def test_dsi():
# load repulsion 724 sphere
sphere = default_sphere
# load icosahedron sphere
sphere2 = create_unit_sphere(5)
btable = np.loadtxt(get_fnames('dsi515btable'))
gtab = gradient_table(btable[:, 0], btable[:, 1:])
data, golden_directions = sticks_and_ball(gtab,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0)],
fractions=[50, 50],
snr=None)
ds = DiffusionSpectrumDeconvModel(gtab)
# repulsion724
dsfit = ds.fit(data)
odf = dsfit.odf(sphere)
directions, _, _ = peak_directions(odf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, golden_directions), 2,
1)
# 5 subdivisions
dsfit = ds.fit(data)
odf2 = dsfit.odf(sphere2)
directions, _, _ = peak_directions(odf2, sphere2, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, golden_directions), 2,
1)
assert_equal(dsfit.pdf().shape, 3 * (ds.qgrid_size, ))
sb_dummies = sticks_and_ball_dummies(gtab)
for sbd in sb_dummies:
data, golden_directions = sb_dummies[sbd]
odf = ds.fit(data).odf(sphere2)
directions, _, _ = peak_directions(odf, sphere2, .35, 25)
if len(directions) <= 3:
assert_equal(len(directions), len(golden_directions))
if len(directions) > 3:
assert_equal(gfa(odf) < 0.1, True)
assert_raises(ValueError,
DiffusionSpectrumDeconvModel,
gtab,
qgrid_size=16)
def test_multivox_dsi():
data, gtab = dsi_deconv_voxels()
DS = DiffusionSpectrumDeconvModel(gtab)
DSfit = DS.fit(data)
PDF = DSfit.pdf()
assert_equal(data.shape[:-1] + (35, 35, 35), PDF.shape)
assert_equal(np.alltrue(np.isreal(PDF)), True)
def test_multivox_dsi():
data, gtab = dsi_deconv_voxels()
DS = DiffusionSpectrumDeconvModel(gtab)
sphere = get_sphere('symmetric724')
DSfit = DS.fit(data)
PDF = DSfit.pdf()
assert_equal(data.shape[:-1] + (35, 35, 35), PDF.shape)
assert_equal(np.alltrue(np.isreal(PDF)), True)
def test_dsi():
# load symmetric 724 sphere
sphere = get_sphere('symmetric724')
# load icosahedron sphere
sphere2 = create_unit_sphere(5)
btable = np.loadtxt(get_data('dsi515btable'))
gtab = gradient_table(btable[:, 0], btable[:, 1:])
data, golden_directions = SticksAndBall(gtab, d=0.0015,
S0=100, angles=[(0, 0), (90, 0)],
fractions=[50, 50], snr=None)
ds = DiffusionSpectrumDeconvModel(gtab)
# symmetric724
dsfit = ds.fit(data)
odf = dsfit.odf(sphere)
directions, _, _ = peak_directions(odf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, golden_directions),
2, 1)
# 5 subdivisions
dsfit = ds.fit(data)
odf2 = dsfit.odf(sphere2)
directions, _, _ = peak_directions(odf2, sphere2, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, golden_directions),
2, 1)
assert_equal(dsfit.pdf().shape, 3 * (ds.qgrid_size, ))
sb_dummies = sticks_and_ball_dummies(gtab)
for sbd in sb_dummies:
data, golden_directions = sb_dummies[sbd]
odf = ds.fit(data).odf(sphere2)
directions, _, _ = peak_directions(odf, sphere2, .35, 25)
if len(directions) <= 3:
assert_equal(len(directions), len(golden_directions))
if len(directions) > 3:
assert_equal(gfa(odf) < 0.1, True)
assert_raises(ValueError, DiffusionSpectrumDeconvModel, gtab, qgrid_size=16)
def dsid(training, category, snr, denoised, odeconv, tv, method, weight=0.1):
data, affine, gtab, mask, evals, S0, prefix = prepare(training,
category,
snr,
denoised,
odeconv,
tv,
method)
model = DiffusionSpectrumDeconvModel(gtab)
fit = model.fit(data, mask)
sphere = get_sphere('symmetric724')
odf = fit.odf(sphere)
if odeconv == True:
odf_sh = sf_to_sh(odf, sphere, sh_order=8,
basis_type='mrtrix')
# # nib.save(nib.Nifti1Image(odf_sh, affine), model_tag + 'odf_sh.nii.gz')
reg_sphere = get_sphere('symmetric724')
fodf_sh = odf_sh_to_sharp(odf_sh,
reg_sphere, basis='mrtrix', ratio=3.8 / 16.6,
sh_order=8, Lambda=1., tau=1.)
# # nib.save(nib.Nifti1Image(odf_sh, affine), model_tag + 'fodf_sh.nii.gz')
fodf_sh[np.isnan(fodf_sh)]=0
r, theta, phi = cart2sphere(sphere.x, sphere.y, sphere.z)
B_regul, m, n = real_sph_harm_mrtrix(8, theta[:, None], phi[:, None])
fodf = np.dot(fodf_sh, B_regul.T)
odf = fodf
if tv == True:
odf = tv_denoise_4d(odf, weight=weight)
save_odfs_peaks(training, odf, affine, sphere, dres, prefix)
odf_gt = multi_tensor_odf(sphere.vertices,
evals,
angles=directions,
fractions=fractions)
"""
Perform the reconstructions with standard DSI and DSI with deconvolution.
"""
dsi_model = DiffusionSpectrumModel(gtab)
dsi_odf = dsi_model.fit(signal).odf(sphere)
dsid_model = DiffusionSpectrumDeconvModel(gtab)
dsid_odf = dsid_model.fit(signal).odf(sphere)
"""
Finally, we can visualize the ground truth ODF, together with the DSI and DSI
with deconvolution ODFs and observe that with the deconvolved method it is
easier to resolve the correct fiber directions because the ODF is sharper.
"""
from dipy.viz import window, actor
# Enables/disables interactive visualization
interactive = False
ren = window.Renderer()
# concatenate data as 4D array
odfs = np.vstack((odf_gt, dsi_odf, dsid_odf))[:, None, None]
sphere = get_sphere('symmetric724').subdivide(1)
odf_gt = multi_tensor_odf(sphere.vertices, evals, angles=directions,
fractions=fractions)
"""
Perform the reconstructions with standard DSI and DSI with deconvolution.
"""
dsi_model = DiffusionSpectrumModel(gtab)
dsi_odf = dsi_model.fit(signal).odf(sphere)
dsid_model = DiffusionSpectrumDeconvModel(gtab)
dsid_odf = dsid_model.fit(signal).odf(sphere)
"""
Finally, we can visualize the ground truth ODF, together with the DSI and DSI
with deconvolution ODFs and observe that with the deconvolved method it is
easier to resolve the correct fiber directions because the ODF is sharper.
"""
from dipy.viz import fvtk
ren = fvtk.ren()
odfs = np.vstack((odf_gt, dsi_odf, dsid_odf))[:, None, None]
odf_actor = fvtk.sphere_funcs(odfs, sphere)
odf_actor.RotateX(90)
gqi_vector = np.real(H(proj * model.Lambda / np.pi))
return np.dot(data, gqi_vector), proj
odf, proj = optimal_transform(gqi_model, data, sphere)
from dipy.viz import fvtk
r = fvtk.ren()
fvtk.add(r, fvtk.sphere_funcs(gqi_odf, sphere))
fvtk.show(r)
dsi_model = DiffusionSpectrumDeconvModel(gtab)
dsi_odf = dsi_model.fit(data).odf(sphere)
fvtk.clear(r)
fvtk.add(r, fvtk.sphere_funcs(dsi_odf, sphere))
fvtk.show(r)
fvtk.clear(r)
fvtk.add(r, fvtk.sphere_funcs(odf, sphere))
fvtk.show(r)
def investigate_internals():
def bvl_min_max(b_vector, sphere, sampling_length):
bv = np.dot(gqi_model.b_vector, sphere.vertices.T)
bv.max()
gqdir, _, _ = peak_directions(gqodf, sphere, .35, 15)
print angular_similarity(sticks, gqdir)
grid_size = 35
dds = DiffusionSpectrumDeconvModel(gtab_full,
qgrid_size=grid_size,
r_start=0.2 * (grid_size // 2),
r_end=0.7 * (grid_size // 2),
r_step=0.02 * (grid_size // 2),
filter_width=np.inf,
normalize_peaks=False)
ddsfit = dds.fit(SS)
ddsodf = ddsfit.odf(sphere)
ddsdir, _, _ = peak_directions(ddsodf, sphere, .35, 15)
print angular_similarity(sticks, ddsdir)
grid_size = 35
ds = DiffusionSpectrumModel(gtab_full,
qgrid_size=grid_size,
r_start=0.2 * (grid_size // 2),
r_end=0.7 * (grid_size // 2),
r_step=0.02 * (grid_size // 2),
filter_width=np.inf,
normalize_peaks=False)
