def afficher_tenseurs(fa_,evec,eva) :
cfa = dti.color_fa(fa_, evec)
sphere = dpd.default_sphere
ren = window.Renderer()
ren.add(actor.tensor_slicer(eva, evec, scalar_colors=cfa, sphere=sphere,
scale=0.5))
window.record(ren, out_path='tensor.png', size=(1200, 1200))
def visualize(evals,evecs,viz_scale=0.5, fname='tensor_ellipsoids.png', size=(1000,1000)):
# Do vizualisation
interactive = True
ren = window.Scene()
from dipy.data import get_sphere
#sphere = get_sphere('symmetric362')
#sphere = get_sphere('repulsion724')
sphere = get_sphere('symmetric642')
# Calculate the colors. See dipy documentation.
from dipy.reconst.dti import fractional_anisotropy, color_fa
FA = fractional_anisotropy(evals)
#print(FA)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
RGB = color_fa(FA, evecs)
k=0
cfa = RGB[:, :, k:k+1]
# Normalizing like this increases the contrast, but this will make the contrast different across plots
#cfa /= cfa.max()
# imgplot = plt.imshow(FA, cmap='gray')
# plt.show()
ren.add(actor.tensor_slicer(evals, evecs, sphere=sphere, scalar_colors=cfa, scale=viz_scale, norm=False))
if interactive:
window.show(ren)
window.record(ren, n_frames=1, out_path=fname, size=(1000, 1000))
fit_wls = dti_wls.fit(data)
fa1 = fit_wls.fa
evals1 = fit_wls.evals
evecs1 = fit_wls.evecs
cfa1 = dti.color_fa(fa1, evecs1)
sphere = dpd.default_sphere
"""
We visualize the ODFs in the ROI using ``dipy.viz`` module:
"""
ren = window.Renderer()
ren.add(
actor.tensor_slicer(evals1,
evecs1,
scalar_colors=cfa1,
sphere=sphere,
scale=0.3))
print('Saving illustration as tensor_ellipsoids_wls.png')
window.record(ren, out_path='tensor_ellipsoids_wls.png', size=(600, 600))
if interactive:
window.show(ren)
"""
.. figure:: tensor_ellipsoids_wls.png
:align: center
Tensor Ellipsoids.
"""
window.clear(ren)
"""
ren = window.Renderer()
evals = tenfit.evals[13:43, 44:74, 28:29]
evecs = tenfit.evecs[13:43, 44:74, 28:29]
"""
We can color the ellipsoids using the ``color_fa`` values that we calculated
above. In this example we additionally normalize the values to increase the
contrast.
"""
cfa = RGB[13:43, 44:74, 28:29]
cfa /= cfa.max()
ren.add(actor.tensor_slicer(evals, evecs, scalar_colors=cfa, sphere=sphere, scale=0.3))
print('Saving illustration as tensor_ellipsoids.png')
window.record(ren, n_frames=1, out_path='tensor_ellipsoids.png', size=(600, 600))
if interactive:
window.show(ren)
"""
.. figure:: tensor_ellipsoids.png
:align: center
Tensor Ellipsoids.
"""
window.clear(ren)
"""
fit_wls = dti_wls.fit(data)
fa1 = fit_wls.fa
evals1 = fit_wls.evals
evecs1 = fit_wls.evecs
cfa1 = dti.color_fa(fa1, evecs1)
sphere = dpd.get_sphere('symmetric724')
"""
We visualize the ODFs in the ROI using ``dipy.viz`` module:
"""
ren = window.Renderer()
ren.add(actor.tensor_slicer(evals1, evecs1, scalar_colors=cfa1, sphere=sphere, scale=0.3))
print('Saving illustration as tensor_ellipsoids_wls.png')
window.record(ren, out_path='tensor_ellipsoids_wls.png', size=(600, 600))
if interactive:
window.show(ren)
"""
.. figure:: tensor_ellipsoids_wls.png
:align: center
Tensor Ellipsoids.
"""
window.clear(ren)
"""
ren = window.Renderer()
evals = tenfit.evals[13:43, 44:74, 28:29]
evecs = tenfit.evecs[13:43, 44:74, 28:29]
"""
We can color the ellipsoids using the ``color_fa`` values that we calculated
above. In this example we additionally normalize the values to increase the
contrast.
"""
cfa = RGB[13:43, 44:74, 28:29]
cfa /= cfa.max()
ren.add(actor.tensor_slicer(evals, evecs, scalar_colors=cfa, sphere=sphere, scale=0.3))
print('Saving illustration as tensor_ellipsoids.png')
window.record(ren, n_frames=1, out_path='tensor_ellipsoids.png', size=(600, 600))
if interactive:
window.show(ren)
"""
.. figure:: tensor_ellipsoids.png
:align: center
Tensor Ellipsoids.
"""
window.clear(ren)
def test_tensor_slicer(interactive=False):
evals = np.array([1.4, .35, .35]) * 10 ** (-3)
evecs = np.eye(3)
mevals = np.zeros((3, 2, 4, 3))
mevecs = np.zeros((3, 2, 4, 3, 3))
mevals[..., :] = evals
mevecs[..., :, :] = evecs
from dipy.data import get_sphere
sphere = get_sphere('symmetric724')
affine = np.eye(4)
renderer = window.Renderer()
tensor_actor = actor.tensor_slicer(mevals, mevecs, affine=affine,
sphere=sphere, scale=.3)
I, J, K = mevals.shape[:3]
renderer.add(tensor_actor)
renderer.reset_camera()
renderer.reset_clipping_range()
tensor_actor.display_extent(0, 1, 0, J, 0, K)
tensor_actor.GetProperty().SetOpacity(1.0)
if interactive:
window.show(renderer, reset_camera=False)
npt.assert_equal(renderer.GetActors().GetNumberOfItems(), 1)
# Test extent
big_extent = renderer.GetActors().GetLastActor().GetBounds()
big_extent_x = abs(big_extent[1] - big_extent[0])
tensor_actor.display(x=2)
if interactive:
window.show(renderer, reset_camera=False)
small_extent = renderer.GetActors().GetLastActor().GetBounds()
small_extent_x = abs(small_extent[1] - small_extent[0])
npt.assert_equal(big_extent_x > small_extent_x, True)
# Test empty mask
empty_actor = actor.tensor_slicer(mevals, mevecs, affine=affine,
mask=np.zeros(mevals.shape[:3]),
sphere=sphere, scale=.3)
npt.assert_equal(empty_actor.GetMapper(), None)
# Test mask
mask = np.ones(mevals.shape[:3])
mask[:2, :3, :3] = 0
cfa = color_fa(fractional_anisotropy(mevals), mevecs)
tensor_actor = actor.tensor_slicer(mevals, mevecs, affine=affine, mask=mask,
scalar_colors=cfa, sphere=sphere, scale=.3)
renderer.clear()
renderer.add(tensor_actor)
renderer.reset_camera()
renderer.reset_clipping_range()
if interactive:
window.show(renderer, reset_camera=False)
mask_extent = renderer.GetActors().GetLastActor().GetBounds()
mask_extent_x = abs(mask_extent[1] - mask_extent[0])
npt.assert_equal(big_extent_x > mask_extent_x, True)
# test display
tensor_actor.display()
current_extent = renderer.GetActors().GetLastActor().GetBounds()
current_extent_x = abs(current_extent[1] - current_extent[0])
npt.assert_equal(big_extent_x > current_extent_x, True)
if interactive:
window.show(renderer, reset_camera=False)
tensor_actor.display(y=1)
current_extent = renderer.GetActors().GetLastActor().GetBounds()
current_extent_y = abs(current_extent[3] - current_extent[2])
big_extent_y = abs(big_extent[3] - big_extent[2])
npt.assert_equal(big_extent_y > current_extent_y, True)
if interactive:
window.show(renderer, reset_camera=False)
tensor_actor.display(z=1)
current_extent = renderer.GetActors().GetLastActor().GetBounds()
current_extent_z = abs(current_extent[5] - current_extent[4])
big_extent_z = abs(big_extent[5] - big_extent[4])
npt.assert_equal(big_extent_z > current_extent_z, True)
if interactive:
window.show(renderer, reset_camera=False)
def dMRI2ODF_DTI(PATH):
'''
Input the dMRI data
return the ODF
'''
dMRI_path = PATH + 'data.nii.gz'
mask_path = PATH + 'nodif_brain_mask.nii.gz'
dMRI_img = nib.load(dMRI_path)
dMRI_data = dMRI_img.get_fdata()
mask_img = nib.load(mask_path)
mask = mask_img.get_fdata()
########## subsample ##########
# dMRI_data = dMRI_data[45:-48,50:-65,51:-54,...]
# mask = mask[45:-48,50:-65,51:-54]
# breakpoint()
dMRI_data = dMRI_data[:, 87, ...]
mask = mask[:, 87, ...]
for cnt in range(10):
fig = plt.imshow(dMRI_data[:, :, cnt].transpose(1, 0),
cmap='Greys',
interpolation='nearest')
plt.axis('off')
# plt.imshow(dMRI_data[:,15,:,cnt].transpose(1,0),cmap='Greys')
plt.savefig(str(cnt) + '.png',
bbox_inches='tight',
dpi=300,
transparent=True,
pad_inches=0)
# breakpoint()
bval = PATH + "bvals"
bvec = PATH + "bvecs"
radial_order = 6
zeta = 700
lambdaN = 1e-8
lambdaL = 1e-8
gtab = gradient_table(bvals=bval, bvecs=bvec)
asm = ShoreModel(gtab,
radial_order=radial_order,
zeta=zeta,
lambdaN=lambdaN,
lambdaL=lambdaL)
asmfit = asm.fit(dMRI_data, mask=mask)
sphere = get_sphere('symmetric362')
dMRI_odf = asmfit.odf(sphere)
dMRI_odf[dMRI_odf <= 0] = 0
tenmodel = dti.TensorModel(gtab)
tenfit = tenmodel.fit(dMRI_data, mask)
dMRI_dti = tenfit.quadratic_form
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
RGB = color_fa(FA, tenfit.evecs)
evals = tenfit.evals + 1e-20
evecs = tenfit.evecs
cfa = RGB + 1e-20
cfa /= cfa.max()
evals = np.expand_dims(evals, 2)
evecs = np.expand_dims(evecs, 2)
cfa = np.expand_dims(cfa, 2)
ren = window.Scene()
sphere = get_sphere('symmetric362')
ren.add(
actor.tensor_slicer(evals,
evecs,
scalar_colors=cfa,
sphere=sphere,
scale=0.5))
window.record(ren,
n_frames=1,
out_path='../data/tensor.png',
size=(5000, 5000))
odf_ = dMRI_odf
ren = window.Scene()
sfu = actor.odf_slicer(np.expand_dims(odf_, 2),
sphere=sphere,
colormap="plasma",
scale=0.5)
ren.add(sfu)
window.record(ren,
n_frames=1,
out_path='../data/odfs.png',
size=(5000, 5000))
return None
def test_tensor_slicer(interactive=False):
evals = np.array([1.4, .35, .35]) * 10 ** (-3)
evecs = np.eye(3)
mevals = np.zeros((3, 2, 4, 3))
mevecs = np.zeros((3, 2, 4, 3, 3))
mevals[..., :] = evals
mevecs[..., :, :] = evecs
from dipy.data import get_sphere
sphere = get_sphere('symmetric724')
affine = np.eye(4)
renderer = window.Renderer()
tensor_actor = actor.tensor_slicer(mevals, mevecs, affine=affine,
sphere=sphere, scale=.3)
I, J, K = mevals.shape[:3]
renderer.add(tensor_actor)
renderer.reset_camera()
renderer.reset_clipping_range()
tensor_actor.display_extent(0, 1, 0, J, 0, K)
tensor_actor.GetProperty().SetOpacity(1.0)
if interactive:
window.show(renderer, reset_camera=False)
npt.assert_equal(renderer.GetActors().GetNumberOfItems(), 1)
# Test extent
big_extent = renderer.GetActors().GetLastActor().GetBounds()
big_extent_x = abs(big_extent[1] - big_extent[0])
tensor_actor.display(x=2)
if interactive:
window.show(renderer, reset_camera=False)
small_extent = renderer.GetActors().GetLastActor().GetBounds()
small_extent_x = abs(small_extent[1] - small_extent[0])
npt.assert_equal(big_extent_x > small_extent_x, True)
# Test empty mask
empty_actor = actor.tensor_slicer(mevals, mevecs, affine=affine,
mask=np.zeros(mevals.shape[:3]),
sphere=sphere, scale=.3)
npt.assert_equal(empty_actor.GetMapper(), None)
# Test mask
mask = np.ones(mevals.shape[:3])
mask[:2, :3, :3] = 0
cfa = color_fa(fractional_anisotropy(mevals), mevecs)
tensor_actor = actor.tensor_slicer(mevals, mevecs, affine=affine, mask=mask,
scalar_colors=cfa, sphere=sphere, scale=.3)
renderer.clear()
renderer.add(tensor_actor)
renderer.reset_camera()
renderer.reset_clipping_range()
if interactive:
window.show(renderer, reset_camera=False)
mask_extent = renderer.GetActors().GetLastActor().GetBounds()
mask_extent_x = abs(mask_extent[1] - mask_extent[0])
npt.assert_equal(big_extent_x > mask_extent_x, True)
# test display
tensor_actor.display()
current_extent = renderer.GetActors().GetLastActor().GetBounds()
current_extent_x = abs(current_extent[1] - current_extent[0])
npt.assert_equal(big_extent_x > current_extent_x, True)
if interactive:
window.show(renderer, reset_camera=False)
tensor_actor.display(y=1)
current_extent = renderer.GetActors().GetLastActor().GetBounds()
current_extent_y = abs(current_extent[3] - current_extent[2])
big_extent_y = abs(big_extent[3] - big_extent[2])
npt.assert_equal(big_extent_y > current_extent_y, True)
if interactive:
window.show(renderer, reset_camera=False)
tensor_actor.display(z=1)
current_extent = renderer.GetActors().GetLastActor().GetBounds()
current_extent_z = abs(current_extent[5] - current_extent[4])
big_extent_z = abs(big_extent[5] - big_extent[4])
npt.assert_equal(big_extent_z > current_extent_z, True)
if interactive:
window.show(renderer, reset_camera=False)
