def __init__(self, gtab, fit_method="WLS", *args, **kwargs):
""" Initialize a KurtosisMicrostrutureModel class instance [1]_.
Parameters
----------
gtab : GradientTable class instance
fit_method : str or callable
str can be one of the following:
'OLS' or 'ULLS' to fit the diffusion tensor and kurtosis tensor
using the ordinary linear least squares solution
dki.ols_fit_dki
'WLS' or 'UWLLS' to fit the diffusion tensor and kurtosis tensor
using the ordinary linear least squares solution
dki.wls_fit_dki
callable has to have the signature:
fit_method(design_matrix, data, *args, **kwargs)
args, kwargs : arguments and key-word arguments passed to the
fit_method. See dki.ols_fit_dki, dki.wls_fit_dki for details
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
"""
DiffusionKurtosisModel.__init__(self,
gtab,
fit_method="WLS",
*args,
**kwargs)
def __init__(self, gtab, S0=None, mask=None, **kwargs):
"""Instantiate the wrapped tensor model."""
from dipy.reconst.dki import DiffusionKurtosisModel
self._S0 = None
if S0 is not None:
self._S0 = np.clip(
S0.astype("float32") / S0.max(),
a_min=1e-5,
a_max=1.0,
)
self._mask = mask
if mask is None and S0 is not None:
self._mask = self._S0 > np.percentile(self._S0, 35)
if self._mask is not None:
self._S0 = self._S0[self._mask.astype(bool)]
kwargs = {
k: v
for k, v in kwargs.items() if k in (
"min_signal",
"return_S0_hat",
"fit_method",
"weighting",
"sigma",
"jac",
)
}
self._model = DiffusionKurtosisModel(gtab, **kwargs)
def __init__(self, gtab, fit_method="WLS", *args, **kwargs):
""" Initialize a KurtosisMicrostrutureModel class instance [1]_.
Parameters
----------
gtab : GradientTable class instance
fit_method : str or callable
str can be one of the following:
'OLS' or 'ULLS' to fit the diffusion tensor and kurtosis tensor
using the ordinary linear least squares solution
dki.ols_fit_dki
'WLS' or 'UWLLS' to fit the diffusion tensor and kurtosis tensor
using the ordinary linear least squares solution
dki.wls_fit_dki
callable has to have the signature:
fit_method(design_matrix, data, *args, **kwargs)
args, kwargs : arguments and key-word arguments passed to the
fit_method. See dki.ols_fit_dki, dki.wls_fit_dki for details
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
"""
DiffusionKurtosisModel.__init__(self, gtab, fit_method="WLS", *args,
**kwargs)
class DKIModel:
"""A wrapper of :obj:`dipy.reconst.dki.DiffusionKurtosisModel."""
__slots__ = ("_model", "_S0", "_mask")
def __init__(self, gtab, S0=None, mask=None, **kwargs):
"""Instantiate the wrapped tensor model."""
from dipy.reconst.dki import DiffusionKurtosisModel
self._S0 = None
if S0 is not None:
self._S0 = np.clip(
S0.astype("float32") / S0.max(),
a_min=1e-5,
a_max=1.0,
)
self._mask = mask
if mask is None and S0 is not None:
self._mask = self._S0 > np.percentile(self._S0, 35)
if self._mask is not None:
self._S0 = self._S0[self._mask.astype(bool)]
kwargs = {
k: v
for k, v in kwargs.items()
if k
in (
"min_signal",
"return_S0_hat",
"fit_method",
"weighting",
"sigma",
"jac",
)
}
self._model = DiffusionKurtosisModel(gtab, **kwargs)
def fit(self, data, **kwargs):
"""Clean-up permitted args and kwargs, and call model's fit."""
self._model = self._model.fit(data[self._mask, ...])
def predict(self, gradient, **kwargs):
"""Propagate model parameters and call predict."""
predicted = np.squeeze(
self._model.predict(
_rasb2dipy(gradient),
S0=self._S0,
)
)
if predicted.ndim == 3:
return predicted
retval = np.zeros_like(self._mask, dtype="float32")
retval[self._mask, ...] = predicted
return retval
def get_dki_model(self, gtab):
return DiffusionKurtosisModel(gtab)
def get_dki_model(self, gtab, fit_method):
return DiffusionKurtosisModel(gtab, fit_method=fit_method)
def tensorFit(self, m='dti', bv=None, removea0=0, mask=True):
if bv != None:
self.backups = [self.pdata, self.avbvals, self.nbvals]
self.pdata, self.avbvals = selectBvals(self, bv)
self.avbvals = np.array(self.avbvals)
self.nbvals = len(bv)
if removea0 != 0:
print "Removing %s A0 images." % removea0
self.pdata = self.pdata[:, :, :, removea0:]
self.nA0 -= removea0
self.avbvals = self.avbvals[removea0:]
self.bvals = self.bvals[removea0:]
# thresh=np.mean(self.pdata[:5,:5,:,0])
if mask:
print "Generating brain mask: Erosion>Dilation>MedianOtsu>LargestComponent"
self.mask = ndi.binary_dilation(
ndi.binary_erosion(ndi.binary_dilation(brain_mask(self.pdata))))
self.mask = largest_component(self.mask)
else:
print "Not using brain mask."
self.mask = np.zeros(self.shape[:-1]) + 1
bvalmat = np.array(self.avbvals)
self.bvecmat = construct_bvecs(self)
if len(self.bvecmat) != len(self.avbvals):
print "Error. Cannot process this image."
print bvalmat.shape
self.gtab = gradient_table(
bvalmat, self.bvecmat
) #creates a gradient table with b-vals and diffusion dirs for processing
if m == 'dti':
from dipy.reconst.dti import TensorModel
starttime = datetime.now()
print "Fitting tensor model."
ten = TensorModel(self.gtab)
self.tenfit = ten.fit(self.pdata, self.mask)
time = datetime.now() - starttime
tenfile = self.name + "_tenfit.pickle"
print "Tensor fit completed in %r seconds. Saving file %s." % (
time.seconds, tenfile)
# with open(tenfile, 'w') as f: TOO INEFFICIENT
# pickle.dump([self, self.tenfit], f)
from dipy.reconst.dti import color_fa
self.cfa = color_fa(self.tenfit.fa, self.tenfit.evecs)
if m == 'RESTORE':
from dipy.reconst.dti import TensorModel
mean_std = 2.5 * np.mean(
np.std(self.pdata[..., self.gtab.b0s_mask],
-1)) #conservative thresh
starttime = datetime.now()
print "Fitting tensor model using the RESTORE method. Sigma=", mean_std
ten = TensorModel(self.gtab, fit_method="RESTORE", sigma=mean_std)
self.tenfit = ten.fit(self.pdata, self.mask)
time = datetime.now() - starttime
tenfile = self.name + "_tenfit.pickle"
print "Tensor fit completed in %r seconds. Saving file %s." % (
time.seconds, tenfile)
# with open(tenfile, 'w') as f: TOO INEFFICIENT
# pickle.dump([self, self.tenfit], f)
from dipy.reconst.dti import color_fa
self.cfa = color_fa(self.tenfit.fa, self.tenfit.evecs)
elif m == 'dki':
from dipy.reconst.dki import DiffusionKurtosisModel
starttime = datetime.now()
print "Fitting kurtosis tensor."
kurt = DiffusionKurtosisModel(self.gtab)
self.tenfit = kurt.fit(self.pdata)
time = datetime.now() - starttime
print "Kurtosis fit completed in %r seconds." % time.seconds
if bv != None:
self.pdata = self.backups[0]
self.avbvals = self.backups[1]
self.nbvals = self.backups[2]
