def t1w2dwi_align(self):
"""
A function to perform alignment from T1w --> MNI and T1w_MNI --> DWI. Uses a local optimisation
cost function to get the two images close, and then uses bbr to obtain a good alignment of brain boundaries.
Assumes input dwi is already preprocessed and brain extracted.
"""
# Create linear transform/ initializer T1w-->MNI
regutils.align(self.t1w_brain, self.input_mni_brain, xfm=self.t12mni_xfm_init, bins=None, interp="spline",
out=None, dof=12, cost='mutualinfo', searchrad=True)
# Attempt non-linear registration of T1 to MNI template
if self.simple is False:
try:
print('Running non-linear registration: T1w-->MNI ...')
# Use FNIRT to nonlinearly align T1 to MNI template
regutils.align_nonlinear(self.t1w_brain, self.input_mni, xfm=self.t12mni_xfm_init,
out=self.t1_aligned_mni, warp=self.warp_t1w2mni, ref_mask=self.input_mni_mask,
config=self.input_mni_sched)
# Get warp from MNI -> T1
regutils.inverse_warp(self.t1w_brain, self.mni2t1w_warp, self.warp_t1w2mni)
# Get mat from MNI -> T1
os.system("convert_xfm -omat {} -inverse {}".format(self.mni2t1_xfm_init, self.t12mni_xfm_init))
except RuntimeError('Error: FNIRT failed!'):
pass
else:
# Falling back to linear registration
regutils.align(self.t1w_brain, self.input_mni_brain, xfm=self.t12mni_xfm, init=self.t12mni_xfm_init,
bins=None, dof=12, cost='mutualinfo', searchrad=True, interp="spline",
out=self.t1_aligned_mni, sch=None)
# Align T1w-->DWI
regutils.align(self.fa_path, self.t1w_brain, xfm=self.t1w2dwi_xfm, bins=None, interp="spline", dof=6,
cost='mutualinfo', out=None, searchrad=True, sch=None)
os.system("convert_xfm -omat {} -inverse {}".format(self.dwi2t1w_xfm, self.t1w2dwi_xfm))
if self.simple is False:
# Flirt bbr
try:
print('Running FLIRT BBR registration: T1w-->DWI ...')
regutils.align(self.fa_path, self.t1w_brain, wmseg=self.wm_edge, xfm=self.dwi2t1w_bbr_xfm,
init=self.dwi2t1w_xfm, bins=256, dof=7, searchrad=True, interp="spline", out=None,
cost='bbr', sch="${FSLDIR}/etc/flirtsch/bbr.sch")
os.system("convert_xfm -omat {} -inverse {}".format(self.t1w2dwi_bbr_xfm, self.dwi2t1w_bbr_xfm))
# Apply the alignment
regutils.align(self.t1w_brain, self.fa_path, init=self.t1w2dwi_bbr_xfm, xfm=self.t1wtissue2dwi_xfm,
bins=None, interp="spline", dof=7, cost='mutualinfo', out=self.t1w2dwi, searchrad=True,
sch=None)
except RuntimeError('Error: FLIRT BBR failed!'):
pass
else:
# Apply the alignment
regutils.align(self.t1w_brain, self.fa_path, init=self.t1w2dwi_xfm, xfm=self.t1wtissue2dwi_xfm, bins=None,
interp="spline", dof=6, cost='mutualinfo', out=self.t1w2dwi, searchrad=True, sch=None)
return
def test_invwarp():
base_dir = str(Path(__file__).parent/"examples")
anat_dir = f"{base_dir}/003/anat"
ref = f"{anat_dir}/sub-003_T1w.nii.gz"
warp = f"{anat_dir}/highres2standard_warp"
out = f"{anat_dir}/highres2standard_warp_inv.nii.gz"
reg_utils.inverse_warp(ref, out, warp)
out_warp = nib.load(out)
assert out_warp is not None
def t1w2mni_align(self):
"""
A function to perform alignment from T1w --> MNI.
"""
# Create linear transform/ initializer T1w-->MNI
regutils.align(self.t1w_brain, self.input_mni_brain, xfm=self.t12mni_xfm_init, bins=None, interp="spline",
out=None, dof=12, cost='mutualinfo', searchrad=True)
# Attempt non-linear registration of T1 to MNI template
try:
print('Running non-linear registration: T1w-->MNI ...')
# Use FNIRT to nonlinearly align T1 to MNI template
regutils.align_nonlinear(self.t1w_brain, self.input_mni, xfm=self.t12mni_xfm_init, out=self.t1_aligned_mni,
warp=self.warp_t1w2mni, ref_mask=self.input_mni_mask, config=self.input_mni_sched)
# Get warp from MNI -> T1
regutils.inverse_warp(self.t1w_brain, self.mni2t1w_warp, self.warp_t1w2mni)
# Get mat from MNI -> T1
os.system("convert_xfm -omat {} -inverse {}".format(self.mni2t1_xfm_init, self.t12mni_xfm_init))
except RuntimeError('Error: FNIRT failed!'):
pass
def t1w2mni_align(self):
"""
A function to perform alignment from T1w --> MNI template.
"""
import time
# Create linear transform/ initializer T1w-->MNI
regutils.align(
self.t1w_brain,
self.input_mni_brain,
xfm=self.t12mni_xfm_init,
bins=None,
interp="spline",
out=None,
dof=12,
cost="mutualinfo",
searchrad=True,
)
time.sleep(0.5)
# Attempt non-linear registration of T1 to MNI template
if self.simple is False:
try:
print(
f"Learning a non-linear mapping from T1w --> "
f"{self.template_name} ..."
)
# Use FNIRT to nonlinearly align T1 to MNI template
regutils.align_nonlinear(
self.t1w_brain,
self.input_mni,
xfm=self.t12mni_xfm_init,
out=self.t1_aligned_mni,
warp=self.warp_t1w2mni,
ref_mask=self.input_mni_mask,
)
time.sleep(0.5)
# Get warp from MNI -> T1
regutils.inverse_warp(
self.t1w_brain, self.mni2t1w_warp, self.warp_t1w2mni
)
time.sleep(0.5)
# Get mat from MNI -> T1
self.mni2t1_xfm = regutils.invert_xfm(self.t12mni_xfm_init,
self.mni2t1_xfm)
time.sleep(0.5)
except BaseException:
# Falling back to linear registration
regutils.align(
self.t1w_brain,
self.input_mni_brain,
xfm=self.mni2t1_xfm,
init=self.t12mni_xfm_init,
bins=None,
dof=12,
cost="mutualinfo",
searchrad=True,
interp="spline",
out=self.t1_aligned_mni,
sch=None,
)
time.sleep(0.5)
# Get mat from MNI -> T1
self.mni2t1_xfm = regutils.invert_xfm(self.t12mni_xfm,
self.mni2t1_xfm)
time.sleep(0.5)
else:
# Falling back to linear registration
regutils.align(
self.t1w_brain,
self.input_mni_brain,
xfm=self.t12mni_xfm,
init=self.t12mni_xfm_init,
bins=None,
dof=12,
cost="mutualinfo",
searchrad=True,
interp="spline",
out=self.t1_aligned_mni,
sch=None,
)
time.sleep(0.5)
# Get mat from MNI -> T1
self.t12mni_xfm = regutils.invert_xfm(self.mni2t1_xfm,
self.t12mni_xfm)
time.sleep(0.5)
