def _run_interface(self, runtime):
# resampling the reference image to the dimension of the EPI
import SimpleITK as sitk
import os
from rabies.preprocess_pkg.utils import run_command
img = sitk.ReadImage(self.inputs.in_file, self.inputs.rabies_data_type)
if not self.inputs.resampling_dim == 'origin':
shape = self.inputs.resampling_dim.split('x')
spacing = (float(shape[0]), float(shape[1]), float(shape[2]))
else:
spacing = img.GetSpacing()[:3]
resampled = resample_image_spacing(
sitk.ReadImage(self.inputs.ref_file, self.inputs.rabies_data_type),
spacing)
sitk.WriteImage(resampled, 'resampled.nii.gz')
# tranforms is a list of transform files, set in order of call within antsApplyTransforms
transform_string = ""
for transform, inverse in zip(self.inputs.transforms,
self.inputs.inverses):
if bool(inverse):
transform_string += "-t [%s,1] " % (transform, )
else:
transform_string += "-t %s " % (transform, )
print("Splitting bold file into lists of single volumes")
[bold_volumes,
num_volumes] = split_volumes(self.inputs.in_file, "bold_",
self.inputs.rabies_data_type)
if self.inputs.apply_motcorr:
motcorr_params = self.inputs.motcorr_params
ref_img = os.path.abspath('resampled.nii.gz')
warped_volumes = []
for x in range(0, num_volumes):
warped_vol_fname = os.path.abspath("deformed_volume" + str(x) +
".nii.gz")
warped_volumes.append(warped_vol_fname)
if self.inputs.apply_motcorr:
command = 'antsMotionCorrStats -m %s -o motcorr_vol%s.mat -t %s' % (
motcorr_params, x, x)
rc = run_command(command)
command = 'antsApplyTransforms -i %s %s-t motcorr_vol%s.mat -n BSpline[5] -r %s -o %s' % (
bold_volumes[x], transform_string, x, ref_img,
warped_vol_fname)
rc = run_command(command)
else:
command = 'antsApplyTransforms -i %s %s-n BSpline[5] -r %s -o %s' % (
bold_volumes[x], transform_string, ref_img,
warped_vol_fname)
rc = run_command(command)
# change image to specified data type
sitk.WriteImage(
sitk.ReadImage(warped_vol_fname, self.inputs.rabies_data_type),
warped_vol_fname)
setattr(self, 'out_files', warped_volumes)
return runtime
def _run_interface(self, runtime):
import os
import numpy as np
import SimpleITK as sitk
from rabies.preprocess_pkg.utils import resample_image_spacing, run_command
cwd = os.getcwd()
out_dir = '%s/anat_preproc/' % (cwd, )
command = 'mkdir -p %s' % (out_dir, )
rc = run_command(command)
import pathlib # Better path manipulation
filename_split = pathlib.Path(self.inputs.nii_anat).name.rsplit(".nii")
dir_path = os.path.dirname(os.path.realpath(__file__))
output_anat = '%s%s_preproc.nii.gz' % (
out_dir,
filename_split[0],
)
# resample the anatomical image to the resolution of the provided template
anat_image = sitk.ReadImage(self.inputs.nii_anat,
self.inputs.rabies_data_type)
anat_dim = anat_image.GetSpacing()
template_image = sitk.ReadImage(self.inputs.template_anat,
self.inputs.rabies_data_type)
template_dim = template_image.GetSpacing()
if not (np.array(anat_dim) == np.array(template_dim)).sum() == 3:
print('Anat image will be resampled to the template resolution.')
resampled_anat = resample_image_spacing(anat_image, template_dim)
input_anat = out_dir + filename_split[0] + '_resampled.nii.gz'
sitk.WriteImage(resampled_anat, input_anat)
else:
input_anat = self.inputs.nii_anat
if self.inputs.disable_anat_preproc:
# resample image to specified data format
sitk.WriteImage(
sitk.ReadImage(input_anat, self.inputs.rabies_data_type),
output_anat)
else:
command = 'bash %s/../shell_scripts/anat_preproc.sh %s %s %s %s %s' % (
dir_path, input_anat, self.inputs.template_anat,
self.inputs.template_mask, output_anat, self.inputs.reg_script)
rc = run_command(command)
# resample image to specified data format
sitk.WriteImage(
sitk.ReadImage(output_anat, self.inputs.rabies_data_type),
output_anat)
setattr(self, 'preproc_anat', output_anat)
return runtime
def resample_template(template_file,
mask_file,
file_list,
spacing='inputs_defined',
rabies_data_type=8):
import os
import SimpleITK as sitk
import numpy as np
from rabies.preprocess_pkg.utils import resample_image_spacing, run_command
import logging
log = logging.getLogger('root')
if spacing == 'inputs_defined':
file_list = list(np.asarray(file_list).flatten())
img = sitk.ReadImage(file_list[0], rabies_data_type)
low_dim = np.asarray(img.GetSpacing()[:3]).min()
for file in file_list[1:]:
img = sitk.ReadImage(file, rabies_data_type)
new_low_dim = np.asarray(img.GetSpacing()[:3]).min()
if new_low_dim < low_dim:
low_dim = new_low_dim
spacing = (low_dim, low_dim, low_dim)
template_image = sitk.ReadImage(template_file, rabies_data_type)
template_dim = template_image.GetSpacing()
if np.asarray(template_dim[:3]).min() > low_dim:
log.info("The template retains its original resolution.")
return template_file
else:
shape = spacing.split('x')
spacing = (float(shape[0]), float(shape[1]), float(shape[2]))
log.info("Resampling template to %sx%sx%smm dimensions." % (
spacing[0],
spacing[1],
spacing[2],
))
resampled_template = os.path.abspath("resampled_template.nii.gz")
sitk.WriteImage(
resample_image_spacing(sitk.ReadImage(template_file, rabies_data_type),
spacing), resampled_template)
# also resample the brain mask to ensure stable registrations further down
resampled_mask = os.path.abspath("resampled_mask.nii.gz")
command = 'antsApplyTransforms -d 3 -i %s -r %s -o %s --verbose -n GenericLabel' % (
mask_file,
resampled_template,
resampled_mask,
)
rc = run_command(command)
return resampled_template, resampled_mask
def _run_interface(self, runtime):
from rabies.preprocess_pkg.utils import flatten_list
merged = flatten_list(list(self.inputs.mask_dict_list))
mask_dict = merged[0] # all mask files are assumed to be identical
brain_mask_file = mask_dict['brain_mask_file']
WM_mask_file = mask_dict['WM_mask_file']
CSF_mask_file = mask_dict['CSF_mask_file']
# resample the template to the EPI dimensions
from rabies.preprocess_pkg.utils import resample_image_spacing
resampled = resample_image_spacing(
sitk.ReadImage(mask_dict['preprocess_anat_template']),
sitk.ReadImage(brain_mask_file).GetSpacing(),
resampling_interpolation='BSpline')
template_file = os.path.abspath('display_template.nii.gz')
sitk.WriteImage(resampled, template_file)
if self.inputs.DSURQE_regions:
right_hem_mask_file = resample_mask(
os.environ['RABIES'] +
'/template_files/DSURQE_100micron_right_hem_mask.nii.gz',
brain_mask_file)
left_hem_mask_file = resample_mask(
os.environ['RABIES'] +
'/template_files/DSURQE_100micron_left_hem_mask.nii.gz',
brain_mask_file)
else:
right_hem_mask_file = ''
left_hem_mask_file = ''
prior_maps = resample_IC_file(self.inputs.prior_maps, brain_mask_file)
edge_mask_file = os.path.abspath('edge_mask.nii.gz')
compute_edge_mask(brain_mask_file, edge_mask_file, num_edge_voxels=1)
mask_file_dict = {
'template_file': template_file,
'brain_mask': brain_mask_file,
'WM_mask': WM_mask_file,
'CSF_mask': CSF_mask_file,
'edge_mask': edge_mask_file,
'right_hem_mask': right_hem_mask_file,
'left_hem_mask': left_hem_mask_file,
'prior_maps': prior_maps
}
setattr(self, 'mask_file_dict', mask_file_dict)
return runtime
def resample_template(template_file,
file_list,
spacing='inputs_defined',
rabies_data_type=8):
import os
import SimpleITK as sitk
import numpy as np
from rabies.preprocess_pkg.utils import resample_image_spacing
if spacing == 'inputs_defined':
file_list = list(np.asarray(file_list).flatten())
img = sitk.ReadImage(file_list[0], rabies_data_type)
low_dim = np.asarray(img.GetSpacing()[:3]).min()
for file in file_list[1:]:
img = sitk.ReadImage(file, rabies_data_type)
new_low_dim = np.asarray(img.GetSpacing()[:3]).min()
if new_low_dim < low_dim:
low_dim = new_low_dim
spacing = (low_dim, low_dim, low_dim)
template_image = sitk.ReadImage(template_file, rabies_data_type)
template_dim = template_image.GetSpacing()
if np.asarray(template_dim[:3]).min() > low_dim:
print("The template retains its original resolution.")
return template_file
else:
shape = spacing.split('x')
spacing = (float(shape[0]), float(shape[1]), float(shape[2]))
print("Resampling template to %sx%sx%smm dimensions." % (
spacing[0],
spacing[1],
spacing[2],
))
resampled_template = os.path.abspath("resampled_template.nii.gz")
sitk.WriteImage(
resample_image_spacing(sitk.ReadImage(template_file, rabies_data_type),
spacing), resampled_template)
return resampled_template
def _run_interface(self, runtime):
import os
import numpy as np
import SimpleITK as sitk
import pathlib # Better path manipulation
filename_split = pathlib.Path(
self.inputs.name_source).name.rsplit(".nii")
import rabies
from rabies.preprocess_pkg.utils import run_command, resample_image_spacing
dir_path = os.path.dirname(os.path.realpath(rabies.__file__))
reg_script_path = dir_path + '/shell_scripts/antsRegistration_rigid.sh'
cwd = os.getcwd()
warped_image = '%s/%s_output_warped_image.nii.gz' % (cwd,
filename_split[0])
resampled = '%s/%s_resampled.nii.gz' % (cwd, filename_split[0])
resampled_mask = '%s/%s_resampled_mask.nii.gz' % (cwd,
filename_split[0])
biascor_EPI = '%s/%s_bias_cor.nii.gz' % (
cwd,
filename_split[0],
)
# resample to isotropic resolution based on lowest dimension
input_ref_EPI_img = sitk.ReadImage(self.inputs.input_ref_EPI,
self.inputs.rabies_data_type)
dim = input_ref_EPI_img.GetSpacing()
low_dim = np.asarray(dim).min()
#sitk.WriteImage(resample_image_spacing(
# input_ref_EPI_img, (low_dim, low_dim, low_dim)), resampled)
# the -b will be rounded up to the nearest multiple of 10 of the image largest dimension
largest_dim = (np.array(input_ref_EPI_img.GetSize()) *
np.array(input_ref_EPI_img.GetSpacing())).max()
b_value = int(np.ceil(largest_dim / 10) * 10)
bias_cor_input = self.inputs.input_ref_EPI
otsu_bias_cor(target=bias_cor_input,
otsu_ref=bias_cor_input,
out_name='corrected_iter1.nii.gz',
b_value=b_value)
otsu_bias_cor(target=bias_cor_input,
otsu_ref='corrected_iter1.nii.gz',
out_name='corrected_iter2.nii.gz',
b_value=b_value)
command = 'bash %s %s %s %s %s' % (
reg_script_path,
'corrected_iter2.nii.gz',
self.inputs.anat,
self.inputs.anat_mask,
filename_split[0],
)
rc = run_command(command)
command = 'antsApplyTransforms -d 3 -i %s -t [%s_output_0GenericAffine.mat,1] -r %s -o %s -n GenericLabel' % (
self.inputs.anat_mask, filename_split[0], 'corrected_iter2.nii.gz',
resampled_mask)
rc = run_command(command)
otsu_bias_cor(target=bias_cor_input,
otsu_ref='corrected_iter2.nii.gz',
out_name=cwd + '/final_otsu.nii.gz',
b_value=b_value,
mask=resampled_mask)
# resample to anatomical image resolution
dim = sitk.ReadImage(self.inputs.anat,
self.inputs.rabies_data_type).GetSpacing()
low_dim = np.asarray(dim).min()
sitk.WriteImage(
resample_image_spacing(
sitk.ReadImage(cwd + '/final_otsu.nii.gz',
self.inputs.rabies_data_type),
(low_dim, low_dim, low_dim)), biascor_EPI)
sitk.WriteImage(
sitk.ReadImage(biascor_EPI, self.inputs.rabies_data_type),
biascor_EPI)
sitk.WriteImage(
sitk.ReadImage(warped_image, self.inputs.rabies_data_type),
warped_image)
sitk.WriteImage(
sitk.ReadImage(resampled_mask, self.inputs.rabies_data_type),
resampled_mask)
setattr(self, 'corrected_EPI', biascor_EPI)
setattr(self, 'warped_EPI', warped_image)
setattr(self, 'resampled_mask', resampled_mask)
return runtime
def _run_interface(self, runtime):
import os
import numpy as np
import SimpleITK as sitk
import pathlib # Better path manipulation
filename_split = pathlib.Path(
self.inputs.name_source).name.rsplit(".nii")
import rabies
from rabies.preprocess_pkg.utils import run_command, resample_image_spacing
dir_path = os.path.dirname(os.path.realpath(rabies.__file__))
reg_script_path = dir_path + '/shell_scripts/antsRegistration_rigid.sh'
bias_cor_script_path = dir_path + '/shell_scripts/iter_bias_cor.sh'
cwd = os.getcwd()
warped_image = '%s/%s_output_warped_image.nii.gz' % (cwd,
filename_split[0])
resampled_mask = '%s/%s_resampled_mask.nii.gz' % (cwd,
filename_split[0])
biascor_EPI = '%s/%s_bias_cor.nii.gz' % (
cwd,
filename_split[0],
)
# resample to isotropic resolution based on lowest dimension
input_ref_EPI = sitk.ReadImage(self.inputs.input_ref_EPI,
self.inputs.rabies_data_type)
dim = input_ref_EPI.GetSpacing()
low_dim = np.asarray(dim).min()
from rabies.preprocess_pkg.utils import resample_image_spacing
sitk.WriteImage(
resample_image_spacing(input_ref_EPI, (low_dim, low_dim, low_dim)),
cwd + '/resampled.nii.gz')
command = 'bash %s %s %s %s %s %s' % (
bias_cor_script_path, self.inputs.input_ref_EPI, self.inputs.anat,
self.inputs.anat_mask, filename_split[0], reg_script_path)
from rabies.preprocess_pkg.utils import run_command
rc = run_command(command)
# resample to anatomical image resolution
dim = sitk.ReadImage(self.inputs.anat,
self.inputs.rabies_data_type).GetSpacing()
low_dim = np.asarray(dim).min()
sitk.WriteImage(
resample_image_spacing(
sitk.ReadImage(cwd + '/iter_corrected.nii.gz',
self.inputs.rabies_data_type),
(low_dim, low_dim, low_dim)), biascor_EPI)
sitk.WriteImage(
sitk.ReadImage(biascor_EPI, self.inputs.rabies_data_type),
biascor_EPI)
sitk.WriteImage(
sitk.ReadImage(warped_image, self.inputs.rabies_data_type),
warped_image)
sitk.WriteImage(
sitk.ReadImage(resampled_mask, self.inputs.rabies_data_type),
resampled_mask)
setattr(self, 'corrected_EPI', biascor_EPI)
setattr(self, 'warped_EPI', warped_image)
setattr(self, 'resampled_mask', resampled_mask)
return runtime
def _run_interface(self, runtime):
import os
import numpy as np
import SimpleITK as sitk
from rabies.preprocess_pkg.utils import resample_image_spacing, run_command
import pathlib # Better path manipulation
filename_split = pathlib.Path(self.inputs.nii_anat).name.rsplit(".nii")
cwd = os.getcwd()
output_anat = '%s/%s_preproc.nii.gz' % (
cwd,
filename_split[0],
)
# resample the anatomical image to the resolution of the provided template
anat_image = sitk.ReadImage(self.inputs.nii_anat,
self.inputs.rabies_data_type)
anat_dim = anat_image.GetSpacing()
template_image = sitk.ReadImage(self.inputs.template_anat,
self.inputs.rabies_data_type)
template_dim = template_image.GetSpacing()
if not (np.array(anat_dim) == np.array(template_dim)).sum() == 3:
import logging
log = logging.getLogger('root')
log.debug(
'Anat image will be resampled to the template resolution.')
resampled_anat = resample_image_spacing(anat_image, template_dim)
input_anat = '%s/%s_resampled.nii.gz' % (
cwd,
filename_split[0],
)
sitk.WriteImage(resampled_anat, input_anat)
else:
input_anat = self.inputs.nii_anat
if self.inputs.disable_anat_preproc:
# resample image to specified data format
sitk.WriteImage(
sitk.ReadImage(input_anat, self.inputs.rabies_data_type),
output_anat)
init_denoise = output_anat
resampled_mask = self.inputs.template_mask
init_denoise = cwd + '/denoise.nii.gz'
resampled_mask = cwd + '/resampled_mask.nii.gz'
else:
if self.inputs.bias_cor_method == 'otsu_reg':
bias_correction = OtsuEPIBiasCorrection(
input_ref_EPI=input_anat,
anat=self.inputs.template_anat,
anat_mask=self.inputs.template_mask,
name_source=self.inputs.nii_anat,
rabies_data_type=self.inputs.rabies_data_type)
out = bias_correction.run()
output_anat = out.outputs.corrected_EPI
resampled_mask = out.outputs.denoise_mask
init_denoise = out.outputs.init_denoise
elif self.inputs.bias_cor_method == 'thresh_reg':
bias_correction = ThreshBiasCorrection(
input_ref_EPI=input_anat,
anat=self.inputs.template_anat,
anat_mask=self.inputs.template_mask,
name_source=self.inputs.nii_anat,
rabies_data_type=self.inputs.rabies_data_type)
out = bias_correction.run()
output_anat = out.outputs.corrected_EPI
resampled_mask = out.outputs.denoise_mask
init_denoise = out.outputs.init_denoise
elif self.inputs.bias_cor_method == 'mouse-preprocessing-v5.sh':
command = 'rabies-mouse-preprocessing-v5.sh %s %s %s %s' % (
input_anat, output_anat, self.inputs.template_anat,
self.inputs.template_mask)
rc = run_command(command)
resampled_mask = output_anat.split(
'.nii.gz')[0] + '_mask.nii.gz'
init_denoise = output_anat.split(
'.nii.gz')[0] + '_init_denoise.nii.gz'
else:
raise ValueError("Wrong --anat_bias_cor_method.")
# resample image to specified data format
sitk.WriteImage(
sitk.ReadImage(output_anat, self.inputs.rabies_data_type),
output_anat)
setattr(self, 'anat_preproc', output_anat)
setattr(self, 'init_denoise', init_denoise)
setattr(self, 'denoise_mask', resampled_mask)
return runtime
def _run_interface(self, runtime):
import os
import numpy as np
import SimpleITK as sitk
import pathlib # Better path manipulation
filename_split = pathlib.Path(
self.inputs.name_source).name.rsplit(".nii")
from rabies.preprocess_pkg.utils import run_command, resample_image_spacing
from rabies.preprocess_pkg.registration import run_antsRegistration
cwd = os.getcwd()
resampled_mask = '%s/%s_resampled_mask.nii.gz' % (cwd,
filename_split[0])
biascor_EPI = '%s/%s_bias_cor.nii.gz' % (
cwd,
filename_split[0],
)
command = 'ImageMath 3 null_mask.nii.gz ThresholdAtMean %s 0' % (
self.inputs.input_ref_EPI)
rc = run_command(command)
command = 'ImageMath 3 thresh_mask.nii.gz ThresholdAtMean %s 2' % (
self.inputs.input_ref_EPI)
rc = run_command(command)
command = 'N4BiasFieldCorrection -d 3 -s 4 -i %s -b 20 -s 1 -c [100x100x100x100,1e-6] -w thresh_mask.nii.gz -x null_mask.nii.gz -o corrected.nii.gz' % (
self.inputs.input_ref_EPI)
rc = run_command(command)
command = 'DenoiseImage -d 3 -i corrected.nii.gz -o denoise.nii.gz'
rc = run_command(command)
[affine, warp, inverse_warp, warped_image
] = run_antsRegistration(reg_method='Rigid',
moving_image=cwd + '/denoise.nii.gz',
fixed_image=self.inputs.anat,
anat_mask=self.inputs.anat_mask)
command = 'antsApplyTransforms -d 3 -i %s -t [%s,1] -r %s -o %s -n GenericLabel' % (
self.inputs.anat_mask, affine, self.inputs.input_ref_EPI,
resampled_mask)
rc = run_command(command)
command = 'N4BiasFieldCorrection -d 3 -s 2 -i %s -b 20 -s 1 -c [100x100x100x100,1e-6] -w %s -x null_mask.nii.gz -o %s' % (
self.inputs.input_ref_EPI, resampled_mask,
cwd + '/iter_corrected.nii.gz')
rc = run_command(command)
command = 'DenoiseImage -d 3 -i iter_corrected.nii.gz -o iter_corrected_denoise.nii.gz'
rc = run_command(command)
# resample to anatomical image resolution
dim = sitk.ReadImage(self.inputs.anat,
self.inputs.rabies_data_type).GetSpacing()
low_dim = np.asarray(dim).min()
sitk.WriteImage(
resample_image_spacing(
sitk.ReadImage(cwd + '/iter_corrected_denoise.nii.gz',
self.inputs.rabies_data_type),
(low_dim, low_dim, low_dim)), biascor_EPI)
sitk.WriteImage(
sitk.ReadImage(biascor_EPI, self.inputs.rabies_data_type),
biascor_EPI)
sitk.WriteImage(
sitk.ReadImage(warped_image, self.inputs.rabies_data_type),
warped_image)
sitk.WriteImage(
sitk.ReadImage(resampled_mask, self.inputs.rabies_data_type),
resampled_mask)
setattr(self, 'corrected_EPI', biascor_EPI)
setattr(self, 'warped_EPI', warped_image)
setattr(self, 'denoise_mask', resampled_mask)
setattr(self, 'init_denoise', cwd + '/denoise.nii.gz')
return runtime
def _run_interface(self, runtime):
import os
import numpy as np
import SimpleITK as sitk
import pathlib # Better path manipulation
filename_split = pathlib.Path(
self.inputs.name_source).name.rsplit(".nii")
from rabies.preprocess_pkg.utils import run_command, resample_image_spacing
from rabies.preprocess_pkg.registration import run_antsRegistration
cwd = os.getcwd()
resampled = '%s/%s_resampled.nii.gz' % (cwd, filename_split[0])
resampled_mask = '%s/%s_resampled_mask.nii.gz' % (cwd,
filename_split[0])
biascor_EPI = '%s/%s_bias_cor.nii.gz' % (
cwd,
filename_split[0],
)
input_ref_EPI_img = sitk.ReadImage(self.inputs.input_ref_EPI,
self.inputs.rabies_data_type)
# the -b will be rounded up to the nearest multiple of 10 of the image largest dimension
largest_dim = (np.array(input_ref_EPI_img.GetSize()) *
np.array(input_ref_EPI_img.GetSpacing())).max()
b_value = int(np.ceil(largest_dim / 10) * 10)
bias_cor_input = self.inputs.input_ref_EPI
otsu_bias_cor(target=bias_cor_input,
otsu_ref=bias_cor_input,
out_name=cwd + '/corrected_iter1.nii.gz',
b_value=b_value)
otsu_bias_cor(target=bias_cor_input,
otsu_ref=cwd + '/corrected_iter1.nii.gz',
out_name=cwd + '/corrected_iter2.nii.gz',
b_value=b_value)
[affine, warp, inverse_warp, warped_image
] = run_antsRegistration(reg_method='Rigid',
moving_image=cwd + '/corrected_iter2.nii.gz',
fixed_image=self.inputs.anat,
anat_mask=self.inputs.anat_mask)
command = 'antsApplyTransforms -d 3 -i %s -t [%s,1] -r %s -o %s -n GenericLabel' % (
self.inputs.anat_mask, affine, cwd + '/corrected_iter2.nii.gz',
resampled_mask)
rc = run_command(command)
otsu_bias_cor(target=bias_cor_input,
otsu_ref=cwd + '/corrected_iter2.nii.gz',
out_name=cwd + '/final_otsu.nii.gz',
b_value=b_value,
mask=resampled_mask)
# resample to anatomical image resolution
dim = sitk.ReadImage(self.inputs.anat,
self.inputs.rabies_data_type).GetSpacing()
low_dim = np.asarray(dim).min()
sitk.WriteImage(
resample_image_spacing(
sitk.ReadImage(cwd + '/final_otsu.nii.gz',
self.inputs.rabies_data_type),
(low_dim, low_dim, low_dim)), biascor_EPI)
sitk.WriteImage(
sitk.ReadImage(cwd + '/corrected_iter2.nii.gz',
self.inputs.rabies_data_type),
cwd + '/corrected_iter2.nii.gz')
sitk.WriteImage(
sitk.ReadImage(biascor_EPI, self.inputs.rabies_data_type),
biascor_EPI)
sitk.WriteImage(
sitk.ReadImage(warped_image, self.inputs.rabies_data_type),
warped_image)
sitk.WriteImage(
sitk.ReadImage(resampled_mask, self.inputs.rabies_data_type),
resampled_mask)
setattr(self, 'init_denoise', cwd + '/corrected_iter2.nii.gz')
setattr(self, 'corrected_EPI', biascor_EPI)
setattr(self, 'warped_EPI', warped_image)
setattr(self, 'denoise_mask', resampled_mask)
return runtime