def run(self,
pam_files,
stopping_files,
seeding_files,
stopping_thr=0.2,
seed_density=1,
use_sh=False,
out_dir='',
out_tractogram='tractogram.trk'):
""" Workflow for deterministic using a saved peaks and metrics (PAM)
file as input.
Parameters
----------
pam_files : string
Path to the peaks and metrics files. This path may contain
wildcards to use multiple masks at once.
stopping_files : string
Path of FA or other images used for stopping criteria for tracking.
seeding_files : string
A binary image showing where we need to seed for tracking.
stopping_thr : float, optional
Threshold applied to stopping volume's data to identify where
tracking has to stop (default 0.25).
seed_density : int, optional
Number of seeds per dimension inside voxel (default 1).
For example, seed_density of 2 means 8 regularly distributed points
in the voxel. And seed density of 1 means 1 point at the center
of the voxel.
use_sh : bool, optional
Use spherical harmonics saved in peaks to find the
maximum peak cone. (default False)
out_dir : string, optional
Output directory (default input file directory)
out_tractogram : string, optional
Name of the tractogram file to be saved (default 'tractogram.trk')
References
----------
Garyfallidis, University of Cambridge, PhD thesis 2012.
Amirbekian, University of California San Francisco, PhD thesis 2017.
"""
io_it = self.get_io_iterator()
for pams_path, stopping_path, seeding_path, out_tract in io_it:
logging.info('Deterministic tracking on {0}'.format(pams_path))
pam = load_peaks(pams_path, verbose=False)
self._core_run(stopping_path, stopping_thr, seeding_path,
seed_density, use_sh, pam, out_tract)
def reconst_flow_core(flow):
with TemporaryDirectory() as out_dir:
data_path, bval_path, bvec_path = get_data('small_64D')
vol_img = nib.load(data_path)
volume = vol_img.get_data()
mask = np.ones_like(volume[:, :, :, 0])
mask_img = nib.Nifti1Image(mask.astype(np.uint8), vol_img.get_affine())
mask_path = join(out_dir, 'tmp_mask.nii.gz')
nib.save(mask_img, mask_path)
reconst_flow = flow()
reconst_flow.run(data_path,
bval_path,
bvec_path,
mask_path,
out_dir=out_dir,
extract_pam_values=True)
gfa_path = reconst_flow.last_generated_outputs['out_gfa']
gfa_data = nib.load(gfa_path).get_data()
assert_equal(gfa_data.shape, volume.shape[:-1])
peaks_dir_path = reconst_flow.last_generated_outputs['out_peaks_dir']
peaks_dir_data = nib.load(peaks_dir_path).get_data()
assert_equal(peaks_dir_data.shape[-1], 15)
assert_equal(peaks_dir_data.shape[:-1], volume.shape[:-1])
peaks_idx_path = \
reconst_flow.last_generated_outputs['out_peaks_indices']
peaks_idx_data = nib.load(peaks_idx_path).get_data()
assert_equal(peaks_idx_data.shape[-1], 5)
assert_equal(peaks_idx_data.shape[:-1], volume.shape[:-1])
peaks_vals_path = \
reconst_flow.last_generated_outputs['out_peaks_values']
peaks_vals_data = nib.load(peaks_vals_path).get_data()
assert_equal(peaks_vals_data.shape[-1], 5)
assert_equal(peaks_vals_data.shape[:-1], volume.shape[:-1])
shm_path = reconst_flow.last_generated_outputs['out_shm']
shm_data = nib.load(shm_path).get_data()
assert_equal(shm_data.shape[-1], 45)
assert_equal(shm_data.shape[:-1], volume.shape[:-1])
pam = load_peaks(reconst_flow.last_generated_outputs['out_pam'])
npt.assert_allclose(pam.peak_dirs.reshape(peaks_dir_data.shape),
peaks_dir_data)
npt.assert_allclose(pam.peak_values, peaks_vals_data)
npt.assert_allclose(pam.peak_indices, peaks_idx_data)
npt.assert_allclose(pam.shm_coeff, shm_data)
npt.assert_allclose(pam.gfa, gfa_data)
def run(self, pam_files, stopping_files, seeding_files,
stopping_thr=0.2,
seed_density=1,
use_sh=False,
out_dir='',
out_tractogram='tractogram.trk'):
""" Workflow for deterministic using a saved peaks and metrics (PAM)
file as input.
Parameters
----------
pam_files : string
Path to the peaks and metrics files. This path may contain
wildcards to use multiple masks at once.
stopping_files : string
Path of FA or other images used for stopping criteria for tracking.
seeding_files : string
A binary image showing where we need to seed for tracking.
stopping_thr : float, optional
Threshold applied to stopping volume's data to identify where
tracking has to stop (default 0.25).
seed_density : int, optional
Number of seeds per dimension inside voxel (default 1).
For example, seed_density of 2 means 8 regularly distributed points
in the voxel. And seed density of 1 means 1 point at the center
of the voxel.
use_sh : bool, optional
Use spherical harmonics saved in peaks to find the
maximum peak cone. (default False)
out_dir : string, optional
Output directory (default input file directory)
out_tractogram : string, optional
Name of the tractogram file to be saved (default 'tractogram.trk')
References
----------
Garyfallidis, University of Cambridge, PhD thesis 2012.
Amirbekian, University of California San Francisco, PhD thesis 2017.
"""
io_it = self.get_io_iterator()
for pams_path, stopping_path, seeding_path, out_tract in io_it:
logging.info('Deterministic tracking on {0}'
.format(pams_path))
pam = load_peaks(pams_path, verbose=False)
self._core_run(stopping_path, stopping_thr, seeding_path,
seed_density, use_sh, pam, out_tract)
def test_io_peaks():
with InTemporaryDirectory():
fname = 'test.pam5'
pam = PeaksAndMetrics()
pam.affine = np.eye(4)
pam.peak_dirs = np.random.rand(10, 10, 10, 5, 3)
pam.peak_values = np.zeros((10, 10, 10, 5))
pam.peak_indices = np.zeros((10, 10, 10, 5))
pam.shm_coeff = np.zeros((10, 10, 10, 45))
pam.sphere = default_sphere
pam.B = np.zeros((45, default_sphere.vertices.shape[0]))
pam.total_weight = 0.5
pam.ang_thr = 60
pam.gfa = np.zeros((10, 10, 10))
pam.qa = np.zeros((10, 10, 10, 5))
pam.odf = np.zeros((10, 10, 10, default_sphere.vertices.shape[0]))
save_peaks(fname, pam)
pam2 = load_peaks(fname, verbose=True)
npt.assert_array_equal(pam.peak_dirs, pam2.peak_dirs)
pam2.affine = None
fname2 = 'test2.pam5'
save_peaks(fname2, pam2, np.eye(4))
pam2_res = load_peaks(fname2, verbose=True)
npt.assert_array_equal(pam.peak_dirs, pam2_res.peak_dirs)
pam3 = load_peaks(fname2, verbose=False)
for attr in [
'peak_dirs', 'peak_values', 'peak_indices', 'gfa', 'qa',
'shm_coeff', 'B', 'odf'
]:
npt.assert_array_equal(getattr(pam3, attr), getattr(pam, attr))
npt.assert_equal(pam3.total_weight, pam.total_weight)
npt.assert_equal(pam3.ang_thr, pam.ang_thr)
npt.assert_array_almost_equal(pam3.sphere.vertices,
pam.sphere.vertices)
fname3 = 'test3.pam5'
pam4 = PeaksAndMetrics()
npt.assert_raises(ValueError, save_peaks, fname3, pam4)
fname4 = 'test4.pam5'
del pam.affine
save_peaks(fname4, pam, affine=None)
fname5 = 'test5.pkm'
npt.assert_raises(IOError, save_peaks, fname5, pam)
pam.affine = np.eye(4)
fname6 = 'test6.pam5'
save_peaks(fname6, pam, verbose=True)
del pam.shm_coeff
save_peaks(fname6, pam, verbose=False)
pam.shm_coeff = np.zeros((10, 10, 10, 45))
del pam.odf
save_peaks(fname6, pam)
pam_tmp = load_peaks(fname6, True)
npt.assert_equal(pam_tmp.odf, None)
fname7 = 'test7.paw'
npt.assert_raises(IOError, load_peaks, fname7)
del pam.shm_coeff
save_peaks(fname6, pam, verbose=True)
fname_shm = 'shm.nii.gz'
fname_dirs = 'dirs.nii.gz'
fname_values = 'values.nii.gz'
fname_indices = 'indices.nii.gz'
fname_gfa = 'gfa.nii.gz'
pam.shm_coeff = np.ones((10, 10, 10, 45))
peaks_to_niftis(pam,
fname_shm,
fname_dirs,
fname_values,
fname_indices,
fname_gfa,
reshape_dirs=False)
os.path.isfile(fname_shm)
os.path.isfile(fname_dirs)
os.path.isfile(fname_values)
os.path.isfile(fname_indices)
os.path.isfile(fname_gfa)
def reconst_flow_core(flow):
with TemporaryDirectory() as out_dir:
data_path, bval_path, bvec_path = get_fnames('small_64D')
volume, affine = load_nifti(data_path)
mask = np.ones_like(volume[:, :, :, 0])
mask_path = pjoin(out_dir, 'tmp_mask.nii.gz')
save_nifti(mask_path, mask.astype(np.uint8), affine)
reconst_flow = flow()
for sh_order in [4, 6, 8]:
if flow.get_short_name() == 'csd':
reconst_flow.run(data_path,
bval_path,
bvec_path,
mask_path,
sh_order=sh_order,
out_dir=out_dir,
extract_pam_values=True)
elif flow.get_short_name() == 'csa':
reconst_flow.run(data_path,
bval_path,
bvec_path,
mask_path,
sh_order=sh_order,
odf_to_sh_order=sh_order,
out_dir=out_dir,
extract_pam_values=True)
gfa_path = reconst_flow.last_generated_outputs['out_gfa']
gfa_data = load_nifti_data(gfa_path)
npt.assert_equal(gfa_data.shape, volume.shape[:-1])
peaks_dir_path =\
reconst_flow.last_generated_outputs['out_peaks_dir']
peaks_dir_data = load_nifti_data(peaks_dir_path)
npt.assert_equal(peaks_dir_data.shape[-1], 15)
npt.assert_equal(peaks_dir_data.shape[:-1], volume.shape[:-1])
peaks_idx_path = \
reconst_flow.last_generated_outputs['out_peaks_indices']
peaks_idx_data = load_nifti_data(peaks_idx_path)
npt.assert_equal(peaks_idx_data.shape[-1], 5)
npt.assert_equal(peaks_idx_data.shape[:-1], volume.shape[:-1])
peaks_vals_path = \
reconst_flow.last_generated_outputs['out_peaks_values']
peaks_vals_data = load_nifti_data(peaks_vals_path)
npt.assert_equal(peaks_vals_data.shape[-1], 5)
npt.assert_equal(peaks_vals_data.shape[:-1], volume.shape[:-1])
shm_path = reconst_flow.last_generated_outputs['out_shm']
shm_data = load_nifti_data(shm_path)
# Test that the number of coefficients is what you would expect
# given the order of the sh basis:
npt.assert_equal(shm_data.shape[-1],
sph_harm_ind_list(sh_order)[0].shape[0])
npt.assert_equal(shm_data.shape[:-1], volume.shape[:-1])
pam = load_peaks(reconst_flow.last_generated_outputs['out_pam'])
npt.assert_allclose(pam.peak_dirs.reshape(peaks_dir_data.shape),
peaks_dir_data)
npt.assert_allclose(pam.peak_values, peaks_vals_data)
npt.assert_allclose(pam.peak_indices, peaks_idx_data)
npt.assert_allclose(pam.shm_coeff, shm_data)
npt.assert_allclose(pam.gfa, gfa_data)
bvals, bvecs = read_bvals_bvecs(bval_path, bvec_path)
bvals[0] = 5.
bvecs = generate_bvecs(len(bvals))
tmp_bval_path = pjoin(out_dir, "tmp.bval")
tmp_bvec_path = pjoin(out_dir, "tmp.bvec")
np.savetxt(tmp_bval_path, bvals)
np.savetxt(tmp_bvec_path, bvecs.T)
reconst_flow._force_overwrite = True
if flow.get_short_name() == 'csd':
reconst_flow = flow()
reconst_flow._force_overwrite = True
reconst_flow.run(data_path,
bval_path,
bvec_path,
mask_path,
out_dir=out_dir,
frf=[15, 5, 5])
reconst_flow = flow()
reconst_flow._force_overwrite = True
reconst_flow.run(data_path,
bval_path,
bvec_path,
mask_path,
out_dir=out_dir,
frf='15, 5, 5')
reconst_flow = flow()
reconst_flow._force_overwrite = True
reconst_flow.run(data_path,
bval_path,
bvec_path,
mask_path,
out_dir=out_dir,
frf=None)
reconst_flow2 = flow()
reconst_flow2._force_overwrite = True
reconst_flow2.run(data_path,
bval_path,
bvec_path,
mask_path,
out_dir=out_dir,
frf=None,
roi_center=[5, 5, 5])
else:
with npt.assert_raises(BaseException):
npt.assert_warns(UserWarning,
reconst_flow.run,
data_path,
tmp_bval_path,
tmp_bvec_path,
mask_path,
out_dir=out_dir,
extract_pam_values=True)
# test parallel implementation
reconst_flow = flow()
reconst_flow._force_overwrite = True
reconst_flow.run(data_path,
bval_path,
bvec_path,
mask_path,
out_dir=out_dir,
parallel=True,
nbr_processes=None)
reconst_flow = flow()
reconst_flow._force_overwrite = True
reconst_flow.run(data_path,
bval_path,
bvec_path,
mask_path,
out_dir=out_dir,
parallel=True,
nbr_processes=2)
def buan_bundle_profiles(model_bundle_folder,
bundle_folder,
orig_bundle_folder,
metric_folder,
group_id,
subject,
no_disks=100,
out_dir=''):
"""
Applies statistical analysis on bundles and saves the results
in a directory specified by ``out_dir``.
Parameters
----------
model_bundle_folder : string
Path to the input model bundle files. This path may contain
wildcards to process multiple inputs at once.
bundle_folder : string
Path to the input bundle files in common space. This path may
contain wildcards to process multiple inputs at once.
orig_folder : string
Path to the input bundle files in native space. This path may
contain wildcards to process multiple inputs at once.
metric_folder : string
Path to the input dti metric or/and peak files. It will be used as
metric for statistical analysis of bundles.
group_id : integer
what group subject belongs to either 0 for control or 1 for patient.
subject : string
subject id e.g. 10001.
no_disks : integer, optional
Number of disks used for dividing bundle into disks.
out_dir : string, optional
Output directory. (default current directory)
References
----------
.. [Chandio2020] Chandio, B.Q., Risacher, S.L., Pestilli, F., Bullock, D.,
Yeh, FC., Koudoro, S., Rokem, A., Harezlak, J., and Garyfallidis, E.
Bundle analytics, a computational framework for investigating the
shapes and profiles of brain pathways across populations.
Sci Rep 10, 17149 (2020)
"""
t = time()
dt = dict()
mb = glob(os.path.join(model_bundle_folder, "*.trk"))
print(mb)
mb.sort()
bd = glob(os.path.join(bundle_folder, "*.trk"))
bd.sort()
print(bd)
org_bd = glob(os.path.join(orig_bundle_folder, "*.trk"))
org_bd.sort()
print(org_bd)
n = len(org_bd)
n = len(mb)
for io in range(n):
mbundles = load_tractogram(mb[io],
reference='same',
bbox_valid_check=False).streamlines
bundles = load_tractogram(bd[io],
reference='same',
bbox_valid_check=False).streamlines
orig_bundles = load_tractogram(org_bd[io],
reference='same',
bbox_valid_check=False).streamlines
if len(orig_bundles) > 5:
indx = assignment_map(bundles, mbundles, no_disks)
ind = np.array(indx)
metric_files_names_dti = glob(
os.path.join(metric_folder, "*.nii.gz"))
metric_files_names_csa = glob(os.path.join(metric_folder,
"*.pam5"))
_, affine = load_nifti(metric_files_names_dti[0])
affine_r = np.linalg.inv(affine)
transformed_orig_bundles = transform_streamlines(
orig_bundles, affine_r)
for mn in range(len(metric_files_names_dti)):
ab = os.path.split(metric_files_names_dti[mn])
metric_name = ab[1]
fm = metric_name[:-7]
bm = os.path.split(mb[io])[1][:-4]
logging.info("bm = " + bm)
dt = dict()
logging.info("metric = " + metric_files_names_dti[mn])
metric, _ = load_nifti(metric_files_names_dti[mn])
anatomical_measures(transformed_orig_bundles, metric, dt, fm,
bm, subject, group_id, ind, out_dir)
for mn in range(len(metric_files_names_csa)):
ab = os.path.split(metric_files_names_csa[mn])
metric_name = ab[1]
fm = metric_name[:-5]
bm = os.path.split(mb[io])[1][:-4]
logging.info("bm = " + bm)
logging.info("metric = " + metric_files_names_csa[mn])
dt = dict()
metric = load_peaks(metric_files_names_csa[mn])
peak_values(transformed_orig_bundles, metric, dt, fm, bm,
subject, group_id, ind, out_dir)
print("total time taken in minutes = ", (-t + time()) / 60)
def buan_bundle_profiles(model_bundle_folder, bundle_folder,
orig_bundle_folder, metric_folder, group_id, subject,
no_disks=100, out_dir=''):
"""
Applies statistical analysis on bundles and saves the results
in a directory specified by ``out_dir``.
Parameters
----------
model_bundle_folder : string
Path to the input model bundle files. This path may contain
wildcards to process multiple inputs at once.
bundle_folder : string
Path to the input bundle files in common space. This path may
contain wildcards to process multiple inputs at once.
orig_folder : string
Path to the input bundle files in native space. This path may
contain wildcards to process multiple inputs at once.
metric_folder : string
Path to the input dti metric or/and peak files. It will be used as
metric for statistical analysis of bundles.
group_id : integer
what group subject belongs to either 0 for control or 1 for patient
subject : string
subject id e.g. 10001
no_disks : integer, optional
Number of disks used for dividing bundle into disks. (Default 100)
out_dir : string, optional
Output directory (default input file directory)
References
----------
.. [Chandio19] Chandio, B.Q., S. Koudoro, D. Reagan, J. Harezlak,
E. Garyfallidis, Bundle Analytics: a computational and statistical
analyses framework for tractometric studies, Proceedings of:
International Society of Magnetic Resonance in Medicine (ISMRM),
Montreal, Canada, 2019.
"""
t = time()
dt = dict()
mb = glob(os.path.join(model_bundle_folder, "*.trk"))
print(mb)
mb.sort()
bd = glob(os.path.join(bundle_folder, "*.trk"))
bd.sort()
print(bd)
org_bd = glob(os.path.join(orig_bundle_folder, "*.trk"))
org_bd.sort()
print(org_bd)
n = len(org_bd)
n = len(mb)
for io in range(n):
mbundles = load_tractogram(mb[io], reference='same',
bbox_valid_check=False).streamlines
bundles = load_tractogram(bd[io], reference='same',
bbox_valid_check=False).streamlines
orig_bundles = load_tractogram(org_bd[io], reference='same',
bbox_valid_check=False).streamlines
if len(orig_bundles) > 5:
indx = assignment_map(bundles, mbundles, no_disks)
ind = np.array(indx)
metric_files_names_dti = glob(os.path.join(metric_folder,
"*.nii.gz"))
metric_files_names_csa = glob(os.path.join(metric_folder,
"*.pam5"))
_, affine = load_nifti(metric_files_names_dti[0])
affine_r = np.linalg.inv(affine)
transformed_orig_bundles = transform_streamlines(orig_bundles,
affine_r)
for mn in range(len(metric_files_names_dti)):
ab = os.path.split(metric_files_names_dti[mn])
metric_name = ab[1]
fm = metric_name[:-7]
bm = os.path.split(mb[io])[1][:-4]
logging.info("bm = " + bm)
dt = dict()
logging.info("metric = " + metric_files_names_dti[mn])
metric, _ = load_nifti(metric_files_names_dti[mn])
anatomical_measures(transformed_orig_bundles, metric, dt, fm,
bm, subject, group_id, ind, out_dir)
for mn in range(len(metric_files_names_csa)):
ab = os.path.split(metric_files_names_csa[mn])
metric_name = ab[1]
fm = metric_name[:-5]
bm = os.path.split(mb[io])[1][:-4]
logging.info("bm = " + bm)
logging.info("metric = " + metric_files_names_csa[mn])
dt = dict()
metric = load_peaks(metric_files_names_csa[mn])
peak_values(transformed_orig_bundles, metric, dt, fm, bm,
subject, group_id, ind, out_dir)
print("total time taken in minutes = ", (-t + time())/60)
def reconst_flow_core(flow):
with TemporaryDirectory() as out_dir:
data_path, bval_path, bvec_path = get_fnames('small_64D')
vol_img = nib.load(data_path)
volume = vol_img.get_data()
mask = np.ones_like(volume[:, :, :, 0])
mask_img = nib.Nifti1Image(mask.astype(np.uint8), vol_img.affine)
mask_path = pjoin(out_dir, 'tmp_mask.nii.gz')
nib.save(mask_img, mask_path)
reconst_flow = flow()
for sh_order in [4, 6, 8]:
if flow.get_short_name() == 'csd':
reconst_flow.run(data_path, bval_path, bvec_path, mask_path,
sh_order=sh_order,
out_dir=out_dir, extract_pam_values=True)
elif flow.get_short_name() == 'csa':
reconst_flow.run(data_path, bval_path, bvec_path, mask_path,
sh_order=sh_order,
odf_to_sh_order=sh_order,
out_dir=out_dir, extract_pam_values=True)
gfa_path = reconst_flow.last_generated_outputs['out_gfa']
gfa_data = nib.load(gfa_path).get_data()
npt.assert_equal(gfa_data.shape, volume.shape[:-1])
peaks_dir_path =\
reconst_flow.last_generated_outputs['out_peaks_dir']
peaks_dir_data = nib.load(peaks_dir_path).get_data()
npt.assert_equal(peaks_dir_data.shape[-1], 15)
npt.assert_equal(peaks_dir_data.shape[:-1], volume.shape[:-1])
peaks_idx_path = \
reconst_flow.last_generated_outputs['out_peaks_indices']
peaks_idx_data = nib.load(peaks_idx_path).get_data()
npt.assert_equal(peaks_idx_data.shape[-1], 5)
npt.assert_equal(peaks_idx_data.shape[:-1], volume.shape[:-1])
peaks_vals_path = \
reconst_flow.last_generated_outputs['out_peaks_values']
peaks_vals_data = nib.load(peaks_vals_path).get_data()
npt.assert_equal(peaks_vals_data.shape[-1], 5)
npt.assert_equal(peaks_vals_data.shape[:-1], volume.shape[:-1])
shm_path = reconst_flow.last_generated_outputs['out_shm']
shm_data = nib.load(shm_path).get_data()
# Test that the number of coefficients is what you would expect
# given the order of the sh basis:
npt.assert_equal(shm_data.shape[-1],
sph_harm_ind_list(sh_order)[0].shape[0])
npt.assert_equal(shm_data.shape[:-1], volume.shape[:-1])
pam = load_peaks(reconst_flow.last_generated_outputs['out_pam'])
npt.assert_allclose(pam.peak_dirs.reshape(peaks_dir_data.shape),
peaks_dir_data)
npt.assert_allclose(pam.peak_values, peaks_vals_data)
npt.assert_allclose(pam.peak_indices, peaks_idx_data)
npt.assert_allclose(pam.shm_coeff, shm_data)
npt.assert_allclose(pam.gfa, gfa_data)
bvals, bvecs = read_bvals_bvecs(bval_path, bvec_path)
bvals[0] = 5.
bvecs = generate_bvecs(len(bvals))
tmp_bval_path = pjoin(out_dir, "tmp.bval")
tmp_bvec_path = pjoin(out_dir, "tmp.bvec")
np.savetxt(tmp_bval_path, bvals)
np.savetxt(tmp_bvec_path, bvecs.T)
reconst_flow._force_overwrite = True
with npt.assert_raises(BaseException):
npt.assert_warns(UserWarning, reconst_flow.run, data_path,
tmp_bval_path, tmp_bvec_path, mask_path,
out_dir=out_dir, extract_pam_values=True)
if flow.get_short_name() == 'csd':
reconst_flow = flow()
reconst_flow._force_overwrite = True
reconst_flow.run(data_path, bval_path, bvec_path, mask_path,
out_dir=out_dir, frf=[15, 5, 5])
reconst_flow = flow()
reconst_flow._force_overwrite = True
reconst_flow.run(data_path, bval_path, bvec_path, mask_path,
out_dir=out_dir, frf='15, 5, 5')
reconst_flow = flow()
reconst_flow._force_overwrite = True
reconst_flow.run(data_path, bval_path, bvec_path, mask_path,
out_dir=out_dir, frf=None)
reconst_flow2 = flow()
reconst_flow2._force_overwrite = True
reconst_flow2.run(data_path, bval_path, bvec_path, mask_path,
out_dir=out_dir, frf=None,
roi_center=[10, 10, 10])
def run(self,
input_files,
cluster=False,
cluster_thr=15.,
random_colors=False,
length_gt=0,
length_lt=1000,
clusters_gt=0,
clusters_lt=10**8,
native_coords=False,
stealth=False,
emergency_header='icbm_2009a',
bg_color=(0, 0, 0),
disable_order_transparency=False,
buan=False,
buan_thr=0.5,
buan_highlight=(1, 0, 0),
out_dir='',
out_stealth_png='tmp.png'):
""" Interactive medical visualization - Invert the Horizon!
Interact with any number of .trk, .tck or .dpy tractograms and anatomy
files .nii or .nii.gz. Cluster streamlines on loading.
Parameters
----------
input_files : variable string
cluster : bool, optional
Enable QuickBundlesX clustering.
cluster_thr : float, optional
Distance threshold used for clustering. Default value 15.0 for
small animal brains you may need to use something smaller such
as 2.0. The distance is in mm. For this parameter to be active
``cluster`` should be enabled.
random_colors : bool, optional
Given multiple tractograms have been included then each tractogram
will be shown with different color.
length_gt : float, optional
Clusters with average length greater than ``length_gt`` amount
in mm will be shown.
length_lt : float, optional
Clusters with average length less than ``length_lt`` amount in
mm will be shown.
clusters_gt : int, optional
Clusters with size greater than ``clusters_gt`` will be shown.
clusters_lt : int, optional
Clusters with size less than ``clusters_gt`` will be shown.
native_coords : bool, optional
Show results in native coordinates.
stealth : bool, optional
Do not use interactive mode just save figure.
emergency_header : str, optional
If no anatomy reference is provided an emergency header is
provided. Current options 'icbm_2009a' and 'icbm_2009c'.
bg_color : variable float, optional
Define the background color of the scene. Colors can be defined
with 1 or 3 values and should be between [0-1].
disable_order_transparency : bool, optional
Use depth peeling to sort transparent objects.
If True also enables anti-aliasing.
buan : bool, optional
Enables BUAN framework visualization.
buan_thr : float, optional
Uses the threshold value to highlight segments on the
bundle which have pvalues less than this threshold.
buan_highlight : variable float, optional
Define the bundle highlight area color. Colors can be defined
with 1 or 3 values and should be between [0-1].
For example, a value of (1, 0, 0) would mean the red color.
out_dir : str, optional
Output directory. (default current directory)
out_stealth_png : str, optional
Filename of saved picture.
References
----------
.. [Horizon_ISMRM19] Garyfallidis E., M-A. Cote, B.Q. Chandio,
S. Fadnavis, J. Guaje, R. Aggarwal, E. St-Onge, K.S. Juneja,
S. Koudoro, D. Reagan, DIPY Horizon: fast, modular, unified and
adaptive visualization, Proceedings of: International Society of
Magnetic Resonance in Medicine (ISMRM), Montreal, Canada, 2019.
"""
verbose = True
tractograms = []
images = []
pams = []
numpy_files = []
interactive = not stealth
world_coords = not native_coords
bundle_colors = None
mni_2009a = {}
mni_2009a['affine'] = np.array([[1., 0., 0.,
-98.], [0., 1., 0., -134.],
[0., 0., 1., -72.], [0., 0., 0., 1.]])
mni_2009a['dims'] = (197, 233, 189)
mni_2009a['vox_size'] = (1., 1., 1.)
mni_2009a['vox_space'] = 'RAS'
mni_2009c = {}
mni_2009c['affine'] = np.array([[1., 0., 0.,
-96.], [0., 1., 0., -132.],
[0., 0., 1., -78.], [0., 0., 0., 1.]])
mni_2009c['dims'] = (193, 229, 193)
mni_2009c['vox_size'] = (1., 1., 1.)
mni_2009c['vox_space'] = 'RAS'
if emergency_header == 'icbm_2009a':
hdr = mni_2009c
else:
hdr = mni_2009c
emergency_ref = create_nifti_header(hdr['affine'], hdr['dims'],
hdr['vox_size'])
io_it = self.get_io_iterator()
for input_output in io_it:
fname = input_output[0]
if verbose:
print('Loading file ...')
print(fname)
print('\n')
fl = fname.lower()
ends = fl.endswith
if ends('.trk'):
sft = load_tractogram(fname, 'same', bbox_valid_check=False)
tractograms.append(sft)
if ends('.dpy') or ends('.tck'):
sft = load_tractogram(fname, emergency_ref)
tractograms.append(sft)
if ends('.nii.gz') or ends('.nii'):
data, affine = load_nifti(fname)
images.append((data, affine))
if verbose:
print('Affine to RAS')
np.set_printoptions(3, suppress=True)
print(affine)
np.set_printoptions()
if ends(".pam5"):
pam = load_peaks(fname)
pams.append(pam)
if verbose:
print('Peak_dirs shape')
print(pam.peak_dirs.shape)
if ends(".npy"):
data = np.load(fname)
numpy_files.append(data)
if verbose:
print('numpy array length')
print(len(data))
if buan:
bundle_colors = []
for i in range(len(numpy_files)):
n = len(numpy_files[i])
pvalues = numpy_files[i]
bundle = tractograms[i].streamlines
indx = assignment_map(bundle, bundle, n)
ind = np.array(indx)
nb_lines = len(bundle)
lines_range = range(nb_lines)
points_per_line = [len(bundle[i]) for i in lines_range]
points_per_line = np.array(points_per_line, np.intp)
cols_arr = line_colors(bundle)
colors_mapper = np.repeat(lines_range, points_per_line, axis=0)
vtk_colors = numpy_to_vtk_colors(255 * cols_arr[colors_mapper])
colors = numpy_support.vtk_to_numpy(vtk_colors)
colors = (colors - np.min(colors)) / np.ptp(colors)
for i in range(n):
if pvalues[i] < buan_thr:
colors[ind == i] = buan_highlight
bundle_colors.append(colors)
if len(bg_color) == 1:
bg_color *= 3
elif len(bg_color) != 3:
raise ValueError('You need 3 values to set up backgound color. '
'e.g --bg_color 0.5 0.5 0.5')
order_transparent = not disable_order_transparency
horizon(tractograms=tractograms,
images=images,
pams=pams,
cluster=cluster,
cluster_thr=cluster_thr,
random_colors=random_colors,
bg_color=bg_color,
order_transparent=order_transparent,
length_gt=length_gt,
length_lt=length_lt,
clusters_gt=clusters_gt,
clusters_lt=clusters_lt,
world_coords=world_coords,
interactive=interactive,
buan=buan,
buan_colors=bundle_colors,
out_png=pjoin(out_dir, out_stealth_png))
def run(self,
pam_files,
wm_files,
gm_files,
csf_files,
seeding_files,
step_size=0.2,
back_tracking_dist=2,
front_tracking_dist=1,
max_trial=20,
particle_count=15,
seed_density=1,
pmf_threshold=0.1,
max_angle=30.,
out_dir='',
out_tractogram='tractogram.trk'):
"""Workflow for Particle Filtering Tracking.
This workflow use a saved peaks and metrics (PAM) file as input.
Parameters
----------
pam_files : string
Path to the peaks and metrics files. This path may contain
wildcards to use multiple masks at once.
wm_files : string
Path of White matter for stopping criteria for tracking.
gm_files : string
Path of grey matter for stopping criteria for tracking.
csf_files : string
Path of cerebrospinal fluid for stopping criteria for tracking.
seeding_files : string
A binary image showing where we need to seed for tracking.
step_size : float, optional
Step size used for tracking.
back_tracking_dist : float, optional
Distance in mm to back track before starting the particle filtering
tractography. The total particle filtering tractography distance is
equal to back_tracking_dist + front_tracking_dist.
By default this is set to 2 mm.
front_tracking_dist : float, optional
Distance in mm to run the particle filtering tractography after the
the back track distance. The total particle filtering tractography
distance is equal to back_tracking_dist + front_tracking_dist. By
default this is set to 1 mm.
max_trial : int, optional
Maximum number of trial for the particle filtering tractography
(Prevents infinite loops, default=20).
particle_count : int, optional
Number of particles to use in the particle filter. (default 15)
seed_density : int, optional
Number of seeds per dimension inside voxel (default 1).
For example, seed_density of 2 means 8 regularly distributed
points in the voxel. And seed density of 1 means 1 point at the
center of the voxel.
pmf_threshold : float, optional
Threshold for ODF functions. (default 0.1)
max_angle : float, optional
Maximum angle between tract segments. This angle can be more
generous (larger) than values typically used with probabilistic
direction getters. The angle range is (0, 90)
out_dir : string, optional
Output directory (default input file directory)
out_tractogram : string, optional
Name of the tractogram file to be saved (default 'tractogram.trk')
References
----------
Girard, G., Whittingstall, K., Deriche, R., & Descoteaux, M.
Towards quantitative connectivity analysis: reducing
tractography biases. NeuroImage, 98, 266-278, 2014..
"""
io_it = self.get_io_iterator()
for pams_path, wm_path, gm_path, csf_path, seeding_path, out_tract \
in io_it:
logging.info(
'Particle Filtering tracking on {0}'.format(pams_path))
pam = load_peaks(pams_path, verbose=False)
wm, affine, voxel_size = load_nifti(wm_path, return_voxsize=True)
gm, _ = load_nifti(gm_path)
csf, _ = load_nifti(csf_path)
avs = sum(voxel_size) / len(voxel_size) # average_voxel_size
classifier = CmcTissueClassifier.from_pve(wm,
gm,
csf,
step_size=step_size,
average_voxel_size=avs)
logging.info('classifier done')
seed_mask, _ = load_nifti(seeding_path)
seeds = utils.seeds_from_mask(
seed_mask,
density=[seed_density, seed_density, seed_density],
affine=affine)
logging.info('seeds done')
dg = ProbabilisticDirectionGetter
direction_getter = dg.from_shcoeff(pam.shm_coeff,
max_angle=max_angle,
sphere=pam.sphere,
pmf_threshold=pmf_threshold)
streamlines = ParticleFilteringTracking(
direction_getter,
classifier,
seeds,
affine,
step_size=step_size,
pft_back_tracking_dist=back_tracking_dist,
pft_front_tracking_dist=front_tracking_dist,
pft_max_trial=max_trial,
particle_count=particle_count)
logging.info('ParticleFilteringTracking initiated')
tractogram = Tractogram(streamlines, affine_to_rasmm=np.eye(4))
save(tractogram, out_tract)
logging.info('Saved {0}'.format(out_tract))
def run(self,
pam_files,
stopping_files,
seeding_files,
stopping_thr=0.2,
seed_density=1,
tracking_method="deterministic",
pmf_threshold=0.1,
max_angle=30.,
out_dir='',
out_tractogram='tractogram.trk'):
"""Workflow for Local Fiber Tracking.
This workflow use a saved peaks and metrics (PAM) file as input.
Parameters
----------
pam_files : string
Path to the peaks and metrics files. This path may contain
wildcards to use multiple masks at once.
stopping_files : string
Path of FA or other images used for stopping criteria for tracking.
seeding_files : string
A binary image showing where we need to seed for tracking.
stopping_thr : float, optional
Threshold applied to stopping volume's data to identify where
tracking has to stop (default 0.25).
seed_density : int, optional
Number of seeds per dimension inside voxel (default 1).
For example, seed_density of 2 means 8 regularly distributed
points in the voxel. And seed density of 1 means 1 point at the
center of the voxel.
tracking_method : string, optional
Select direction getter strategy:
- "eudx" (Uses the peaks saved in the pam_files)
- "deterministic" or "det" for a deterministic tracking
(Uses the sh saved in the pam_files, default)
- "probabilistic" or "prob" for a Probabilistic tracking
(Uses the sh saved in the pam_files)
- "closestpeaks" or "cp" for a ClosestPeaks tracking
(Uses the sh saved in the pam_files)
pmf_threshold : float, optional
Threshold for ODF functions. (default 0.1)
max_angle : float, optional
Maximum angle between tract segments. This angle can be more
generous (larger) than values typically used with probabilistic
direction getters. The angle range is (0, 90)
out_dir : string, optional
Output directory (default input file directory)
out_tractogram : string, optional
Name of the tractogram file to be saved (default 'tractogram.trk')
References
----------
Garyfallidis, University of Cambridge, PhD thesis 2012.
Amirbekian, University of California San Francisco, PhD thesis 2017.
"""
io_it = self.get_io_iterator()
for pams_path, stopping_path, seeding_path, out_tract in io_it:
logging.info('Local tracking on {0}'.format(pams_path))
pam = load_peaks(pams_path, verbose=False)
dg = self._get_direction_getter(tracking_method,
pam,
pmf_threshold=pmf_threshold,
max_angle=max_angle)
self._core_run(stopping_path, stopping_thr, seeding_path,
seed_density, dg, out_tract)
def tracking(folder):
print('Tracking in ' + folder)
output_folder = folder + 'dipy_out/'
# make a folder to save new data into
try:
Path(output_folder).mkdir(parents=True, exist_ok=True)
except OSError:
print('Could not create output dir. Aborting...')
return
# load data
print('Loading data...')
img = nib.load(folder + 'data.nii.gz')
dmri = np.asarray(img.dataobj)
affine = img.affine
mask, _ = load_nifti(folder + 'nodif_brain_mask.nii.gz')
bvals, bvecs = read_bvals_bvecs(folder + 'bvals', folder + 'bvecs')
gtab = gradient_table(bvals, bvecs)
# extract peaksoutput_folder + 'peak_vals.nii.gz'
if Path(output_folder + 'peaks.pam5').exists():
peaks = load_peaks(output_folder + 'peaks.pam5')
else:
print('Extracting peaks...')
response, ration = auto_response(gtab, dmri, roi_radius=10, fa_thr=.7)
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
peaks = peaks_from_model(model=csd_model,
data=dmri,
sphere=default_sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=True)
save_peaks(output_folder + 'peaks.pam5', peaks, affine)
scaled = peaks.peak_dirs * np.repeat(
np.expand_dims(peaks.peak_values, -1), 3, -1)
cropped = scaled[:, :, :, :3, :].reshape(dmri.shape[:3] + (9, ))
save_nifti(output_folder + 'peaks.nii.gz', cropped, affine)
#save_nifti(output_folder + 'peak_dirs.nii.gz', peaks.peak_dirs, affine)
#save_nifti(output_folder + 'peak_vals.nii.gz', peaks.peak_values, affine)
# tracking
print('Tracking...')
maskdata, mask = median_otsu(dmri,
vol_idx=range(0, dmri.shape[3]),
median_radius=3,
numpass=1,
autocrop=True,
dilate=2)
tensor_model = TensorModel(gtab, fit_method='WLS')
tensor_fit = tensor_model.fit(maskdata)
fa = fractional_anisotropy(tensor_fit.evals)
fa[np.isnan(fa)] = 0
bla = np.average(fa)
tissue_classifier = ThresholdStoppingCriterion(fa, .1)
seeds = random_seeds_from_mask(fa > 1e-5, affine, seeds_count=1)
streamline_generator = LocalTracking(direction_getter=peaks,
stopping_criterion=tissue_classifier,
seeds=seeds,
affine=affine,
step_size=.5)
streamlines = Streamlines(streamline_generator)
save_trk(StatefulTractogram(streamlines, img, Space.RASMM),
output_folder + 'whole_brain.trk')
def run(self,
input_files,
cluster=False,
cluster_thr=15.,
random_colors=False,
length_gt=0,
length_lt=1000,
clusters_gt=0,
clusters_lt=10**8,
native_coords=False,
stealth=False,
out_dir='',
out_stealth_png='tmp.png'):
""" Highly interactive visualization - invert the Horizon!
Interact with any number of .trk, .tck or .dpy tractograms and anatomy
files .nii or .nii.gz. Cluster streamlines on loading.
Parameters
----------
input_files : variable string
cluster : bool
cluster_thr : float
random_colors : bool
length_gt : float
length_lt : float
clusters_gt : int
clusters_lt : int
native_coords : bool
stealth : bool
out_dir : string
out_stealth_png : string
References
----------
.. [Horizon_ISMRM19] Garyfallidis E., M-A. Cote, B.Q. Chandio,
S. Fadnavis, J. Guaje, R. Aggarwal, E. St-Onge, K.S. Juneja,
S. Koudoro, D. Reagan, DIPY Horizon: fast, modular, unified and
adaptive visualization, Proceedings of: International Society of
Magnetic Resonance in Medicine (ISMRM), Montreal, Canada, 2019.
"""
verbose = True
tractograms = []
images = []
pams = []
interactive = not stealth
world_coords = not native_coords
io_it = self.get_io_iterator()
for input_output in io_it:
fname = input_output[0]
if verbose:
print('Loading file ...')
print(fname)
print('\n')
fl = fname.lower()
ends = fl.endswith
if ends('.trk') or ends('.tck'):
streamlines = nib.streamlines.load(fname).streamlines
tractograms.append(streamlines)
elif ends('dpy'):
dpy_obj = Dpy(fname, mode='r')
streamlines = list(dpy_obj.read_tracks())
dpy_obj.close()
if ends('.nii.gz') or ends('.nii'):
data, affine = load_nifti(fname)
images.append((data, affine))
if verbose:
print('Affine to RAS')
np.set_printoptions(3, suppress=True)
print(affine)
np.set_printoptions()
if ends(".pam5"):
pam = load_peaks(fname)
pams.append(pam)
if verbose:
print('Peak_dirs shape')
print(pam.peak_dirs.shape)
horizon(tractograms=tractograms,
images=images,
pams=pams,
cluster=cluster,
cluster_thr=cluster_thr,
random_colors=random_colors,
length_gt=length_gt,
length_lt=length_lt,
clusters_gt=clusters_gt,
clusters_lt=clusters_lt,
world_coords=world_coords,
interactive=interactive,
out_png=pjoin(out_dir, out_stealth_png))
def reconst_flow_core(flow):
with TemporaryDirectory() as out_dir:
data_path, bval_path, bvec_path = get_data('small_64D')
vol_img = nib.load(data_path)
volume = vol_img.get_data()
mask = np.ones_like(volume[:, :, :, 0])
mask_img = nib.Nifti1Image(mask.astype(np.uint8), vol_img.affine)
mask_path = join(out_dir, 'tmp_mask.nii.gz')
nib.save(mask_img, mask_path)
reconst_flow = flow()
reconst_flow.run(data_path, bval_path, bvec_path, mask_path,
out_dir=out_dir, extract_pam_values=True)
gfa_path = reconst_flow.last_generated_outputs['out_gfa']
gfa_data = nib.load(gfa_path).get_data()
assert_equal(gfa_data.shape, volume.shape[:-1])
peaks_dir_path = reconst_flow.last_generated_outputs['out_peaks_dir']
peaks_dir_data = nib.load(peaks_dir_path).get_data()
assert_equal(peaks_dir_data.shape[-1], 15)
assert_equal(peaks_dir_data.shape[:-1], volume.shape[:-1])
peaks_idx_path = \
reconst_flow.last_generated_outputs['out_peaks_indices']
peaks_idx_data = nib.load(peaks_idx_path).get_data()
assert_equal(peaks_idx_data.shape[-1], 5)
assert_equal(peaks_idx_data.shape[:-1], volume.shape[:-1])
peaks_vals_path = \
reconst_flow.last_generated_outputs['out_peaks_values']
peaks_vals_data = nib.load(peaks_vals_path).get_data()
assert_equal(peaks_vals_data.shape[-1], 5)
assert_equal(peaks_vals_data.shape[:-1], volume.shape[:-1])
shm_path = reconst_flow.last_generated_outputs['out_shm']
shm_data = nib.load(shm_path).get_data()
assert_equal(shm_data.shape[-1], 45)
assert_equal(shm_data.shape[:-1], volume.shape[:-1])
pam = load_peaks(reconst_flow.last_generated_outputs['out_pam'])
npt.assert_allclose(pam.peak_dirs.reshape(peaks_dir_data.shape),
peaks_dir_data)
npt.assert_allclose(pam.peak_values, peaks_vals_data)
npt.assert_allclose(pam.peak_indices, peaks_idx_data)
npt.assert_allclose(pam.shm_coeff, shm_data)
npt.assert_allclose(pam.gfa, gfa_data)
if flow.get_short_name() == 'csd':
reconst_flow = flow()
reconst_flow._force_overwrite = True
reconst_flow.run(data_path, bval_path, bvec_path, mask_path,
out_dir=out_dir, frf=[15, 5, 5])
reconst_flow = flow()
reconst_flow._force_overwrite = True
reconst_flow.run(data_path, bval_path, bvec_path, mask_path,
out_dir=out_dir, frf='15, 5, 5')
reconst_flow = flow()
reconst_flow._force_overwrite = True
reconst_flow.run(data_path, bval_path, bvec_path, mask_path,
out_dir=out_dir, frf=None)
reconst_flow2 = flow()
reconst_flow2._force_overwrite = True
reconst_flow2.run(data_path, bval_path, bvec_path, mask_path,
out_dir=out_dir, frf=None,
roi_center=[10, 10, 10])
def run(self,
pam_files,
stopping_files,
seeding_files,
use_binary_mask=False,
stopping_thr=0.2,
seed_density=1,
step_size=0.5,
tracking_method="eudx",
pmf_threshold=0.1,
max_angle=30.,
out_dir='',
out_tractogram='tractogram.trk',
save_seeds=False):
"""Workflow for Local Fiber Tracking.
This workflow use a saved peaks and metrics (PAM) file as input.
Parameters
----------
pam_files : string
Path to the peaks and metrics files. This path may contain
wildcards to use multiple masks at once.
stopping_files : string
Path to images (e.g. FA) used for stopping criterion for tracking.
seeding_files : string
A binary image showing where we need to seed for tracking.
use_binary_mask : bool, optional
If True, uses a binary stopping criterion. If the provided
`stopping_files` are not binary, `stopping_thr` will be used to
binarize the images.
stopping_thr : float, optional
Threshold applied to stopping volume's data to identify where
tracking has to stop (default 0.2).
seed_density : int, optional
Number of seeds per dimension inside voxel (default 1).
For example, seed_density of 2 means 8 regularly distributed
points in the voxel. And seed density of 1 means 1 point at the
center of the voxel.
step_size : float, optional
Step size used for tracking (default 0.5mm).
tracking_method : string, optional
Select direction getter strategy :
- "eudx" (Uses the peaks saved in the pam_files)
- "deterministic" or "det" for a deterministic tracking
(Uses the sh saved in the pam_files, default)
- "probabilistic" or "prob" for a Probabilistic tracking
(Uses the sh saved in the pam_files)
- "closestpeaks" or "cp" for a ClosestPeaks tracking
(Uses the sh saved in the pam_files)
pmf_threshold : float, optional
Threshold for ODF functions (default 0.1).
max_angle : float, optional
Maximum angle between streamline segments (range [0, 90],
default 30).
out_dir : string, optional
Output directory (default input file directory).
out_tractogram : string, optional
Name of the tractogram file to be saved (default 'tractogram.trk').
save_seeds : bool, optional
If true, save the seeds associated to their streamline
in the 'data_per_streamline' Tractogram dictionary using
'seeds' as the key.
References
----------
Garyfallidis, University of Cambridge, PhD thesis 2012.
Amirbekian, University of California San Francisco, PhD thesis 2017.
"""
io_it = self.get_io_iterator()
for pams_path, stopping_path, seeding_path, out_tract in io_it:
logging.info('Local tracking on {0}'.format(pams_path))
pam = load_peaks(pams_path, verbose=False)
dg = self._get_direction_getter(tracking_method,
pam,
pmf_threshold=pmf_threshold,
max_angle=max_angle)
self._core_run(stopping_path, use_binary_mask, stopping_thr,
seeding_path, seed_density, step_size, dg,
out_tract, save_seeds)
def run(self, pam_files, stopping_files, seeding_files,
stopping_thr=0.2,
seed_density=1,
step_size=0.5,
tracking_method="eudx",
pmf_threshold=0.1,
max_angle=30.,
out_dir='',
out_tractogram='tractogram.trk'):
"""Workflow for Local Fiber Tracking.
This workflow use a saved peaks and metrics (PAM) file as input.
Parameters
----------
pam_files : string
Path to the peaks and metrics files. This path may contain
wildcards to use multiple masks at once.
stopping_files : string
Path to images (e.g. FA) used for stopping criteria for tracking.
seeding_files : string
A binary image showing where we need to seed for tracking.
stopping_thr : float, optional
Threshold applied to stopping volume's data to identify where
tracking has to stop (default 0.25).
seed_density : int, optional
Number of seeds per dimension inside voxel (default 1).
For example, seed_density of 2 means 8 regularly distributed
points in the voxel. And seed density of 1 means 1 point at the
center of the voxel.
step_size : float, optional
Step size used for tracking (default 0.5mm).
tracking_method : string, optional
Select direction getter strategy:
- "eudx" (Uses the peaks saved in the pam_files)
- "deterministic" or "det" for a deterministic tracking
(Uses the sh saved in the pam_files, default)
- "probabilistic" or "prob" for a Probabilistic tracking
(Uses the sh saved in the pam_files)
- "closestpeaks" or "cp" for a ClosestPeaks tracking
(Uses the sh saved in the pam_files)
pmf_threshold : float, optional
Threshold for ODF functions (default 0.1).
max_angle : float, optional
Maximum angle between streamline segments (range [0, 90],
default 30).
out_dir : string, optional
Output directory (default input file directory).
out_tractogram : string, optional
Name of the tractogram file to be saved (default 'tractogram.trk').
References
----------
Garyfallidis, University of Cambridge, PhD thesis 2012.
Amirbekian, University of California San Francisco, PhD thesis 2017.
"""
io_it = self.get_io_iterator()
for pams_path, stopping_path, seeding_path, out_tract in io_it:
logging.info('Local tracking on {0}'
.format(pams_path))
pam = load_peaks(pams_path, verbose=False)
dg = self._get_direction_getter(tracking_method, pam,
pmf_threshold=pmf_threshold,
max_angle=max_angle)
self._core_run(stopping_path, stopping_thr, seeding_path,
seed_density, step_size, dg, out_tract)
def run(self,
pam_files,
wm_files,
gm_files,
csf_files,
seeding_files,
step_size=0.2,
seed_density=1,
pmf_threshold=0.1,
max_angle=20.,
pft_back=2,
pft_front=1,
pft_count=15,
out_dir='',
out_tractogram='tractogram.trk',
save_seeds=False):
"""Workflow for Particle Filtering Tracking.
This workflow use a saved peaks and metrics (PAM) file as input.
Parameters
----------
pam_files : string
Path to the peaks and metrics files. This path may contain
wildcards to use multiple masks at once.
wm_files : string
Path to white matter partial volume estimate for tracking (CMC).
gm_files : string
Path to grey matter partial volume estimate for tracking (CMC).
csf_files : string
Path to cerebrospinal fluid partial volume estimate for tracking
(CMC).
seeding_files : string
A binary image showing where we need to seed for tracking.
step_size : float, optional
Step size used for tracking (default 0.2mm).
seed_density : int, optional
Number of seeds per dimension inside voxel (default 1).
For example, seed_density of 2 means 8 regularly distributed
points in the voxel. And seed density of 1 means 1 point at the
center of the voxel.
pmf_threshold : float, optional
Threshold for ODF functions (default 0.1).
max_angle : float, optional
Maximum angle between streamline segments (range [0, 90],
default 20).
pft_back : float, optional
Distance in mm to back track before starting the particle filtering
tractography (default 2mm). The total particle filtering
tractography distance is equal to back_tracking_dist +
front_tracking_dist.
pft_front : float, optional
Distance in mm to run the particle filtering tractography after the
the back track distance (default 1mm). The total particle filtering
tractography distance is equal to back_tracking_dist +
front_tracking_dist.
pft_count : int, optional
Number of particles to use in the particle filter (default 15).
out_dir : string, optional
Output directory (default input file directory)
out_tractogram : string, optional
Name of the tractogram file to be saved (default 'tractogram.trk')
save_seeds : bool, optional
If true, save the seeds associated to their streamline
in the 'data_per_streamline' Tractogram dictionary using
'seeds' as the key
References
----------
Girard, G., Whittingstall, K., Deriche, R., & Descoteaux, M. Towards
quantitative connectivity analysis: reducing tractography biases.
NeuroImage, 98, 266-278, 2014.
"""
io_it = self.get_io_iterator()
for pams_path, wm_path, gm_path, csf_path, seeding_path, out_tract \
in io_it:
logging.info(
'Particle Filtering tracking on {0}'.format(pams_path))
pam = load_peaks(pams_path, verbose=False)
wm, affine, voxel_size = load_nifti(wm_path, return_voxsize=True)
gm, _ = load_nifti(gm_path)
csf, _ = load_nifti(csf_path)
avs = sum(voxel_size) / len(voxel_size) # average_voxel_size
stopping_criterion = CmcStoppingCriterion.from_pve(
wm, gm, csf, step_size=step_size, average_voxel_size=avs)
logging.info('stopping criterion done')
seed_mask, _ = load_nifti(seeding_path)
seeds = utils.seeds_from_mask(
seed_mask,
density=[seed_density, seed_density, seed_density],
affine=affine)
logging.info('seeds done')
dg = ProbabilisticDirectionGetter
direction_getter = dg.from_shcoeff(pam.shm_coeff,
max_angle=max_angle,
sphere=pam.sphere,
pmf_threshold=pmf_threshold)
tracking_result = ParticleFilteringTracking(
direction_getter,
stopping_criterion,
seeds,
affine,
step_size=step_size,
pft_back_tracking_dist=pft_back,
pft_front_tracking_dist=pft_front,
pft_max_trial=20,
particle_count=pft_count,
save_seeds=save_seeds)
logging.info('ParticleFilteringTracking initiated')
if save_seeds:
streamlines, seeds = zip(*tracking_result)
seeds = {'seeds': seeds}
else:
streamlines = list(tracking_result)
seeds = {}
sft = StatefulTractogram(streamlines,
seeding_path,
Space.RASMM,
data_per_streamline=seeds)
save_tractogram(sft, out_tract, bbox_valid_check=False)
logging.info('Saved {0}'.format(out_tract))
def run(self, pam_files, wm_files, gm_files, csf_files, seeding_files,
step_size=0.2,
seed_density=1,
pmf_threshold=0.1,
max_angle=20.,
pft_back=2,
pft_front=1,
pft_count=15,
out_dir='',
out_tractogram='tractogram.trk'):
"""Workflow for Particle Filtering Tracking.
This workflow use a saved peaks and metrics (PAM) file as input.
Parameters
----------
pam_files : string
Path to the peaks and metrics files. This path may contain
wildcards to use multiple masks at once.
wm_files : string
Path to white matter partial volume estimate for tracking (CMC).
gm_files : string
Path to grey matter partial volume estimate for tracking (CMC).
csf_files : string
Path to cerebrospinal fluid partial volume estimate for tracking
(CMC).
seeding_files : string
A binary image showing where we need to seed for tracking.
step_size : float, optional
Step size used for tracking (default 0.2mm).
seed_density : int, optional
Number of seeds per dimension inside voxel (default 1).
For example, seed_density of 2 means 8 regularly distributed
points in the voxel. And seed density of 1 means 1 point at the
center of the voxel.
pmf_threshold : float, optional
Threshold for ODF functions (default 0.1).
max_angle : float, optional
Maximum angle between streamline segments (range [0, 90],
default 20).
pft_back : float, optional
Distance in mm to back track before starting the particle filtering
tractography (defaul 2mm). The total particle filtering
tractography distance is equal to back_tracking_dist +
front_tracking_dist.
pft_front : float, optional
Distance in mm to run the particle filtering tractography after the
the back track distance (default 1mm). The total particle filtering
tractography distance is equal to back_tracking_dist +
front_tracking_dist.
pft_count : int, optional
Number of particles to use in the particle filter (default 15).
out_dir : string, optional
Output directory (default input file directory)
out_tractogram : string, optional
Name of the tractogram file to be saved (default 'tractogram.trk')
References
----------
Girard, G., Whittingstall, K., Deriche, R., & Descoteaux, M. Towards
quantitative connectivity analysis: reducing tractography biases.
NeuroImage, 98, 266-278, 2014.
"""
io_it = self.get_io_iterator()
for pams_path, wm_path, gm_path, csf_path, seeding_path, out_tract \
in io_it:
logging.info('Particle Filtering tracking on {0}'
.format(pams_path))
pam = load_peaks(pams_path, verbose=False)
wm, affine, voxel_size = load_nifti(wm_path, return_voxsize=True)
gm, _ = load_nifti(gm_path)
csf, _ = load_nifti(csf_path)
avs = sum(voxel_size) / len(voxel_size) # average_voxel_size
classifier = CmcTissueClassifier.from_pve(wm, gm, csf,
step_size=step_size,
average_voxel_size=avs)
logging.info('classifier done')
seed_mask, _ = load_nifti(seeding_path)
seeds = utils.seeds_from_mask(seed_mask,
density=[seed_density, seed_density,
seed_density],
affine=affine)
logging.info('seeds done')
dg = ProbabilisticDirectionGetter
direction_getter = dg.from_shcoeff(pam.shm_coeff,
max_angle=max_angle,
sphere=pam.sphere,
pmf_threshold=pmf_threshold)
streamlines_generator = ParticleFilteringTracking(
direction_getter,
classifier,
seeds, affine,
step_size=step_size,
pft_back_tracking_dist=pft_back,
pft_front_tracking_dist=pft_front,
pft_max_trial=20,
particle_count=pft_count)
logging.info('ParticleFilteringTracking initiated')
tractogram = Tractogram(streamlines_generator,
affine_to_rasmm=np.eye(4))
save(tractogram, out_tract)
logging.info('Saved {0}'.format(out_tract))
def bundle_analysis(model_bundle_folder,
bundle_folder,
orig_bundle_folder,
metric_folder,
group,
subject,
no_disks=100,
out_dir=''):
"""
Applies statistical analysis on bundles and saves the results
in a directory specified by ``out_dir``.
Parameters
----------
model_bundle_folder : string
Path to the input model bundle files. This path may contain
wildcards to process multiple inputs at once.
bundle_folder : string
Path to the input bundle files in common space. This path may
contain wildcards to process multiple inputs at once.
orig_folder : string
Path to the input bundle files in native space. This path may
contain wildcards to process multiple inputs at once.
metric_folder : string
Path to the input dti metric or/and peak files. It will be used as
metric for statistical analysis of bundles.
group : string
what group subject belongs to e.g. control or patient
subject : string
subject id e.g. 10001
no_disks : integer, optional
Number of disks used for dividing bundle into disks. (Default 100)
out_dir : string, optional
Output directory (default input file directory)
References
----------
.. [Chandio19] Chandio, B.Q., S. Koudoro, D. Reagan, J. Harezlak,
E. Garyfallidis, Bundle Analytics: a computational and statistical
analyses framework for tractometric studies, Proceedings of:
International Society of Magnetic Resonance in Medicine (ISMRM),
Montreal, Canada, 2019.
"""
dt = dict()
mb = os.listdir(model_bundle_folder)
mb.sort()
bd = os.listdir(bundle_folder)
bd.sort()
org_bd = os.listdir(orig_bundle_folder)
org_bd.sort()
n = len(org_bd)
for io in range(n):
mbundles, _ = load_trk(os.path.join(model_bundle_folder, mb[io]))
bundles, _ = load_trk(os.path.join(bundle_folder, bd[io]))
orig_bundles, _ = load_trk(os.path.join(orig_bundle_folder,
org_bd[io]))
mbundle_streamlines = set_number_of_points(mbundles,
nb_points=no_disks)
metric = AveragePointwiseEuclideanMetric()
qb = QuickBundles(threshold=25., metric=metric)
clusters = qb.cluster(mbundle_streamlines)
centroids = Streamlines(clusters.centroids)
print('Number of centroids ', len(centroids.data))
print('Model bundle ', mb[io])
print('Number of streamlines in bundle in common space ', len(bundles))
print('Number of streamlines in bundle in original space ',
len(orig_bundles))
_, indx = cKDTree(centroids.data, 1,
copy_data=True).query(bundles.data, k=1)
metric_files_names = os.listdir(metric_folder)
_, affine = load_nifti(os.path.join(metric_folder, "fa.nii.gz"))
affine_r = np.linalg.inv(affine)
transformed_orig_bundles = transform_streamlines(
orig_bundles, affine_r)
for mn in range(0, len(metric_files_names)):
ind = np.array(indx)
fm = metric_files_names[mn][:2]
bm = mb[io][:-4]
dt = dict()
metric_name = os.path.join(metric_folder, metric_files_names[mn])
if metric_files_names[mn][2:] == '.nii.gz':
metric, _ = load_nifti(metric_name)
dti_measures(transformed_orig_bundles, metric, dt, fm, bm,
subject, group, ind, out_dir)
else:
fm = metric_files_names[mn][:3]
metric = load_peaks(metric_name)
peak_values(bundles, metric, dt, fm, bm, subject, group, ind,
out_dir)
def test_io_peaks():
with InTemporaryDirectory():
fname = 'test.pam5'
sphere = get_sphere('repulsion724')
pam = PeaksAndMetrics()
pam.affine = np.eye(4)
pam.peak_dirs = np.random.rand(10, 10, 10, 5, 3)
pam.peak_values = np.zeros((10, 10, 10, 5))
pam.peak_indices = np.zeros((10, 10, 10, 5))
pam.shm_coeff = np.zeros((10, 10, 10, 45))
pam.sphere = sphere
pam.B = np.zeros((45, sphere.vertices.shape[0]))
pam.total_weight = 0.5
pam.ang_thr = 60
pam.gfa = np.zeros((10, 10, 10))
pam.qa = np.zeros((10, 10, 10, 5))
pam.odf = np.zeros((10, 10, 10, sphere.vertices.shape[0]))
save_peaks(fname, pam)
pam2 = load_peaks(fname, verbose=True)
npt.assert_array_equal(pam.peak_dirs, pam2.peak_dirs)
pam2.affine = None
fname2 = 'test2.pam5'
save_peaks(fname2, pam2, np.eye(4))
pam2_res = load_peaks(fname2, verbose=True)
npt.assert_array_equal(pam.peak_dirs, pam2_res.peak_dirs)
pam3 = load_peaks(fname2, verbose=False)
for attr in ['peak_dirs', 'peak_values', 'peak_indices',
'gfa', 'qa', 'shm_coeff', 'B', 'odf']:
npt.assert_array_equal(getattr(pam3, attr),
getattr(pam, attr))
npt.assert_equal(pam3.total_weight, pam.total_weight)
npt.assert_equal(pam3.ang_thr, pam.ang_thr)
npt.assert_array_almost_equal(pam3.sphere.vertices,
pam.sphere.vertices)
fname3 = 'test3.pam5'
pam4 = PeaksAndMetrics()
npt.assert_raises(ValueError, save_peaks, fname3, pam4)
fname4 = 'test4.pam5'
del pam.affine
save_peaks(fname4, pam, affine=None)
fname5 = 'test5.pkm'
npt.assert_raises(IOError, save_peaks, fname5, pam)
pam.affine = np.eye(4)
fname6 = 'test6.pam5'
save_peaks(fname6, pam, verbose=True)
del pam.shm_coeff
save_peaks(fname6, pam, verbose=False)
pam.shm_coeff = np.zeros((10, 10, 10, 45))
del pam.odf
save_peaks(fname6, pam)
pam_tmp = load_peaks(fname6, True)
npt.assert_equal(pam_tmp.odf, None)
fname7 = 'test7.paw'
npt.assert_raises(IOError, load_peaks, fname7)
del pam.shm_coeff
save_peaks(fname6, pam, verbose=True)
fname_shm = 'shm.nii.gz'
fname_dirs = 'dirs.nii.gz'
fname_values = 'values.nii.gz'
fname_indices = 'indices.nii.gz'
fname_gfa = 'gfa.nii.gz'
pam.shm_coeff = np.ones((10, 10, 10, 45))
peaks_to_niftis(pam, fname_shm, fname_dirs, fname_values,
fname_indices, fname_gfa, reshape_dirs=False)
os.path.isfile(fname_shm)
os.path.isfile(fname_dirs)
os.path.isfile(fname_values)
os.path.isfile(fname_indices)
os.path.isfile(fname_gfa)
def bundle_analysis(model_bundle_folder, bundle_folder, orig_bundle_folder,
metric_folder, group, subject, no_disks=100,
out_dir=''):
"""
Applies statistical analysis on bundles and saves the results
in a directory specified by ``out_dir``.
Parameters
----------
model_bundle_folder : string
Path to the input model bundle files. This path may contain
wildcards to process multiple inputs at once.
bundle_folder : string
Path to the input bundle files in common space. This path may
contain wildcards to process multiple inputs at once.
orig_folder : string
Path to the input bundle files in native space. This path may
contain wildcards to process multiple inputs at once.
metric_folder : string
Path to the input dti metric or/and peak files. It will be used as
metric for statistical analysis of bundles.
group : string
what group subject belongs to e.g. control or patient
subject : string
subject id e.g. 10001
no_disks : integer, optional
Number of disks used for dividing bundle into disks. (Default 100)
out_dir : string, optional
Output directory (default input file directory)
References
----------
.. [Chandio19] Chandio, B.Q., S. Koudoro, D. Reagan, J. Harezlak,
E. Garyfallidis, Bundle Analytics: a computational and statistical
analyses framework for tractometric studies, Proceedings of:
International Society of Magnetic Resonance in Medicine (ISMRM),
Montreal, Canada, 2019.
"""
dt = dict()
mb = os.listdir(model_bundle_folder)
mb.sort()
bd = os.listdir(bundle_folder)
bd.sort()
org_bd = os.listdir(orig_bundle_folder)
org_bd.sort()
n = len(org_bd)
for io in range(n):
mbundles, _ = load_trk(os.path.join(model_bundle_folder, mb[io]))
bundles, _ = load_trk(os.path.join(bundle_folder, bd[io]))
orig_bundles, _ = load_trk(os.path.join(orig_bundle_folder,
org_bd[io]))
mbundle_streamlines = set_number_of_points(mbundles,
nb_points=no_disks)
metric = AveragePointwiseEuclideanMetric()
qb = QuickBundles(threshold=25., metric=metric)
clusters = qb.cluster(mbundle_streamlines)
centroids = Streamlines(clusters.centroids)
print('Number of centroids ', len(centroids.data))
print('Model bundle ', mb[io])
print('Number of streamlines in bundle in common space ',
len(bundles))
print('Number of streamlines in bundle in original space ',
len(orig_bundles))
_, indx = cKDTree(centroids.data, 1,
copy_data=True).query(bundles.data, k=1)
metric_files_names = os.listdir(metric_folder)
_, affine = load_nifti(os.path.join(metric_folder, "fa.nii.gz"))
affine_r = np.linalg.inv(affine)
transformed_orig_bundles = transform_streamlines(orig_bundles,
affine_r)
for mn in range(0, len(metric_files_names)):
ind = np.array(indx)
fm = metric_files_names[mn][:2]
bm = mb[io][:-4]
dt = dict()
metric_name = os.path.join(metric_folder,
metric_files_names[mn])
if metric_files_names[mn][2:] == '.nii.gz':
metric, _ = load_nifti(metric_name)
dti_measures(transformed_orig_bundles, metric, dt, fm,
bm, subject, group, ind, out_dir)
else:
fm = metric_files_names[mn][:3]
metric = load_peaks(metric_name)
peak_values(bundles, metric, dt, fm, bm, subject, group,
ind, out_dir)
def run(self,
input_files,
cluster=False,
cluster_thr=15.,
random_colors=False,
length_gt=0,
length_lt=1000,
clusters_gt=0,
clusters_lt=10**8,
native_coords=False,
stealth=False,
emergency_header='icbm_2009a',
bg_color=(0, 0, 0),
disable_order_transparency=False,
out_dir='',
out_stealth_png='tmp.png'):
""" Interactive medical visualization - Invert the Horizon!
Interact with any number of .trk, .tck or .dpy tractograms and anatomy
files .nii or .nii.gz. Cluster streamlines on loading.
Parameters
----------
input_files : variable string
cluster : bool, optional
Enable QuickBundlesX clustering
cluster_thr : float, optional
Distance threshold used for clustering. Default value 15.0 for
small animal brains you may need to use something smaller such
as 2.0. The distance is in mm. For this parameter to be active
``cluster`` should be enabled
random_colors : bool, optional
Given multiple tractograms have been included then each tractogram
will be shown with different color
length_gt : float, optional
Clusters with average length greater than ``length_gt`` amount
in mm will be shown
length_lt : float, optional
Clusters with average length less than ``length_lt`` amount in
mm will be shown
clusters_gt : int, optional
Clusters with size greater than ``clusters_gt`` will be shown.
clusters_lt : int, optional
Clusters with size less than ``clusters_gt`` will be shown.
native_coords : bool, optional
Show results in native coordinates
stealth : bool, optional
Do not use interactive mode just save figure.
emergency_header : str, optional
If no anatomy reference is provided an emergency header is
provided. Current options 'icbm_2009a' and 'icbm_2009c'.
bg_color : variable float, optional
Define the background color of the scene. Colors can be defined
with 1 or 3 values and should be between [0-1].
Default is black (e.g --bg_color 0 0 0 or --bg_color 0).
disable_order_transparency : bool, optional
Default False. Use depth peeling to sort transparent objects.
If True also enables anti-aliasing.
out_dir : str, optional
Output directory. Default current directory.
out_stealth_png : str, optional
Filename of saved picture.
References
----------
.. [Horizon_ISMRM19] Garyfallidis E., M-A. Cote, B.Q. Chandio,
S. Fadnavis, J. Guaje, R. Aggarwal, E. St-Onge, K.S. Juneja,
S. Koudoro, D. Reagan, DIPY Horizon: fast, modular, unified and
adaptive visualization, Proceedings of: International Society of
Magnetic Resonance in Medicine (ISMRM), Montreal, Canada, 2019.
"""
verbose = True
tractograms = []
images = []
pams = []
interactive = not stealth
world_coords = not native_coords
mni_2009a = {}
mni_2009a['affine'] = np.array([[1., 0., 0.,
-98.], [0., 1., 0., -134.],
[0., 0., 1., -72.], [0., 0., 0., 1.]])
mni_2009a['dims'] = (197, 233, 189)
mni_2009a['vox_size'] = (1., 1., 1.)
mni_2009a['vox_space'] = 'RAS'
mni_2009c = {}
mni_2009c['affine'] = np.array([[1., 0., 0.,
-96.], [0., 1., 0., -132.],
[0., 0., 1., -78.], [0., 0., 0., 1.]])
mni_2009c['dims'] = (193, 229, 193)
mni_2009c['vox_size'] = (1., 1., 1.)
mni_2009c['vox_space'] = 'RAS'
if emergency_header == 'icbm_2009a':
hdr = mni_2009c
else:
hdr = mni_2009c
emergency_ref = create_nifti_header(hdr['affine'], hdr['dims'],
hdr['vox_size'])
io_it = self.get_io_iterator()
for input_output in io_it:
fname = input_output[0]
if verbose:
print('Loading file ...')
print(fname)
print('\n')
fl = fname.lower()
ends = fl.endswith
if ends('.trk'):
sft = load_tractogram(fname, 'same', bbox_valid_check=False)
tractograms.append(sft)
if ends('.dpy') or ends('.tck'):
sft = load_tractogram(fname, emergency_ref)
tractograms.append(sft)
if ends('.nii.gz') or ends('.nii'):
data, affine = load_nifti(fname)
images.append((data, affine))
if verbose:
print('Affine to RAS')
np.set_printoptions(3, suppress=True)
print(affine)
np.set_printoptions()
if ends(".pam5"):
pam = load_peaks(fname)
pams.append(pam)
if verbose:
print('Peak_dirs shape')
print(pam.peak_dirs.shape)
if len(bg_color) == 1:
bg_color *= 3
elif len(bg_color) != 3:
raise ValueError('You need 3 values to set up backgound color. '
'e.g --bg_color 0.5 0.5 0.5')
order_transparent = not disable_order_transparency
horizon(tractograms=tractograms,
images=images,
pams=pams,
cluster=cluster,
cluster_thr=cluster_thr,
random_colors=random_colors,
bg_color=bg_color,
order_transparent=order_transparent,
length_gt=length_gt,
length_lt=length_lt,
clusters_gt=clusters_gt,
clusters_lt=clusters_lt,
world_coords=world_coords,
interactive=interactive,
out_png=pjoin(out_dir, out_stealth_png))
