def test_split_dki_param():
dkiM = dki.DiffusionKurtosisModel(gtab_2s, fit_method="OLS")
dkiF = dkiM.fit(DWI)
evals, evecs, kt = dki.split_dki_param(dkiF.model_params)
assert_array_almost_equal(evals, dkiF.evals)
assert_array_almost_equal(evecs, dkiF.evecs)
assert_array_almost_equal(kt, dkiF.kt)
def test_split_dki_param():
dkiM = dki.DiffusionKurtosisModel(gtab_2s, fit_method="OLS")
dkiF = dkiM.fit(DWI)
evals, evecs, kt = dki.split_dki_param(dkiF.model_params)
assert_array_almost_equal(evals, dkiF.evals)
assert_array_almost_equal(evecs, dkiF.evecs)
assert_array_almost_equal(kt, dkiF.kt)
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 diffusion_components(dki_params,
sphere='repulsion100',
awf=None,
mask=None):
""" Extracts the restricted and hindered diffusion tensors of well aligned
fibers from diffusion kurtosis imaging parameters [1]_.
Parameters
----------
dki_params : ndarray (x, y, z, 27) or (n, 27)
All parameters estimated from the diffusion kurtosis model.
Parameters are ordered as follows:
1) Three diffusion tensor's eigenvalues
2) Three lines of the eigenvector matrix each containing the first,
second and third coordinates of the eigenvector
3) Fifteen elements of the kurtosis tensor
sphere : Sphere class instance, optional
The sphere providing sample directions to sample the restricted and
hindered cellular diffusion tensors. For more details see Fieremans
et al., 2011.
awf : ndarray (optional)
Array containing values of the axonal water fraction that has the shape
dki_params.shape[:-1]. If not given this will be automatically computed
using :func:`axonal_water_fraction`" with function's default precision.
mask : ndarray (optional)
A boolean array used to mark the coordinates in the data that should be
analyzed that has the shape dki_params.shape[:-1]
Returns
-------
edt : ndarray (x, y, z, 6) or (n, 6)
Parameters of the hindered diffusion tensor.
idt : ndarray (x, y, z, 6) or (n, 6)
Parameters of the restricted diffusion tensor.
Notes
-----
In the original article of DKI microstructural model [1]_, the hindered and
restricted tensors were definde as the intra-cellular and extra-cellular
diffusion compartments respectively.
References
----------
.. [1] Fieremans E, Jensen JH, Helpern JA, 2011. White matter
characterization with diffusional kurtosis imaging.
Neuroimage 58(1):177-88. doi: 10.1016/j.neuroimage.2011.06.006
"""
shape = dki_params.shape[:-1]
# load gradient directions
if not isinstance(sphere, dps.Sphere):
sphere = get_sphere(sphere)
# select voxels where to apply the single fiber model
if mask is None:
mask = np.ones(shape, dtype='bool')
else:
if mask.shape != shape:
raise ValueError("Mask is not the same shape as dki_params.")
else:
mask = np.array(mask, dtype=bool, copy=False)
# check or compute awf values
if awf is None:
awf = axonal_water_fraction(dki_params, sphere=sphere, mask=mask)
else:
if awf.shape != shape:
raise ValueError("awf array is not the same shape as dki_params.")
# Initialize hindered and restricted diffusion tensors
edt_all = np.zeros(shape + (6, ))
idt_all = np.zeros(shape + (6, ))
# Generate matrix that converts apparent diffusion coefficients to tensors
B = np.zeros((sphere.x.size, 6))
B[:, 0] = sphere.x * sphere.x # Bxx
B[:, 1] = sphere.x * sphere.y * 2. # Bxy
B[:, 2] = sphere.y * sphere.y # Byy
B[:, 3] = sphere.x * sphere.z * 2. # Bxz
B[:, 4] = sphere.y * sphere.z * 2. # Byz
B[:, 5] = sphere.z * sphere.z # Bzz
pinvB = np.linalg.pinv(B)
# Compute hindered and restricted diffusion tensors for all voxels
evals, evecs, kt = split_dki_param(dki_params)
dt = lower_triangular(vec_val_vect(evecs, evals))
md = mean_diffusivity(evals)
index = ndindex(mask.shape)
for idx in index:
if not mask[idx]:
continue
# sample apparent diffusion and kurtosis values
di = directional_diffusion(dt[idx], sphere.vertices)
ki = directional_kurtosis(dt[idx],
md[idx],
kt[idx],
sphere.vertices,
adc=di,
min_kurtosis=0)
edi = di * (1 + np.sqrt(ki * awf[idx] / (3.0 - 3.0 * awf[idx])))
edt = np.dot(pinvB, edi)
edt_all[idx] = edt
# We only move on if there is an axonal water fraction.
# Otherwise, remaining params are already zero, so move on
if awf[idx] == 0:
continue
# Convert apparent diffusion and kurtosis values to apparent diffusion
# values of the hindered and restricted diffusion
idi = di * (1 - np.sqrt(ki * (1.0 - awf[idx]) / (3.0 * awf[idx])))
# generate hindered and restricted diffusion tensors
idt = np.dot(pinvB, idi)
idt_all[idx] = idt
return edt_all, idt_all
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 dki_prediction(dki_params, gtab, S0=150):
"""
In Dipy versions < 0.12, there is a bug in DKI prediction, that doesn't
allow using volumes of S0. This is temporary fix until the bug is fixed
upstream. See: https://github.com/nipy/dipy/pull/1028
For now, we provide this as a fix, to monkey-patch into dipy.reconst.dki
Parameters
----------
dki_params : ndarray (x, y, z, 27) or (n, 27)
All parameters estimated from the diffusion kurtosis model.
Parameters are ordered as follows:
1) Three diffusion tensor's eigenvalues
2) Three lines of the eigenvector matrix each containing the first,
second and third coordinates of the eigenvector
3) Fifteen elements of the kurtosis tensor
gtab : a GradientTable class instance
The gradient table for this prediction
S0 : float or ndarray (optional)
The non diffusion-weighted signal in every voxel, or across all
voxels. Default: 150
Returns
--------
S : (..., N) ndarray
Simulated signal based on the DKI model:
.. math::
S=S_{0}e^{-bD+\frac{1}{6}b^{2}D^{2}K}
"""
evals, evecs, kt = dki.split_dki_param(dki_params)
# Define DKI design matrix according to given gtab
A = dki.design_matrix(gtab)
# Flat parameters and initialize pred_sig
fevals = evals.reshape((-1, evals.shape[-1]))
fevecs = evecs.reshape((-1,) + evecs.shape[-2:])
fkt = kt.reshape((-1, kt.shape[-1]))
pred_sig = np.zeros((len(fevals), len(gtab.bvals)))
if isinstance(S0, np.ndarray):
S0_vol = np.reshape(S0, (len(fevals)))
else:
S0_vol = S0
# looping for all voxels
for v in range(len(pred_sig)):
DT = np.dot(np.dot(fevecs[v], np.diag(fevals[v])), fevecs[v].T)
dt = dki.lower_triangular(DT)
MD = (dt[0] + dt[2] + dt[5]) / 3
if isinstance(S0_vol, np.ndarray):
this_S0 = S0_vol[v]
else:
this_S0 = S0_vol
X = np.concatenate((dt, fkt[v] * MD * MD,
np.array([np.log(this_S0)])),
axis=0)
pred_sig[v] = np.exp(np.dot(A, X))
# Reshape data according to the shape of dki_params
pred_sig = pred_sig.reshape(dki_params.shape[:-1] + (pred_sig.shape[-1],))
return pred_sig
def diffusion_components(dki_params, sphere='repulsion100', awf=None,
mask=None):
""" Extracts the restricted and hindered diffusion tensors of well aligned
fibers from diffusion kurtosis imaging parameters [1]_.
Parameters
----------
dki_params : ndarray (x, y, z, 27) or (n, 27)
All parameters estimated from the diffusion kurtosis model.
Parameters are ordered as follows:
1) Three diffusion tensor's eigenvalues
2) Three lines of the eigenvector matrix each containing the first,
second and third coordinates of the eigenvector
3) Fifteen elements of the kurtosis tensor
sphere : Sphere class instance, optional
The sphere providing sample directions to sample the restricted and
hindered cellular diffusion tensors. For more details see Fieremans
et al., 2011.
awf : ndarray (optional)
Array containing values of the axonal water fraction that has the shape
dki_params.shape[:-1]. If not given this will be automatically computed
using :func:`axonal_water_fraction`" with function's default precision.
mask : ndarray (optional)
A boolean array used to mark the coordinates in the data that should be
analyzed that has the shape dki_params.shape[:-1]
Returns
--------
edt : ndarray (x, y, z, 6) or (n, 6)
Parameters of the hindered diffusion tensor.
idt : ndarray (x, y, z, 6) or (n, 6)
Parameters of the restricted diffusion tensor.
Note
----
In the original article of DKI microstructural model [1]_, the hindered and
restricted tensors were definde as the intra-cellular and extra-cellular
diffusion compartments respectively.
References
----------
.. [1] Fieremans E, Jensen JH, Helpern JA, 2011. White matter
characterization with diffusional kurtosis imaging.
Neuroimage 58(1):177-88. doi: 10.1016/j.neuroimage.2011.06.006
"""
shape = dki_params.shape[:-1]
# load gradient directions
if not isinstance(sphere, dps.Sphere):
sphere = get_sphere(sphere)
# select voxels where to apply the single fiber model
if mask is None:
mask = np.ones(shape, dtype='bool')
else:
if mask.shape != shape:
raise ValueError("Mask is not the same shape as dki_params.")
else:
mask = np.array(mask, dtype=bool, copy=False)
# check or compute awf values
if awf is None:
awf = axonal_water_fraction(dki_params, sphere=sphere, mask=mask)
else:
if awf.shape != shape:
raise ValueError("awf array is not the same shape as dki_params.")
# Initialize hindered and restricted diffusion tensors
edt_all = np.zeros(shape + (6,))
idt_all = np.zeros(shape + (6,))
# Generate matrix that converts apparant diffusion coefficients to tensors
B = np.zeros((sphere.x.size, 6))
B[:, 0] = sphere.x * sphere.x # Bxx
B[:, 1] = sphere.x * sphere.y * 2. # Bxy
B[:, 2] = sphere.y * sphere.y # Byy
B[:, 3] = sphere.x * sphere.z * 2. # Bxz
B[:, 4] = sphere.y * sphere.z * 2. # Byz
B[:, 5] = sphere.z * sphere.z # Bzz
pinvB = np.linalg.pinv(B)
# Compute hindered and restricted diffusion tensors for all voxels
evals, evecs, kt = split_dki_param(dki_params)
dt = lower_triangular(vec_val_vect(evecs, evals))
md = mean_diffusivity(evals)
index = ndindex(mask.shape)
for idx in index:
if not mask[idx]:
continue
# sample apparent diffusion and kurtosis values
di = directional_diffusion(dt[idx], sphere.vertices)
ki = directional_kurtosis(dt[idx], md[idx], kt[idx], sphere.vertices,
adc=di, min_kurtosis=0)
edi = di * (1 + np.sqrt(ki * awf[idx] / (3.0 - 3.0 * awf[idx])))
edt = np.dot(pinvB, edi)
edt_all[idx] = edt
# We only move on if there is an axonal water fraction.
# Otherwise, remaining params are already zero, so move on
if awf[idx] == 0:
continue
# Convert apparent diffusion and kurtosis values to apparent diffusion
# values of the hindered and restricted diffusion
idi = di * (1 - np.sqrt(ki * (1.0 - awf[idx]) / (3.0 * awf[idx])))
# generate hindered and restricted diffusion tensors
idt = np.dot(pinvB, idi)
idt_all[idx] = idt
return edt_all, idt_all