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 run_group_ICA(bold_file_list, mask_file, dim, tr):
import os
import pandas as pd
# create a filelist.txt
file_path = os.path.abspath('filelist.txt')
from rabies.preprocess_pkg.utils import flatten_list
merged = flatten_list(list(bold_file_list))
df = pd.DataFrame(data=merged)
df.to_csv(file_path, header=False, sep=',', index=False)
from rabies.preprocess_pkg.utils import run_command
out_dir = os.path.abspath('group_melodic.ica')
command = 'melodic -i %s -m %s -o %s --tr=%s -d %s --report' % (
file_path, mask_file, out_dir, tr, dim)
rc = run_command(command)
IC_file = out_dir + '/melodic_IC.nii.gz'
return out_dir, IC_file
def _run_interface(self, runtime):
import os
import pandas as pd
import pathlib
from rabies.preprocess_pkg.utils import run_command
cwd = os.getcwd()
template_folder = self.inputs.output_folder + '/ants_dbm_outputs/'
if os.path.isdir(template_folder):
# remove previous run
command = 'rm -r %s' % (template_folder, )
rc = run_command(command)
command = 'mkdir -p %s' % (template_folder, )
rc = run_command(command)
# create a csv file of the input image list
csv_path = cwd + '/commonspace_input_files.csv'
from rabies.preprocess_pkg.utils import flatten_list
merged = flatten_list(list(self.inputs.moving_image))
if len(merged) == 1:
import logging
log = logging.getLogger('root')
log.info(
"Only a single scan was provided as input for commonspace registration. Commonspace registration "
"won't be run, and the output template will be the input scan."
)
# create an identity transform as a surrogate for the commonspace transforms
import SimpleITK as sitk
dimension = 3
identity = sitk.Transform(dimension, sitk.sitkIdentity)
file = merged[0]
filename_template = pathlib.Path(file).name.rsplit(".nii")[0]
transform_file = template_folder + filename_template + '_identity.mat'
sitk.WriteTransform(identity, transform_file)
setattr(self, 'warped_image', file)
setattr(self, 'affine_list', [transform_file])
setattr(self, 'warp_list', [transform_file])
setattr(self, 'inverse_warp_list', [transform_file])
setattr(self, 'warped_anat_list', [file])
return runtime
df = pd.DataFrame(data=merged)
df.to_csv(csv_path, header=False, sep=',', index=False)
command = 'cd %s ; ants_dbm.sh %s %s %s %s %s %s' % (
template_folder, csv_path, self.inputs.template_anat,
self.inputs.cluster_type, self.inputs.walltime,
self.inputs.memory_request, self.inputs.local_threads)
rc = run_command(command)
# verify that all outputs are present
ants_dbm_template = template_folder + \
'/output/secondlevel/secondlevel_template0.nii.gz'
if not os.path.isfile(ants_dbm_template):
raise ValueError(ants_dbm_template + " doesn't exists.")
affine_list = []
warp_list = []
inverse_warp_list = []
warped_anat_list = []
i = 0
for file in merged:
file = str(file)
filename_template = pathlib.Path(file).name.rsplit(".nii")[0]
anat_to_template_inverse_warp = '%s/output/secondlevel/secondlevel_%s%s1InverseWarp.nii.gz' % (
template_folder,
filename_template,
str(i),
)
if not os.path.isfile(anat_to_template_inverse_warp):
raise ValueError(anat_to_template_inverse_warp +
" file doesn't exists.")
anat_to_template_warp = '%s/output/secondlevel/secondlevel_%s%s1Warp.nii.gz' % (
template_folder,
filename_template,
str(i),
)
if not os.path.isfile(anat_to_template_warp):
raise ValueError(anat_to_template_warp +
" file doesn't exists.")
anat_to_template_affine = '%s/output/secondlevel/secondlevel_%s%s0GenericAffine.mat' % (
template_folder,
filename_template,
str(i),
)
if not os.path.isfile(anat_to_template_affine):
raise ValueError(anat_to_template_affine +
" file doesn't exists.")
warped_anat = '%s/output/secondlevel/secondlevel_template0%s%sWarpedToTemplate.nii.gz' % (
template_folder,
filename_template,
str(i),
)
if not os.path.isfile(warped_anat):
raise ValueError(warped_anat + " file doesn't exists.")
inverse_warp_list.append(anat_to_template_inverse_warp)
warp_list.append(anat_to_template_warp)
affine_list.append(anat_to_template_affine)
warped_anat_list.append(warped_anat)
i += 1
setattr(self, 'warped_image', ants_dbm_template)
setattr(self, 'affine_list', affine_list)
setattr(self, 'warp_list', warp_list)
setattr(self, 'inverse_warp_list', inverse_warp_list)
setattr(self, 'warped_anat_list', warped_anat_list)
return runtime
def _run_interface(self, runtime):
import os
import pandas as pd
import pathlib
cwd = os.getcwd()
template_folder = self.inputs.output_folder + '/ants_dbm_outputs/'
if os.path.isdir(template_folder):
print(
'Previous ants_dbm_outputs/ folder detected. Inputs from a previous run may cause issues for the commonspace registration, so consider removing the previous folder before running again.'
)
command = 'mkdir -p %s' % (template_folder, )
from rabies.preprocess_pkg.utils import run_command
rc = run_command(command)
# create a csv file of the input image list
csv_path = cwd + '/commonspace_input_files.csv'
from rabies.preprocess_pkg.utils import flatten_list
merged = flatten_list(list(self.inputs.moving_image))
if len(merged) == 1:
print(
"Only a single scan was provided as input for commonspace registration. Commonspace registration "
"won't be run, and the output template will be the input scan."
)
# create an identity transform as a surrogate for the commonspace transforms
import SimpleITK as sitk
dimension = 3
identity = sitk.Transform(dimension, sitk.sitkIdentity)
file = merged[0]
filename_template = pathlib.Path(file).name.rsplit(".nii")[0]
transform_file = template_folder + filename_template + '_identity.mat'
sitk.WriteTransform(identity, transform_file)
setattr(self, 'warped_image', file)
setattr(self, 'affine_list', [transform_file])
setattr(self, 'warp_list', [transform_file])
setattr(self, 'inverse_warp_list', [transform_file])
setattr(self, 'warped_anat_list', [file])
return runtime
df = pd.DataFrame(data=merged)
df.to_csv(csv_path, header=False, sep=',', index=False)
import rabies
dir_path = os.path.dirname(os.path.realpath(rabies.__file__))
model_script_path = dir_path + '/shell_scripts/ants_dbm.sh'
command = 'cd %s ; bash %s %s %s %s %s %s %s' % (
template_folder, model_script_path, csv_path,
self.inputs.template_anat, self.inputs.cluster_type,
self.inputs.walltime, self.inputs.memory_request,
self.inputs.local_threads)
import subprocess
try:
process = subprocess.run(
command,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
check=True,
shell=True,
)
except Exception as e:
print(e.output.decode("utf-8"))
#log.warning(e.output.decode("utf-8"))
#rc = run_command(command)
# verify that all outputs are present
ants_dbm_template = template_folder + \
'/output/secondlevel/secondlevel_template0.nii.gz'
if not os.path.isfile(ants_dbm_template):
raise ValueError(ants_dbm_template + " doesn't exists.")
affine_list = []
warp_list = []
inverse_warp_list = []
warped_anat_list = []
i = 0
for file in merged:
file = str(file)
filename_template = pathlib.Path(file).name.rsplit(".nii")[0]
anat_to_template_inverse_warp = '%s/output/secondlevel/secondlevel_%s%s1InverseWarp.nii.gz' % (
template_folder,
filename_template,
str(i),
)
if not os.path.isfile(anat_to_template_inverse_warp):
raise ValueError(anat_to_template_inverse_warp +
" file doesn't exists.")
anat_to_template_warp = '%s/output/secondlevel/secondlevel_%s%s1Warp.nii.gz' % (
template_folder,
filename_template,
str(i),
)
if not os.path.isfile(anat_to_template_warp):
raise ValueError(anat_to_template_warp +
" file doesn't exists.")
anat_to_template_affine = '%s/output/secondlevel/secondlevel_%s%s0GenericAffine.mat' % (
template_folder,
filename_template,
str(i),
)
if not os.path.isfile(anat_to_template_affine):
raise ValueError(anat_to_template_affine +
" file doesn't exists.")
warped_anat = '%s/output/secondlevel/secondlevel_template0%s%sWarpedToTemplate.nii.gz' % (
template_folder,
filename_template,
str(i),
)
if not os.path.isfile(warped_anat):
raise ValueError(warped_anat + " file doesn't exists.")
inverse_warp_list.append(anat_to_template_inverse_warp)
warp_list.append(anat_to_template_warp)
affine_list.append(anat_to_template_affine)
warped_anat_list.append(warped_anat)
i += 1
setattr(self, 'warped_image', ants_dbm_template)
setattr(self, 'affine_list', affine_list)
setattr(self, 'warp_list', warp_list)
setattr(self, 'inverse_warp_list', inverse_warp_list)
setattr(self, 'warped_anat_list', warped_anat_list)
return runtime
def _run_interface(self, runtime):
from rabies.preprocess_pkg.utils import flatten_list
merged = flatten_list(list(self.inputs.spatial_info_list))
if len(merged) < 3:
import logging
log = logging.getLogger('root')
log.warning(
"Cannot run statistics on a sample size smaller than 3, so an empty figure is generated."
)
fig, axes = plt.subplots()
fig.savefig(os.path.abspath('empty_dataset_diagnosis.png'),
bbox_inches='tight')
setattr(self, 'figure_dataset_diagnosis',
os.path.abspath('empty_dataset_diagnosis.png'))
return runtime
dict_keys = [
'temporal_std', 'VE_spatial', 'GS_corr', 'DVARS_corr', 'FD_corr',
'DR_BOLD', 'prior_modeling_maps'
]
voxelwise_list = []
for spatial_info in merged:
sub_list = [spatial_info[key] for key in dict_keys]
voxelwise_sub = np.array(sub_list[:5])
if len(sub_list[6]) > 0:
voxelwise_sub = np.concatenate(
(voxelwise_sub, np.array(sub_list[5]), np.array(
sub_list[6])),
axis=0)
else:
voxelwise_sub = np.concatenate(
(voxelwise_sub, np.array(sub_list[5])), axis=0)
voxelwise_list.append(voxelwise_sub)
num_DR_maps = len(sub_list[5])
num_prior_maps = len(sub_list[6])
voxelwise_array = np.array(voxelwise_list)
label_name = [
'temporal_std', 'VE_spatial', 'GS_corr', 'DVARS_corr', 'FD_corr'
]
label_name += [
'BOLD Dual Regression map %s' % (i) for i in range(num_DR_maps)
]
label_name += [
'BOLD Dual Convergence map %s' % (i) for i in range(num_prior_maps)
]
template_file = self.inputs.mask_file_dict['template_file']
mask_file = self.inputs.mask_file_dict['brain_mask']
from rabies.preprocess_pkg.preprocess_visual_QC import plot_3d, otsu_scaling
scaled = otsu_scaling(template_file)
ncols = 5
fig, axes = plt.subplots(nrows=voxelwise_array.shape[1],
ncols=ncols,
figsize=(12 * ncols,
2 * voxelwise_array.shape[1]))
for i, x_label in zip(range(voxelwise_array.shape[1]), label_name):
for j, y_label in zip(range(ncols), label_name[:ncols]):
ax = axes[i, j]
if i <= j:
ax.axis('off')
continue
X = voxelwise_array[:, i, :]
Y = voxelwise_array[:, j, :]
corr = elementwise_corrcoef(X, Y)
plot_3d([ax],
scaled,
fig,
vmin=0,
vmax=1,
cmap='gray',
alpha=1,
cbar=False,
num_slices=6,
planes=('coronal'))
analysis_functions.recover_3D(
mask_file, corr).to_filename('temp_img.nii.gz')
sitk_img = sitk.ReadImage('temp_img.nii.gz')
plot_3d([ax],
sitk_img,
fig,
vmin=-0.7,
vmax=0.7,
cmap='cold_hot',
alpha=1,
cbar=True,
threshold=0.1,
num_slices=6,
planes=('coronal'))
ax.set_title('Cross-correlation for %s and %s' %
(x_label, y_label),
fontsize=15)
fig.savefig(os.path.abspath('dataset_diagnosis.png'),
bbox_inches='tight')
setattr(self, 'figure_dataset_diagnosis',
os.path.abspath('dataset_diagnosis.png'))
return runtime