def test_mcsd_model_delta():
sh_order = 8
gtab = get_3shell_gtab()
shells = np.unique(gtab.bvals // 100.) * 100.
response = sim_response(sh_order, shells, evals_d)
model = MultiShellDeconvModel(gtab, response)
iso = response.iso
theta, phi = default_sphere.theta, default_sphere.phi
B = shm.real_sph_harm(response.m, response.n, theta[:, None], phi[:, None])
wm_delta = model.delta.copy()
# set isotropic components to zero
wm_delta[:iso] = 0.
wm_delta = _expand(model.m, iso, wm_delta)
for i, s in enumerate(shells):
g = GradientTable(default_sphere.vertices * s)
signal = model.predict(wm_delta, g)
expected = np.dot(response.response[i, iso:], B.T)
npt.assert_array_almost_equal(signal, expected)
signal = model.predict(wm_delta, gtab)
fit = model.fit(signal)
m = model.m
npt.assert_array_almost_equal(fit.shm_coeff[m != 0], 0., 2)
def test_mcsd_model_delta():
sh_order = 8
gtab = get_3shell_gtab()
response = multi_shell_fiber_response(sh_order, [0, 1000, 2000, 3500],
wm_response,
gm_response,
csf_response)
model = MultiShellDeconvModel(gtab, response)
iso = response.iso
theta, phi = default_sphere.theta, default_sphere.phi
B = shm.real_sh_descoteaux_from_index(
response.m, response.n, theta[:, None], phi[:, None])
wm_delta = model.delta.copy()
# set isotropic components to zero
wm_delta[:iso] = 0.
wm_delta = _expand(model.m, iso, wm_delta)
for i, s in enumerate([0, 1000, 2000, 3500]):
g = GradientTable(default_sphere.vertices * s)
signal = model.predict(wm_delta, g)
expected = np.dot(response.response[i, iso:], B.T)
npt.assert_array_almost_equal(signal, expected)
signal = model.predict(wm_delta, gtab)
fit = model.fit(signal)
m = model.m
npt.assert_array_almost_equal(fit.shm_coeff[m != 0], 0., 2)
def test_MultiShellDeconvModel():
gtab = get_3shell_gtab()
mevals = np.array([wm_response[0, :3], wm_response[0, :3]])
angles = [(0, 0), (60, 0)]
S_wm, sticks = multi_tensor(gtab,
mevals,
wm_response[0, 3],
angles=angles,
fractions=[30., 70.],
snr=None)
S_gm = gm_response[0, 3] * np.exp(-gtab.bvals * gm_response[0, 0])
S_csf = csf_response[0, 3] * np.exp(-gtab.bvals * csf_response[0, 0])
sh_order = 8
response = multi_shell_fiber_response(sh_order, [0, 1000, 2000, 3500],
wm_response, gm_response,
csf_response)
model = MultiShellDeconvModel(gtab, response)
vf = [0.325, 0.2, 0.475]
signal = sum(i * j for i, j in zip(vf, [S_csf, S_gm, S_wm]))
fit = model.fit(signal)
# Testing both ways to predict
S_pred_fit = fit.predict()
S_pred_model = model.predict(fit.all_shm_coeff)
npt.assert_array_almost_equal(S_pred_fit, S_pred_model, 0)
npt.assert_array_almost_equal(S_pred_fit, signal, 0)
using ``all_shm_coeff`` for each compartment (isotropic) and
``shm_coeff`` for white matter.
"""
vf = mcsd_fit.volume_fractions
sh_coeff = mcsd_fit.all_shm_coeff
csf_sh_coeff = sh_coeff[..., 0]
gm_sh_coeff = sh_coeff[..., 1]
wm_sh_coeff = mcsd_fit.shm_coeff
"""
The model allows to predict a signal from sh coefficients. There are two ways of
doing this.
"""
mcsd_pred = mcsd_fit.predict()
mcsd_pred = mcsd_model.predict(mcsd_fit.all_shm_coeff)
"""
From the fit obtained in the previous step, we generate the ODFs which can be
visualized as follows:
"""
mcsd_odf = mcsd_fit.odf(sphere)
print("ODF")
print(mcsd_odf.shape)
print(mcsd_odf[40, 40, 0])
fodf_spheres = actor.odf_slicer(mcsd_odf,
sphere=sphere,
scale=1,
norm=False,
