def test_compartments():
# test for failure if no. of compartments less than 2
gtab = get_3shell_gtab()
sh_order = 8
response = multi_shell_fiber_response(sh_order, [0, 1000, 2000, 3500],
evals_d, csf_md, gm_md)
npt.assert_raises(ValueError, MultiShellDeconvModel, gtab, response, iso=1)
def test_MultiShellDeconvModel():
gtab = get_3shell_gtab()
S0 = 1.
evals = np.array([.992, .254, .254]) * 1e-3
mevals = np.array([evals, evals])
angles = [(0, 0), (60, 0)]
S_wm, sticks = multi_tensor(gtab, mevals, S0, angles=angles,
fractions=[30., 70.], snr=None)
S_gm = np.exp(-gtab.bvals * gm_md)
S_csf = np.exp(-gtab.bvals * csf_md)
sh_order = 8
response = multi_shell_fiber_response(sh_order, [0, 1000, 2000, 3500],
evals_d, csf_md, gm_md)
model = MultiShellDeconvModel(gtab, response)
vf = [1.3, .8, 1.9]
signal = sum(i * j for i, j in zip(vf, [S_csf, S_gm, S_wm]))
fit = model.fit(signal)
npt.assert_array_almost_equal(fit.volume_fractions, vf, 0)
S_pred = fit.predict()
npt.assert_array_almost_equal(S_pred, signal, 0)
def test_mcsd_model_delta():
sh_order = 8
gtab = get_3shell_gtab()
shells = np.unique(gtab.bvals // 100.) * 100.
response = multi_shell_fiber_response(sh_order, shells, evals_d, csf_md,
gm_md)
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)
The ``auto_response`` function will calculate FA for an ROI of radius equal to
``roi_radius`` in the center of the volume and return the response function
estimated in that region for the voxels with FA higher than 0.7.
"""
response, ratio = auto_response(gtab, denoised_arr, roi_radius=10, fa_thr=0.7)
evals_d = response[0]
"""
We will now use the evals obtained from the ``auto_response`` to generate the
``multi_shell_fiber_response`` rquired by the MSMT-CSD model. Note that we
mead diffusivities of ``csf`` and ``gm`` as inputs to generate th response.
"""
response_mcsd = multi_shell_fiber_response(sh_order=8,
bvals=bvals,
evals=evals_d,
csf_md=csf_md,
gm_md=gm_md)
"""
Now we build the MSMT-CSD model with the ``response_mcsd`` as input. We then
call the ``fit`` function to fit one slice of the 3D data and visualize it.
"""
mcsd_model = MultiShellDeconvModel(gtab, response_mcsd)
mcsd_fit = mcsd_model.fit(denoised_arr[:, :, 10:11])
"""
From the fit obtained in the previous step, we generate the ODFs which can be
visualized as follows:
"""
mcsd_odf = mcsd_fit.odf(sphere)