def t1w2dwi_align(self):
"""
A function to perform alignment from T1w_MNI --> DWI. Uses a local
optimization 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.
"""
import time
# Align T1w-->DWI
regutils.align(
self.ap_path,
self.t1w_brain,
xfm=self.t1w2dwi_xfm,
bins=None,
interp="spline",
dof=6,
cost="mutualinfo",
out=None,
searchrad=True,
sch=None,
)
time.sleep(0.5)
self.dwi2t1w_xfm = regutils.invert_xfm(self.t1w2dwi_xfm,
self.dwi2t1w_xfm)
time.sleep(0.5)
if self.simple is False:
# Flirt bbr
try:
print("Learning a Boundary-Based Mapping from 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",
)
time.sleep(0.5)
self.t1w2dwi_bbr_xfm = regutils.invert_xfm(
self.dwi2t1w_bbr_xfm, self.t1w2dwi_bbr_xfm)
time.sleep(0.5)
# Apply the alignment
regutils.align(
self.t1w_brain,
self.ap_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,
)
time.sleep(0.5)
except BaseException:
# Apply the alignment
regutils.align(
self.t1w_brain,
self.ap_path,
init=self.t1w2dwi_xfm,
xfm=self.t1wtissue2dwi_xfm,
bins=None,
interp="spline",
dof=7,
cost="mutualinfo",
out=self.t1w2dwi,
searchrad=True,
sch=None,
)
time.sleep(0.5)
else:
# Apply the alignment
regutils.align(
self.t1w_brain,
self.ap_path,
init=self.t1w2dwi_xfm,
xfm=self.t1wtissue2dwi_xfm,
bins=None,
interp="spline",
dof=6,
cost="mutualinfo",
out=self.t1w2dwi,
searchrad=True,
sch=None,
)
time.sleep(0.5)
return
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)
