def _run_interface(self, runtime):
from dipy.reconst import shm
from dipy.data import get_sphere
from dipy.reconst.peaks import peaks_from_model
gtab = self._get_gradient_table()
img = nb.load(self.inputs.in_file)
data = img.get_data()
affine = img.affine
mask = None
if isdefined(self.inputs.mask_file):
mask = nb.load(self.inputs.mask_file).get_data()
# Fit it
model = shm.QballModel(gtab, 8)
sphere = get_sphere('symmetric724')
peaks = peaks_from_model(model=model,
data=data,
relative_peak_threshold=.5,
min_separation_angle=25,
sphere=sphere,
mask=mask)
apm = shm.anisotropic_power(peaks.shm_coeff)
out_file = self._gen_filename('apm')
nb.Nifti1Image(apm.astype("float32"), affine).to_filename(out_file)
IFLOGGER.info('APM qball image saved as %s', out_file)
return runtime
"""
As one can see from its shape, the selected data contains a total of 67
volumes of images corresponding to all the diffusion gradient directions
of the selected b-values and call the ``mppca`` as follows:
"""
denoised_arr = mppca(data, mask=mask, patch_radius=2)
"""
Now we will use the denoised array (``denoised_arr``) obtained from ``mppca``
in the rest of the steps in the tutorial.
As for the next step, we generate the anisotropic powermap introduced by
[DellAcqua2014]_. To do so, we make use of the Q-ball Model as follows:
"""
qball_model = shm.QballModel(gtab, 8)
"""
We generate the peaks from the ``qball_model`` as follows:
"""
peaks = dp.peaks_from_model(model=qball_model,
data=denoised_arr,
relative_peak_threshold=.5,
min_separation_angle=25,
sphere=sphere,
mask=mask)
ap = shm.anisotropic_power(peaks.shm_coeff)
plt.matshow(np.rot90(ap[:, :, 10]), cmap=plt.cm.bone)
#plt.savefig("anisotropic_power_map.png")