def merge_tsv(
bids_directory: str,
output_tsv: str,
caps_directory: Optional[str] = None,
pipelines: Optional[List[str]] = None,
volume_atlas_selection: Optional[List[str]] = None,
freesurfer_atlas_selection: Optional[List[str]] = None,
pvc_restriction: Optional[int] = None,
pet_tracers_selection: Optional[List[str]] = None,
group_selection: Optional[List[str]] = None,
subjects_sessions_tsv: Optional[str] = None,
ignore_scan_files: bool = False,
ignore_session_scan_files: bool = False,
) -> None:
"""Merge clinical data into a single TSV file."""
from clinica.iotools.utils.data_handling import create_merge_file
from clinica.utils.inputs import check_bids_folder
check_bids_folder(bids_directory)
create_merge_file(
bids_directory,
output_tsv,
caps_dir=caps_directory,
pipelines=pipelines,
ignore_scan_files=ignore_scan_files,
ignore_sessions_files=ignore_session_scan_files,
volume_atlas_selection=volume_atlas_selection,
freesurfer_atlas_selection=freesurfer_atlas_selection,
pvc_restriction=pvc_restriction,
tsv_file=subjects_sessions_tsv,
group_selection=group_selection,
tracers_selection=pet_tracers_selection,
)
def run_command(self, args):
from clinica.iotools.utils import data_handling as dt
from clinica.utils.inputs import check_bids_folder
check_bids_folder(args.bids_directory)
dt.compute_missing_mods(args.bids_directory, args.out_directory,
args.output_prefix)
def check_missing_modalities(
bids_directory: str,
output_directory: str,
output_prefix: str = "missing_mods",
) -> None:
"""Check missing modalities in a BIDS dataset."""
from clinica.iotools.utils.data_handling import compute_missing_mods
from clinica.utils.inputs import check_bids_folder
check_bids_folder(bids_directory)
compute_missing_mods(bids_directory, output_directory, output_prefix)
def run_command(self, args):
import os
from clinica.iotools.utils import data_handling as dt
from clinica.utils.inputs import check_bids_folder
from clinica.utils.stream import cprint
check_bids_folder(args.bids_directory)
output_directory = os.path.dirname(os.path.abspath(args.out_tsv))
os.makedirs(output_directory, exist_ok=True)
dt.create_subs_sess_list(args.bids_directory, output_directory,
os.path.basename(args.out_tsv))
cprint("The TSV file was saved to %s" % os.path.abspath(args.out_tsv))
def run_command(self, args):
from clinica.iotools.utils import data_handling as dt
from clinica.utils.inputs import check_bids_folder
check_bids_folder(args.bids_directory)
dt.create_merge_file(args.bids_directory,
args.out_tsv,
caps_dir=args.caps_directory,
pipelines=args.pipelines,
atlas_selection=args.atlas_selection,
pvc_restriction=args.pvc_restriction,
tsv_file=args.subjects_sessions_tsv,
group_selection=args.group_selection)
def create_subjects_visits(bids_directory: str, output_tsv: str) -> None:
"""Export participants with their sessions."""
from os import makedirs
from os.path import basename, dirname
from clinica.iotools.utils.data_handling import create_subs_sess_list
from clinica.utils.inputs import check_bids_folder
from clinica.utils.stream import cprint
check_bids_folder(bids_directory)
output_directory = dirname(output_tsv)
makedirs(output_directory, exist_ok=True)
create_subs_sess_list(bids_directory, output_directory,
basename(output_tsv))
cprint(f"The TSV file was saved to {output_tsv}.")
def run_command(self, args):
import os
import errno
from clinica.iotools.utils import data_handling as dt
from clinica.utils.stream import cprint
from clinica.utils.inputs import check_bids_folder
check_bids_folder(args.bids_directory)
output_directory = os.path.dirname(os.path.abspath(args.out_tsv))
if not os.path.exists(output_directory):
try:
os.makedirs(output_directory)
except OSError as exc: # Guard against race condition
if exc.errno != errno.EEXIST:
raise
dt.create_subs_sess_list(args.bids_directory, output_directory,
os.path.basename(args.out_tsv))
cprint("The TSV file was saved to %s" % os.path.abspath(args.out_tsv))
def run_command(self, args):
from clinica.iotools.utils import data_handling as dt
from clinica.utils.inputs import check_bids_folder
check_bids_folder(args.bids_directory)
dt.create_merge_file(
args.bids_directory,
args.out_tsv,
caps_dir=args.caps_directory,
pipelines=args.pipelines,
ignore_scan_files=args.ignore_scan_files,
ignore_sessions_files=args.ignore_session_scan_files,
volume_atlas_selection=args.volume_atlas_selection,
freesurfer_atlas_selection=args.freesurfer_atlas_selection,
pvc_restriction=args.pvc_restriction,
tsv_file=args.subjects_sessions_tsv,
group_selection=args.group_selection,
tracers_selection=args.pet_tracers_selection,
)
def center_all_nifti(bids_dir, output_dir, modality, center_all_files=False):
"""
Center all the NIfTI images of the input BIDS folder into the empty output_dir specified in argument.
All the files from bids_dir are copied into output_dir, then all the NIfTI images we can found are replaced by their
centered version if their center if off the origin by more than 50 mm.
Args:
bids_dir: (str) path to bids directory
output_dir: (str) path to EMPTY output directory
modality: (list of str) modalities to convert
center_all_files: (bool) center only files that may cause problem for SPM if false. If true, center all NIfTI
Returns:
List of the centered files
"""
from colorama import Fore
from clinica.utils.inputs import check_bids_folder
from clinica.utils.exceptions import ClinicaBIDSError
from os.path import join, basename
from glob import glob
from os import listdir
from os.path import isdir, isfile
from shutil import copy2, copytree
# output and input must be different, so that we do not mess with user's data
if bids_dir == output_dir:
raise ClinicaBIDSError(
Fore.RED +
'[Error] Input BIDS and output directories must be different' +
Fore.RESET)
assert isinstance(modality, list), 'modality arg must be a list of str'
# check that input is a BIDS dir
check_bids_folder(bids_dir)
for f in listdir(bids_dir):
if isdir(join(bids_dir, f)) and not isdir(join(output_dir, f)):
copytree(join(bids_dir, f), join(output_dir, f))
elif isfile(join(bids_dir, f)) and not isfile(join(output_dir, f)):
copy2(join(bids_dir, f), output_dir)
pattern = join(output_dir, '**/*.nii*')
nifti_files = glob(pattern, recursive=True)
# Now filter this list by elements in modality list
# For each file:
# if any modality name (lowercase) is found in the basename of the file:
# keep the file
nifti_files_filtered = [
f for f in nifti_files
if any(elem.lower() in basename(f).lower() for elem in modality)
]
# Remove those who are centered
if not center_all_files:
nifti_files_filtered = [
file for file in nifti_files_filtered if not is_centered(file)
]
all_errors = []
for f in nifti_files_filtered:
print('Handling ' + f)
_, current_error = center_nifti_origin(f, f)
if current_error:
all_errors.append(current_error)
if len(all_errors) > 0:
final_error_msg = Fore.RED + '[Error] Clinica encoutered ' + str(len(all_errors)) \
+ ' error(s) while trying to center all NIfTI images.\n'
for error in all_errors:
final_error_msg += '\n' + error
raise RuntimeError(final_error_msg)
return nifti_files_filtered
def preprocessing_t1w(bids_directory,
caps_directory,
tsv,
working_directory=None):
"""
This preprocessing pipeline includes globally three steps:
1) N4 bias correction (performed with ANTS).
2) Linear registration to MNI (MNI icbm152 nlinear sym template)
(performed with ANTS) - RegistrationSynQuick.
3) Cropping the background (in order to save computational power).
4) Histogram-based intensity normalization. This is a custom function
performed by the binary ImageMath included with ANTS.
Parameters
----------
bids_directory: str
Folder with BIDS structure.
caps_directory: str
Folder where CAPS structure will be stored.
working_directory: str
Folder containing a temporary space to save intermediate results.
"""
from clinica.utils.inputs import check_bids_folder
from clinica.utils.participant import get_subject_session_list
from clinica.utils.exceptions import ClinicaBIDSError, ClinicaException
from clinica.utils.inputs import clinica_file_reader
from clinica.utils.input_files import T1W_NII
from clinicadl.tools.inputs.input import fetch_file
from os.path import dirname, join, abspath, split, exists
from os import pardir
check_bids_folder(bids_directory)
input_dir = bids_directory
is_bids_dir = True
base_dir = working_directory
root = dirname(abspath(join(abspath(__file__), pardir)))
path_to_mask = join(root, 'resources', 'masks')
ref_template = join(path_to_mask, 'mni_icbm152_t1_tal_nlin_sym_09c.nii')
ref_crop = join(path_to_mask, 'ref_cropped_template.nii.gz')
url1 = "https://aramislab.paris.inria.fr/files/data/img_t1_linear/ref_cropped_template.nii.gz"
url2 = "https://aramislab.paris.inria.fr/files/data/img_t1_linear/mni_icbm152_t1_tal_nlin_sym_09c.nii"
if not (exists(ref_template)):
try:
fetch_file(url2, ref_template)
except IOError as err:
print(
'Unable to download required template (mni_icbm152) for processing:',
err)
if not (exists(ref_crop)):
try:
fetch_file(url1, ref_crop)
except IOError as err:
print(
'Unable to download required template (ref_crop) for processing:',
err)
sessions, subjects = get_subject_session_list(input_dir, tsv, is_bids_dir,
False, base_dir)
from clinica.utils.exceptions import ClinicaBIDSError, ClinicaException
from clinica.utils.inputs import clinica_file_reader
from clinica.utils.input_files import T1W_NII
import nipype.pipeline.engine as npe
import nipype.interfaces.utility as nutil
from nipype.interfaces import ants
from clinica.utils.filemanip import get_subject_id
# Inputs from anat/ folder
# ========================
# T1w file:
try:
t1w_files = clinica_file_reader(subjects, sessions, bids_directory,
T1W_NII)
except ClinicaException as e:
err = 'Clinica faced error(s) while trying to read files in your CAPS directory.\n' + str(
e)
raise ClinicaBIDSError(err)
def get_input_fields():
""""Specify the list of possible inputs of this pipelines.
Returns:
A list of (string) input fields name.
"""
return ['t1w']
read_node = npe.Node(
name="ReadingFiles",
iterables=[
('t1w', t1w_files),
],
synchronize=True,
interface=nutil.IdentityInterface(fields=get_input_fields()))
image_id_node = npe.Node(interface=nutil.Function(
input_names=['bids_or_caps_file'],
output_names=['image_id'],
function=get_subject_id),
name='ImageID')
## The core (processing) nodes
# 1. N4biascorrection by ANTS. It uses nipype interface.
n4biascorrection = npe.Node(name='n4biascorrection',
interface=ants.N4BiasFieldCorrection(
dimension=3,
save_bias=True,
bspline_fitting_distance=600))
# 2. `RegistrationSynQuick` by *ANTS*. It uses nipype interface.
ants_registration_node = npe.Node(name='antsRegistrationSynQuick',
interface=ants.RegistrationSynQuick())
ants_registration_node.inputs.fixed_image = ref_template
ants_registration_node.inputs.transform_type = 'a'
ants_registration_node.inputs.dimension = 3
# 3. Crop image (using nifti). It uses custom interface, from utils file
from .T1_linear_utils import crop_nifti
cropnifti = npe.Node(name='cropnifti',
interface=nutil.Function(
function=crop_nifti,
input_names=['input_img', 'ref_crop'],
output_names=['output_img', 'crop_template']))
cropnifti.inputs.ref_crop = ref_crop
#### Deprecrecated ####
#### This step was not used in the final version ####
# 4. Histogram-based intensity normalization. This is a custom function
# performed by the binary `ImageMath` included with *ANTS*.
# from .T1_linear_utils import ants_histogram_intensity_normalization
#
# ## histogram-based intensity normalization
# intensitynorm = npe.Node(
# name='intensitynormalization',
# interface=nutil.Function(
# input_names=['image_dimension', 'crop_template', 'input_img'],
# output_names=['output_img'],
# function=ants_histogram_intensity_normalization
# )
# )
# intensitynorm.inputs.image_dimension = 3
## DataSink and the output node
from .T1_linear_utils import (container_from_filename, get_data_datasink)
# Create node to write selected files into the CAPS
from nipype.interfaces.io import DataSink
get_ids = npe.Node(interface=nutil.Function(
input_names=['image_id'],
output_names=['image_id_out', 'subst_ls'],
function=get_data_datasink),
name="GetIDs")
# Find container path from t1w filename
# =====================================
container_path = npe.Node(nutil.Function(
input_names=['bids_or_caps_filename'],
output_names=['container'],
function=container_from_filename),
name='ContainerPath')
write_node = npe.Node(name="WriteCaps", interface=DataSink())
write_node.inputs.base_directory = caps_directory
write_node.inputs.parameterization = False
## Connectiong the workflow
from clinica.utils.nipype import fix_join
wf = npe.Workflow(name='t1_linear_dl', base_dir=working_directory)
wf.connect([
(read_node, image_id_node, [('t1w', 'bids_or_caps_file')]),
(read_node, container_path, [('t1w', 'bids_or_caps_filename')]),
(image_id_node, ants_registration_node, [('image_id', 'output_prefix')
]),
(read_node, n4biascorrection, [("t1w", "input_image")]),
(n4biascorrection, ants_registration_node, [('output_image',
'moving_image')]),
(ants_registration_node, cropnifti, [('warped_image', 'input_img')]),
(ants_registration_node, write_node, [('out_matrix', 'affine_mat')]),
# Connect to DataSink
(container_path, write_node, [(('container', fix_join, 't1_linear'),
'container')]),
(image_id_node, get_ids, [('image_id', 'image_id')]),
(get_ids, write_node, [('image_id_out', '@image_id')]),
(get_ids, write_node, [('subst_ls', 'substitutions')]),
#(get_ids, write_node, [('regexp_subst_ls', 'regexp_substitutions')]),
(n4biascorrection, write_node, [('output_image', '@outfile_corr')]),
(ants_registration_node, write_node, [('warped_image', '@outfile_reg')
]),
(cropnifti, write_node, [('output_img', '@outfile_crop')]),
])
return wf
def __init__(
self,
bids_directory=None,
caps_directory=None,
tsv_file=None,
overwrite_caps=False,
base_dir=None,
parameters={},
name=None,
):
"""Init a Pipeline object.
Args:
bids_directory (str, optional): Path to a BIDS directory. Defaults to None.
caps_directory (str, optional): Path to a CAPS directory. Defaults to None.
tsv_file (str, optional): Path to a subjects-sessions `.tsv` file. Defaults to None.
overwrite_caps (bool, optional): Overwrite or not output directory.. Defaults to False.
base_dir (str, optional): Working directory (attribute of Nipype::Workflow class). Defaults to None.
parameters (dict, optional): Pipeline parameters. Defaults to {}.
name (str, optional): Pipeline name. Defaults to None.
Raises:
RuntimeError: [description]
"""
import inspect
import os
from tempfile import mkdtemp
from colorama import Fore
from clinica.utils.exceptions import ClinicaException
from clinica.utils.inputs import check_bids_folder, check_caps_folder
from clinica.utils.participant import get_subject_session_list
self._is_built = False
self._overwrite_caps = overwrite_caps
self._bids_directory = bids_directory
self._caps_directory = caps_directory
self._verbosity = "debug"
self._tsv_file = tsv_file
self._info_file = os.path.join(
os.path.dirname(os.path.abspath(inspect.getfile(self.__class__))),
"info.json",
)
self._info = {}
if base_dir is None:
self.base_dir = mkdtemp()
self._base_dir_was_specified = False
else:
self.base_dir = base_dir
self._base_dir_was_specified = True
if name:
self._name = name
else:
self._name = self.__class__.__name__
self._parameters = parameters
if self._bids_directory is None:
if self._caps_directory is None:
raise RuntimeError(
f"{Fore.RED}[Error] The {self._name} pipeline does not contain "
f"BIDS nor CAPS directory at the initialization.{Fore.RESET}"
)
check_caps_folder(self._caps_directory)
input_dir = self._caps_directory
is_bids_dir = False
else:
check_bids_folder(self._bids_directory)
input_dir = self._bids_directory
is_bids_dir = True
self._sessions, self._subjects = get_subject_session_list(
input_dir, tsv_file, is_bids_dir, False, base_dir
)
self.init_nodes()
def __init__(self,
bids_directory=None,
caps_directory=None,
tsv_file=None,
overwrite_caps=False,
base_dir=None,
parameters={},
name=None):
"""Init a Pipeline object.
Args:
bids_directory (optional): Path to a BIDS directory.
caps_directory (optional): Path to a CAPS directory.
tsv_file (optional): Path to a subjects-sessions `.tsv` file.
overwrite_caps (optional): Boolean which specifies overwritten of output directory.
base_dir (optional): Working directory (attribute of Nipype::Workflow class).
name (optional): Pipeline name.
"""
import inspect
import os
from tempfile import mkdtemp
from colorama import Fore
from clinica.utils.inputs import check_caps_folder
from clinica.utils.inputs import check_bids_folder
from clinica.utils.exceptions import ClinicaException
from clinica.utils.participant import get_subject_session_list
self._is_built = False
self._overwrite_caps = overwrite_caps
self._bids_directory = bids_directory
self._caps_directory = caps_directory
self._verbosity = 'debug'
self._tsv_file = tsv_file
self._info_file = os.path.join(
os.path.dirname(os.path.abspath(inspect.getfile(self.__class__))),
'info.json')
self._info = {}
if base_dir is None:
self.base_dir = mkdtemp()
self._base_dir_was_specified = False
else:
self.base_dir = base_dir
self._base_dir_was_specified = True
if name:
self._name = name
else:
self._name = self.__class__.__name__
self._parameters = parameters
if self._bids_directory is None:
if self._caps_directory is None:
raise RuntimeError(
'%s[Error] The %s pipeline does not contain BIDS nor CAPS directory at the initialization.%s'
% (Fore.RED, self._name, Fore.RESET))
check_caps_folder(self._caps_directory)
input_dir = self._caps_directory
is_bids_dir = False
else:
check_bids_folder(self._bids_directory)
input_dir = self._bids_directory
is_bids_dir = True
self._sessions, self._subjects = get_subject_session_list(
input_dir, tsv_file, is_bids_dir, False, base_dir)
self.init_nodes()
def preprocessing_t1w(bids_directory,
caps_directory,
tsv,
working_directory=None):
"""
This preprocessing pipeline includes globally three steps:
1) N4 bias correction (performed with ANTS).
2) Linear registration to MNI (MNI icbm152 nlinear sym template)
(performed with ANTS) - RegistrationSynQuick.
3) Cropping the background (in order to save computational power).
4) Histogram-based intensity normalization. This is a custom function
performed by the binary ImageMath included with ANTS.
Parameters
----------
bids_directory: str
Folder with BIDS structure.
caps_directory: str
Folder where CAPS structure will be stored.
working_directory: str
Folder containing a temporary space to save intermediate results.
"""
from os.path import dirname, join, abspath, split, exists
from os import pardir, makedirs
from pathlib import Path
from clinica.utils.inputs import check_bids_folder
from clinica.utils.participant import get_subject_session_list
from clinica.utils.filemanip import get_subject_id
from clinica.utils.exceptions import ClinicaBIDSError, ClinicaException
from clinica.utils.inputs import clinica_file_reader
from clinica.utils.input_files import T1W_NII
from clinica.utils.check_dependency import check_ants
from clinicadl.tools.inputs.input import fetch_file
from clinicadl.tools.inputs.input import RemoteFileStructure
import nipype.pipeline.engine as npe
import nipype.interfaces.utility as nutil
from nipype.interfaces import ants
check_ants()
check_bids_folder(bids_directory)
input_dir = abspath(bids_directory)
caps_directory = abspath(caps_directory)
is_bids_dir = True
base_dir = abspath(working_directory)
home = str(Path.home())
cache_clinicadl = join(home, '.cache', 'clinicadl', 'ressources', 'masks')
url_aramis = 'https://aramislab.paris.inria.fr/files/data/img_t1_linear/'
FILE1 = RemoteFileStructure(
filename='ref_cropped_template.nii.gz',
url=url_aramis,
checksum=
'67e1e7861805a8fd35f7fcf2bdf9d2a39d7bcb2fd5a201016c4d2acdd715f5b3')
FILE2 = RemoteFileStructure(
filename='mni_icbm152_t1_tal_nlin_sym_09c.nii',
url=url_aramis,
checksum=
'93359ab97c1c027376397612a9b6c30e95406c15bf8695bd4a8efcb2064eaa34')
if not (exists(cache_clinicadl)):
makedirs(cache_clinicadl)
ref_template = join(cache_clinicadl, FILE2.filename)
ref_crop = join(cache_clinicadl, FILE1.filename)
if not (exists(ref_template)):
try:
ref_template = fetch_file(FILE2, cache_clinicadl)
except IOError as err:
print(
'Unable to download required template (mni_icbm152) for processing:',
err)
if not (exists(ref_crop)):
try:
ref_crop = fetch_file(FILE1, cache_clinicadl)
except IOError as err:
print(
'Unable to download required template (ref_crop) for processing:',
err)
sessions, subjects = get_subject_session_list(input_dir, tsv, is_bids_dir,
False, base_dir)
# Use hash instead of parameters for iterables folder names
# Otherwise path will be too long and generate OSError
from nipype import config
cfg = dict(execution={'parameterize_dirs': False})
config.update_config(cfg)
# Inputs from anat/ folder
# ========================
# T1w file:
try:
t1w_files = clinica_file_reader(subjects, sessions, bids_directory,
T1W_NII)
except ClinicaException as e:
err = 'Clinica faced error(s) while trying to read files in your CAPS directory.\n' + str(
e)
raise ClinicaBIDSError(err)
def get_input_fields():
""""Specify the list of possible inputs of this pipelines.
Returns:
A list of (string) input fields name.
"""
return ['t1w']
read_node = npe.Node(
name="ReadingFiles",
iterables=[
('t1w', t1w_files),
],
synchronize=True,
interface=nutil.IdentityInterface(fields=get_input_fields()))
image_id_node = npe.Node(interface=nutil.Function(
input_names=['bids_or_caps_file'],
output_names=['image_id'],
function=get_subject_id),
name='ImageID')
# The core (processing) nodes
# 1. N4biascorrection by ANTS. It uses nipype interface.
n4biascorrection = npe.Node(name='n4biascorrection',
interface=ants.N4BiasFieldCorrection(
dimension=3,
save_bias=True,
bspline_fitting_distance=600))
# 2. `RegistrationSynQuick` by *ANTS*. It uses nipype interface.
ants_registration_node = npe.Node(name='antsRegistrationSynQuick',
interface=ants.RegistrationSynQuick())
ants_registration_node.inputs.fixed_image = ref_template
ants_registration_node.inputs.transform_type = 'a'
ants_registration_node.inputs.dimension = 3
# 3. Crop image (using nifti). It uses custom interface, from utils file
from .T1_linear_utils import crop_nifti
cropnifti = npe.Node(name='cropnifti',
interface=nutil.Function(
function=crop_nifti,
input_names=['input_img', 'ref_crop'],
output_names=['output_img', 'crop_template']))
cropnifti.inputs.ref_crop = ref_crop
# ********* Deprecrecated ********** #
# ** This step was not used in the final version ** #
# 4. Histogram-based intensity normalization. This is a custom function
# performed by the binary `ImageMath` included with *ANTS*.
# from .T1_linear_utils import ants_histogram_intensity_normalization
#
# # histogram-based intensity normalization
# intensitynorm = npe.Node(
# name='intensitynormalization',
# interface=nutil.Function(
# input_names=['image_dimension', 'crop_template', 'input_img'],
# output_names=['output_img'],
# function=ants_histogram_intensity_normalization
# )
# )
# intensitynorm.inputs.image_dimension = 3
# DataSink and the output node
from .T1_linear_utils import (container_from_filename, get_data_datasink)
# Create node to write selected files into the CAPS
from nipype.interfaces.io import DataSink
get_ids = npe.Node(interface=nutil.Function(
input_names=['image_id'],
output_names=['image_id_out', 'subst_ls'],
function=get_data_datasink),
name="GetIDs")
# Find container path from t1w filename
# =====================================
container_path = npe.Node(nutil.Function(
input_names=['bids_or_caps_filename'],
output_names=['container'],
function=container_from_filename),
name='ContainerPath')
write_node = npe.Node(name="WriteCaps", interface=DataSink())
write_node.inputs.base_directory = caps_directory
write_node.inputs.parameterization = False
# Connectiong the workflow
from clinica.utils.nipype import fix_join
wf = npe.Workflow(name='t1_linear_dl', base_dir=working_directory)
wf.connect([
(read_node, image_id_node, [('t1w', 'bids_or_caps_file')]),
(read_node, container_path, [('t1w', 'bids_or_caps_filename')]),
(image_id_node, ants_registration_node, [('image_id', 'output_prefix')
]),
(read_node, n4biascorrection, [("t1w", "input_image")]),
(n4biascorrection, ants_registration_node, [('output_image',
'moving_image')]),
(ants_registration_node, cropnifti, [('warped_image', 'input_img')]),
(ants_registration_node, write_node, [('out_matrix', '@affine_mat')]),
# Connect to DataSink
(container_path, write_node, [(('container', fix_join, 't1_linear'),
'container')]),
(image_id_node, get_ids, [('image_id', 'image_id')]),
(get_ids, write_node, [('image_id_out', '@image_id')]),
(get_ids, write_node, [('subst_ls', 'substitutions')]),
# (get_ids, write_node, [('regexp_subst_ls', 'regexp_substitutions')]),
(n4biascorrection, write_node, [('output_image', '@outfile_corr')]),
(ants_registration_node, write_node, [('warped_image', '@outfile_reg')
]),
(cropnifti, write_node, [('output_img', '@outfile_crop')]),
])
return wf
