def seg_setup(alg):
MNI_T2_img = afd.read_mni_template()
img = nib.load("dwi.nii")
mapping = reg.read_mapping("mapping.nii.gz", img, MNI_T2_img)
if alg == "waypoint":
bundles = api.make_bundle_dict(
bundle_names=[
"CST",
"ARC",
],
resample_to=MNI_T2_img,
) # CST ARC
else:
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 test_AFQ_custom_bundle_dict():
bids_path = create_dummy_bids_path(3, 1)
bundle_dict = api.make_bundle_dict()
my_afq = api.AFQ(
bids_path,
dmriprep="synthetic",
bundle_info=bundle_dict)
def test_make_bundle_dict():
"""
Tests bundle dict
"""
# test defaults
afq_bundles = api.make_bundle_dict()
# bundles restricted within hemisphere
# NOTE: FA and FP cross midline so are removed
# NOTE: all others generate two bundles
num_hemi_bundles = (len(api.BUNDLES) - 2) * 2
# bundles that cross the midline
num_whole_bundles = 2
assert len(afq_bundles) == num_hemi_bundles + num_whole_bundles
# Arcuate Fasciculus
afq_bundles = api.make_bundle_dict(bundle_names=["ARC"])
assert len(afq_bundles) == 2
# Forceps Minor
afq_bundles = api.make_bundle_dict(bundle_names=["FA"])
assert len(afq_bundles) == 1
# Cingulum Hippocampus
# not included but exists in templates
afq_bundles = api.make_bundle_dict(bundle_names=["HCC"])
assert len(afq_bundles) == 2
# Vertical Occipital Fasciculus
# not included and does not exist in templates
afq_bundles = api.make_bundle_dict(bundle_names=["VOF"])
assert len(afq_bundles) == 0
track = config["track"]
# load dwi data and generate gradient table
dwi_img = nib.load(dwi)
gtab = dpg.gradient_table(bvals, bvecs)
# load MNI template and syn register dwi data to MNI
MNI_T2_img = dpd.read_mni_template()
warped_hardi, mapping = reg.syn_register_dwi(dwi, gtab)
# load tractogram
tg = load_tractogram(track, dwi_img)
#tg_acpc = transform_streamlines(tg.streamlines,dwi_img.get_affine())
# download and load waypoint ROIs and make bundle dictionary
bundles = api.make_bundle_dict(resample_to=MNI_T2_img)
bundle_names = list(bundles.keys())
print(f"Space before segmentation: {tg.space}")
# initialize segmentation and segment major fiber groups
print("running AFQ segmentation")
segmentation = seg.Segmentation(return_idx=True)
segmentation.segment(bundles,
tg,
fdata=dwi,
fbval=bvals,
fbvec=bvecs,
mapping=mapping,
reg_template=MNI_T2_img,
reset_tg_space=True)
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))
sl_xform = [sum(d, s) for d, s in zip(delta, sl_xform)]
sl_xform = dts.Streamlines(
warped_hardi, mapping = reg.syn_register_dwi(hardi_fdata, gtab)
reg.write_mapping(mapping, './mapping.nii.gz')
else:
mapping = reg.read_mapping('./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.make_bundle_dict(bundle_names= ["CST", "ARC"],
resample_to=MNI_T2_img)
##########################################################################
# Tracking
# --------
# Streamlines are generate using DTI and a deterministic tractography algorithm.
# For speed, we seed only within the waypoint ROIs for each bundle.
print("Tracking...")
if not op.exists('dti_streamlines.trk'):
seed_roi = np.zeros(img.shape[:-1])
for bundle in bundles:
for idx, roi in enumerate(bundles[bundle]['ROIs']):
if bundles[bundle]['rules'][idx]:
warped_roi = patch_up_roi(
