def atlas2t1w2dwi_align(
uatlas,
uatlas_parcels,
atlas,
t1w_brain,
t1w_brain_mask,
mni2t1w_warp,
t1_aligned_mni,
ap_path,
t1w2dwi_bbr_xfm,
mni2t1_xfm,
t1w2dwi_xfm,
wm_gm_int_in_dwi,
aligned_atlas_t1mni,
aligned_atlas_skull,
dwi_aligned_atlas,
dwi_aligned_atlas_wmgm_int,
B0_mask,
simple,
):
"""
A function to perform atlas alignment atlas --> T1 --> dwi.
Tries nonlinear registration first, and if that fails, does a linear registration instead. For this to succeed,
must first have called t1w2dwi_align.
"""
from nilearn.image import resample_to_img
from pynets.core.utils import checkConsecutive
from pynets.registration import reg_utils as regutils
from nilearn.image import math_img
from nilearn.masking import intersect_masks
template_img = nib.load(t1_aligned_mni)
if uatlas_parcels:
uatlas_res_template = resample_to_img(nib.load(uatlas_parcels),
template_img,
interpolation="nearest")
else:
uatlas_res_template = resample_to_img(nib.load(uatlas),
template_img,
interpolation="nearest")
uatlas_res_template_data = np.asarray(uatlas_res_template.dataobj)
uatlas_res_template_data[
uatlas_res_template_data != uatlas_res_template_data.astype(int)] = 0
uatlas_res_template = nib.Nifti1Image(
uatlas_res_template_data.astype("int32"),
affine=uatlas_res_template.affine,
header=uatlas_res_template.header,
)
nib.save(uatlas_res_template, aligned_atlas_t1mni)
if simple is False:
try:
regutils.apply_warp(
t1w_brain,
aligned_atlas_t1mni,
aligned_atlas_skull,
warp=mni2t1w_warp,
interp="nn",
sup=True,
mask=t1w_brain_mask,
)
# Apply linear transformation from template to dwi space
regutils.align(
aligned_atlas_skull,
ap_path,
init=t1w2dwi_bbr_xfm,
out=dwi_aligned_atlas,
dof=6,
searchrad=True,
interp="nearestneighbour",
cost="mutualinfo",
)
except BaseException:
print(
"Warning: Atlas is not in correct dimensions, or input is low quality,\nusing linear template "
"registration.")
regutils.align(
aligned_atlas_t1mni,
t1w_brain,
init=mni2t1_xfm,
out=aligned_atlas_skull,
dof=6,
searchrad=True,
interp="nearestneighbour",
cost="mutualinfo",
)
regutils.align(
aligned_atlas_skull,
ap_path,
init=t1w2dwi_bbr_xfm,
out=dwi_aligned_atlas,
dof=6,
searchrad=True,
interp="nearestneighbour",
cost="mutualinfo",
)
else:
regutils.align(
aligned_atlas_t1mni,
t1w_brain,
init=mni2t1_xfm,
out=aligned_atlas_skull,
dof=6,
searchrad=True,
interp="nearestneighbour",
cost="mutualinfo",
)
regutils.align(
aligned_atlas_skull,
ap_path,
init=t1w2dwi_xfm,
out=dwi_aligned_atlas,
dof=6,
searchrad=True,
interp="nearestneighbour",
cost="mutualinfo",
)
atlas_img = nib.load(dwi_aligned_atlas)
wm_gm_img = nib.load(wm_gm_int_in_dwi)
wm_gm_mask_img = math_img("img > 0", img=wm_gm_img)
atlas_mask_img = math_img("img > 0", img=atlas_img)
uatlas_res_template_data = np.asarray(atlas_img.dataobj)
uatlas_res_template_data[
uatlas_res_template_data != uatlas_res_template_data.astype(int)] = 0
atlas_img_corr = nib.Nifti1Image(
uatlas_res_template_data.astype("uint32"),
affine=atlas_img.affine,
header=atlas_img.header,
)
dwi_aligned_atlas_wmgm_int_img = intersect_masks(
[wm_gm_mask_img, atlas_mask_img], threshold=0, connected=False)
nib.save(atlas_img_corr, dwi_aligned_atlas)
nib.save(dwi_aligned_atlas_wmgm_int_img, dwi_aligned_atlas_wmgm_int)
os.system(
f"fslmaths {dwi_aligned_atlas} -mas {B0_mask} {dwi_aligned_atlas} "
f"2>/dev/null")
os.system(f"fslmaths {dwi_aligned_atlas_wmgm_int} -mas {B0_mask} "
f"{dwi_aligned_atlas_wmgm_int} 2>/dev/null")
final_dat = atlas_img_corr.get_fdata()
unique_a = sorted(set(np.array(final_dat.flatten().tolist())))
if not checkConsecutive(unique_a):
print("Warning! Non-consecutive integers found in parcellation...")
atlas_img.uncache()
atlas_img_corr.uncache()
atlas_mask_img.uncache()
wm_gm_img.uncache()
wm_gm_mask_img.uncache()
return dwi_aligned_atlas_wmgm_int, dwi_aligned_atlas, aligned_atlas_t1mni
def atlas2t1w2dwi_align(
uatlas,
uatlas_parcels,
atlas,
t1w_brain,
t1w_brain_mask,
mni2t1w_warp,
t1_aligned_mni,
ap_path,
mni2t1_xfm,
t1w2dwi_xfm,
wm_gm_int_in_dwi,
aligned_atlas_t1mni,
aligned_atlas_skull,
dwi_aligned_atlas,
dwi_aligned_atlas_wmgm_int,
B0_mask,
mni2dwi_xfm,
simple,
):
"""
A function to perform atlas alignment atlas --> T1 --> dwi.
Tries nonlinear registration first, and if that fails, does a linear
registration instead. For this to succeed, must first have called
t1w2dwi_align.
"""
import time
from nilearn.image import resample_to_img
from pynets.core.utils import checkConsecutive
from pynets.registration import utils as regutils
from nilearn.image import math_img
from nilearn.masking import intersect_masks
template_img = nib.load(t1_aligned_mni)
if uatlas_parcels:
atlas_img_orig = nib.load(uatlas_parcels)
else:
atlas_img_orig = nib.load(uatlas)
old_count = len(np.unique(np.asarray(atlas_img_orig.dataobj)))
uatlas_res_template = resample_to_img(atlas_img_orig,
template_img,
interpolation="nearest")
uatlas_res_template = nib.Nifti1Image(
np.asarray(uatlas_res_template.dataobj).astype('uint16'),
affine=uatlas_res_template.affine,
header=uatlas_res_template.header,
)
nib.save(uatlas_res_template, aligned_atlas_t1mni)
if simple is False:
try:
regutils.apply_warp(
t1w_brain,
aligned_atlas_t1mni,
aligned_atlas_skull,
warp=mni2t1w_warp,
interp="nn",
sup=True,
mask=t1w_brain_mask,
)
time.sleep(0.5)
# Apply linear transformation from template to dwi space
regutils.applyxfm(ap_path,
aligned_atlas_skull,
t1w2dwi_xfm,
dwi_aligned_atlas,
interp="nearestneighbour")
time.sleep(0.5)
except BaseException:
print(
"Warning: Atlas is not in correct dimensions, or input is low"
" quality,\nusing linear template registration.")
regutils.applyxfm(t1w_brain,
aligned_atlas_t1mni,
mni2t1_xfm,
aligned_atlas_skull,
interp="nearestneighbour")
time.sleep(0.5)
combine_xfms(mni2t1_xfm, t1w2dwi_xfm, mni2dwi_xfm)
time.sleep(0.5)
regutils.applyxfm(ap_path,
aligned_atlas_t1mni,
mni2dwi_xfm,
dwi_aligned_atlas,
interp="nearestneighbour")
time.sleep(0.5)
else:
regutils.applyxfm(t1w_brain,
aligned_atlas_t1mni,
mni2t1_xfm,
aligned_atlas_skull,
interp="nearestneighbour")
time.sleep(0.5)
combine_xfms(mni2t1_xfm, t1w2dwi_xfm, mni2dwi_xfm)
time.sleep(0.5)
regutils.applyxfm(ap_path,
aligned_atlas_t1mni,
mni2dwi_xfm,
dwi_aligned_atlas,
interp="nearestneighbour")
time.sleep(0.5)
atlas_img = nib.load(dwi_aligned_atlas)
wm_gm_img = nib.load(wm_gm_int_in_dwi)
wm_gm_mask_img = math_img("img > 0", img=wm_gm_img)
atlas_mask_img = math_img("img > 0", img=atlas_img)
atlas_img_corr = nib.Nifti1Image(
np.asarray(atlas_img.dataobj).astype('uint16'),
affine=atlas_img.affine,
header=atlas_img.header,
)
# Get the union of masks
dwi_aligned_atlas_wmgm_int_img = intersect_masks(
[wm_gm_mask_img, atlas_mask_img], threshold=0, connected=False)
nib.save(atlas_img_corr, dwi_aligned_atlas)
nib.save(dwi_aligned_atlas_wmgm_int_img, dwi_aligned_atlas_wmgm_int)
dwi_aligned_atlas = regutils.apply_mask_to_image(dwi_aligned_atlas,
B0_mask,
dwi_aligned_atlas)
time.sleep(0.5)
dwi_aligned_atlas_wmgm_int = regutils.apply_mask_to_image(
dwi_aligned_atlas_wmgm_int, B0_mask, dwi_aligned_atlas_wmgm_int)
time.sleep(0.5)
final_dat = atlas_img_corr.get_fdata()
unique_a = sorted(set(np.array(final_dat.flatten().tolist())))
if not checkConsecutive(unique_a):
print("Warning! Non-consecutive integers found in parcellation...")
new_count = len(unique_a)
diff = np.abs(np.int(float(new_count) - float(old_count)))
print(f"Previous label count: {old_count}")
print(f"New label count: {new_count}")
print(f"Labels dropped: {diff}")
atlas_img.uncache()
atlas_img_corr.uncache()
atlas_img_orig.uncache()
atlas_mask_img.uncache()
wm_gm_img.uncache()
wm_gm_mask_img.uncache()
return dwi_aligned_atlas_wmgm_int, dwi_aligned_atlas, aligned_atlas_skull
def atlas2t1w_align(
uatlas,
uatlas_parcels,
atlas,
t1w_brain,
t1w_brain_mask,
t1_aligned_mni,
mni2t1w_warp,
mni2t1_xfm,
gm_mask,
aligned_atlas_t1mni,
aligned_atlas_skull,
aligned_atlas_gm,
simple,
):
"""
A function to perform atlas alignment from atlas --> T1w.
"""
from pynets.registration import reg_utils as regutils
from nilearn.image import resample_to_img
from pynets.core.utils import checkConsecutive
template_img = nib.load(t1_aligned_mni)
if uatlas_parcels:
uatlas_res_template = resample_to_img(nib.load(uatlas_parcels),
template_img,
interpolation="nearest")
else:
uatlas_res_template = resample_to_img(nib.load(uatlas),
template_img,
interpolation="nearest")
uatlas_res_template_data = np.asarray(uatlas_res_template.dataobj)
uatlas_res_template_data[
uatlas_res_template_data != uatlas_res_template_data.astype(int)] = 0
uatlas_res_template = nib.Nifti1Image(
uatlas_res_template_data.astype("uint16"),
affine=uatlas_res_template.affine,
header=uatlas_res_template.header,
)
nib.save(uatlas_res_template, aligned_atlas_t1mni)
if simple is False:
try:
regutils.apply_warp(
t1w_brain,
aligned_atlas_t1mni,
aligned_atlas_skull,
warp=mni2t1w_warp,
interp="nn",
sup=True,
mask=t1w_brain_mask,
)
except BaseException:
print(
"Warning: Atlas is not in correct dimensions, or input is low quality,\nusing linear template "
"registration.")
regutils.align(
aligned_atlas_t1mni,
t1w_brain,
init=mni2t1_xfm,
out=aligned_atlas_skull,
dof=6,
searchrad=True,
interp="nearestneighbour",
cost="mutualinfo",
)
else:
regutils.align(
aligned_atlas_t1mni,
t1w_brain,
init=mni2t1_xfm,
out=aligned_atlas_skull,
dof=6,
searchrad=True,
interp="nearestneighbour",
cost="mutualinfo",
)
os.system(f"fslmaths {aligned_atlas_skull} -mas {gm_mask} "
f"{aligned_atlas_gm} 2>/dev/null")
atlas_img = nib.load(aligned_atlas_gm)
uatlas_res_template_data = np.asarray(atlas_img.dataobj)
uatlas_res_template_data[
uatlas_res_template_data != uatlas_res_template_data.astype(int)] = 0
atlas_img_corr = nib.Nifti1Image(
uatlas_res_template_data.astype("uint32"),
affine=atlas_img.affine,
header=atlas_img.header,
)
nib.save(atlas_img_corr, aligned_atlas_gm)
final_dat = atlas_img_corr.get_fdata()
unique_a = sorted(set(np.array(final_dat.flatten().tolist())))
if not checkConsecutive(unique_a):
old_count = len(np.unique(uatlas_res_template_data))
new_count = len(unique_a)
diff = np.abs(np.int(float(new_count) - float(old_count)))
print("\nWarning! Non-consecutive integers found in parcellation...")
print(f"Previous label count: {old_count}")
print(f"New label count: {new_count}")
print(f"Labels dropped: {diff}")
if diff > 1:
print('Grey-Matter mask too restrictive for this parcellation. '
'Falling back to the T1w mask...')
os.system(f"fslmaths {aligned_atlas_skull} -mas {t1w_brain_mask} "
f"{aligned_atlas_gm} 2>/dev/null")
template_img.uncache()
return aligned_atlas_gm, aligned_atlas_skull
