#=============================================================================
if not os.path.exists(Msym_file) and not os.path.exists(Mdir_file):
print '\tCalculating peaks'
csamodel = shm.CsaOdfModel(gtab, 6)
csapeaks = peaks.peaks_from_model(model=csamodel,
data=dwi_data,
sphere=peaks.default_sphere,
relative_peak_threshold=.8,
min_separation_angle=45,
mask=wm_data_bin)
print '\tTracking'
seeds = utils.seeds_from_mask(parcellation_wm_data, density=2)
condition_seeds = condition_seeds(seeds, np.eye(4),
csapeaks.peak_values.shape[:3])
streamline_generator = EuDX(csapeaks.peak_values,
csapeaks.peak_indices,
odf_vertices=peaks.default_sphere.vertices,
a_low=.05,
step_sz=.5,
seeds=condition_seeds)
affine = streamline_generator.affine
streamlines = list(streamline_generator)
else:
print '\tTracking already complete'
#=============================================================================
# Create two connectivity matrices - symmetric and directional
#=============================================================================
#=============================================================================
if not os.path.exists(Msym_file) and not os.path.exists(Mdir_file):
print '\tCalculating peaks'
csamodel = shm.CsaOdfModel(gtab, 6)
csapeaks = peaks.peaks_from_model(model=csamodel,
data=dwi_data,
sphere=peaks.default_sphere,
relative_peak_threshold=.8,
min_separation_angle=45,
mask=wm_data_bin)
print '\tTracking'
seeds = utils.seeds_from_mask(parcellation_wm_data, density=2)
condition_seeds = condition_seeds(seeds, np.eye(4), csapeaks.peak_values.shape[:3])
streamline_generator = EuDX(csapeaks.peak_values, csapeaks.peak_indices,
odf_vertices=peaks.default_sphere.vertices,
a_low=.05, step_sz=.5, seeds=condition_seeds)
affine = streamline_generator.affine
streamlines = list(streamline_generator)
else:
print '\tTracking already complete'
#=============================================================================
# Create two connectivity matrices - symmetric and directional
#=============================================================================
if not os.path.exists(Msym_file) and not os.path.exists(Mdir_file):
print '\tCreating Connectivity Matrix'
tensor_fit = tensor_model.fit(data, mask) FA = fractional_anisotropy(tensor_fit.evals) stopping_values = np.zeros(csapeaks.peak_values.shape) stopping_values[:] = FA[..., None] ''' Now we can use EuDX to track all of the white matter. To keep things reasonably fast we use `density=2` which will result in 8 seeds per voxel. We'll set `a_low` (the parameter which determines the threshold of FA/QA under which tracking stops) to be very low because we've already applied a white matter mask. ''' print '\tTracking' seeds = utils.seeds_from_mask(mask, density=2) condition_seeds = condition_seeds(seeds, np.eye(4), csapeaks.peak_values.shape[:3], verbose=1) # Here's the line for streamline_generator = EuDX(stopping_values, csapeaks.peak_indices, odf_vertices=peaks.default_sphere.vertices, a_low=.05, step_sz=.5, seeds=condition_seeds) affine = streamline_generator.affine streamlines = list(streamline_generator) ''' Streamlines are a path though the 3d space of an image represented by a set of points. For these points to have a meaningful interpretation, these points must be given in a known coordinate system. The ``affine`` attribute of the ``streamline_generator`` object specifies the coordinate system of the points with respect to the voxel indices of the input data. Next we want to create a matrix of streamline connections. To do this we can use the `connectivity_matrix` function. This function takes a set of streamlines