib.streamlines.Tractogram(eu)
tractogram.affine_to_rasmm = affine
trk_file = nib.streamlines.TrkFile(tractogram, header=hdr)
nib.streamlines.save(trk_file, sl_fname)
if oldval:
os.environ['MKL_NUM_THREADS'] = oldval
else:
del os.environ['MKL_NUM_THREADS']
if ompoldval:
os.environ['OMP_NUM_THREADS'] = ompoldval
else:
del os.environ['OMP_NUM_THREADS']
assert tensor_fa_file
assert tensor_evec_file
assert model_gfa_file
assert tensor_ad_file
assert tensor_rd_file
assert tensor_md_file
assert shm_coeff_file
print('all output files created')
return tensor_fa_file, tensor_evec_file, model_gfa_file, sl_fname, affine, tensor_ad_file, tensor_rd_file, tensor_md_file, shm_coeff_file
def run(self, input_files, bvalues, bvectors, mask_files, b0_threshold=0.0,
save_metrics=[],
out_dir='', out_tensor='tensors.nii.gz', out_fa='fa.nii.gz',
out_ga='ga.nii.gz', out_rgb='rgb.nii.gz', out_md='md.nii.gz',
out_ad='ad.nii.gz', out_rd='rd.nii.gz', out_mode='mode.nii.gz',
out_evec='evecs.nii.gz', out_eval='evals.nii.gz'):
""" Workflow for tensor reconstruction and for computing DTI metrics.
Performs a tensor reconstruction on the files by 'globing'
``input_files`` and saves the DTI metrics in a directory specified by
``out_dir``.
Parameters
----------
input_files : string
Path to the input volumes. This path may contain wildcards to
process multiple inputs at once.
bvalues : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
bvectors : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
mask_files : string
Path to the input masks. This path may contain wildcards to use
multiple masks at once. (default: No mask used)
b0_threshold : float, optional
Threshold used to find b=0 directions (default 0.0)
save_metrics : variable string, optional
List of metrics to save.
Possible values: fa, ga, rgb, md, ad, rd, mode, tensor, evec, eval
(default [] (all))
out_dir : string, optional
Output directory (default input file directory)
out_tensor : string, optional
Name of the tensors volume to be saved (default 'tensors.nii.gz')
out_fa : string, optional
Name of the fractional anisotropy volume to be saved
(default 'fa.nii.gz')
out_ga : string, optional
Name of the geodesic anisotropy volume to be saved
(default 'ga.nii.gz')
out_rgb : string, optional
Name of the color fa volume to be saved (default 'rgb.nii.gz')
out_md : string, optional
Name of the mean diffusivity volume to be saved
(default 'md.nii.gz')
out_ad : string, optional
Name of the axial diffusivity volume to be saved
(default 'ad.nii.gz')
out_rd : string, optional
Name of the radial diffusivity volume to be saved
(default 'rd.nii.gz')
out_mode : string, optional
Name of the mode volume to be saved (default 'mode.nii.gz')
out_evec : string, optional
Name of the eigenvectors volume to be saved
(default 'evecs.nii.gz')
out_eval : string, optional
Name of the eigenvalues to be saved (default 'evals.nii.gz')
"""
io_it = self.get_io_iterator()
for dwi, bval, bvec, mask, otensor, ofa, oga, orgb, omd, oad, orad, \
omode, oevecs, oevals in io_it:
logging.info('Computing DTI metrics for {0}'.format(dwi))
img = nib.load(dwi)
data = img.get_data()
affine = img.get_affine()
if mask is None:
mask = None
else:
mask = nib.load(mask).get_data().astype(np.bool)
tenfit, _ = self.get_fitted_tensor(data, mask, bval, bvec,
b0_threshold)
if not save_metrics:
save_metrics = ['fa', 'md', 'rd', 'ad', 'ga', 'rgb', 'mode',
'evec', 'eval', 'tensor']
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
if 'tensor' in save_metrics:
tensor_vals = lower_triangular(tenfit.quadratic_form)
correct_order = [0, 1, 3, 2, 4, 5]
tensor_vals_reordered = tensor_vals[..., correct_order]
fiber_tensors = nib.Nifti1Image(tensor_vals_reordered.astype(
np.float32), affine)
nib.save(fiber_tensors, otensor)
if 'fa' in save_metrics:
fa_img = nib.Nifti1Image(FA.astype(np.float32), affine)
nib.save(fa_img, ofa)
if 'ga' in save_metrics:
GA = geodesic_anisotropy(tenfit.evals)
ga_img = nib.Nifti1Image(GA.astype(np.float32), affine)
nib.save(ga_img, oga)
if 'rgb' in save_metrics:
RGB = color_fa(FA, tenfit.evecs)
rgb_img = nib.Nifti1Image(np.array(255 * RGB, 'uint8'), affine)
nib.save(rgb_img, orgb)
if 'md' in save_metrics:
MD = mean_diffusivity(tenfit.evals)
md_img = nib.Nifti1Image(MD.astype(np.float32), affine)
nib.save(md_img, omd)
if 'ad' in save_metrics:
AD = axial_diffusivity(tenfit.evals)
ad_img = nib.Nifti1Image(AD.astype(np.float32), affine)
nib.save(ad_img, oad)
if 'rd' in save_metrics:
RD = radial_diffusivity(tenfit.evals)
rd_img = nib.Nifti1Image(RD.astype(np.float32), affine)
nib.save(rd_img, orad)
if 'mode' in save_metrics:
MODE = get_mode(tenfit.quadratic_form)
mode_img = nib.Nifti1Image(MODE.astype(np.float32), affine)
nib.save(mode_img, omode)
if 'evec' in save_metrics:
evecs_img = nib.Nifti1Image(tenfit.evecs.astype(np.float32), affine)
nib.save(evecs_img, oevecs)
if 'eval' in save_metrics:
evals_img = nib.Nifti1Image(tenfit.evals.astype(np.float32), affine)
nib.save(evals_img, oevals)
logging.info('DTI metrics saved in {0}'.
format(os.path.dirname(oevals)))
def run(self,
input_files,
bvalues_files,
bvectors_files,
mask_files,
b0_threshold=50.0,
save_metrics=[],
out_dir='',
out_dt_tensor='dti_tensors.nii.gz',
out_fa='fa.nii.gz',
out_ga='ga.nii.gz',
out_rgb='rgb.nii.gz',
out_md='md.nii.gz',
out_ad='ad.nii.gz',
out_rd='rd.nii.gz',
out_mode='mode.nii.gz',
out_evec='evecs.nii.gz',
out_eval='evals.nii.gz',
out_dk_tensor="dki_tensors.nii.gz",
out_mk="mk.nii.gz",
out_ak="ak.nii.gz",
out_rk="rk.nii.gz"):
""" Workflow for Diffusion Kurtosis reconstruction and for computing
DKI metrics. Performs a DKI reconstruction on the files by 'globing'
``input_files`` and saves the DKI metrics in a directory specified by
``out_dir``.
Parameters
----------
input_files : string
Path to the input volumes. This path may contain wildcards to
process multiple inputs at once.
bvalues_files : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
bvectors_files : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
mask_files : string
Path to the input masks. This path may contain wildcards to use
multiple masks at once. (default: No mask used)
b0_threshold : float, optional
Threshold used to find b0 volumes.
save_metrics : variable string, optional
List of metrics to save.
Possible values: fa, ga, rgb, md, ad, rd, mode, tensor, evec, eval
out_dir : string, optional
Output directory. (default current directory)
out_dt_tensor : string, optional
Name of the tensors volume to be saved.
out_dk_tensor : string, optional
Name of the tensors volume to be saved.
out_fa : string, optional
Name of the fractional anisotropy volume to be saved.
out_ga : string, optional
Name of the geodesic anisotropy volume to be saved.
out_rgb : string, optional
Name of the color fa volume to be saved.
out_md : string, optional
Name of the mean diffusivity volume to be saved.
out_ad : string, optional
Name of the axial diffusivity volume to be saved.
out_rd : string, optional
Name of the radial diffusivity volume to be saved.
out_mode : string, optional
Name of the mode volume to be saved.
out_evec : string, optional
Name of the eigenvectors volume to be saved.
out_eval : string, optional
Name of the eigenvalues to be saved.
out_mk : string, optional
Name of the mean kurtosis to be saved.
out_ak : string, optional
Name of the axial kurtosis to be saved.
out_rk : string, optional
Name of the radial kurtosis to be saved.
References
----------
.. [1] Tabesh, A., Jensen, J.H., Ardekani, B.A., Helpern, J.A., 2011.
Estimation of tensors and tensor-derived measures in diffusional
kurtosis imaging. Magn Reson Med. 65(3), 823-836
.. [2] Jensen, Jens H., Joseph A. Helpern, Anita Ramani, Hanzhang Lu,
and Kyle Kaczynski. 2005. Diffusional Kurtosis Imaging: The
Quantification of Non-Gaussian Water Diffusion by Means of Magnetic
Resonance Imaging. MRM 53 (6):1432-40.
"""
io_it = self.get_io_iterator()
for (dwi, bval, bvec, mask, otensor, ofa, oga, orgb, omd, oad, orad,
omode, oevecs, oevals, odk_tensor, omk, oak, ork) in io_it:
logging.info('Computing DKI metrics for {0}'.format(dwi))
data, affine = load_nifti(dwi)
if mask is not None:
mask = load_nifti_data(mask).astype(bool)
dkfit, _ = self.get_fitted_tensor(data, mask, bval, bvec,
b0_threshold)
if not save_metrics:
save_metrics = [
'mk', 'rk', 'ak', 'fa', 'md', 'rd', 'ad', 'ga', 'rgb',
'mode', 'evec', 'eval', 'dt_tensor', 'dk_tensor'
]
evals, evecs, kt = split_dki_param(dkfit.model_params)
FA = fractional_anisotropy(evals)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
if 'dt_tensor' in save_metrics:
tensor_vals = lower_triangular(dkfit.quadratic_form)
correct_order = [0, 1, 3, 2, 4, 5]
tensor_vals_reordered = tensor_vals[..., correct_order]
save_nifti(otensor, tensor_vals_reordered.astype(np.float32),
affine)
if 'dk_tensor' in save_metrics:
save_nifti(odk_tensor, dkfit.kt.astype(np.float32), affine)
if 'fa' in save_metrics:
save_nifti(ofa, FA.astype(np.float32), affine)
if 'ga' in save_metrics:
GA = geodesic_anisotropy(dkfit.evals)
save_nifti(oga, GA.astype(np.float32), affine)
if 'rgb' in save_metrics:
RGB = color_fa(FA, dkfit.evecs)
save_nifti(orgb, np.array(255 * RGB, 'uint8'), affine)
if 'md' in save_metrics:
MD = mean_diffusivity(dkfit.evals)
save_nifti(omd, MD.astype(np.float32), affine)
if 'ad' in save_metrics:
AD = axial_diffusivity(dkfit.evals)
save_nifti(oad, AD.astype(np.float32), affine)
if 'rd' in save_metrics:
RD = radial_diffusivity(dkfit.evals)
save_nifti(orad, RD.astype(np.float32), affine)
if 'mode' in save_metrics:
MODE = get_mode(dkfit.quadratic_form)
save_nifti(omode, MODE.astype(np.float32), affine)
if 'evec' in save_metrics:
save_nifti(oevecs, dkfit.evecs.astype(np.float32), affine)
if 'eval' in save_metrics:
save_nifti(oevals, dkfit.evals.astype(np.float32), affine)
if 'mk' in save_metrics:
save_nifti(omk, dkfit.mk().astype(np.float32), affine)
if 'ak' in save_metrics:
save_nifti(oak, dkfit.ak().astype(np.float32), affine)
if 'rk' in save_metrics:
save_nifti(ork, dkfit.rk().astype(np.float32), affine)
logging.info('DKI metrics saved in {0}'.format(
os.path.dirname(oevals)))
def run(self,
input_files,
bvalues_files,
bvectors_files,
mask_files,
b0_threshold=0.0,
bvecs_tol=0.01,
save_metrics=[],
out_dir='',
out_tensor='tensors.nii.gz',
out_fa='fa.nii.gz',
out_ga='ga.nii.gz',
out_rgb='rgb.nii.gz',
out_md='md.nii.gz',
out_ad='ad.nii.gz',
out_rd='rd.nii.gz',
out_mode='mode.nii.gz',
out_evec='evecs.nii.gz',
out_eval='evals.nii.gz'):
""" Workflow for tensor reconstruction and for computing DTI metrics.
using Weighted Least-Squares.
Performs a tensor reconstruction on the files by 'globing'
``input_files`` and saves the DTI metrics in a directory specified by
``out_dir``.
Parameters
----------
input_files : string
Path to the input volumes. This path may contain wildcards to
process multiple inputs at once.
bvalues_files : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
bvectors_files : string
Path to the bvectors files. This path may contain wildcards to use
multiple bvectors files at once.
mask_files : string
Path to the input masks. This path may contain wildcards to use
multiple masks at once. (default: No mask used)
b0_threshold : float, optional
Threshold used to find b=0 directions (default 0.0)
bvecs_tol : float, optional
Threshold used to check that norm(bvec) = 1 +/- bvecs_tol
b-vectors are unit vectors (default 0.01)
save_metrics : variable string, optional
List of metrics to save.
Possible values: fa, ga, rgb, md, ad, rd, mode, tensor, evec, eval
(default [] (all))
out_dir : string, optional
Output directory (default input file directory)
out_tensor : string, optional
Name of the tensors volume to be saved (default 'tensors.nii.gz')
out_fa : string, optional
Name of the fractional anisotropy volume to be saved
(default 'fa.nii.gz')
out_ga : string, optional
Name of the geodesic anisotropy volume to be saved
(default 'ga.nii.gz')
out_rgb : string, optional
Name of the color fa volume to be saved (default 'rgb.nii.gz')
out_md : string, optional
Name of the mean diffusivity volume to be saved
(default 'md.nii.gz')
out_ad : string, optional
Name of the axial diffusivity volume to be saved
(default 'ad.nii.gz')
out_rd : string, optional
Name of the radial diffusivity volume to be saved
(default 'rd.nii.gz')
out_mode : string, optional
Name of the mode volume to be saved (default 'mode.nii.gz')
out_evec : string, optional
Name of the eigenvectors volume to be saved
(default 'evecs.nii.gz')
out_eval : string, optional
Name of the eigenvalues to be saved (default 'evals.nii.gz')
References
----------
.. [1] Basser, P.J., Mattiello, J., LeBihan, D., 1994. Estimation of
the effective self-diffusion tensor from the NMR spin echo. J Magn
Reson B 103, 247-254.
.. [2] Basser, P., Pierpaoli, C., 1996. Microstructural and
physiological features of tissues elucidated by quantitative
diffusion-tensor MRI. Journal of Magnetic Resonance 111, 209-219.
.. [3] Lin-Ching C., Jones D.K., Pierpaoli, C. 2005. RESTORE: Robust
estimation of tensors by outlier rejection. MRM 53: 1088-1095
.. [4] hung, SW., Lu, Y., Henry, R.G., 2006. Comparison of bootstrap
approaches for estimation of uncertainties of DTI parameters.
NeuroImage 33, 531-541.
"""
io_it = self.get_io_iterator()
for dwi, bval, bvec, mask, otensor, ofa, oga, orgb, omd, oad, orad, \
omode, oevecs, oevals in io_it:
logging.info('Computing DTI metrics for {0}'.format(dwi))
img = nib.load(dwi)
data = img.get_data()
affine = img.affine
if mask is not None:
mask = nib.load(mask).get_data().astype(np.bool)
tenfit, _ = self.get_fitted_tensor(data, mask, bval, bvec,
b0_threshold, bvecs_tol)
if not save_metrics:
save_metrics = [
'fa', 'md', 'rd', 'ad', 'ga', 'rgb', 'mode', 'evec',
'eval', 'tensor'
]
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
if 'tensor' in save_metrics:
tensor_vals = lower_triangular(tenfit.quadratic_form)
correct_order = [0, 1, 3, 2, 4, 5]
tensor_vals_reordered = tensor_vals[..., correct_order]
fiber_tensors = nib.Nifti1Image(
tensor_vals_reordered.astype(np.float32), affine)
nib.save(fiber_tensors, otensor)
if 'fa' in save_metrics:
fa_img = nib.Nifti1Image(FA.astype(np.float32), affine)
nib.save(fa_img, ofa)
if 'ga' in save_metrics:
GA = geodesic_anisotropy(tenfit.evals)
ga_img = nib.Nifti1Image(GA.astype(np.float32), affine)
nib.save(ga_img, oga)
if 'rgb' in save_metrics:
RGB = color_fa(FA, tenfit.evecs)
rgb_img = nib.Nifti1Image(np.array(255 * RGB, 'uint8'), affine)
nib.save(rgb_img, orgb)
if 'md' in save_metrics:
MD = mean_diffusivity(tenfit.evals)
md_img = nib.Nifti1Image(MD.astype(np.float32), affine)
nib.save(md_img, omd)
if 'ad' in save_metrics:
AD = axial_diffusivity(tenfit.evals)
ad_img = nib.Nifti1Image(AD.astype(np.float32), affine)
nib.save(ad_img, oad)
if 'rd' in save_metrics:
RD = radial_diffusivity(tenfit.evals)
rd_img = nib.Nifti1Image(RD.astype(np.float32), affine)
nib.save(rd_img, orad)
if 'mode' in save_metrics:
MODE = get_mode(tenfit.quadratic_form)
mode_img = nib.Nifti1Image(MODE.astype(np.float32), affine)
nib.save(mode_img, omode)
if 'evec' in save_metrics:
evecs_img = nib.Nifti1Image(tenfit.evecs.astype(np.float32),
affine)
nib.save(evecs_img, oevecs)
if 'eval' in save_metrics:
evals_img = nib.Nifti1Image(tenfit.evals.astype(np.float32),
affine)
nib.save(evals_img, oevals)
dname_ = os.path.dirname(oevals)
if dname_ == '':
logging.info('DTI metrics saved in current directory')
else:
logging.info('DTI metrics saved in {0}'.format(dname_))
def run(self, input_files, bvalues_files, bvectors_files, mask_files,
b0_threshold=50.0, save_metrics=[],
out_dir='', out_dt_tensor='dti_tensors.nii.gz', out_fa='fa.nii.gz',
out_ga='ga.nii.gz', out_rgb='rgb.nii.gz', out_md='md.nii.gz',
out_ad='ad.nii.gz', out_rd='rd.nii.gz', out_mode='mode.nii.gz',
out_evec='evecs.nii.gz', out_eval='evals.nii.gz',
out_dk_tensor="dki_tensors.nii.gz",
out_mk="mk.nii.gz", out_ak="ak.nii.gz", out_rk="rk.nii.gz"):
""" Workflow for Diffusion Kurtosis reconstruction and for computing
DKI metrics. Performs a DKI reconstruction on the files by 'globing'
``input_files`` and saves the DKI metrics in a directory specified by
``out_dir``.
Parameters
----------
input_files : string
Path to the input volumes. This path may contain wildcards to
process multiple inputs at once.
bvalues_files : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
bvectors_files : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
mask_files : string
Path to the input masks. This path may contain wildcards to use
multiple masks at once. (default: No mask used)
b0_threshold : float, optional
Threshold used to find b=0 directions (default 0.0)
save_metrics : variable string, optional
List of metrics to save.
Possible values: fa, ga, rgb, md, ad, rd, mode, tensor, evec, eval
(default [] (all))
out_dir : string, optional
Output directory (default input file directory)
out_dt_tensor : string, optional
Name of the tensors volume to be saved
(default: 'dti_tensors.nii.gz')
out_dk_tensor : string, optional
Name of the tensors volume to be saved
(default 'dki_tensors.nii.gz')
out_fa : string, optional
Name of the fractional anisotropy volume to be saved
(default 'fa.nii.gz')
out_ga : string, optional
Name of the geodesic anisotropy volume to be saved
(default 'ga.nii.gz')
out_rgb : string, optional
Name of the color fa volume to be saved (default 'rgb.nii.gz')
out_md : string, optional
Name of the mean diffusivity volume to be saved
(default 'md.nii.gz')
out_ad : string, optional
Name of the axial diffusivity volume to be saved
(default 'ad.nii.gz')
out_rd : string, optional
Name of the radial diffusivity volume to be saved
(default 'rd.nii.gz')
out_mode : string, optional
Name of the mode volume to be saved (default 'mode.nii.gz')
out_evec : string, optional
Name of the eigenvectors volume to be saved
(default 'evecs.nii.gz')
out_eval : string, optional
Name of the eigenvalues to be saved (default 'evals.nii.gz')
out_mk : string, optional
Name of the mean kurtosis to be saved (default: 'mk.nii.gz')
out_ak : string, optional
Name of the axial kurtosis to be saved (default: 'ak.nii.gz')
out_rk : string, optional
Name of the radial kurtosis to be saved (default: 'rk.nii.gz')
References
----------
.. [1] Tabesh, A., Jensen, J.H., Ardekani, B.A., Helpern, J.A., 2011.
Estimation of tensors and tensor-derived measures in diffusional
kurtosis imaging. Magn Reson Med. 65(3), 823-836
.. [2] Jensen, Jens H., Joseph A. Helpern, Anita Ramani, Hanzhang Lu,
and Kyle Kaczynski. 2005. Diffusional Kurtosis Imaging: The
Quantification of Non-Gaussian Water Diffusion by Means of Magnetic
Resonance Imaging. MRM 53 (6):1432-40.
"""
io_it = self.get_io_iterator()
for (dwi, bval, bvec, mask, otensor, ofa, oga, orgb, omd, oad, orad,
omode, oevecs, oevals, odk_tensor, omk, oak, ork) in io_it:
logging.info('Computing DKI metrics for {0}'.format(dwi))
data, affine = load_nifti(dwi)
if mask is not None:
mask = nib.load(mask).get_data().astype(np.bool)
dkfit, _ = self.get_fitted_tensor(data, mask, bval, bvec,
b0_threshold)
if not save_metrics:
save_metrics = ['mk', 'rk', 'ak', 'fa', 'md', 'rd', 'ad', 'ga',
'rgb', 'mode', 'evec', 'eval', 'dt_tensor',
'dk_tensor']
evals, evecs, kt = split_dki_param(dkfit.model_params)
FA = fractional_anisotropy(evals)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
if 'dt_tensor' in save_metrics:
tensor_vals = lower_triangular(dkfit.quadratic_form)
correct_order = [0, 1, 3, 2, 4, 5]
tensor_vals_reordered = tensor_vals[..., correct_order]
save_nifti(otensor, tensor_vals_reordered.astype(np.float32),
affine)
if 'dk_tensor' in save_metrics:
save_nifti(odk_tensor, dkfit.kt.astype(np.float32), affine)
if 'fa' in save_metrics:
save_nifti(ofa, FA.astype(np.float32), affine)
if 'ga' in save_metrics:
GA = geodesic_anisotropy(dkfit.evals)
save_nifti(oga, GA.astype(np.float32), affine)
if 'rgb' in save_metrics:
RGB = color_fa(FA, dkfit.evecs)
save_nifti(orgb, np.array(255 * RGB, 'uint8'), affine)
if 'md' in save_metrics:
MD = mean_diffusivity(dkfit.evals)
save_nifti(omd, MD.astype(np.float32), affine)
if 'ad' in save_metrics:
AD = axial_diffusivity(dkfit.evals)
save_nifti(oad, AD.astype(np.float32), affine)
if 'rd' in save_metrics:
RD = radial_diffusivity(dkfit.evals)
save_nifti(orad, RD.astype(np.float32), affine)
if 'mode' in save_metrics:
MODE = get_mode(dkfit.quadratic_form)
save_nifti(omode, MODE.astype(np.float32), affine)
if 'evec' in save_metrics:
save_nifti(oevecs, dkfit.evecs.astype(np.float32), affine)
if 'eval' in save_metrics:
save_nifti(oevals, dkfit.evals.astype(np.float32), affine)
if 'mk' in save_metrics:
save_nifti(omk, dkfit.mk().astype(np.float32), affine)
if 'ak' in save_metrics:
save_nifti(oak, dkfit.ak().astype(np.float32), affine)
if 'rk' in save_metrics:
save_nifti(ork, dkfit.rk().astype(np.float32), affine)
logging.info('DKI metrics saved in {0}'.
format(os.path.dirname(oevals)))
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
