def _list_outputs(self):
outputs = self._outputs().get()
outputs['out_file'] = self._gen_outfilename()
output_dir = os.path.dirname(outputs['out_file'])
if isdefined(self.inputs.stats_imgs) and self.inputs.stats_imgs:
if LooseVersion(Info.version()) < LooseVersion('6.0.0'):
# FSL <6.0 outputs have .nii.gz_variance.nii.gz as extension
outputs['variance_img'] = self._gen_fname(
outputs['out_file'] + '_variance.ext', cwd=output_dir)
outputs['std_img'] = self._gen_fname(
outputs['out_file'] + '_sigma.ext', cwd=output_dir)
else:
outputs['variance_img'] = self._gen_fname(
outputs['out_file'], suffix='_variance', cwd=output_dir)
outputs['std_img'] = self._gen_fname(
outputs['out_file'], suffix='_sigma', cwd=output_dir)
# The mean image created if -stats option is specified ('meanvol')
# is missing the top and bottom slices. Therefore we only expose the
# mean image created by -meanvol option ('mean_reg') which isn't
# corrupted.
# Note that the same problem holds for the std and variance image.
if isdefined(self.inputs.mean_vol) and self.inputs.mean_vol:
if LooseVersion(Info.version()) < LooseVersion('6.0.0'):
# FSL <6.0 outputs have .nii.gz_mean_img.nii.gz as extension
outputs['mean_img'] = self._gen_fname(
outputs['out_file'] + '_mean_reg.ext', cwd=output_dir)
else:
outputs['mean_img'] = self._gen_fname(
outputs['out_file'], suffix='_mean_reg', cwd=output_dir)
if isdefined(self.inputs.save_mats) and self.inputs.save_mats:
_, filename = os.path.split(outputs['out_file'])
mat_dir = os.path.join(output_dir, filename + '.mat')
outputs['mat_dir'] = mat_dir
_, _, _, timepoints = load(self.inputs.in_file).shape
outputs['mat_file'] = []
for t in range(timepoints):
outputs['mat_file'].append(
os.path.join(mat_dir, 'MAT_%04d' % t))
if isdefined(self.inputs.save_plots) and self.inputs.save_plots:
# Note - if e.g. out_file has .nii.gz, you get .nii.gz.par,
# which is what mcflirt does!
outputs['par_file'] = outputs['out_file'] + '.par'
if isdefined(self.inputs.save_rms) and self.inputs.save_rms:
outfile = outputs['out_file']
outputs['rms_files'] = [outfile + '_abs.rms', outfile + '_rel.rms']
return outputs
def _get_fsl_slice_output_files(out_base_name, output_type):
ext = Info.output_type_to_ext(output_type)
suffix = '_slice_*' + ext
exact_pattern = '_slice_[0-9][0-9][0-9][0-9]' + ext
fname_template = os.path.abspath(out_base_name + suffix)
fname_exact_pattern = os.path.abspath(out_base_name + exact_pattern)
sliced_files = fnmatch.filter(sorted(glob.glob(fname_template)),
fname_exact_pattern)
return sliced_files
def _gen_fname(self,
basename,
cwd=None,
suffix=None,
change_ext=True,
ext=None):
"""Generate a filename based on the given parameters.
The filename will take the form: cwd/basename<suffix><ext>.
If change_ext is True, it will use the extentions specified in
<instance>intputs.output_type.
Parameters
----------
basename : str
Filename to base the new filename on.
cwd : str
Path to prefix to the new filename. (default is os.getcwd())
suffix : str
Suffix to add to the `basename`. (defaults is '' )
change_ext : bool
Flag to change the filename extension to the FSL output type.
(default True)
Returns
-------
fname : str
New filename based on given parameters.
"""
if basename == '':
msg = 'Unable to generate filename for command %s. ' % self.cmd
msg += 'basename is not set!'
raise ValueError(msg)
if cwd is None:
cwd = os.getcwd()
if ext is None:
ext = Info.output_type_to_ext(self.inputs.output_type)
if change_ext:
if suffix:
suffix = ''.join((suffix, ext))
else:
suffix = ext
if suffix is None:
suffix = ''
fname = fname_presuffix(basename,
suffix=suffix,
use_ext=False,
newpath=cwd)
return fname
def flirt_anatomical_linear_registration(workflow, resource_pool, config):
# resource pool should have:
# anatomical_brain
import os
import sys
import nipype.interfaces.io as nio
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as util
import nipype.interfaces.fsl as fsl
from workflow_utils import check_input_resources, \
check_config_settings
from nipype.interfaces.fsl.base import Info
if "template_brain_for_anat" not in config:
config["template_brain_for_anat"] = Info.standard_image(
"MNI152_T1_2mm_brain.nii.gz")
check_config_settings(config, "template_brain_for_anat")
if "anatomical_brain" not in resource_pool.keys():
from anatomical_preproc import anatomical_skullstrip_workflow
workflow, resource_pool = \
anatomical_skullstrip_workflow(workflow, resource_pool, config)
#check_input_resources(resource_pool, "anatomical_brain")
calc_flirt_warp = pe.Node(interface=fsl.FLIRT(), name='calc_flirt_warp')
calc_flirt_warp.inputs.cost = 'corratio'
if len(resource_pool["anatomical_brain"]) == 2:
node, out_file = resource_pool["anatomical_brain"]
workflow.connect(node, out_file, calc_flirt_warp, 'in_file')
else:
calc_flirt_warp.inputs.in_file = resource_pool["anatomical_brain"]
calc_flirt_warp.inputs.reference = config["template_brain_for_anat"]
resource_pool["flirt_affine_xfm"] = (calc_flirt_warp, 'out_matrix_file')
resource_pool["flirt_linear_warped_image"] = (calc_flirt_warp, 'out_file')
return workflow, resource_pool
def flirt_anatomical_linear_registration(workflow, resource_pool, config):
# resource pool should have:
# anatomical_brain
import os
import sys
import nipype.interfaces.io as nio
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as util
import nipype.interfaces.fsl as fsl
from workflow_utils import check_input_resources, check_config_settings
from nipype.interfaces.fsl.base import Info
if "template_brain_for_anat" not in config:
config["template_brain_for_anat"] = Info.standard_image("MNI152_T1_2mm_brain.nii.gz")
check_config_settings(config, "template_brain_for_anat")
if "anatomical_brain" not in resource_pool.keys():
from anatomical_preproc import anatomical_skullstrip_workflow
workflow, resource_pool = anatomical_skullstrip_workflow(workflow, resource_pool, config)
# check_input_resources(resource_pool, "anatomical_brain")
calc_flirt_warp = pe.Node(interface=fsl.FLIRT(), name="calc_flirt_warp")
calc_flirt_warp.inputs.cost = "corratio"
if len(resource_pool["anatomical_brain"]) == 2:
node, out_file = resource_pool["anatomical_brain"]
workflow.connect(node, out_file, calc_flirt_warp, "in_file")
else:
calc_flirt_warp.inputs.in_file = resource_pool["anatomical_brain"]
calc_flirt_warp.inputs.reference = config["template_brain_for_anat"]
resource_pool["flirt_affine_xfm"] = (calc_flirt_warp, "out_matrix_file")
resource_pool["flirt_linear_warped_image"] = (calc_flirt_warp, "out_file")
return workflow, resource_pool
def _gen_fname(self, basename, cwd=None, suffix=None, change_ext=True,
ext=None):
"""Generate a filename based on the given parameters.
The filename will take the form: cwd/basename<suffix><ext>.
If change_ext is True, it will use the extentions specified in
<instance>intputs.output_type.
Parameters
----------
basename : str
Filename to base the new filename on.
cwd : str
Path to prefix to the new filename. (default is os.getcwd())
suffix : str
Suffix to add to the `basename`. (defaults is '' )
change_ext : bool
Flag to change the filename extension to the FSL output type.
(default True)
Returns
-------
fname : str
New filename based on given parameters.
"""
if basename == '':
msg = 'Unable to generate filename for command %s. ' % self.cmd
msg += 'basename is not set!'
raise ValueError(msg)
if cwd is None:
cwd = os.getcwd()
if ext is None:
ext = Info.output_type_to_ext(self.inputs.output_type)
if change_ext:
if suffix:
suffix = ''.join((suffix, ext))
else:
suffix = ext
if suffix is None:
suffix = ''
fname = fname_presuffix(basename, suffix=suffix,
use_ext=False, newpath=cwd)
return fname
def _format_arg(self, name, spec, value):
if name == "project_data":
if isdefined(value) and value:
_si = self.inputs
if isdefined(_si.use_cingulum_mask) and _si.use_cingulum_mask:
mask_file = Info.standard_image("LowerCingulum_1mm.nii.gz")
else:
mask_file = _si.search_mask_file
if not isdefined(_si.projected_data):
proj_file = self._list_outputs()["projected_data"]
else:
proj_file = _si.projected_data
return spec.argstr % (_si.threshold, _si.distance_map, mask_file, _si.data_file, proj_file)
elif name == "skeleton_file":
if isinstance(value, bool):
return spec.argstr % self._list_outputs()["skeleton_file"]
else:
return spec.argstr % value
return super(TractSkeleton, self)._format_arg(name, spec, value)
def _format_arg(self, name, spec, value):
if name == "project_data":
if isdefined(value) and value:
_si = self.inputs
if isdefined(_si.use_cingulum_mask) and _si.use_cingulum_mask:
mask_file = Info.standard_image("LowerCingulum_1mm.nii.gz")
else:
mask_file = _si.search_mask_file
if not isdefined(_si.projected_data):
proj_file = self._list_outputs()["projected_data"]
else:
proj_file = _si.projected_data
return spec.argstr % (_si.threshold, _si.distance_map,
mask_file, _si.data_file, proj_file)
elif name == "skeleton_file":
if isinstance(value, bool):
return spec.argstr % self._list_outputs()["skeleton_file"]
else:
return spec.argstr % value
return super(TractSkeleton, self)._format_arg(name, spec, value)
def _list_outputs(self):
"""Create a Bunch which contains all possible files generated
by running the interface. Some files are always generated, others
depending on which ``inputs`` options are set.
Returns
-------
outputs : Bunch object
Bunch object containing all possible files generated by
interface object.
If None, file was not generated
Else, contains path, filename of generated outputfile
"""
outputs = self._outputs().get()
ext = Info.output_type_to_ext(self.inputs.output_type)
outbase = 'vol*'
if isdefined(self.inputs.out_base_name):
outbase = '%s*' % self.inputs.out_base_name
outputs['out_files'] = sorted(glob(os.path.join(os.getcwd(),
outbase + ext)))
return outputs
def _list_outputs(self):
"""Create a Bunch which contains all possible files generated
by running the interface. Some files are always generated, others
depending on which ``inputs`` options are set.
Returns
-------
outputs : Bunch object
Bunch object containing all possible files generated by
interface object.
If None, file was not generated
Else, contains path, filename of generated outputfile
"""
outputs = self._outputs().get()
ext = Info.output_type_to_ext(self.inputs.output_type)
outbase = 'vol*'
if isdefined(self.inputs.out_base_name):
outbase = '%s*' % self.inputs.out_base_name
outputs['out_files'] = sorted(glob(os.path.join(os.getcwd(),
outbase + ext)))
return outputs
def _list_fsl_split_outputs(self):
""" Method to list the fsl split interface outputs
Returns
-------
outputs: dict
all the interface outputs
"""
from glob import glob
from nipype.interfaces.fsl.base import Info
from nipype.interfaces.base import isdefined
# Get the nipype outputs
outputs = self._outputs().get()
# Modify the path outputs
ext = Info.output_type_to_ext(self.inputs.output_type)
outbase = 'vol*'
if isdefined(self.inputs.out_base_name):
outbase = '%s*' % self.inputs.out_base_name
outputs['out_files'] = sorted(glob(
os.path.join(self.inputs.output_directory, outbase + ext)))
return outputs
def qap_mask_workflow(workflow, resource_pool, config):
import os
import sys
import nipype.interfaces.io as nio
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as niu
import nipype.interfaces.fsl.maths as fsl
from nipype.interfaces.fsl.base import Info
from qap_workflows_utils import select_thresh, slice_head_mask
from workflow_utils import check_input_resources, check_config_settings
# check_input_resources(resource_pool, 'anatomical_reorient')
# check_input_resources(resource_pool, 'ants_affine_xfm')
if "template_skull_for_anat" not in config:
config["template_skull_for_anat"] = Info.standard_image("MNI152_T1_2mm.nii.gz")
check_config_settings(config, "template_skull_for_anat")
if "flirt_affine_xfm" not in resource_pool.keys():
from anatomical_preproc import flirt_anatomical_linear_registration
workflow, resource_pool = flirt_anatomical_linear_registration(workflow, resource_pool, config)
if "anatomical_reorient" not in resource_pool.keys():
from anatomical_preproc import anatomical_reorient_workflow
workflow, resource_pool = anatomical_reorient_workflow(workflow, resource_pool, config)
select_thresh = pe.Node(
niu.Function(input_names=["input_skull"], output_names=["thresh_out"], function=select_thresh),
name="qap_headmask_select_thresh",
iterfield=["input_skull"],
)
mask_skull = pe.Node(fsl.Threshold(args="-bin"), name="qap_headmask_thresh")
dilate_node = pe.Node(fsl.MathsCommand(args="-dilM -dilM -dilM -dilM -dilM -dilM"), name="qap_headmask_dilate")
erode_node = pe.Node(fsl.MathsCommand(args="-eroF -eroF -eroF -eroF -eroF -eroF"), name="qap_headmask_erode")
slice_head_mask = pe.Node(
niu.Function(
input_names=["infile", "transform", "standard"], output_names=["outfile_path"], function=slice_head_mask
),
name="qap_headmask_slice_head_mask",
)
combine_masks = pe.Node(fsl.BinaryMaths(operation="add", args="-bin"), name="qap_headmask_combine_masks")
if len(resource_pool["anatomical_reorient"]) == 2:
node, out_file = resource_pool["anatomical_reorient"]
workflow.connect(
[
(node, select_thresh, [(out_file, "input_skull")]),
(node, mask_skull, [(out_file, "in_file")]),
(node, slice_head_mask, [(out_file, "infile")]),
]
)
else:
select_thresh.inputs.input_skull = resource_pool["anatomical_reorient"]
mask_skull.inputs.in_file = resource_pool["anatomical_reorient"]
# convert_fsl_xfm.inputs.infile =
# resource_pool['anatomical_reorient']
slice_head_mask.inputs.infile = resource_pool["anatomical_reorient"]
if len(resource_pool["flirt_affine_xfm"]) == 2:
node, out_file = resource_pool["flirt_affine_xfm"]
workflow.connect(node, out_file, slice_head_mask, "transform")
else:
slice_head_mask.inputs.transform = resource_pool["flirt_affine_xfm"]
# convert_fsl_xfm.inputs.standard = config['template_skull_for_anat']
slice_head_mask.inputs.standard = config["template_skull_for_anat"]
workflow.connect(
[
(select_thresh, mask_skull, [("thresh_out", "thresh")]),
# (convert_fsl_xfm, slice_head_mask, [('converted_xfm', 'transform')])
(mask_skull, dilate_node, [("out_file", "in_file")]),
(dilate_node, erode_node, [("out_file", "in_file")]),
(erode_node, combine_masks, [("out_file", "in_file")]),
(slice_head_mask, combine_masks, [("outfile_path", "operand_file")]),
]
)
resource_pool["qap_head_mask"] = (combine_masks, "out_file")
return workflow, resource_pool
def ants_anatomical_linear_registration(workflow, resource_pool, config):
# resource pool should have:
# anatomical_brain
# linear ANTS registration takes roughly 2.5 minutes per subject running
# on one core of an Intel Core i7-4800MQ CPU @ 2.70GHz
import os
import sys
import nipype.interfaces.io as nio
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as util
from anatomical_preproc_utils import ants_lin_reg, \
separate_warps_list
from workflow_utils import check_input_resources, \
check_config_settings
from nipype.interfaces.fsl.base import Info
if "template_brain_for_anat" not in config:
config["template_brain_for_anat"] = Info.standard_image(
"MNI152_T1_2mm_brain.nii.gz")
check_config_settings(config, "template_brain_for_anat")
if "anatomical_brain" not in resource_pool.keys():
from anatomical_preproc import anatomical_skullstrip_workflow
workflow, resource_pool = \
anatomical_skullstrip_workflow(workflow, resource_pool, config)
#check_input_resources(resource_pool, "anatomical_brain")
calc_ants_warp = pe.Node(interface=util.Function(
input_names=['anatomical_brain', 'reference_brain'],
output_names=['warp_list', 'warped_image'],
function=ants_lin_reg),
name='calc_ants_linear_warp')
select_forward_initial = pe.Node(util.Function(
input_names=['warp_list', 'selection'],
output_names=['selected_warp'],
function=separate_warps_list),
name='select_forward_initial')
select_forward_initial.inputs.selection = "Initial"
select_forward_rigid = pe.Node(util.Function(
input_names=['warp_list', 'selection'],
output_names=['selected_warp'],
function=separate_warps_list),
name='select_forward_rigid')
select_forward_rigid.inputs.selection = "Rigid"
select_forward_affine = pe.Node(util.Function(
input_names=['warp_list', 'selection'],
output_names=['selected_warp'],
function=separate_warps_list),
name='select_forward_affine')
select_forward_affine.inputs.selection = "Affine"
if len(resource_pool["anatomical_brain"]) == 2:
node, out_file = resource_pool["anatomical_brain"]
workflow.connect(node, out_file, calc_ants_warp, 'anatomical_brain')
else:
calc_ants_warp.inputs.anatomical_brain = \
resource_pool["anatomical_brain"]
calc_ants_warp.inputs.reference_brain = config["template_brain_for_anat"]
workflow.connect(calc_ants_warp, 'warp_list', select_forward_initial,
'warp_list')
workflow.connect(calc_ants_warp, 'warp_list', select_forward_rigid,
'warp_list')
workflow.connect(calc_ants_warp, 'warp_list', select_forward_affine,
'warp_list')
resource_pool["ants_initial_xfm"] = \
(select_forward_initial, 'selected_warp')
resource_pool["ants_rigid_xfm"] = (select_forward_rigid, 'selected_warp')
resource_pool["ants_affine_xfm"] = \
(select_forward_affine, 'selected_warp')
resource_pool["ants_linear_warped_image"] = \
(calc_ants_warp, 'warped_image')
return workflow, resource_pool
def qap_mask_workflow(workflow, resource_pool, config):
import os
import sys
import nipype.interfaces.io as nio
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as niu
import nipype.interfaces.fsl.maths as fsl
from nipype.interfaces.fsl.base import Info
from qap_workflows_utils import select_thresh, \
slice_head_mask
from workflow_utils import check_input_resources, \
check_config_settings
# check_input_resources(resource_pool, 'anatomical_reorient')
# check_input_resources(resource_pool, 'ants_affine_xfm')
if 'template_skull_for_anat' not in config:
config['template_skull_for_anat'] = Info.standard_image(
'MNI152_T1_2mm.nii.gz')
check_config_settings(config, 'template_skull_for_anat')
if 'flirt_affine_xfm' not in resource_pool.keys():
from anatomical_preproc import flirt_anatomical_linear_registration
workflow, resource_pool = \
flirt_anatomical_linear_registration(workflow, resource_pool,
config)
if 'anatomical_reorient' not in resource_pool.keys():
from anatomical_preproc import anatomical_reorient_workflow
workflow, resource_pool = \
anatomical_reorient_workflow(workflow, resource_pool, config)
select_thresh = pe.Node(niu.Function(input_names=['input_skull'],
output_names=['thresh_out'],
function=select_thresh),
name='qap_headmask_select_thresh',
iterfield=['input_skull'])
mask_skull = pe.Node(fsl.Threshold(args='-bin'),
name='qap_headmask_thresh')
dilate_node = pe.Node(
fsl.MathsCommand(args='-dilM -dilM -dilM -dilM -dilM -dilM'),
name='qap_headmask_dilate')
erode_node = pe.Node(
fsl.MathsCommand(args='-eroF -eroF -eroF -eroF -eroF -eroF'),
name='qap_headmask_erode')
slice_head_mask = pe.Node(niu.Function(
input_names=['infile', 'transform', 'standard'],
output_names=['outfile_path'],
function=slice_head_mask),
name='qap_headmask_slice_head_mask')
combine_masks = pe.Node(fsl.BinaryMaths(operation='add', args='-bin'),
name='qap_headmask_combine_masks')
if len(resource_pool['anatomical_reorient']) == 2:
node, out_file = resource_pool['anatomical_reorient']
workflow.connect([(node, select_thresh, [(out_file, 'input_skull')]),
(node, mask_skull, [(out_file, 'in_file')]),
(node, slice_head_mask, [(out_file, 'infile')])])
else:
select_thresh.inputs.input_skull = resource_pool['anatomical_reorient']
mask_skull.inputs.in_file = resource_pool['anatomical_reorient']
# convert_fsl_xfm.inputs.infile =
# resource_pool['anatomical_reorient']
slice_head_mask.inputs.infile = resource_pool['anatomical_reorient']
if len(resource_pool['flirt_affine_xfm']) == 2:
node, out_file = resource_pool['flirt_affine_xfm']
workflow.connect(node, out_file, slice_head_mask, 'transform')
else:
slice_head_mask.inputs.transform = resource_pool['flirt_affine_xfm']
# convert_fsl_xfm.inputs.standard = config['template_skull_for_anat']
slice_head_mask.inputs.standard = config['template_skull_for_anat']
workflow.connect([
(select_thresh, mask_skull, [('thresh_out', 'thresh')]),
# (convert_fsl_xfm, slice_head_mask, [('converted_xfm', 'transform')])
(mask_skull, dilate_node, [('out_file', 'in_file')]),
(dilate_node, erode_node, [('out_file', 'in_file')]),
(erode_node, combine_masks, [('out_file', 'in_file')]),
(slice_head_mask, combine_masks, [('outfile_path', 'operand_file')])
])
resource_pool['qap_head_mask'] = (combine_masks, 'out_file')
return workflow, resource_pool
def ants_anatomical_linear_registration(workflow, resource_pool, config):
# resource pool should have:
# anatomical_brain
# linear ANTS registration takes roughly 2.5 minutes per subject running
# on one core of an Intel Core i7-4800MQ CPU @ 2.70GHz
import os
import sys
import nipype.interfaces.io as nio
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as util
from anatomical_preproc_utils import ants_lin_reg, separate_warps_list
from workflow_utils import check_input_resources, check_config_settings
from nipype.interfaces.fsl.base import Info
if "template_brain_for_anat" not in config:
config["template_brain_for_anat"] = Info.standard_image("MNI152_T1_2mm_brain.nii.gz")
check_config_settings(config, "template_brain_for_anat")
if "anatomical_brain" not in resource_pool.keys():
from anatomical_preproc import anatomical_skullstrip_workflow
workflow, resource_pool = anatomical_skullstrip_workflow(workflow, resource_pool, config)
# check_input_resources(resource_pool, "anatomical_brain")
calc_ants_warp = pe.Node(
interface=util.Function(
input_names=["anatomical_brain", "reference_brain"],
output_names=["warp_list", "warped_image"],
function=ants_lin_reg,
),
name="calc_ants_linear_warp",
)
select_forward_initial = pe.Node(
util.Function(
input_names=["warp_list", "selection"], output_names=["selected_warp"], function=separate_warps_list
),
name="select_forward_initial",
)
select_forward_initial.inputs.selection = "Initial"
select_forward_rigid = pe.Node(
util.Function(
input_names=["warp_list", "selection"], output_names=["selected_warp"], function=separate_warps_list
),
name="select_forward_rigid",
)
select_forward_rigid.inputs.selection = "Rigid"
select_forward_affine = pe.Node(
util.Function(
input_names=["warp_list", "selection"], output_names=["selected_warp"], function=separate_warps_list
),
name="select_forward_affine",
)
select_forward_affine.inputs.selection = "Affine"
if len(resource_pool["anatomical_brain"]) == 2:
node, out_file = resource_pool["anatomical_brain"]
workflow.connect(node, out_file, calc_ants_warp, "anatomical_brain")
else:
calc_ants_warp.inputs.anatomical_brain = resource_pool["anatomical_brain"]
calc_ants_warp.inputs.reference_brain = config["template_brain_for_anat"]
workflow.connect(calc_ants_warp, "warp_list", select_forward_initial, "warp_list")
workflow.connect(calc_ants_warp, "warp_list", select_forward_rigid, "warp_list")
workflow.connect(calc_ants_warp, "warp_list", select_forward_affine, "warp_list")
resource_pool["ants_initial_xfm"] = (select_forward_initial, "selected_warp")
resource_pool["ants_rigid_xfm"] = (select_forward_rigid, "selected_warp")
resource_pool["ants_affine_xfm"] = (select_forward_affine, "selected_warp")
resource_pool["ants_linear_warped_image"] = (calc_ants_warp, "warped_image")
return workflow, resource_pool