from dipy.sims.voxel import single_tensor_odf
response_odf = single_tensor_odf(sphere.vertices, evals, evecs)
response_actor = fvtk.sphere_funcs(response_odf, sphere)
fvtk.add(ren, response_actor)
print('Saving illustration as csd_response.png')
fvtk.record(ren, out_path='csd_response.png', size=(200, 200))
"""
.. figure:: csd_response.png
:align: center
**Estimated response function**.
"""
fvtk.rm(ren, response_actor)
"""
Depending on the dataset, FA threshold may not be the best way to find the
best possible response function. For one, it depends on the diffusion tensor
(FA and first eigenvector), which has lower accuracy at high
b-values. Alternatively, the response function can be calibrated in a
data-driven manner [Tax2014]_.
First, the data is deconvolved with a 'fat' response function. All voxels that
are considered to contain only one peak in this deconvolution (as determined by
the peak threshold which gives an upper limit of the ratio of the second peak
to the first peak) are maintained, and from these voxels a new response
function is determined. This process is repeated until convergence is
reached. Here we calibrate the response function on a small part of the data.
"""
vol_actor1 = fvtk.slicer(t1_data, affine=affine)
vol_actor1.display(None, None, 20)
fvtk.add(ren, vol_actor1)
# Vertical (sagittal) slice of T1 data
vol_actor2 = fvtk.slicer(t1_data, affine=affine)
vol_actor2.display(35, None, None)
fvtk.add(ren, vol_actor2)
# Show original fibers
fvtk.camera(ren, pos=(-264, 285, 155), focal=(0, -14, 9), viewup=(0, 0, 1),
verbose=False)
fvtk.record(ren, n_frames=1, out_path='OR_before.png', size=(900, 900))
# Show thresholded fibers
fvtk.rm(ren, lineactor)
fvtk.add(ren, actor.line(fbc_sl_thres, clrs_thres, linewidth=0.2))
fvtk.record(ren, n_frames=1, out_path='OR_after.png', size=(900, 900))
"""
.. figure:: OR_before.png
:align: center
The optic radiation obtained through probabilistic tractography colored by
local fiber to bundle coherence.
.. figure:: OR_after.png
:align: center
The tractography result is cleaned (shown in bottom) by removing fibers
with a relative FBC (RFBC) lower than the threshold tau=0.2.
vol_actor1 = fvtk.slicer(t1_data, affine=affine)
vol_actor1.display(None, None, 20)
fvtk.add(ren, vol_actor1)
# Vertical (sagittal) slice of T1 data
vol_actor2 = fvtk.slicer(t1_data, affine=affine)
vol_actor2.display(35, None, None)
fvtk.add(ren, vol_actor2)
# Show original fibers
fvtk.camera(ren, pos=(-264, 285, 155), focal=(0, -14, 9), viewup=(0, 0, 1),
verbose=False)
fvtk.record(ren, n_frames=1, out_path='OR_before.png', size=(900, 900))
# Show thresholded fibers
fvtk.rm(ren, lineactor)
fvtk.add(ren, actor.line(fbc_sl_thres, clrs_thres, linewidth=0.2))
fvtk.record(ren, n_frames=1, out_path='OR_after.png', size=(900, 900))
"""
.. figure:: OR_before.png
:align: center
The optic radiation obtained through probabilistic tractography colored by
local fiber to bundle coherence.
.. figure:: OR_after.png
:align: center
The tractography result is cleaned (shown in bottom) by removing fibers
with a relative FBC (RFBC) lower than the threshold tau=0.2.
sphere = get_sphere('symmetric724')
from dipy.sims.voxel import single_tensor_odf
response_odf = single_tensor_odf(sphere.vertices, evals, evecs)
response_actor = fvtk.sphere_funcs(response_odf, sphere)
fvtk.add(ren, response_actor)
print('Saving illustration as csd_response.png')
fvtk.record(ren, out_path='csd_response.png', size=(200, 200))
"""
.. figure:: csd_response.png
:align: center
**Estimated response function**.
"""
fvtk.rm(ren, response_actor)
"""
Depending on the dataset, FA threshold may not be the best way to find the
best possible response function. For one, it depends on the diffusion tensor
(FA and first eigenvector), which has lower accuracy at high
b-values. Alternatively, the response function can be calibrated in a
data-driven manner [Tax2014]_.
First, the data is deconvolved with a 'fat' response function. All voxels that
are considered to contain only one peak in this deconvolution (as determined by
the peak threshold which gives an upper limit of the ratio of the second peak
to the first peak) are maintained, and from these voxels a new response
function is determined. This process is repeated until convergence is
reached. Here we calibrate the response function on a small part of the data.
"""
