def test_syn_registration():
with nbtmp.InTemporaryDirectory() as tmpdir:
warped_moving, mapping = syn_registration(subset_b0,
subset_t2,
moving_affine=hardi_affine,
static_affine=MNI_T2_affine,
step_length=0.1,
metric='CC',
dim=3,
level_iters=[10, 10, 5],
sigma_diff=2.0,
prealign=None)
npt.assert_equal(warped_moving.shape, subset_t2.shape)
mapping_fname = op.join(tmpdir, 'mapping.nii.gz')
write_mapping(mapping, mapping_fname)
file_mapping = read_mapping(mapping_fname,
subset_b0_img,
subset_t2_img)
# Test that it has the same effect on the data:
warped_from_file = file_mapping.transform(subset_b0)
npt.assert_equal(warped_from_file, warped_moving)
# Test that it is, attribute by attribute, identical:
for k in mapping.__dict__:
assert (np.all(mapping.__getattribute__(k) ==
file_mapping.__getattribute__(k)))
def _bundles(row, wm_labels, odf_model="DTI", directions="det",
force_recompute=False):
bundles_file = _get_fname(row,
'%s_%s_bundles.trk' % (odf_model,
directions))
if not op.exists(bundles_file) or force_recompute:
streamlines_file = _streamlines(row, wm_labels,
odf_model=odf_model,
directions=directions,
force_recompute=force_recompute)
tg = nib.streamlines.load(streamlines_file).tractogram
sl = tg.apply_affine(np.linalg.inv(row['dwi_affine'])).streamlines
bundle_dict = make_bundle_dict()
reg_template = dpd.read_mni_template()
mapping = reg.read_mapping(_mapping(row), row['dwi_file'],
reg_template)
bundles = seg.segment(row['dwi_file'],
row['bval_file'],
row['bvec_file'],
sl,
bundle_dict,
reg_template=reg_template,
mapping=mapping)
tgram = _tgramer(bundles, bundle_dict, row['dwi_affine'])
nib.streamlines.save(tgram, bundles_file)
return bundles_file
def _template_xform(row, reg_template, force_recompute=False):
template_xform_file = _get_fname(row, "_template_xform.nii.gz")
if not op.exists(template_xform_file) or force_recompute:
reg_prealign = np.load(
_reg_prealign(row, force_recompute=force_recompute))
mapping = reg.read_mapping(_mapping(row,
reg_template,
force_recompute=force_recompute),
row['dwi_file'],
reg_template,
prealign=np.linalg.inv(reg_prealign))
template_xform = mapping.transform_inverse(reg_template.get_data())
nib.save(nib.Nifti1Image(template_xform, row['dwi_affine']),
template_xform_file)
return template_xform_file
def prepare_map(self, mapping=None, reg_prealign=None, reg_template=None):
"""
Set mapping between DWI space and a template.
Parameters
----------
mapping : DiffeomorphicMap object, str or nib.Nifti1Image, optional.
A mapping between DWI space and a template.
If None, mapping will be registered from data used in prepare_img.
Default: None.
reg_template : str or nib.Nifti1Image, optional.
Template to use for registration (defaults to the MNI T2)
Default: None.
reg_prealign : array, optional.
The linear transformation to be applied to align input images to
the reference space before warping under the deformation field.
Default: None.
"""
if reg_template is None:
reg_template = afd.read_mni_template()
self.reg_template = reg_template
if mapping is None:
if self.seg_algo == "afq" or self.reg_algo == "syn":
gtab = dpg.gradient_table(self.fbval, self.fbvec)
self.mapping = reg.syn_register_dwi(self.fdata,
gtab,
template=reg_template)[1]
else:
self.mapping = None
elif isinstance(mapping, str) or isinstance(mapping, nib.Nifti1Image):
if reg_prealign is None:
reg_prealign = np.eye(4)
if self.img is None:
self.img, _, _, _ = \
ut.prepare_data(self.fdata,
self.fbval,
self.fbvec,
b0_threshold=self.b0_threshold)
self.mapping = reg.read_mapping(
mapping,
self.img,
reg_template,
prealign=np.linalg.inv(reg_prealign))
else:
self.mapping = mapping
def seg_setup():
MNI_T2_img = afd.read_mni_template()
img = nib.load("dwi.nii")
mapping = reg.read_mapping("mapping.nii.gz", img, MNI_T2_img)
bundles = api.make_bundle_dict(
bundle_names=[
"CST", "UF", "CC_ForcepsMajor", "CC_ForcepsMinor", "OR", "VOF"
],
seg_algo="reco80",
resample_to=MNI_T2_img,
) # CST ARC
return {
"MNI_T2_img": MNI_T2_img,
"img": img,
"mapping": mapping,
"bundles": bundles,
}
def _bundles(row,
wm_labels,
bundle_dict,
reg_template,
odf_model="DTI",
directions="det",
n_seeds=2,
random_seeds=False,
force_recompute=False):
bundles_file = _get_fname(row,
'%s_%s_bundles.trk' % (odf_model, directions))
if not op.exists(bundles_file) or force_recompute:
streamlines_file = _streamlines(row,
wm_labels,
odf_model=odf_model,
directions=directions,
n_seeds=n_seeds,
random_seeds=random_seeds,
force_recompute=force_recompute)
tg = nib.streamlines.load(streamlines_file).tractogram
sl = tg.apply_affine(np.linalg.inv(row['dwi_affine'])).streamlines
reg_prealign = np.load(
_reg_prealign(row, force_recompute=force_recompute))
mapping = reg.read_mapping(_mapping(row, reg_template),
row['dwi_file'],
reg_template,
prealign=np.linalg.inv(reg_prealign))
bundles = seg.segment(row['dwi_file'],
row['bval_file'],
row['bvec_file'],
sl,
bundle_dict,
reg_template=reg_template,
mapping=mapping)
tgram = aus.bundles_to_tgram(bundles, bundle_dict, row['dwi_affine'])
nib.streamlines.save(tgram, bundles_file)
return bundles_file
def _export_rois(self, row):
if self.use_prealign:
reg_prealign = np.load(self._reg_prealign(row))
reg_prealign_inv = np.linalg.inv(reg_prealign)
else:
reg_prealign_inv = None
mapping = reg.read_mapping(self._mapping(row),
row['dwi_file'],
self.reg_template,
prealign=reg_prealign_inv)
rois_dir = op.join(row['results_dir'], 'ROIs')
os.makedirs(rois_dir, exist_ok=True)
for bundle in self.bundle_dict:
for ii, roi in enumerate(self.bundle_dict[bundle]['ROIs']):
if self.bundle_dict[bundle]['rules'][ii]:
inclusion = 'include'
else:
inclusion = 'exclude'
warped_roi = auv.patch_up_roi((mapping.transform_inverse(
roi.get_fdata(), interpolation='linear')) > 0).astype(int)
fname = op.split(
self._get_fname(
row,
f'_desc-ROI-{bundle}-{ii + 1}-{inclusion}.nii.gz'))
fname = op.join(fname[0], rois_dir, fname[1])
# Cast to float32, so that it can be read in by MI-Brain:
nib.save(
nib.Nifti1Image(warped_roi.astype(np.float32),
row['dwi_affine']), fname)
meta = dict()
meta_fname = fname.split('.')[0] + '.json'
afd.write_json(meta_fname, meta)
def test_slr_registration():
# have to import subject sls
file_dict = afd.read_stanford_hardi_tractography()
streamlines = file_dict['tractography_subsampled.trk']
# have to import sls atlas
afd.fetch_hcp_atlas_16_bundles()
atlas_fname = op.join(afd.afq_home, 'hcp_atlas_16_bundles',
'Atlas_in_MNI_Space_16_bundles', 'whole_brain',
'whole_brain_MNI.trk')
hcp_atlas = load_tractogram(atlas_fname, 'same', bbox_valid_check=False)
with nbtmp.InTemporaryDirectory() as tmpdir:
mapping = slr_registration(streamlines,
hcp_atlas.streamlines,
moving_affine=subset_b0_img.affine,
static_affine=subset_t2_img.affine,
moving_shape=subset_b0_img.shape,
static_shape=subset_t2_img.shape,
progressive=False,
greater_than=10,
rm_small_clusters=1,
rng=np.random.RandomState(seed=8))
warped_moving = mapping.transform(subset_b0)
npt.assert_equal(warped_moving.shape, subset_t2.shape)
mapping_fname = op.join(tmpdir, 'mapping.npy')
write_mapping(mapping, mapping_fname)
file_mapping = read_mapping(mapping_fname, subset_b0_img,
subset_t2_img)
# Test that it has the same effect on the data:
warped_from_file = file_mapping.transform(subset_b0)
npt.assert_equal(warped_from_file, warped_moving)
# Test that it is, attribute by attribute, identical:
for k in mapping.__dict__:
assert (np.all(
mapping.__getattribute__(k) == file_mapping.__getattribute__(
k)))
if not op.exists(op.join(working_dir, 'mapping.nii.gz')):
import dipy.core.gradients as dpg
gtab = dpg.gradient_table(hardi_fbval, hardi_fbvec)
b0 = np.mean(img.get_fdata()[..., gtab.b0s_mask], -1)
# Prealign using affine registration
_, prealign = affine_registration(b0, MNI_T2_img.get_fdata(), img.affine,
MNI_T2_img.affine)
# Then register using a non-linear registration using the affine for
# prealignment
warped_hardi, mapping = reg.syn_register_dwi(hardi_fdata,
gtab,
prealign=prealign)
reg.write_mapping(mapping, op.join(working_dir, 'mapping.nii.gz'))
else:
mapping = reg.read_mapping(op.join(working_dir, 'mapping.nii.gz'), img,
MNI_T2_img)
##########################################################################
# Read in bundle specification
# -------------------------------------------
# The waypoint ROIs, in addition to bundle probability maps are stored in this
# data structure. The templates are first resampled into the MNI space, before
# they are brought into the subject's individual native space.
# For speed, we only segment two bundles here.
bundles = api.BundleDict(["CST", "ARC"], resample_to=MNI_T2_img)
##########################################################################
# Tracking
# --------
# Streamlines are generate using DTI and a deterministic tractography
dti_params = {'FA': './dti_FA.nii.gz', 'params': './dti_params.nii.gz'}
FA_img = nib.load(dti_params['FA'])
FA_data = FA_img.get_fdata()
print("Registering to template...")
MNI_T2_img = afd.read_mni_template()
if not op.exists('mapping.nii.gz'):
import dipy.core.gradients as dpg
gtab = dpg.gradient_table(hardi_fbval, hardi_fbvec)
warped_hardi, mapping = reg.syn_register_dwi(hardi_fdata,
gtab,
template=MNI_T2_img)
reg.write_mapping(mapping, './mapping.nii.gz')
else:
mapping = reg.read_mapping('./mapping.nii.gz', img, MNI_T2_img)
bundle_names = ["CST", "UF", "CC_ForcepsMajor", "CC_ForcepsMinor"]
bundles = api.make_bundle_dict(bundle_names=bundle_names, seg_algo="reco")
print("Tracking...")
if not op.exists('dti_streamlines_reco.trk'):
seed_roi = np.zeros(img.shape[:-1])
for bundle in bundles:
if bundle != 'whole_brain':
sl_xform = dts.Streamlines(
dtu.transform_tracking_output(bundles[bundle]['sl'],
MNI_T2_img.affine))
delta = dts.values_from_volume(mapping.backward, sl_xform,
np.eye(4))
else:
dti_params = {'FA': './dti_FA.nii.gz',
'MD': './dti_MD.nii.gz',
'RD': './dti_RD.nii.gz',
'AD': './dti_AD.nii.gz',
'params': './dti_params.nii.gz'}
print("Registering to template...")
MNI_T2_img = dpd.read_mni_template()
if not op.exists('mapping.nii.gz'):
import dipy.core.gradients as dpg
gtab = dpg.gradient_table(fbval, fbvec)
mapping = reg.syn_register_dwi(fdata, gtab)
reg.write_mapping(mapping, './mapping.nii.gz')
else:
mapping = reg.read_mapping('./mapping.nii.gz', img, MNI_T2_img)
print("Tracking...")
if not op.exists('dti_streamlines.trk'):
FA = nib.load(dti_params["FA"]).get_data()
wm_mask = np.zeros_like(FA)
wm_mask[FA > 0.2] = 1
step_size = 1
min_length_mm = 50
streamlines = dts.Streamlines(
aft.track(dti_params['params'],
directions="det",
seed_mask=wm_mask,
seeds=2,
stop_mask=FA,
stop_threshold=0.2,
def segment(fdata,
fbval,
fbvec,
streamlines,
bundles,
reg_template=None,
mapping=None,
as_generator=True,
clip_to_roi=True,
**reg_kwargs):
"""
Segment streamlines into bundles.
Parameters
----------
fdata, fbval, fbvec : str
Full path to data, bvals, bvecs
streamlines : list of 2D arrays
Each array is a streamline, shape (3, N).
bundles: dict
The format is something like::
{'name': {'ROIs':[img, img], 'rules':[True, True]}}
reg_template : str or nib.Nifti1Image, optional.
Template to use for registration (defaults to the MNI T2)
mapping : DiffeomorphicMap object, str or nib.Nifti1Image, optional
A mapping between DWI space and a template. Defaults to generate this.
as_generator : bool, optional
Whether to generate the streamlines here, or return generators.
Default: True.
clip_to_roi : bool, optional
Whether to clip the streamlines between the ROIs
"""
img, data, gtab, mask = ut.prepare_data(fdata, fbval, fbvec)
xform_sl = [
s for s in dtu.move_streamlines(streamlines, np.linalg.inv(img.affine))
]
if reg_template is None:
reg_template = dpd.read_mni_template()
if mapping is None:
mapping = reg.syn_register_dwi(fdata,
gtab,
template=reg_template,
**reg_kwargs)
if isinstance(mapping, str) or isinstance(mapping, nib.Nifti1Image):
mapping = reg.read_mapping(mapping, img, reg_template)
fiber_groups = {}
for bundle in bundles:
select_sl = xform_sl
for ROI, rule in zip(bundles[bundle]['ROIs'],
bundles[bundle]['rules']):
data = ROI.get_data()
warped_ROI = patch_up_roi(
mapping.transform_inverse(data, interpolation='nearest'))
# This function requires lists as inputs:
select_sl = dts.select_by_rois(select_sl,
[warped_ROI.astype(bool)], [rule])
# Next, we reorient each streamline according to an ARBITRARY, but
# CONSISTENT order. To do this, we use the first ROI for which the rule
# is True as the first one to pass through, and the last ROI for which
# the rule is True as the last one to pass through:
# Indices where the 'rule' is True:
idx = np.where(bundles[bundle]['rules'])
orient_ROIs = [
bundles[bundle]['ROIs'][idx[0][0]],
bundles[bundle]['ROIs'][idx[0][-1]]
]
select_sl = dts.orient_by_rois(select_sl,
orient_ROIs[0].get_data(),
orient_ROIs[1].get_data(),
as_generator=True)
# XXX Implement clipping to the ROIs
# if clip_to_roi:
# dts.clip()
if as_generator:
fiber_groups[bundle] = select_sl
else:
fiber_groups[bundle] = list(select_sl)
return fiber_groups
def main():
with open('config.json') as config_json:
config = json.load(config_json)
#Paths to data
data_file = str(config['data_file'])
data_bval = str(config['data_bval'])
data_bvec = str(config['data_bvec'])
img = nib.load(data_file)
"""
print("Calculating DTI...")
if not op.exists('./dti_FA.nii.gz'):
dti_params = dti.fit_dti(data_file, data_bval, data_bvec, out_dir='.')
else:
dti_params = {'FA': './dti_FA.nii.gz',
'params': './dti_params.nii.gz'}
"""
#tg = nib.streamlines.load('track.trk').tractogram
tg = nib.streamlines.load(config['tck_data']).tractogram
streamlines = tg.apply_affine(np.linalg.inv(img.affine)).streamlines
# Use only a small portion of the streamlines, for expedience:
#streamlines = streamlines[::100]
templates = afd.read_templates()
bundle_names = ["CST", "ILF"]
bundles = {}
for name in bundle_names:
for hemi in ['_R', '_L']:
bundles[name + hemi] = {
'ROIs': [
templates[name + '_roi1' + hemi],
templates[name + '_roi1' + hemi]
],
'rules': [True, True]
}
print("Registering to template...")
if not op.exists('mapping.nii.gz'):
gtab = gradient_table(data_bval, data_bvec)
mapping = reg.syn_register_dwi(data_file, gtab)
reg.write_mapping(mapping, './mapping.nii.gz')
else:
mapping = reg.read_mapping('./mapping.nii.gz', img, MNI_T2_img)
"""
MNI_T2_img = dpd.read_mni_template()
bvals, bvecs = read_bvals_bvecs(data_bval, data_bvec)
gtab = gradient_table(bvals, bvecs, b0_threshold=100)
mapping = reg.syn_register_dwi(data_file, gtab)
reg.write_mapping(mapping, './mapping.nii.gz')
"""
print("Segmenting fiber groups...")
fiber_groups = seg.segment(data_file,
data_bval,
data_bvec,
streamlines,
bundles,
reg_template=MNI_T2_img,
mapping=mapping,
as_generator=False,
affine=img.affine)
path = os.getcwd() + '/tract1/'
if not os.path.exists(path):
os.makedirs(path)
for fg in fiber_groups:
streamlines = fiber_groups[fg]
fname = fg + ".tck"
trg = nib.streamlines.Tractogram(streamlines,
affine_to_rasmm=img.affine)
nib.streamlines.save(trg, path + fname)
def segment(fdata, fbval, fbvec, streamlines, bundle_dict, mapping,
reg_prealign=None, b0_threshold=0, reg_template=None,
prob_threshold=0):
"""
Segment streamlines into bundles based on inclusion ROIs.
Parameters
----------
fdata, fbval, fbvec : str
Full path to data, bvals, bvecs
streamlines : list of 2D arrays
Each array is a streamline, shape (3, N).
bundle_dict: dict
The format is something like::
{'name': {'ROIs':[img1, img2],
'rules':[True, True]},
'prob_map': img3,
'cross_midline': False}
mapping : a DiffeomorphicMapping object
Used to align the ROIs to the data.
reg_template : str or nib.Nifti1Image, optional.
Template to use for registration (defaults to the MNI T2)
mapping : DiffeomorphicMap object, str or nib.Nifti1Image, optional
A mapping between DWI space and a template. Defaults to generate
this.
prob_threshold : float.
Initial cleaning of fiber groups is done using probability maps from
[Hua2008]_. Here, we choose an average probability that needs to be
exceeded for an individual streamline to be retained. Default: 0.
References
----------
.. [Hua2008] Hua K, Zhang J, Wakana S, Jiang H, Li X, et al. (2008)
Tract probability maps in stereotaxic spaces: analyses of white
matter anatomy and tract-specific quantification. Neuroimage 39:
336-347
"""
img, _, gtab, _ = ut.prepare_data(fdata, fbval, fbvec,
b0_threshold=b0_threshold)
tol = dts.dist_to_corner(img.affine)
if reg_template is None:
reg_template = dpd.read_mni_template()
# Classify the streamlines and split those that: 1) cross the
# midline, and 2) pass under 10 mm below the mid-point of their
# representation in the template space:
xform_sl, crosses = split_streamlines(streamlines, img)
if isinstance(mapping, str) or isinstance(mapping, nib.Nifti1Image):
if reg_prealign is None:
reg_prealign = np.eye(4)
mapping = reg.read_mapping(mapping, img, reg_template,
prealign=reg_prealign)
fiber_probabilities = np.zeros((len(xform_sl), len(bundle_dict)))
# For expedience, we approximate each streamline as a 100 point curve:
fgarray = _resample_bundle(xform_sl, 100)
streamlines_in_bundles = np.zeros((len(xform_sl), len(bundle_dict)))
min_dist_coords = np.zeros((len(xform_sl), len(bundle_dict), 2))
fiber_groups = {}
for bundle_idx, bundle in enumerate(bundle_dict):
rules = bundle_dict[bundle]['rules']
include_rois = []
exclude_rois = []
for rule_idx, rule in enumerate(rules):
roi = bundle_dict[bundle]['ROIs'][rule_idx]
if not isinstance(roi, np.ndarray):
roi = roi.get_data()
warped_roi = auv.patch_up_roi(
(mapping.transform_inverse(
roi,
interpolation='linear')) > 0)
if rule:
# include ROI:
include_rois.append(np.array(np.where(warped_roi)).T)
else:
# Exclude ROI:
exclude_rois.append(np.array(np.where(warped_roi)).T)
crosses_midline = bundle_dict[bundle]['cross_midline']
# The probability map if doesn't exist is all ones with the same
# shape as the ROIs:
prob_map = bundle_dict[bundle].get('prob_map', np.ones(roi.shape))
if not isinstance(prob_map, np.ndarray):
prob_map = prob_map.get_data()
warped_prob_map = mapping.transform_inverse(prob_map,
interpolation='nearest')
fiber_probabilities = dts.values_from_volume(warped_prob_map,
fgarray)
fiber_probabilities = np.mean(fiber_probabilities, -1)
for sl_idx, sl in enumerate(xform_sl):
if fiber_probabilities[sl_idx] > prob_threshold:
if crosses_midline is not None:
if crosses[sl_idx]:
# This means that the streamline does
# cross the midline:
if crosses_midline:
# This is what we want, keep going
pass
else:
# This is not what we want, skip to next streamline
continue
is_close, dist = _check_sl_with_inclusion(sl, include_rois,
tol)
if is_close:
is_far = _check_sl_with_exclusion(sl, exclude_rois,
tol)
if is_far:
min_dist_coords[sl_idx, bundle_idx, 0] =\
np.argmin(dist[0], 0)[0]
min_dist_coords[sl_idx, bundle_idx, 1] =\
np.argmin(dist[1], 0)[0]
streamlines_in_bundles[sl_idx, bundle_idx] =\
fiber_probabilities[sl_idx]
# Eliminate any fibers not selected using the plane ROIs:
possible_fibers = np.sum(streamlines_in_bundles, -1) > 0
xform_sl = xform_sl[possible_fibers]
streamlines_in_bundles = streamlines_in_bundles[possible_fibers]
min_dist_coords = min_dist_coords[possible_fibers]
bundle_choice = np.argmax(streamlines_in_bundles, -1)
# We do another round through, so that we can orient all the
# streamlines within a bundle in the same orientation with respect to
# the ROIs. This order is ARBITRARY but CONSISTENT (going from ROI0
# to ROI1).
for bundle_idx, bundle in enumerate(bundle_dict):
select_idx = np.where(bundle_choice == bundle_idx)
# Use a list here, because Streamlines don't support item assignment:
select_sl = list(xform_sl[select_idx])
if len(select_sl) == 0:
fiber_groups[bundle] = dts.Streamlines([])
# There's nothing here, move to the next bundle:
continue
# Sub-sample min_dist_coords:
min_dist_coords_bundle = min_dist_coords[select_idx]
for idx in range(len(select_sl)):
min0 = min_dist_coords_bundle[idx, bundle_idx, 0]
min1 = min_dist_coords_bundle[idx, bundle_idx, 1]
if min0 > min1:
select_sl[idx] = select_sl[idx][::-1]
# Set this to nibabel.Streamlines object for output:
select_sl = dts.Streamlines(select_sl)
fiber_groups[bundle] = select_sl
return fiber_groups
def segment(fdata, fbval, fbvec, streamlines, bundles,
reg_template=None, mapping=None, as_generator=True, **reg_kwargs):
"""
generate : bool
Whether to generate the streamlines here, or return generators.
reg_template : template to use for registration (defaults to the MNI T2)
bundles: dict
The format is something like::
{'name': {'ROIs':[img, img], 'rules':[True, True]}}
"""
img, data, gtab, mask = ut.prepare_data(fdata, fbval, fbvec)
xform_sl = [s for s in dtu.move_streamlines(streamlines,
np.linalg.inv(img.affine))]
if reg_template is None:
reg_template = dpd.read_mni_template()
if mapping is None:
mapping = reg.syn_register_dwi(fdata, gtab, template=reg_template,
**reg_kwargs)
if isinstance(mapping, str) or isinstance(mapping, nib.Nifti1Image):
mapping = reg.read_mapping(mapping, img, reg_template)
fiber_groups = {}
for bundle in bundles:
select_sl = xform_sl
for ROI, rule in zip(bundles[bundle]['ROIs'],
bundles[bundle]['rules']):
data = ROI.get_data()
warped_ROI = patch_up_roi(mapping.transform_inverse(
data,
interpolation='nearest'))
# This function requires lists as inputs:
select_sl = dts.select_by_rois(select_sl,
[warped_ROI.astype(bool)],
[rule])
# Next, we reorient each streamline according to an ARBITRARY, but
# CONSISTENT order. To do this, we use the first ROI for which the rule
# is True as the first one to pass through, and the last ROI for which
# the rule is True as the last one to pass through:
# Indices where the 'rule' is True:
idx = np.where(bundles[bundle]['rules'])
orient_ROIs = [bundles[bundle]['ROIs'][idx[0][0]],
bundles[bundle]['ROIs'][idx[0][-1]]]
select_sl = dts.orient_by_rois(select_sl,
orient_ROIs[0].get_data(),
orient_ROIs[1].get_data(),
in_place=True)
if as_generator:
fiber_groups[bundle] = select_sl
else:
fiber_groups[bundle] = list(select_sl)
return fiber_groups
def segment(fdata, fbval, fbvec, streamlines, bundles,
reg_template=None, mapping=None, prob_threshold=0,
**reg_kwargs):
"""
Segment streamlines into bundles based on inclusion ROIs.
Parameters
----------
fdata, fbval, fbvec : str
Full path to data, bvals, bvecs
streamlines : list of 2D arrays
Each array is a streamline, shape (3, N).
bundles: dict
The format is something like::
{'name': {'ROIs':[img1, img2],
'rules':[True, True]},
'prob_map': img3,
'cross_midline': False}
reg_template : str or nib.Nifti1Image, optional.
Template to use for registration (defaults to the MNI T2)
mapping : DiffeomorphicMap object, str or nib.Nifti1Image, optional
A mapping between DWI space and a template. Defaults to generate
this.
prob_threshold : float.
Initial cleaning of fiber groups is done using probability maps from
[Hua2008]_. Here, we choose an average probability that needs to be
exceeded for an individual streamline to be retained. Default: 0.
References
----------
.. [Hua2008] Hua K, Zhang J, Wakana S, Jiang H, Li X, et al. (2008)
Tract probability maps in stereotaxic spaces: analyses of white
matter anatomy and tract-specific quantification. Neuroimage 39:
336-347
"""
img, _, gtab, _ = ut.prepare_data(fdata, fbval, fbvec)
tol = dts.dist_to_corner(img.affine)
xform_sl = dts.Streamlines(dtu.move_streamlines(streamlines,
np.linalg.inv(img.affine)))
if reg_template is None:
reg_template = dpd.read_mni_template()
if mapping is None:
mapping = reg.syn_register_dwi(fdata, gtab, template=reg_template,
**reg_kwargs)
if isinstance(mapping, str) or isinstance(mapping, nib.Nifti1Image):
mapping = reg.read_mapping(mapping, img, reg_template)
fiber_probabilities = np.zeros((len(xform_sl), len(bundles)))
# For expedience, we approximate each streamline as a 100 point curve:
fgarray = _resample_bundle(xform_sl, 100)
streamlines_in_bundles = np.zeros((len(xform_sl), len(bundles)))
min_dist_coords = np.zeros((len(xform_sl), len(bundles), 2))
fiber_groups = {}
for bundle_idx, bundle in enumerate(bundles):
# Get the ROI coordinates:
ROI0 = bundles[bundle]['ROIs'][0]
ROI1 = bundles[bundle]['ROIs'][1]
if not isinstance(ROI0, np.ndarray):
ROI0 = ROI0.get_data()
warped_ROI0 = patch_up_roi(
mapping.transform_inverse(
ROI0,
interpolation='nearest')).astype(bool)
if not isinstance(ROI1, np.ndarray):
ROI1 = ROI1.get_data()
warped_ROI1 = patch_up_roi(
mapping.transform_inverse(
ROI1,
interpolation='nearest')).astype(bool)
roi_coords0 = np.array(np.where(warped_ROI0)).T
roi_coords1 = np.array(np.where(warped_ROI1)).T
crosses_midline = bundles[bundle]['cross_midline']
# The probability map if doesn't exist is all ones with the same
# shape as the ROIs:
prob_map = bundles[bundle].get('prob_map', np.ones(ROI0.shape))
if not isinstance(prob_map, np.ndarray):
prob_map = prob_map.get_data()
warped_prob_map = mapping.transform_inverse(prob_map,
interpolation='nearest')
fiber_probabilities = dts.values_from_volume(warped_prob_map,
fgarray)
fiber_probabilities = np.mean(fiber_probabilities, -1)
for sl_idx, sl in enumerate(xform_sl):
if fiber_probabilities[sl_idx] > prob_threshold:
if crosses_midline is not None:
if (np.any(sl[:, 0] > img.shape[0] // 2) and
np.any(sl[:, 0] < img.shape[0] // 2)):
# This means that the streamline does
# cross the midline:
if crosses_midline:
# This is what we want, keep going
pass
else:
# This is not what we want, skip to next streamline
continue
dist0 = cdist(sl, roi_coords0, 'euclidean')
if np.min(dist0) <= tol:
dist1 = cdist(sl, roi_coords1, 'euclidean')
if np.min(dist1) <= tol:
min_dist_coords[sl_idx, bundle_idx, 0] =\
np.argmin(dist0, 0)[0]
min_dist_coords[sl_idx, bundle_idx, 1] =\
np.argmin(dist1, 0)[0]
streamlines_in_bundles[sl_idx, bundle_idx] =\
fiber_probabilities[sl_idx]
# Eliminate any fibers not selected using the plane ROIs:
possible_fibers = np.sum(streamlines_in_bundles, -1) > 0
xform_sl = xform_sl[possible_fibers]
streamlines_in_bundles = streamlines_in_bundles[possible_fibers]
min_dist_coords = min_dist_coords[possible_fibers]
bundle_choice = np.argmax(streamlines_in_bundles, -1)
for bundle_idx, bundle in enumerate(bundles):
print(bundle)
select_idx = np.where(bundle_choice == bundle_idx)
# Use a list here, because Streamlines don't support item assignment:
select_sl = list(xform_sl[select_idx])
# Sub-sample min_dist_coords:
min_dist_coords_bundle = min_dist_coords[select_idx]
if len(select_sl) == 0:
fiber_groups[bundle] = dts.Streamlines([])
# There's nothing here, move to the next bundle:
continue
for idx in range(len(select_sl)):
min0 = min_dist_coords_bundle[idx, bundle_idx, 0]
min1 = min_dist_coords_bundle[idx, bundle_idx, 1]
if min0 > min1:
select_sl[idx] = select_sl[idx][::-1]
# We'll set this to Streamlines object for the next steps (e.g.,
# cleaning) because these objects support indexing with arrays:
select_sl = dts.Streamlines(select_sl)
fiber_groups[bundle] = select_sl
return fiber_groups
