def load_tractography_dataset_from_dwi_and_tractogram(dwi,
tractogram,
volume_manager,
use_sh_coeffs=False,
bvals=None,
bvecs=None,
step_size=None,
mean_centering=True):
# Load signal
signal = nib.load(dwi)
signal.get_data() # Forces loading volume in-memory.
basename = re.sub('(\.gz|\.nii.gz)$', '', dwi)
bvals = basename + '.bvals' if bvals is None else bvals
bvecs = basename + '.bvecs' if bvecs is None else bvecs
gradients = gradient_table(bvals, bvecs)
tracto_data = TractographyData(signal, gradients)
# Load streamlines
tfile = nib.streamlines.load(tractogram)
tractogram = tfile.tractogram
# Resample streamline to have a fixed step size, if needed.
if step_size is not None:
print("Resampling streamlines to have a step size of {}mm".format(
step_size))
streamlines = tractogram.streamlines
streamlines._lengths = streamlines._lengths.astype(int)
streamlines._offsets = streamlines._offsets.astype(int)
lengths = length(streamlines)
nb_points = np.ceil(lengths / step_size).astype(int)
new_streamlines = (set_number_of_points(s, n)
for s, n in zip(streamlines, nb_points))
tractogram = nib.streamlines.Tractogram(new_streamlines,
affine_to_rasmm=np.eye(4))
# Compute matrix that brings streamlines back to diffusion voxel space.
rasmm2vox_affine = np.linalg.inv(signal.affine)
tractogram.apply_affine(rasmm2vox_affine)
# Add streamlines to the TractogramData
tracto_data.add(tractogram.streamlines, "tractogram")
dwi = tracto_data.signal
bvals = tracto_data.gradients.bvals
bvecs = tracto_data.gradients.bvecs
if use_sh_coeffs:
# Use 45 spherical harmonic coefficients to represent the diffusion signal.
volume = neurotools.get_spherical_harmonics_coefficients(
dwi, bvals, bvecs,
mean_centering=mean_centering).astype(np.float32)
else:
# Resample the diffusion signal to have 100 directions.
volume = neurotools.resample_dwi(dwi,
bvals,
bvecs,
mean_centering=mean_centering).astype(
np.float32)
tracto_data.signal.uncache(
) # Free some memory as we don't need the original signal.
subject_id = volume_manager.register(volume)
tracto_data.subject_id = subject_id
return TractographyDataset([tracto_data], "dataset", keep_on_cpu=True)
