def dipy_gt():
print("Loading slice...")
sample_slice, qvecs, gtab = load_slice('./test_data/david_data', '3112_BL_data_subject_space.nii.gz', gtab=True)
mapmodel = GeneralizedQSamplingModel(gtab)
mapfit = mapmodel.fit(sample_slice)
sphere = get_sphere('repulsion724')
odfs = mapfit.odf(sphere)
vis_2d_field(odfs, sphere)
def test_gqi():
#load symmetric 724 sphere
sphere = get_sphere('symmetric724')
#load icosahedron sphere
sphere2 = create_unit_sphere(5)
btable = np.loadtxt(get_data('dsi515btable'))
bvals = btable[:, 0]
bvecs = btable[:, 1:]
gtab = gradient_table(bvals, bvecs)
data, golden_directions = SticksAndBall(gtab,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0)],
fractions=[50, 50],
snr=None)
gq = GeneralizedQSamplingModel(gtab, method='gqi2', sampling_length=1.4)
#symmetric724
gqfit = gq.fit(data)
odf = gqfit.odf(sphere)
directions, values, indices = peak_directions(odf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, golden_directions), 2,
1)
#5 subdivisions
gqfit = gq.fit(data)
odf2 = gqfit.odf(sphere2)
directions, values, indices = peak_directions(odf2, sphere2, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, golden_directions), 2,
1)
sb_dummies = sticks_and_ball_dummies(gtab)
for sbd in sb_dummies:
data, golden_directions = sb_dummies[sbd]
odf = gq.fit(data).odf(sphere2)
directions, values, indices = 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)
def test_mvoxel_gqi():
data, gtab = dsi_voxels()
gq = GeneralizedQSamplingModel(gtab, 'standard')
sphere = get_sphere('symmetric724')
gq.direction_finder.config(sphere=sphere,
min_separation_angle=25,
relative_peak_threshold=.35)
gqfit = gq.fit(data)
directions = gqfit.directions
assert_equal(directions[0, 0, 0].shape[0], 2)
assert_equal(directions[-1, -1, -1].shape[0], 2)
def gqi(training, category, snr, denoised, odeconv, tv, method, weight=0.1, sl=3.):
data, affine, gtab, mask, evals, S0, prefix = prepare(training,
category,
snr,
denoised,
odeconv,
tv,
method)
model = GeneralizedQSamplingModel(gtab,
method='gqi2',
sampling_length=sl,
normalize_peaks=False)
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)
def test_gqi():
# load symmetric 724 sphere
sphere = get_sphere('symmetric724')
# load icosahedron sphere
sphere2 = create_unit_sphere(5)
btable = np.loadtxt(get_fnames('dsi515btable'))
bvals = btable[:, 0]
bvecs = btable[:, 1:]
gtab = gradient_table(bvals, bvecs)
data, golden_directions = SticksAndBall(gtab, d=0.0015,
S0=100, angles=[(0, 0), (90, 0)],
fractions=[50, 50], snr=None)
gq = GeneralizedQSamplingModel(gtab, method='gqi2', sampling_length=1.4)
# symmetric724
gqfit = gq.fit(data)
odf = gqfit.odf(sphere)
directions, values, indices = peak_directions(odf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, golden_directions), 2,
1)
# 5 subdivisions
gqfit = gq.fit(data)
odf2 = gqfit.odf(sphere2)
directions, values, indices = peak_directions(odf2, sphere2, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, golden_directions), 2,
1)
sb_dummies = sticks_and_ball_dummies(gtab)
for sbd in sb_dummies:
data, golden_directions = sb_dummies[sbd]
odf = gq.fit(data).odf(sphere2)
directions, values, indices = 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)
def test_mvoxel_gqi():
data, gtab = dsi_voxels()
sphere = get_sphere('symmetric724')
gq = GeneralizedQSamplingModel(gtab, 'standard')
gqfit = gq.fit(data)
all_odfs = gqfit.odf(sphere)
# Check that the first and last voxels each have 2 peaks
odf = all_odfs[0, 0, 0]
directions, values, indices = peak_directions(odf, sphere, .35, 25)
assert_equal(directions.shape[0], 2)
odf = all_odfs[-1, -1, -1]
directions, values, indices = peak_directions(odf, sphere, .35, 25)
assert_equal(directions.shape[0], 2)
def test_mvoxel_gqi():
data, gtab = dsi_voxels()
sphere = get_sphere('symmetric724')
gq = GeneralizedQSamplingModel(gtab, 'standard')
gqfit = gq.fit(data)
all_odfs = gqfit.odf(sphere)
# Check that the first and last voxels each have 2 peaks
odf = all_odfs[0, 0, 0]
directions, values, indices = peak_directions(odf, sphere, .35, 25)
assert_equal(directions.shape[0], 2)
odf = all_odfs[-1, -1, -1]
directions, values, indices = peak_directions(odf, sphere, .35, 25)
assert_equal(directions.shape[0], 2)
def test_gqi():
#load symmetric 724 sphere
sphere = get_sphere('symmetric724')
#load icosahedron sphere
sphere2 = create_unit_sphere(5)
btable = np.loadtxt(get_data('dsi515btable'))
bvals = btable[:,0]
bvecs = btable[:,1:]
data, golden_directions = SticksAndBall(bvals, bvecs, d=0.0015,
S0=100, angles=[(0, 0), (90, 0)],
fractions=[50, 50], snr=None)
gtab = gradient_table(bvals, bvecs)
gq = GeneralizedQSamplingModel(gtab, method='gqi2', sampling_length=1.4)
#symmetric724
gq.direction_finder.config(sphere=sphere, min_separation_angle=25,
relative_peak_threshold=.35)
gqfit = gq.fit(data)
odf = gqfit.odf(sphere)
#from dipy.viz._show_odfs import show_odfs
#show_odfs(odf[None,None,None,:], (sphere.vertices, sphere.faces))
directions = gqfit.directions
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, golden_directions), 2, 1)
#5 subdivisions
gq.direction_finder.config(sphere=sphere2, min_separation_angle=25,
relative_peak_threshold=.35)
gqfit = gq.fit(data)
odf2 = gqfit.odf(sphere2)
directions = gqfit.directions
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, golden_directions), 2, 1)
#show_odfs(odf[None,None,None,:], (sphere.vertices, sphere.faces))
sb_dummies=sticks_and_ball_dummies(gtab)
for sbd in sb_dummies:
data, golden_directions = sb_dummies[sbd]
odf = gq.fit(data).odf(sphere2)
directions = gq.fit(data).directions
#show_odfs(odf[None, None, None, :], (sphere2.vertices, sphere2.faces))
if len(directions) <= 3:
assert_equal(len(gq.fit(data).directions), len(golden_directions))
if len(directions) > 3:
assert_equal(gfa(gq.fit(data).odf(sphere2)) < 0.1, True)
"""
Instantiate the model and apply it to the data.
"""
gqmodel = GeneralizedQSamplingModel(gtab, sampling_length=3)
"""
The parameter ``sampling_length`` is used here to
Lets just use one slice only from the data.
"""
dataslice = data[:, :, data.shape[2] // 2]
mask = dataslice[..., 0] > 50
gqfit = gqmodel.fit(dataslice, mask=mask)
"""
Load an ODF reconstruction sphere
"""
sphere = get_sphere('repulsion724')
"""
Calculate the ODFs with this specific sphere
"""
ODF = gqfit.odf(sphere)
print('ODF.shape (%d, %d, %d)' % ODF.shape)
"""
ODF.shape ``(96, 96, 724)``
def test_gqi():
# load odf sphere
vertices, faces = sphere_vf_from("symmetric724")
edges = unique_edges(faces)
half_vertices, half_edges, half_faces = reduce_antipodal(vertices, faces)
# load bvals and gradients
btable = np.loadtxt(get_data("dsi515btable"))
bvals = btable[:, 0]
bvecs = btable[:, 1:]
S, stics = SticksAndBall(
bvals, bvecs, d=0.0015, S0=100, angles=[(0, 0), (90, 0), (90, 90)], fractions=[50, 50, 0], snr=None
)
# pdf0,odf0,peaks0=standard_dsi_algorithm(S,bvals,bvecs)
S2 = S.copy()
S2 = S2.reshape(1, len(S))
odf_sphere = (vertices, faces)
ds = GeneralizedQSamplingModel(bvals, bvecs, odf_sphere)
dsfit = ds.fit(S)
assert_equal((dsfit.peak_values > 0).sum(), 3)
# change thresholds
ds.relative_peak_threshold = 0.5
ds.angular_distance_threshold = 30
dsfit = ds.fit(S)
assert_equal((dsfit.peak_values > 0).sum(), 2)
# 1 fiber
S, stics = SticksAndBall(
bvals, bvecs, d=0.0015, S0=100, angles=[(0, 0), (90, 0), (90, 90)], fractions=[100, 0, 0], snr=None
)
ds = GeneralizedQSamplingModel(bvals, bvecs, odf_sphere)
ds.relative_peak_threshold = 0.5
ds.angular_distance_threshold = 20
dsfit = ds.fit(S)
QA = dsfit.qa
# 1/0
assert_equal(np.sum(QA > 0), 1)
# 2 fibers
S, stics = SticksAndBall(
bvals, bvecs, d=0.0015, S0=100, angles=[(0, 0), (90, 0), (90, 90)], fractions=[50, 50, 0], snr=None
)
ds = GeneralizedQSamplingModel(bvals, bvecs, odf_sphere)
ds.relative_peak_threshold = 0.5
ds.angular_distance_threshold = 20
dsfit = ds.fit(S)
QA = dsfit.qa
assert_equal(np.sum(QA > 0), 2)
# 3 fibers
S, stics = SticksAndBall(
bvals, bvecs, d=0.0015, S0=100, angles=[(0, 0), (90, 0), (90, 90)], fractions=[33, 33, 33], snr=None
)
ds = GeneralizedQSamplingModel(bvals, bvecs, odf_sphere)
ds.relative_peak_threshold = 0.5
dsfit = ds.fit(S)
QA = dsfit.qa
assert_equal(np.sum(QA > 0), 3)
# isotropic
S, stics = SticksAndBall(
bvals, bvecs, d=0.0015, S0=100, angles=[(0, 0), (90, 0), (90, 90)], fractions=[0, 0, 0], snr=None
)
ds = GeneralizedQSamplingModel(bvals, bvecs, odf_sphere)
dsfit = ds.fit(S)
QA = dsfit.qa
assert_equal(np.sum(QA > 0), 0)
# 3 fibers DSI2
S, stics = SticksAndBall(
bvals, bvecs, d=0.0015, S0=100, angles=[(0, 0), (90, 0), (90, 90)], fractions=[33, 33, 33], snr=None
)
ds = GeneralizedQSamplingModel(bvals, bvecs, odf_sphere, squared=True)
ds.relative_peak_threshold = 0.5
dsfit = ds.fit(S, gfa_thr=0.05)
QA = dsfit.qa
# 3 fibers DSI2 with a 3D volume
data = np.zeros((3, 3, 3, len(S)))
data[..., :] = S.copy()
dsfit = ds.fit(data, gfa_thr=0.05)
# 1/0
assert_array_almost_equal(np.sum(dsfit.peak_values > 0, axis=-1), 3 * np.ones((3, 3, 3)))
[0.0017, 0.0003, 0.0003],
[0.0017, 0.0003, 0.0003]])
# ang = [(20, 10), (70, 20), (45, 60)]
ang = [(0, 0), (45, 45), (90, 90)]
data, sticks = MultiTensor(gtab, mevals, S0=100, angles=ang,
fractions=[33.3, 33.3, 33.4], snr=100)
gqi_model = GeneralizedQSamplingModel(gtab,
method='gqi2',
sampling_length=4.,
normalize_peaks=False)
gqi_fit = gqi_model.fit(data)
sphere = get_sphere('symmetric724')
gqi_odf = gqi_fit.odf(sphere)
def optimal_transform(model, data, sphere):
from dipy.reconst.gqi import squared_radial_component
H = squared_radial_component
b_vector = model.b_vector
bnorm = np.sqrt(np.sum(b_vector ** 2, axis=1))
# # linearize
# b_vector2 = b_vector * bnorm[:, None]
#1/0
data = data[25 - 10:25 + 10, 25 - 10:25 + 10, 25]
mask = mask[25 - 10:25 + 10, 25 - 10:25 + 10, 25]
# data = data[:, :, 25]
model_tag = 'dsdeconv_'
model = GeneralizedQSamplingModel(gtab,
method='gqi2',
sampling_length=3.,
normalize_peaks=False)
#model = DiffusionSpectrumDeconvModel(gtab)
fit = model.fit(data, mask)
sphere = get_sphere('symmetric724')
odf = fit.odf(sphere)
#nib.save(nib.Nifti1Image(odf, affine), model_tag + 'odf.nii.gz')
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')
Instantiate the Model and apply it to the data.
"""
gqmodel = GeneralizedQSamplingModel(gtab, sampling_length=3)
"""
The parameter `sampling_length` is used here to
Lets just use one slice only from the data.
"""
dataslice = data[:, :, data.shape[2] / 2]
mask = dataslice[..., 0] > 50
gqfit = gqmodel.fit(dataslice, mask=mask)
"""
Load an odf reconstruction sphere
"""
sphere = get_sphere('symmetric724')
"""
Calculate the ODFs with this specific sphere
"""
ODF = gqfit.odf(sphere)
print('ODF.shape (%d, %d, %d)' % ODF.shape)
fvtk.show(r)
# from pylab import plot, show
# plot(S, 'b')
# plot(S2, 'r')
# show()
# # plot(np.abs(S - S2))
# plot((S - S2))
# show()
gq = GeneralizedQSamplingModel(gtab_full, sampling_length=3.5)
gqfit = gq.fit(SS)
gqodf = gqfit.odf(sphere)
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)
"""
w=10
data_roi = data[:, :, 32]
#data_roi = data[48-w:48+w, 48-w:48+w, 32] #27-w:27+w,:]
data_roi = data_roi[:,:,None,:]
data = data_roi.astype('f8')
"""
S0 = data[..., 0]
mask = S0 > 50
gq = GeneralizedQSamplingModel(gtab)
gq.direction_finder.config(sphere=sphere,
min_separation_angle=25,
relative_peak_threshold=.35)
gqfit = gq.fit(data, mask)
gfa = gqfit.gfa
peak_values = gqfit.peak_values
peak_indices = gqfit.peak_indices
qa = gqfit.qa
#ODF = gqfit.odf(sphere)
"""
data, gtab = half_to_full_qspace(data, gtab)
ds = DiffusionSpectrumModel(gtab)
ds.direction_finder.config(sphere=sphere,
min_separation_angle=25,
relative_peak_threshold=.35)
dsfit = ds.fit(data, mask)
def test_gqi():
#load odf sphere
vertices, faces = sphere_vf_from('symmetric724')
edges = unique_edges(faces)
half_vertices, half_edges, half_faces = reduce_antipodal(vertices, faces)
#load bvals and gradients
btable = np.loadtxt(get_data('dsi515btable'))
bvals = btable[:, 0]
bvecs = btable[:, 1:]
S, stics = SticksAndBall(bvals,
bvecs,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[50, 50, 0],
snr=None)
#pdf0,odf0,peaks0=standard_dsi_algorithm(S,bvals,bvecs)
S2 = S.copy()
S2 = S2.reshape(1, len(S))
odf_sphere = (vertices, faces)
ds = GeneralizedQSamplingModel(bvals, bvecs, odf_sphere)
dsfit = ds.fit(S)
assert_equal((dsfit.peak_values > 0).sum(), 3)
#change thresholds
ds.relative_peak_threshold = 0.5
ds.angular_distance_threshold = 30
dsfit = ds.fit(S)
assert_equal((dsfit.peak_values > 0).sum(), 2)
#1 fiber
S, stics = SticksAndBall(bvals,
bvecs,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[100, 0, 0],
snr=None)
ds = GeneralizedQSamplingModel(bvals, bvecs, odf_sphere)
ds.relative_peak_threshold = 0.5
ds.angular_distance_threshold = 20
dsfit = ds.fit(S)
QA = dsfit.qa
#1/0
assert_equal(np.sum(QA > 0), 1)
#2 fibers
S, stics = SticksAndBall(bvals,
bvecs,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[50, 50, 0],
snr=None)
ds = GeneralizedQSamplingModel(bvals, bvecs, odf_sphere)
ds.relative_peak_threshold = 0.5
ds.angular_distance_threshold = 20
dsfit = ds.fit(S)
QA = dsfit.qa
assert_equal(np.sum(QA > 0), 2)
#3 fibers
S, stics = SticksAndBall(bvals,
bvecs,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[33, 33, 33],
snr=None)
ds = GeneralizedQSamplingModel(bvals, bvecs, odf_sphere)
ds.relative_peak_threshold = 0.5
dsfit = ds.fit(S)
QA = dsfit.qa
assert_equal(np.sum(QA > 0), 3)
#isotropic
S, stics = SticksAndBall(bvals,
bvecs,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[0, 0, 0],
snr=None)
ds = GeneralizedQSamplingModel(bvals, bvecs, odf_sphere)
dsfit = ds.fit(S)
QA = dsfit.qa
assert_equal(np.sum(QA > 0), 0)
#3 fibers DSI2
S, stics = SticksAndBall(bvals,
bvecs,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0), (90, 90)],
fractions=[33, 33, 33],
snr=None)
ds = GeneralizedQSamplingModel(bvals, bvecs, odf_sphere, squared=True)
ds.relative_peak_threshold = 0.5
dsfit = ds.fit(S, gfa_thr=0.05)
QA = dsfit.qa
#3 fibers DSI2 with a 3D volume
data = np.zeros((3, 3, 3, len(S)))
data[..., :] = S.copy()
dsfit = ds.fit(data, gfa_thr=0.05)
#1/0
assert_array_almost_equal(np.sum(dsfit.peak_values > 0, axis=-1),
3 * np.ones((3, 3, 3)))
from dipy.reconst.odf import gfa
from dipy.reconst.dsi import DiffusionSpectrumDeconvModel
from dipy.data import get_sphere
from dipy.viz.mayavi.spheres import show_odfs
from load_data import get_train_dsi
data, affine, gtab = get_train_dsi(30)
gqi_model = GeneralizedQSamplingModel(gtab,
method='gqi2',
sampling_length=3,
normalize_peaks=True)
crop = 20
gqi_fit = gqi_model.fit(data[crop:29,crop:29,crop:29])
sphere = get_sphere('symmetric724')
gqi_odf = gqi_fit.odf(sphere)
gqi_gfa = gfa(gqi_odf)
import nibabel as nib
affine[:3,3] += crop
print affine
nib.save(nib.Nifti1Image(gqi_odf, affine), 'gqi_odf_norm.nii.gz')
nib.save(nib.Nifti1Image(gqi_gfa, affine), 'gfa_norm.nii.gz')
import numpy as np
np.savetxt('sphere724.txt', sphere.vertices)
bvals, bvecs = read_bvals_bvecs(fdsi_bvals, fdsi_bvecs)
gtab = gradient_table(bvals, bvecs)
#sb = sticks_and_ball_dummies(gtab)
S, sticks = SticksAndBall(gtab, d=0.0015, S0=100,
angles=[(0, 0), (30, 0)],
fractions=[50, 50], snr=30.0)
sphere = get_sphere('symmetric724')
sphere = sphere.subdivide(n=1)
r=np.zeros(sphere.vertices.shape[0])
sampling_lengths=[1.25,1.5,2.,2.5,3.,3.5,4.]
for ss in sampling_lengths:
gqi_model = GeneralizedQSamplingModel(gtab,
method='gqi2',
sampling_length=ss,
normalize_peaks=False)
gqi_fit = gqi_model.fit(S)
gqi_odf = gqi_fit.odf(sphere)
r=np.vstack((r,gqi_odf))
show_odfs(r[:,None,None], sphere)
