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 axonal_diffusivity(self):
""" Returns the axonal diffusivity defined as the restricted diffusion
tensor trace [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 trace(self.restricted_evals)
def axonal_diffusivity(self):
""" Returns the axonal diffusivity defined as the restricted diffusion
tensor trace [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 trace(self.restricted_evals)
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