def test_det_track():
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_csd_flow = ReconstCSDFlow()
reconst_csd_flow.run(data_path,
bval_path,
bvec_path,
mask_path,
out_dir=out_dir,
extract_pam_values=True)
pam_path = reconst_csd_flow.last_generated_outputs['out_pam']
gfa_path = reconst_csd_flow.last_generated_outputs['out_gfa']
# Create seeding mask by thresholding the gfa
mask_flow = MaskFlow()
mask_flow.run(gfa_path, 0.8, out_dir=out_dir)
seeds_path = mask_flow.last_generated_outputs['out_mask']
# Put identity in gfa path to prevent impossible to use
# local tracking because of affine containing shearing.
gfa_img = nib.load(gfa_path)
save_nifti(gfa_path, gfa_img.get_data(), np.eye(4), gfa_img.header)
# Test tracking with pam no sh
det_track_pam = DetTrackPAMFlow()
assert_equal(det_track_pam.get_short_name(), 'det_track')
det_track_pam.run(pam_path, gfa_path, seeds_path)
tractogram_path = \
det_track_pam.last_generated_outputs['out_tractogram']
assert_false(is_tractogram_empty(tractogram_path))
# Test tracking with pam with sh
det_track_pam.run(pam_path, gfa_path, seeds_path, use_sh=True)
tractogram_path = \
det_track_pam.last_generated_outputs['out_tractogram']
assert_false(is_tractogram_empty(tractogram_path))
def dmri_recon(dwi_file, bval_file, bvec_file, mask, b0_threshold,
recon='csd', num_threads=2):
# define input parameters
csd_resp_fa_thresh = 0.5 # dropped from 0.7
# define output filenames
import os
out_pam = os.path.abspath('peaks.pam5')
out_shm = os.path.abspath('shm.nii.gz')
out_peaks_dir = os.path.abspath('peaks_dirs.nii.gz')
out_peaks_values = os.path.abspath('peaks_values.nii.gz')
out_peaks_indices = os.path.abspath('peaks_indices.nii.gz')
out_gfa = os.path.abspath('gfa.nii.gz')
# import and run/fit model
if recon == 'csd':
from dipy.workflows.reconst import ReconstCSDFlow
csd = ReconstCSDFlow()
csd.run(dwi_file, bval_file, bvec_file, mask,
b0_threshold=b0_threshold, bvecs_tol=0.01,
roi_center=None, roi_radius=10,
fa_thr=csd_resp_fa_thresh, frf=None,
extract_pam_values=True,
odf_to_sh_order=8,
out_dir='',
out_pam=out_pam, out_shm=out_shm,
out_peaks_dir=out_peaks_dir,
out_peaks_values=out_peaks_values,
out_peaks_indices=out_peaks_indices, out_gfa=out_gfa)
elif recon == 'csa':
from dipy.workflows.reconst import ReconstCSAFlow
csa = ReconstCSAFlow()
csa.run(dwi_file, bval_file, bvec_file, mask,
odf_to_sh_order=8,
b0_threshold=b0_threshold, bvecs_tol=0.01,
extract_pam_values=True,
out_dir='',
out_pam=out_pam, out_shm=out_shm,
out_peaks_dir=out_peaks_dir,
out_peaks_values=out_peaks_values,
out_peaks_indices=out_peaks_indices, out_gfa=out_gfa)
return (out_pam, out_shm, out_peaks_dir,
out_peaks_values, out_peaks_indices, out_gfa)
def test_det_track():
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_csd_flow = ReconstCSDFlow()
reconst_csd_flow.run(data_path, bval_path, bvec_path, mask_path,
out_dir=out_dir, extract_pam_values=True)
pam_path = reconst_csd_flow.last_generated_outputs['out_pam']
gfa_path = reconst_csd_flow.last_generated_outputs['out_gfa']
# Create seeding mask by thresholding the gfa
mask_flow = MaskFlow()
mask_flow.run(gfa_path, 0.8, out_dir=out_dir)
seeds_path = mask_flow.last_generated_outputs['out_mask']
# Put identity in gfa path to prevent impossible to use
# local tracking because of affine containing shearing.
gfa_img = nib.load(gfa_path)
save_nifti(gfa_path, gfa_img.get_data(), np.eye(4), gfa_img.header)
# Test tracking with pam no sh
det_track_pam = DetTrackPAMFlow()
assert_equal(det_track_pam.get_short_name(), 'det_track')
det_track_pam.run(pam_path, gfa_path, seeds_path)
tractogram_path = \
det_track_pam.last_generated_outputs['out_tractogram']
assert_false(is_tractogram_empty(tractogram_path))
# Test tracking with pam with sh
det_track_pam.run(pam_path, gfa_path, seeds_path, use_sh=True)
tractogram_path = \
det_track_pam.last_generated_outputs['out_tractogram']
assert_false(is_tractogram_empty(tractogram_path))
def test_particle_filtering_traking_workflows():
with TemporaryDirectory() as out_dir:
dwi_path, bval_path, bvec_path = get_fnames('small_64D')
vol_img = nib.load(dwi_path)
volume = vol_img.get_data()
# Create some mask
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)
simple_wm = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 0, 1, 0, 0],
[0, 0, 1, 0, 1, 0, 1, 0, 0, 0],
[0, 0, 1, 0, 1, 1, 0, 1, 0, 0],
[0, 0, 0, 1, 1, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
])
simple_wm = np.dstack([np.zeros(simple_wm.shape),
np.zeros(simple_wm.shape),
simple_wm, simple_wm, simple_wm,
simple_wm, simple_wm, simple_wm,
np.zeros(simple_wm.shape),
np.zeros(simple_wm.shape)])
simple_gm = np.array([[0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 1, 1, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 1, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 1],
[1, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 1, 0, 1, 0],
[0, 1, 1, 0, 1, 1, 1, 0, 1, 1],
[0, 0, 0, 1, 0, 0, 0, 1, 1, 0],
])
simple_gm = np.dstack([np.zeros(simple_gm.shape),
np.zeros(simple_gm.shape),
simple_gm, simple_gm, simple_gm,
simple_gm, simple_gm, simple_gm,
np.zeros(simple_gm.shape),
np.zeros(simple_gm.shape)])
simple_csf = np.ones(simple_wm.shape) - simple_wm - simple_gm
wm_path = join(out_dir, 'tmp_wm.nii.gz')
gm_path = join(out_dir, 'tmp_gm.nii.gz')
csf_path = join(out_dir, 'tmp_csf.nii.gz')
for path, arr in zip([wm_path, gm_path, csf_path],
[simple_wm, simple_gm, simple_csf]):
nib.save(nib.Nifti1Image(arr.astype(np.uint8), vol_img.affine),
path)
# CSD Reconstruction
reconst_csd_flow = ReconstCSDFlow()
reconst_csd_flow.run(dwi_path, bval_path, bvec_path, mask_path,
out_dir=out_dir, extract_pam_values=True)
pam_path = reconst_csd_flow.last_generated_outputs['out_pam']
gfa_path = reconst_csd_flow.last_generated_outputs['out_gfa']
# Create seeding mask by thresholding the gfa
mask_flow = MaskFlow()
mask_flow.run(gfa_path, 0.8, out_dir=out_dir)
seeds_path = mask_flow.last_generated_outputs['out_mask']
# Test tracking
pf_track_pam = PFTrackingPAMFlow()
assert_equal(pf_track_pam.get_short_name(), 'track_pft')
pf_track_pam.run(pam_path, wm_path, gm_path, csf_path, seeds_path)
tractogram_path = \
pf_track_pam.last_generated_outputs['out_tractogram']
assert_false(is_tractogram_empty(tractogram_path))
# Test that tracking returns seeds
pf_track_pam = PFTrackingPAMFlow()
pf_track_pam._force_overwrite = True
pf_track_pam.run(pam_path,
wm_path,
gm_path,
csf_path,
seeds_path,
save_seeds=True)
tractogram_path = \
pf_track_pam.last_generated_outputs['out_tractogram']
assert_true(tractogram_has_seeds(tractogram_path))
assert_true(seeds_are_same_space_as_streamlines(tractogram_path))
def test_local_fiber_tracking_workflow():
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 = join(out_dir, 'tmp_mask.nii.gz')
nib.save(mask_img, mask_path)
reconst_csd_flow = ReconstCSDFlow()
reconst_csd_flow.run(data_path, bval_path, bvec_path, mask_path,
out_dir=out_dir, extract_pam_values=True)
pam_path = reconst_csd_flow.last_generated_outputs['out_pam']
gfa_path = reconst_csd_flow.last_generated_outputs['out_gfa']
# Create seeding mask by thresholding the gfa
mask_flow = MaskFlow()
mask_flow.run(gfa_path, 0.8, out_dir=out_dir)
seeds_path = mask_flow.last_generated_outputs['out_mask']
mask_path = mask_flow.last_generated_outputs['out_mask']
# Put identity in gfa path to prevent impossible to use
# local tracking because of affine containing shearing.
gfa_img = nib.load(gfa_path)
save_nifti(gfa_path, gfa_img.get_data(), np.eye(4), gfa_img.header)
# Test tracking with pam no sh
lf_track_pam = LocalFiberTrackingPAMFlow()
lf_track_pam._force_overwrite = True
assert_equal(lf_track_pam.get_short_name(), 'track_local')
lf_track_pam.run(pam_path, gfa_path, seeds_path)
tractogram_path = \
lf_track_pam.last_generated_outputs['out_tractogram']
assert_false(is_tractogram_empty(tractogram_path))
# Test tracking with binary tissue classifier
lf_track_pam = LocalFiberTrackingPAMFlow()
lf_track_pam._force_overwrite = True
lf_track_pam.run(pam_path, mask_path, seeds_path,
use_binary_mask=True)
tractogram_path = \
lf_track_pam.last_generated_outputs['out_tractogram']
assert_false(is_tractogram_empty(tractogram_path))
# Test tracking with pam with sh
lf_track_pam = LocalFiberTrackingPAMFlow()
lf_track_pam._force_overwrite = True
lf_track_pam.run(pam_path, gfa_path, seeds_path,
tracking_method="eudx")
tractogram_path = \
lf_track_pam.last_generated_outputs['out_tractogram']
assert_false(is_tractogram_empty(tractogram_path))
# Test tracking with pam with sh and deterministic getter
lf_track_pam = LocalFiberTrackingPAMFlow()
lf_track_pam._force_overwrite = True
lf_track_pam.run(pam_path, gfa_path, seeds_path,
tracking_method="deterministic")
tractogram_path = \
lf_track_pam.last_generated_outputs['out_tractogram']
assert_false(is_tractogram_empty(tractogram_path))
# Test tracking with pam with sh and probabilistic getter
lf_track_pam = LocalFiberTrackingPAMFlow()
lf_track_pam._force_overwrite = True
lf_track_pam.run(pam_path, gfa_path, seeds_path,
tracking_method="probabilistic")
tractogram_path = \
lf_track_pam.last_generated_outputs['out_tractogram']
assert_false(is_tractogram_empty(tractogram_path))
# Test tracking with pam with sh and closest peaks getter
lf_track_pam = LocalFiberTrackingPAMFlow()
lf_track_pam._force_overwrite = True
lf_track_pam.run(pam_path, gfa_path, seeds_path,
tracking_method="closestpeaks")
tractogram_path = \
lf_track_pam.last_generated_outputs['out_tractogram']
assert_false(is_tractogram_empty(tractogram_path))
# Test that tracking returns seeds
lf_track_pam = LocalFiberTrackingPAMFlow()
lf_track_pam._force_overwrite = True
lf_track_pam.run(pam_path, gfa_path, seeds_path,
tracking_method="deterministic",
save_seeds=True)
tractogram_path = \
lf_track_pam.last_generated_outputs['out_tractogram']
assert_true(tractogram_has_seeds(tractogram_path))
assert_true(seeds_are_same_space_as_streamlines(tractogram_path))
def test_local_fiber_tracking_workflow():
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 = join(out_dir, 'tmp_mask.nii.gz')
nib.save(mask_img, mask_path)
reconst_csd_flow = ReconstCSDFlow()
reconst_csd_flow.run(data_path, bval_path, bvec_path, mask_path,
out_dir=out_dir, extract_pam_values=True)
pam_path = reconst_csd_flow.last_generated_outputs['out_pam']
gfa_path = reconst_csd_flow.last_generated_outputs['out_gfa']
# Create seeding mask by thresholding the gfa
mask_flow = MaskFlow()
mask_flow.run(gfa_path, 0.8, out_dir=out_dir)
seeds_path = mask_flow.last_generated_outputs['out_mask']
# Put identity in gfa path to prevent impossible to use
# local tracking because of affine containing shearing.
gfa_img = nib.load(gfa_path)
save_nifti(gfa_path, gfa_img.get_data(), np.eye(4), gfa_img.header)
# Test tracking with pam no sh
lf_track_pam = LocalFiberTrackingPAMFlow()
lf_track_pam._force_overwrite = True
assert_equal(lf_track_pam.get_short_name(), 'track_local')
lf_track_pam.run(pam_path, gfa_path, seeds_path)
tractogram_path = \
lf_track_pam.last_generated_outputs['out_tractogram']
assert_false(is_tractogram_empty(tractogram_path))
# Test tracking with pam with sh
lf_track_pam = LocalFiberTrackingPAMFlow()
lf_track_pam._force_overwrite = True
lf_track_pam.run(pam_path, gfa_path, seeds_path,
tracking_method="eudx")
tractogram_path = \
lf_track_pam.last_generated_outputs['out_tractogram']
assert_false(is_tractogram_empty(tractogram_path))
# Test tracking with pam with sh and deterministic getter
lf_track_pam = LocalFiberTrackingPAMFlow()
lf_track_pam._force_overwrite = True
lf_track_pam.run(pam_path, gfa_path, seeds_path,
tracking_method="deterministic")
tractogram_path = \
lf_track_pam.last_generated_outputs['out_tractogram']
assert_false(is_tractogram_empty(tractogram_path))
# Test tracking with pam with sh and probabilistic getter
lf_track_pam = LocalFiberTrackingPAMFlow()
lf_track_pam._force_overwrite = True
lf_track_pam.run(pam_path, gfa_path, seeds_path,
tracking_method="probabilistic")
tractogram_path = \
lf_track_pam.last_generated_outputs['out_tractogram']
assert_false(is_tractogram_empty(tractogram_path))
# Test tracking with pam with sh and closestpeaks getter
lf_track_pam = LocalFiberTrackingPAMFlow()
lf_track_pam._force_overwrite = True
lf_track_pam.run(pam_path, gfa_path, seeds_path,
tracking_method="closestpeaks")
tractogram_path = \
lf_track_pam.last_generated_outputs['out_tractogram']
assert_false(is_tractogram_empty(tractogram_path))
def test_particule_filtering_traking_workflows():
with TemporaryDirectory() as out_dir:
dwi_path, bval_path, bvec_path = get_fnames('small_64D')
vol_img = nib.load(dwi_path)
volume = vol_img.get_data()
# Create some mask
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)
simple_wm = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 0, 1, 0, 0],
[0, 0, 1, 0, 1, 0, 1, 0, 0, 0],
[0, 0, 1, 0, 1, 1, 0, 1, 0, 0],
[0, 0, 0, 1, 1, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
])
simple_wm = np.dstack([np.zeros(simple_wm.shape),
np.zeros(simple_wm.shape),
simple_wm, simple_wm, simple_wm,
simple_wm, simple_wm, simple_wm,
np.zeros(simple_wm.shape),
np.zeros(simple_wm.shape)])
simple_gm = np.array([[0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 1, 1, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 1, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 1],
[1, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 1, 0, 1, 0],
[0, 1, 1, 0, 1, 1, 1, 0, 1, 1],
[0, 0, 0, 1, 0, 0, 0, 1, 1, 0],
])
simple_gm = np.dstack([np.zeros(simple_gm.shape),
np.zeros(simple_gm.shape),
simple_gm, simple_gm, simple_gm,
simple_gm, simple_gm, simple_gm,
np.zeros(simple_gm.shape),
np.zeros(simple_gm.shape)])
simple_csf = np.ones(simple_wm.shape) - simple_wm - simple_gm
wm_path = join(out_dir, 'tmp_wm.nii.gz')
gm_path = join(out_dir, 'tmp_gm.nii.gz')
csf_path = join(out_dir, 'tmp_csf.nii.gz')
for path, arr in zip([wm_path, gm_path, csf_path],
[simple_wm, simple_gm, simple_csf]):
nib.save(nib.Nifti1Image(arr.astype(np.uint8), vol_img.affine),
path)
# CSD Reconstruction
reconst_csd_flow = ReconstCSDFlow()
reconst_csd_flow.run(dwi_path, bval_path, bvec_path, mask_path,
out_dir=out_dir, extract_pam_values=True)
pam_path = reconst_csd_flow.last_generated_outputs['out_pam']
gfa_path = reconst_csd_flow.last_generated_outputs['out_gfa']
# Create seeding mask by thresholding the gfa
mask_flow = MaskFlow()
mask_flow.run(gfa_path, 0.8, out_dir=out_dir)
seeds_path = mask_flow.last_generated_outputs['out_mask']
# Test tracking
pf_track_pam = PFTrackingPAMFlow()
assert_equal(pf_track_pam.get_short_name(), 'track_pft')
pf_track_pam.run(pam_path, wm_path, gm_path, csf_path, seeds_path)
tractogram_path = \
pf_track_pam.last_generated_outputs['out_tractogram']
assert_false(is_tractogram_empty(tractogram_path))
def test_local_fiber_tracking_workflow():
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], dtype=np.uint8)
mask_path = join(out_dir, 'tmp_mask.nii.gz')
save_nifti(mask_path, mask, affine)
reconst_csd_flow = ReconstCSDFlow()
reconst_csd_flow.run(data_path,
bval_path,
bvec_path,
mask_path,
out_dir=out_dir,
extract_pam_values=True)
pam_path = reconst_csd_flow.last_generated_outputs['out_pam']
gfa_path = reconst_csd_flow.last_generated_outputs['out_gfa']
# Create seeding mask by thresholding the gfa
mask_flow = MaskFlow()
mask_flow.run(gfa_path, 0.8, out_dir=out_dir)
seeds_path = mask_flow.last_generated_outputs['out_mask']
mask_path = mask_flow.last_generated_outputs['out_mask']
gfa_img, gfa_affine = load_nifti(gfa_path)
save_nifti(gfa_path, gfa_img, gfa_affine)
# Test tracking with pam no sh
lf_track_pam = LocalFiberTrackingPAMFlow()
lf_track_pam._force_overwrite = True
assert_equal(lf_track_pam.get_short_name(), 'track_local')
lf_track_pam.run(pam_path, gfa_path, seeds_path)
tractogram_path = \
lf_track_pam.last_generated_outputs['out_tractogram']
assert_false(is_tractogram_empty(tractogram_path))
# Test tracking with binary stopping criterion
lf_track_pam = LocalFiberTrackingPAMFlow()
lf_track_pam._force_overwrite = True
lf_track_pam.run(pam_path, mask_path, seeds_path, use_binary_mask=True)
tractogram_path = \
lf_track_pam.last_generated_outputs['out_tractogram']
assert_false(is_tractogram_empty(tractogram_path))
# Test tracking with pam with sh
lf_track_pam = LocalFiberTrackingPAMFlow()
lf_track_pam._force_overwrite = True
lf_track_pam.run(pam_path,
gfa_path,
seeds_path,
tracking_method="eudx")
tractogram_path = \
lf_track_pam.last_generated_outputs['out_tractogram']
assert_false(is_tractogram_empty(tractogram_path))
# Test tracking with pam with sh and deterministic getter
lf_track_pam = LocalFiberTrackingPAMFlow()
lf_track_pam._force_overwrite = True
lf_track_pam.run(pam_path,
gfa_path,
seeds_path,
tracking_method="deterministic")
tractogram_path = \
lf_track_pam.last_generated_outputs['out_tractogram']
assert_false(is_tractogram_empty(tractogram_path))
# Test tracking with pam with sh and probabilistic getter
lf_track_pam = LocalFiberTrackingPAMFlow()
lf_track_pam._force_overwrite = True
lf_track_pam.run(pam_path,
gfa_path,
seeds_path,
tracking_method="probabilistic")
tractogram_path = \
lf_track_pam.last_generated_outputs['out_tractogram']
assert_false(is_tractogram_empty(tractogram_path))
# Test tracking with pam with sh and closest peaks getter
lf_track_pam = LocalFiberTrackingPAMFlow()
lf_track_pam._force_overwrite = True
lf_track_pam.run(pam_path,
gfa_path,
seeds_path,
tracking_method="closestpeaks")
tractogram_path = \
lf_track_pam.last_generated_outputs['out_tractogram']
assert_false(is_tractogram_empty(tractogram_path))
# Test that tracking returns seeds
lf_track_pam = LocalFiberTrackingPAMFlow()
lf_track_pam._force_overwrite = True
lf_track_pam.run(pam_path,
gfa_path,
seeds_path,
tracking_method="deterministic",
save_seeds=True)
tractogram_path = \
lf_track_pam.last_generated_outputs['out_tractogram']
assert_true(tractogram_has_seeds(tractogram_path))
assert_true(seeds_are_same_space_as_streamlines(tractogram_path))
def test_particle_filtering_traking_workflows():
with TemporaryDirectory() as out_dir:
dwi_path, bval_path, bvec_path = get_fnames('small_64D')
volume, affine = load_nifti(dwi_path)
# Create some mask
mask = np.ones_like(volume[:, :, :, 0], dtype=np.uint8)
mask_path = join(out_dir, 'tmp_mask.nii.gz')
save_nifti(mask_path, mask, affine)
simple_wm = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 1, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 1, 0, 1, 0, 0],
[0, 0, 1, 0, 1, 0, 1, 0, 0, 0],
[0, 0, 1, 0, 1, 1, 0, 1, 0, 0],
[0, 0, 0, 1, 1, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
])
simple_wm = np.dstack([np.zeros(simple_wm.shape),
np.zeros(simple_wm.shape),
simple_wm, simple_wm, simple_wm,
simple_wm, simple_wm, simple_wm,
np.zeros(simple_wm.shape),
np.zeros(simple_wm.shape)])
simple_gm = np.array([[0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 1, 1, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 1, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 1],
[1, 0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 0, 0, 0, 0, 1, 0, 1, 0],
[0, 1, 1, 0, 1, 1, 1, 0, 1, 1],
[0, 0, 0, 1, 0, 0, 0, 1, 1, 0],
])
simple_gm = np.dstack([np.zeros(simple_gm.shape),
np.zeros(simple_gm.shape),
simple_gm, simple_gm, simple_gm,
simple_gm, simple_gm, simple_gm,
np.zeros(simple_gm.shape),
np.zeros(simple_gm.shape)])
simple_csf = np.ones(simple_wm.shape) - simple_wm - simple_gm
wm_path = join(out_dir, 'tmp_wm.nii.gz')
gm_path = join(out_dir, 'tmp_gm.nii.gz')
csf_path = join(out_dir, 'tmp_csf.nii.gz')
for path, arr in zip([wm_path, gm_path, csf_path],
[simple_wm, simple_gm, simple_csf]):
save_nifti(path, arr.astype(np.uint8), affine)
# CSD Reconstruction
reconst_csd_flow = ReconstCSDFlow()
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
reconst_csd_flow.run(dwi_path, bval_path, bvec_path, mask_path,
out_dir=out_dir, extract_pam_values=True)
pam_path = reconst_csd_flow.last_generated_outputs['out_pam']
gfa_path = reconst_csd_flow.last_generated_outputs['out_gfa']
# Create seeding mask by thresholding the gfa
mask_flow = MaskFlow()
mask_flow.run(gfa_path, 0.8, out_dir=out_dir)
seeds_path = mask_flow.last_generated_outputs['out_mask']
# Test tracking
pf_track_pam = PFTrackingPAMFlow()
assert_equal(pf_track_pam.get_short_name(), 'track_pft')
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
pf_track_pam.run(pam_path, wm_path, gm_path, csf_path, seeds_path)
tractogram_path = \
pf_track_pam.last_generated_outputs['out_tractogram']
assert_false(is_tractogram_empty(tractogram_path))
# Test that tracking returns seeds
pf_track_pam = PFTrackingPAMFlow()
pf_track_pam._force_overwrite = True
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
pf_track_pam.run(pam_path,
wm_path,
gm_path,
csf_path,
seeds_path,
save_seeds=True)
tractogram_path = \
pf_track_pam.last_generated_outputs['out_tractogram']
assert_true(tractogram_has_seeds(tractogram_path))
assert_true(seeds_are_same_space_as_streamlines(tractogram_path))