def predicted_signal(self, bvecs, bvals, axial_diffusivity,
radial_diffusivity):
"""
Compute the fiber contribution to the *relative signal* along its
coords.
Notes
-----
The calculation is based on a simplified Stejskal/Tanner equation:
.. math::
\frac{S/S_0} = exp^{-bval (\vec{b}*Q*\vec{b}^T)}
Where $S0$ is the unweighted signal and $\vec{b} * Q * \vec{b}^t$ is
the ADC for each tensor.
To get the diffusion signal measured, you will have to multiply back by
the $S_0$ in each voxel.
Parameters
----------
"""
# Gotta have those tensors:
tens = self.tensors(axial_diffusivity, radial_diffusivity)
# Preallocate:
sig = np.empty((tens.shape[0], len(bvals)))
for t_idx, ten in enumerate(tens):
ADC = ozt.apparent_diffusion_coef(bvecs, ten.reshape((3, 3)))
# Call S/T with the ADC as input:
sig[t_idx] = ozt.stejskal_tanner(1, bvals, ADC)
return sig
def model_adc(self):
out = np.empty(self.signal.shape)
tensors_flat = self.tensors[self.mask].reshape((-1, 3, 3))
adc_flat = np.empty(self.signal[self.mask].shape)
for ii in xrange(len(adc_flat)):
adc_flat[ii] = ozt.apparent_diffusion_coef(
self.bvecs[:, self.b_idx], tensors_flat[ii])
out[self.mask] = adc_flat
return out
def model_adc(self):
out = np.empty(self.signal.shape)
tensors_flat = self.tensors[self.mask].reshape((-1,3,3))
adc_flat = np.empty(self.signal[self.mask].shape)
for ii in xrange(len(adc_flat)):
adc_flat[ii] = ozt.apparent_diffusion_coef(
self.bvecs[:,self.b_idx],
tensors_flat[ii])
out[self.mask] = adc_flat
return out
def predict_adc(self, sphere):
"""
The ADC predicted on a sphere (containing points other than the bvecs)
"""
out = ozu.nans(self.signal.shape[:3] + (sphere.shape[-1], ))
tensors_flat = self.tensors[self.mask].reshape((-1, 3, 3))
pred_adc_flat = np.empty((np.sum(self.mask), sphere.shape[-1]))
for ii in xrange(len(pred_adc_flat)):
pred_adc_flat[ii] = ozt.apparent_diffusion_coef(
sphere, tensors_flat[ii])
out[self.mask] = pred_adc_flat
return out
def predict_adc(self, sphere):
"""
The ADC predicted on a sphere (containing points other than the bvecs)
"""
out = ozu.nans(self.signal.shape[:3] + (sphere.shape[-1],))
tensors_flat = self.tensors[self.mask].reshape((-1,3,3))
pred_adc_flat = np.empty((np.sum(self.mask), sphere.shape[-1]))
for ii in xrange(len(pred_adc_flat)):
pred_adc_flat[ii] = ozt.apparent_diffusion_coef(sphere,
tensors_flat[ii])
out[self.mask] = pred_adc_flat
return out
def predicted_signal(self,
bvecs,
bvals,
axial_diffusivity,
radial_diffusivity):
"""
Compute the fiber contribution to the *relative signal* along its
coords.
Notes
-----
The calculation is based on a simplified Stejskal/Tanner equation:
.. math::
\frac{S/S_0} = exp^{-bval (\vec{b}*Q*\vec{b}^T)}
Where $S0$ is the unweighted signal and $\vec{b} * Q * \vec{b}^t$ is
the ADC for each tensor.
To get the diffusion signal measured, you will have to multiply back by
the $S_0$ in each voxel.
Parameters
----------
"""
# Gotta have those tensors:
tens = self.tensors(axial_diffusivity,
radial_diffusivity)
# Preallocate:
sig = np.empty((tens.shape[0], len(bvals)))
for t_idx, ten in enumerate(tens):
ADC = ozt.apparent_diffusion_coef(bvecs, ten.reshape((3,3)))
# Call S/T with the ADC as input:
sig[t_idx] = ozt.stejskal_tanner(1, bvals, ADC)
return sig