def test_whole_brain_slr():
streams, hdr = nib.trackvis.read(get_fnames('fornix'))
fornix = [s[0] for s in streams]
f = Streamlines(fornix)
f1 = f.copy()
f2 = f.copy()
# check translation
f2._data += np.array([50, 0, 0])
moved, transform, qb_centroids1, qb_centroids2 = whole_brain_slr(
f1, f2, x0='affine', verbose=True, rm_small_clusters=2,
greater_than=0, less_than=np.inf,
qbx_thr=[5, 2, 1], progressive=False)
# we can check the quality of registration by comparing the matrices
# MAM streamline distances before and after SLR
D12 = bundles_distances_mam(f1, f2)
D1M = bundles_distances_mam(f1, moved)
d12_minsum = np.sum(np.min(D12, axis=0))
d1m_minsum = np.sum(np.min(D1M, axis=0))
print("distances= ", d12_minsum, " ", d1m_minsum)
assert_equal(d1m_minsum < d12_minsum, True)
assert_array_almost_equal(transform[:3, 3], [-50, -0, -0], 2)
# check rotation
mat = compose_matrix44([0, 0, 0, 15, 0, 0])
f3 = f.copy()
f3 = transform_streamlines(f3, mat)
moved, transform, qb_centroids1, qb_centroids2 = slr_with_qbx(
f1, f3, verbose=False, rm_small_clusters=1, greater_than=20,
less_than=np.inf, qbx_thr=[2],
progressive=True)
# we can also check the quality by looking at the decomposed transform
assert_array_almost_equal(decompose_matrix44(transform)[3], -15, 2)
moved, transform, qb_centroids1, qb_centroids2 = slr_with_qbx(
f1, f3, verbose=False, rm_small_clusters=1, select_random=400,
greater_than=20, less_than=np.inf, qbx_thr=[2],
progressive=True)
# we can also check the quality by looking at the decomposed transform
assert_array_almost_equal(decompose_matrix44(transform)[3], -15, 2)
def run(self,
static_files,
moving_files,
x0='affine',
rm_small_clusters=50,
qbx_thr=[40, 30, 20, 15],
num_threads=None,
greater_than=50,
less_than=250,
nb_pts=20,
progressive=True,
out_dir='',
out_moved='moved.trk',
out_affine='affine.txt',
out_stat_centroids='static_centroids.trk',
out_moving_centroids='moving_centroids.trk',
out_moved_centroids='moved_centroids.trk'):
""" Streamline-based linear registration.
For efficiency we apply the registration on cluster centroids and
remove small clusters.
Parameters
----------
static_files : string
moving_files : string
x0 : string, optional
rigid, similarity or affine transformation model (default affine)
rm_small_clusters : int, optional
Remove clusters that have less than `rm_small_clusters`
(default 50)
qbx_thr : variable int, optional
Thresholds for QuickBundlesX (default [40, 30, 20, 15])
num_threads : int, optional
Number of threads. If None (default) then all available threads
will be used. Only metrics using OpenMP will use this variable.
greater_than : int, optional
Keep streamlines that have length greater than
this value (default 50)
less_than : int, optional
Keep streamlines have length less than this value (default 250)
np_pts : int, optional
Number of points for discretizing each streamline (default 20)
progressive : boolean, optional
(default True)
out_dir : string, optional
Output directory (default input file directory)
out_moved : string, optional
Filename of moved tractogram (default 'moved.trk')
out_affine : string, optional
Filename of affine for SLR transformation (default 'affine.txt')
out_stat_centroids : string, optional
Filename of static centroids (default 'static_centroids.trk')
out_moving_centroids : string, optional
Filename of moving centroids (default 'moving_centroids.trk')
out_moved_centroids : string, optional
Filename of moved centroids (default 'moved_centroids.trk')
Notes
-----
The order of operations is the following. First short or long
streamlines are removed. Second the tractogram or a random selection
of the tractogram is clustered with QuickBundlesX. Then SLR
[Garyfallidis15]_ is applied.
References
----------
.. [Garyfallidis15] Garyfallidis et al. "Robust and efficient linear
registration of white-matter fascicles in the space of
streamlines", NeuroImage, 117, 124--140, 2015
.. [Garyfallidis14] Garyfallidis et al., "Direct native-space fiber
bundle alignment for group comparisons", ISMRM, 2014.
.. [Garyfallidis17] Garyfallidis et al. Recognition of white matter
bundles using local and global streamline-based registration
and clustering, NeuroImage, 2017.
"""
io_it = self.get_io_iterator()
logging.info("QuickBundlesX clustering is in use")
logging.info('QBX thresholds {0}'.format(qbx_thr))
for static_file, moving_file, out_moved_file, out_affine_file, \
static_centroids_file, moving_centroids_file, \
moved_centroids_file in io_it:
logging.info('Loading static file {0}'.format(static_file))
logging.info('Loading moving file {0}'.format(moving_file))
static_obj = nib.streamlines.load(static_file)
moving_obj = nib.streamlines.load(moving_file)
static, static_header = static_obj.streamlines, static_obj.header
moving, moving_header = moving_obj.streamlines, moving_obj.header
moved, affine, centroids_static, centroids_moving = \
slr_with_qbx(
static, moving, x0, rm_small_clusters=rm_small_clusters,
greater_than=greater_than, less_than=less_than,
qbx_thr=qbx_thr)
logging.info('Saving output file {0}'.format(out_moved_file))
new_tractogram = nib.streamlines.Tractogram(
moved, affine_to_rasmm=np.eye(4))
nib.streamlines.save(new_tractogram,
out_moved_file,
header=moving_header)
logging.info('Saving output file {0}'.format(out_affine_file))
np.savetxt(out_affine_file, affine)
logging.info(
'Saving output file {0}'.format(static_centroids_file))
new_tractogram = nib.streamlines.Tractogram(
centroids_static, affine_to_rasmm=np.eye(4))
nib.streamlines.save(new_tractogram,
static_centroids_file,
header=static_header)
logging.info(
'Saving output file {0}'.format(moving_centroids_file))
new_tractogram = nib.streamlines.Tractogram(
centroids_moving, affine_to_rasmm=np.eye(4))
nib.streamlines.save(new_tractogram,
moving_centroids_file,
header=moving_header)
centroids_moved = transform_streamlines(centroids_moving, affine)
logging.info('Saving output file {0}'.format(moved_centroids_file))
new_tractogram = nib.streamlines.Tractogram(
centroids_moved, affine_to_rasmm=np.eye(4))
nib.streamlines.save(new_tractogram,
moved_centroids_file,
header=moving_header)
def test_whole_brain_slr():
streams, hdr = nib.trackvis.read(get_fnames('fornix'))
fornix = [s[0] for s in streams]
f = Streamlines(fornix)
f1 = f.copy()
f2 = f.copy()
# check translation
f2._data += np.array([50, 0, 0])
moved, transform, qb_centroids1, qb_centroids2 = whole_brain_slr(
f1,
f2,
x0='affine',
verbose=True,
rm_small_clusters=2,
greater_than=0,
less_than=np.inf,
qbx_thr=[5, 2, 1],
progressive=False)
# we can check the quality of registration by comparing the matrices
# MAM streamline distances before and after SLR
D12 = bundles_distances_mam(f1, f2)
D1M = bundles_distances_mam(f1, moved)
d12_minsum = np.sum(np.min(D12, axis=0))
d1m_minsum = np.sum(np.min(D1M, axis=0))
print("distances= ", d12_minsum, " ", d1m_minsum)
assert_equal(d1m_minsum < d12_minsum, True)
assert_array_almost_equal(transform[:3, 3], [-50, -0, -0], 2)
# check rotation
mat = compose_matrix44([0, 0, 0, 15, 0, 0])
f3 = f.copy()
f3 = transform_streamlines(f3, mat)
moved, transform, qb_centroids1, qb_centroids2 = slr_with_qbx(
f1,
f3,
verbose=False,
rm_small_clusters=1,
greater_than=20,
less_than=np.inf,
qbx_thr=[2],
progressive=True)
# we can also check the quality by looking at the decomposed transform
assert_array_almost_equal(decompose_matrix44(transform)[3], -15, 2)
moved, transform, qb_centroids1, qb_centroids2 = slr_with_qbx(
f1,
f3,
verbose=False,
rm_small_clusters=1,
select_random=400,
greater_than=20,
less_than=np.inf,
qbx_thr=[2],
progressive=True)
# we can also check the quality by looking at the decomposed transform
assert_array_almost_equal(decompose_matrix44(transform)[3], -15, 2)
def run(self, static_files, moving_files,
x0='affine',
rm_small_clusters=50,
qbx_thr=[40, 30, 20, 15],
num_threads=None,
greater_than=50,
less_than=250,
nb_pts=20,
progressive=True,
out_dir='',
out_moved='moved.trk',
out_affine='affine.txt',
out_stat_centroids='static_centroids.trk',
out_moving_centroids='moving_centroids.trk',
out_moved_centroids='moved_centroids.trk'):
""" Streamline-based linear registration.
For efficiency we apply the registration on cluster centroids and
remove small clusters.
Parameters
----------
static_files : string
moving_files : string
x0 : string, optional
rigid, similarity or affine transformation model (default affine)
rm_small_clusters : int, optional
Remove clusters that have less than `rm_small_clusters`
(default 50)
qbx_thr : variable int, optional
Thresholds for QuickBundlesX (default [40, 30, 20, 15])
num_threads : int, optional
Number of threads. If None (default) then all available threads
will be used. Only metrics using OpenMP will use this variable.
greater_than : int, optional
Keep streamlines that have length greater than
this value (default 50)
less_than : int, optional
Keep streamlines have length less than this value (default 250)
np_pts : int, optional
Number of points for discretizing each streamline (default 20)
progressive : boolean, optional
(default True)
out_dir : string, optional
Output directory (default input file directory)
out_moved : string, optional
Filename of moved tractogram (default 'moved.trk')
out_affine : string, optional
Filename of affine for SLR transformation (default 'affine.txt')
out_stat_centroids : string, optional
Filename of static centroids (default 'static_centroids.trk')
out_moving_centroids : string, optional
Filename of moving centroids (default 'moving_centroids.trk')
out_moved_centroids : string, optional
Filename of moved centroids (default 'moved_centroids.trk')
Notes
-----
The order of operations is the following. First short or long
streamlines are removed. Second the tractogram or a random selection
of the tractogram is clustered with QuickBundlesX. Then SLR
[Garyfallidis15]_ is applied.
References
----------
.. [Garyfallidis15] Garyfallidis et al. "Robust and efficient linear
registration of white-matter fascicles in the space of
streamlines", NeuroImage, 117, 124--140, 2015
.. [Garyfallidis14] Garyfallidis et al., "Direct native-space fiber
bundle alignment for group comparisons", ISMRM, 2014.
.. [Garyfallidis17] Garyfallidis et al. Recognition of white matter
bundles using local and global streamline-based registration
and clustering, Neuroimage, 2017.
"""
io_it = self.get_io_iterator()
logging.info("QuickBundlesX clustering is in use")
logging.info('QBX thresholds {0}'.format(qbx_thr))
for static_file, moving_file, out_moved_file, out_affine_file, \
static_centroids_file, moving_centroids_file, \
moved_centroids_file in io_it:
logging.info('Loading static file {0}'.format(static_file))
logging.info('Loading moving file {0}'.format(moving_file))
static, static_header = load_trk(static_file)
moving, moving_header = load_trk(moving_file)
moved, affine, centroids_static, centroids_moving = \
slr_with_qbx(
static, moving, x0, rm_small_clusters=rm_small_clusters,
greater_than=greater_than, less_than=less_than,
qbx_thr=qbx_thr)
logging.info('Saving output file {0}'.format(out_moved_file))
save_trk(out_moved_file, moved, affine=np.eye(4),
header=static_header)
logging.info('Saving output file {0}'.format(out_affine_file))
np.savetxt(out_affine_file, affine)
logging.info('Saving output file {0}'
.format(static_centroids_file))
save_trk(static_centroids_file, centroids_static, affine=np.eye(4),
header=static_header)
logging.info('Saving output file {0}'
.format(moving_centroids_file))
save_trk(moving_centroids_file, centroids_moving,
affine=np.eye(4),
header=static_header)
centroids_moved = transform_streamlines(centroids_moving, affine)
logging.info('Saving output file {0}'
.format(moved_centroids_file))
save_trk(moved_centroids_file, centroids_moved, affine=np.eye(4),
header=static_header)