def spm_mrpet_preprocessing(wf_name="spm_mrpet_preproc"):
""" Run the PET pre-processing workflow against the
gunzip_pet.in_file files.
It depends on the anat_preproc_workflow, so if this
has not been run, this function will run it too.
# TODO: organize the anat2pet hack/condition somehow:
If anat2pet:
- SPM12 Coregister T1 and tissues to PET
- PETPVC the PET image in PET space
- SPM12 Warp PET to MNI
else:
- SPM12 Coregister PET to T1
- PETPVC the PET image in anatomical space
- SPM12 Warp PET in anatomical space to MNI through the
`anat_to_mni_warp`.
Parameters
----------
wf_name: str
Name of the workflow.
Nipype Inputs
-------------
pet_input.in_file: traits.File
The raw NIFTI_GZ PET image file
pet_input.anat: traits.File
Path to the high-contrast anatomical image.
Reference file of the warp_field, i.e., the
anatomical image in its native space.
pet_input.anat_to_mni_warp: traits.File
The warp field from the transformation of the
anatomical image to the standard MNI space.
pet_input.atlas_anat: traits.File
The atlas file in anatomical space.
pet_input.tissues: list of traits.File
List of tissues files from the New Segment process.
At least the first 3 tissues must be present.
Nipype outputs
--------------
pet_output.pvc_out: existing file
The results of the PVC process
pet_output.brain_mask: existing file
A brain mask calculated with the tissues file.
pet_output.coreg_ref: existing file
The coregistered reference image to PET space.
pet_output.coreg_others: list of existing files
List of coregistered files from coreg_pet.apply_to_files
pet_output.pvc_warped: existing file
Results from PETPVC normalized to MNI.
The result of every internal pre-processing step
is normalized to MNI here.
pet_output.warp_field: existing files
Spatial normalization parameters .mat files
pet_output.gm_norm: existing file
The output of the grey matter intensity
normalization process.
This is the last step in the PET signal correction,
before registration.
pet_output.atlas_pet: existing file
Atlas image warped to PET space.
If the `atlas_file` option is an existing file and
`normalize_atlas` is True.
Returns
-------
wf: nipype Workflow
"""
# specify input and output fields
in_fields = ["in_file", "anat", "anat_to_mni_warp", "tissues"]
out_fields = [
"brain_mask",
"coreg_others",
"coreg_ref",
"pvc_warped",
"pet_warped", # 'pet_warped' is a dummy entry to keep the fields pattern.
"warp_field",
"pvc_out",
"pvc_mask",
"gm_norm"
]
do_atlas, _ = check_atlas_file()
if do_atlas:
in_fields += ["atlas_anat"]
out_fields += ["atlas_pet"]
# input
pet_input = setup_node(IdentityInterface(fields=in_fields,
mandatory_inputs=True),
name="pet_input")
# workflow to perform partial volume correction
petpvc = petpvc_workflow(wf_name="petpvc")
merge_list = setup_node(Merge(4), name='merge_for_unzip')
gunzipper = pe.MapNode(Gunzip(), name="gunzip", iterfield=['in_file'])
warp_pet = setup_node(spm_normalize(), name="warp_pet")
tpm_bbox = setup_node(Function(function=get_bounding_box,
input_names=["in_file"],
output_names=["bbox"]),
name="tpm_bbox")
tpm_bbox.inputs.in_file = spm_tpm_priors_path()
# output
pet_output = setup_node(IdentityInterface(fields=out_fields),
name="pet_output")
# Create the workflow object
wf = pe.Workflow(name=wf_name)
# check how to perform the registration, to decide how to build the pipeline
anat2pet = get_config_setting('registration.anat2pet', False)
if anat2pet:
wf.connect([
# inputs
(pet_input, petpvc, [("in_file", "pvc_input.in_file"),
("anat", "pvc_input.reference_file"),
("tissues", "pvc_input.tissues")]),
# gunzip some files for SPM Normalize
(petpvc, merge_list, [("pvc_output.pvc_out", "in1"),
("pvc_output.brain_mask", "in2"),
("pvc_output.gm_norm", "in3")]),
(pet_input, merge_list, [("in_file", "in4")]),
(merge_list, gunzipper, [("out", "in_file")]),
# warp the PET PVCed to MNI
(petpvc, warp_pet, [("pvc_output.coreg_ref", "image_to_align")]),
(gunzipper, warp_pet, [("out_file", "apply_to_files")]),
(tpm_bbox, warp_pet, [("bbox", "write_bounding_box")]),
# output
(petpvc, pet_output, [("pvc_output.pvc_out", "pvc_out"),
("pvc_output.brain_mask", "brain_mask"),
("pvc_output.coreg_ref", "coreg_ref"),
("pvc_output.coreg_others", "coreg_others"),
("pvc_output.gm_norm", "gm_norm")]),
# output
(warp_pet, pet_output, [("normalized_files", "pvc_warped"),
("deformation_field", "warp_field")]),
])
else: # PET 2 ANAT
collector = setup_node(Merge(2), name='merge_for_warp')
apply_warp = setup_node(spm_apply_deformations(), name="warp_pet")
wf.connect([
# inputs
(pet_input, petpvc, [("in_file", "pvc_input.in_file"),
("anat", "pvc_input.reference_file"),
("tissues", "pvc_input.tissues")]),
# gunzip some files for SPM Normalize
(petpvc, merge_list, [("pvc_output.pvc_out", "in1"),
("pvc_output.brain_mask", "in2"),
("pvc_output.gm_norm", "in3")]),
(pet_input, merge_list, [("in_file", "in4")]),
(merge_list, gunzipper, [("out", "in_file")]),
# warp the PET PVCed to MNI
(gunzipper, collector, [("out_file", "in1")]),
(petpvc, collector, [("pvc_output.coreg_ref", "in2")]),
(pet_input, apply_warp, [("anat_to_mni_warp", "deformation_file")
]),
(collector, apply_warp, [("out", "apply_to_files")]),
(tpm_bbox, apply_warp, [("bbox", "write_bounding_box")]),
# output
(petpvc, pet_output, [("pvc_output.pvc_out", "pvc_out"),
("pvc_output.brain_mask", "brain_mask"),
("pvc_output.petpvc_mask", "petpvc_mask"),
("pvc_output.coreg_ref", "coreg_ref"),
("pvc_output.coreg_others", "coreg_others"),
("pvc_output.gm_norm", "gm_norm")]),
# output
(apply_warp, pet_output, [("normalized_files", "pvc_warped"),
("deformation_field", "warp_field")]),
])
if do_atlas:
coreg_atlas = setup_node(spm_coregister(cost_function="mi"),
name="coreg_atlas")
# set the registration interpolation to nearest neighbour.
coreg_atlas.inputs.write_interp = 0
wf.connect([
(pet_input, coreg_atlas, [("anat", "source")]),
(petpvc, coreg_atlas, [("pvc_output.coreg_ref", "target")]),
(pet_input, coreg_atlas, [("atlas_anat", "apply_to_files")]),
(coreg_atlas, pet_output, [("coregistered_files", "atlas_pet")]),
])
return wf
def attach_spm_mrpet_preprocessing(main_wf,
wf_name="spm_mrpet_preproc",
do_group_template=False):
""" Attach a PET pre-processing workflow that uses SPM12 to `main_wf`.
This workflow needs MRI based workflow.
This function is using the workflows defined in the function above:
spm_mrpet_preprocessing or spm_mrpet_grouptemplate_preprocessing. Depending
if group template is enabled.
Nipype Inputs for `main_wf`
---------------------------
Note: The `main_wf` workflow is expected to have an `input_files` and a
`datasink` nodes.
input_files.select.pet: input node
datasink: nipype Node
Parameters
----------
main_wf: nipype Workflow
wf_name: str
Name of the preprocessing workflow
do_group_template: bool
If True will attach the group template creation and pre-processing pipeline.
Nipype Workflow Dependencies
----------------------------
This workflow depends on:
- spm_anat_preproc
Returns
-------
main_wf: nipype Workflow
"""
# Dependency workflows
in_files = get_input_node(main_wf)
datasink = get_datasink(main_wf)
anat_output = get_interface_node(main_wf, "anat_output")
# The base name of the 'pet' file for the substitutions
anat_fbasename = remove_ext(
os.path.basename(get_input_file_name(in_files, 'anat')))
pet_fbasename = remove_ext(
os.path.basename(get_input_file_name(in_files, 'pet')))
# get the PET preprocessing pipeline
if do_group_template:
pet_wf = spm_mrpet_grouptemplate_preprocessing(wf_name=wf_name)
template_name = 'grptemplate'
output_subfolder = 'group_template'
else:
pet_wf = spm_mrpet_preprocessing(wf_name=wf_name)
template_name = 'stdtemplate'
output_subfolder = 'std_template'
# dataSink output substitutions
regexp_subst = [
(r"/{pet}_.*_pvc.nii.gz$", "/{pet}_pvc.nii.gz"),
(r"/{pet}_.*_pvc_maths.nii.gz$", "/{pet}_pvc_norm.nii.gz"),
(r"/{pet}_.*_pvc_intnormed.nii.gz$", "/{pet}_pvc_norm.nii.gz"),
(r"/tissues_brain_mask.nii$", "/brain_mask_anat.nii"),
(r"/w{pet}.nii", "/{pet}_{template}.nii"),
(r"/w{pet}_.*_pvc.nii$", "/{pet}_pvc_{template}.nii"),
(r"/w{pet}_.*_pvc_maths.nii$", "/{pet}_pvc_norm_{template}.nii"),
(r"/w{pet}_.*_pvc_intnormed.nii$", "/{pet}_pvc_norm_{template}.nii"),
(r"/wbrain_mask.nii", "/brain_mask_{template}.nii"),
(r"/r{pet}.nii", "/{pet}_anat.nii"),
(r"/r{pet}_.*_pvc.nii$", "/{pet}_pvc_anat.nii"),
(r"/r{pet}_.*_pvc_maths.nii$", "/{pet}_pvc_norm_anat.nii"),
(r"/r{pet}_.*_pvc_intnormed.nii$", "/{pet}_pvc_norm_anat.nii"),
(r"/y_rm{anat}_corrected.nii", "/{anat}_{pet}_warpfield.nii"),
(r"/rm{anat}_corrected.nii$", "/{anat}_{pet}.nii"),
(r"/rc1{anat}_corrected.nii$", "/gm_{pet}.nii"),
(r"/rc2{anat}_corrected.nii$", "/wm_{pet}.nii"),
(r"/rc3{anat}_corrected.nii$", "/csf_{pet}.nii"),
]
regexp_subst = format_pair_list(regexp_subst,
pet=pet_fbasename,
anat=anat_fbasename,
template=template_name)
# prepare substitution for atlas_file, if any
do_atlas, atlas_file = check_atlas_file()
if do_atlas:
atlas_basename = remove_ext(os.path.basename(atlas_file))
regexp_subst.extend([(r"/[\w]*{atlas}\.nii$", "/{atlas}_{pet}.nii")])
regexp_subst = format_pair_list(regexp_subst,
pet=pet_fbasename,
atlas=atlas_basename)
regexp_subst += extension_duplicates(regexp_subst)
regexp_subst = concat_to_pair_list(regexp_subst, prefix='/mrpet')
datasink.inputs.regexp_substitutions = extend_trait_list(
datasink.inputs.regexp_substitutions, regexp_subst)
# Connect the nodes
main_wf.connect([
# pet file input
(in_files, pet_wf, [("pet", "pet_input.in_file")]),
# pet to anat registration
(anat_output, pet_wf, [("anat_biascorr", "pet_input.anat"),
("tissues_native", "pet_input.tissues")]),
(
pet_wf,
datasink,
[
("pet_output.gm_norm", "mrpet.@norm"),
("pet_output.coreg_others",
"mrpet.tissues"), # careful changing this, look regexp_subst
("pet_output.coreg_ref", "mrpet.@anat"),
("pet_output.pvc_mask", "mrpet.@pvc_mask"),
("pet_output.pvc_out", "mrpet.@pvc"),
("pet_output.brain_mask", "mrpet.@brain_mask"),
("pet_output.pvc_warped",
"mrpet.{}.@pvc".format(output_subfolder)),
("pet_output.warp_field",
"mrpet.{}.@warp_field".format(output_subfolder)),
("pet_output.pet_warped",
"mrpet.{}.@pet_warped".format(output_subfolder)),
])
])
if not do_group_template:
# Connect the nodes
main_wf.connect([
# pet to anat registration
(anat_output, pet_wf, [("warp_forward",
"pet_input.anat_to_mni_warp")]),
])
if do_atlas:
main_wf.connect([
(anat_output, pet_wf, [("atlas_anat", "pet_input.atlas_anat")]),
(pet_wf, datasink, [("pet_output.atlas_pet", "mrpet.@atlas")]),
])
return main_wf
def attach_spm_anat_preprocessing(main_wf, wf_name="spm_anat_preproc"):
""" Attach the SPM12 anatomical MRI pre-processing workflow to
the `main_wf`.
Parameters
----------
main_wf: nipype Workflow
wf_name: str
Name of the preprocessing workflow
Nipype Inputs for `main_wf`
---------------------------
Note: The `main_wf` workflow is expected to have an
`input_files` and a `datasink` nodes.
input_files.anat: input node
datasink: nipype Node
Returns
-------
main_wf: nipype Workflow
"""
in_files = get_input_node(main_wf)
datasink = get_datasink(main_wf)
# The workflow box
anat_wf = spm_anat_preprocessing(wf_name=wf_name)
# The base name of the 'anat' file for the substitutions
anat_fbasename = remove_ext(
os.path.basename(get_input_file_name(in_files, 'anat')))
# dataSink output substitutions
regexp_subst = [
(r"/{anat}_.*corrected_seg8.mat$", "/{anat}_to_mni_affine.mat"),
(r"/m{anat}.*_corrected.nii$", "/{anat}_biascorrected.nii"),
(r"/wm{anat}.*_corrected.nii$", "/{anat}_mni.nii"),
(r"/y_{anat}.*nii$", "/{anat}_to_mni_field.nii"),
(r"/iy_{anat}.*nii$", "/{anat}_to_mni_inv_field.nii"),
(r"/mwc1{anat}.*nii$", "/{anat}_gm_mod_mni.nii"),
(r"/mwc2{anat}.*nii$", "/{anat}_wm_mod_mni.nii"),
(r"/mwc3{anat}.*nii$", "/{anat}_csf_mod_mni.nii"),
(r"/mwc4{anat}.*nii$", "/{anat}_nobrain_mod_mni.nii"),
(r"/c1{anat}.*nii$", "/{anat}_gm.nii"),
(r"/c2{anat}.*nii$", "/{anat}_wm.nii"),
(r"/c3{anat}.*nii$", "/{anat}_csf.nii"),
(r"/c4{anat}.*nii$", "/{anat}_nobrain.nii"),
(r"/c5{anat}.*nii$", "/{anat}_nobrain_mask.nii"),
(r"/direct_cortical_thickness.nii$",
"/{anat}_gm_cortical_thickness.nii"),
(r"/direct_warped_white_matter.nii$",
"/{anat}_warped_white_matter.nii"),
]
regexp_subst = format_pair_list(regexp_subst, anat=anat_fbasename)
# prepare substitution for atlas_file, if any
do_atlas, atlas_file = check_atlas_file()
if do_atlas:
atlas_basename = remove_ext(os.path.basename(atlas_file))
regexp_subst.extend([
(r"/w{atlas}\.nii$", "/{atlas}_anat_space.nii"),
])
regexp_subst = format_pair_list(regexp_subst,
anat=anat_fbasename,
atlas=atlas_basename)
# add nii.gz patterns
regexp_subst += extension_duplicates(regexp_subst)
# add parent folder to paths
regexp_subst = concat_to_pair_list(regexp_subst, prefix='/anat')
datasink.inputs.regexp_substitutions = extend_trait_list(
datasink.inputs.regexp_substitutions, regexp_subst)
main_wf.connect([
(in_files, anat_wf, [("anat", "anat_input.in_file")]),
(anat_wf, datasink, [
("anat_output.anat_mni", "anat.@mni"),
("anat_output.tissues_warped", "anat.tissues.warped"),
("anat_output.tissues_native", "anat.tissues.native"),
("anat_output.affine_transform", "anat.transform.@linear"),
("anat_output.warp_forward", "anat.transform.@forward"),
("anat_output.warp_inverse", "anat.transform.@inverse"),
("anat_output.anat_biascorr", "anat.@biascor"),
("anat_output.brain_mask", "anat.@brain_mask"),
]),
])
# check optional outputs
if do_atlas:
main_wf.connect([
(anat_wf, datasink, [("anat_output.atlas_anat", "anat.@atlas")]),
])
do_cortical_thickness = get_config_setting(
'anat_preproc.do_cortical_thickness', False)
if do_cortical_thickness:
main_wf.connect([
(anat_wf, datasink, [
("anat_output.cortical_thickness", "anat.@cortical_thickness"),
("anat_output.warped_white_matter",
"anat.@warped_white_matter"),
]),
])
return main_wf
def spm_mrpet_grouptemplate_preprocessing(
wf_name="spm_mrpet_grouptemplate_preproc"):
""" Run the PET pre-processing workflow against the gunzip_pet.in_file files.
It depends on the anat_preproc_workflow, so if this has not been run, this function
will run it too.
This is identical to the workflow defined in `spm_mrpet_preprocessing`,
with the only difference that we now normalize all subjects agains a custom
template using the spm Old Normalize interface.
It does:
- SPM12 Coregister T1 and tissues to PET
- PVC the PET image in PET space
- SPM12 Warp PET to the given template
Parameters
----------
wf_name: str
Name of the workflow.
Nipype Inputs
-------------
pet_input.in_file: traits.File
The raw NIFTI_GZ PET image file.
pet_input.atlas_anat: traits.File
The atlas file in anatomical space.
pet_input.anat: traits.File
Path to the high-contrast anatomical image.
Reference file of the warp_field, i.e., the anatomical image in its native space.
pet_input.tissues: list of traits.File
List of tissues files from the New Segment process. At least the first
3 tissues must be present.
pet_input.pet_template: traits.File
The template file for inter-subject registration reference.
Nipype outputs
--------------
pet_output.pvc_out: existing file
The results of the PVC process.
pet_output.brain_mask: existing file
A brain mask calculated with the tissues file.
pet_output.coreg_ref: existing file
The coregistered reference image to PET space.
pet_output.coreg_others: list of existing files
List of coregistered files from coreg_pet.apply_to_files.
pet_output.pet_warped: existing file
PET image normalized to the group template.
pet_output.pvc_warped: existing file
The outputs of the PETPVC workflow normalized to the group template.
The result of every internal pre-processing step is normalized to the
group template here.
pet_output.warp_field: existing files
Spatial normalization parameters .mat files.
pet_output.gm_norm: existing file
The output of the grey matter intensity normalization process.
This is the last step in the PET signal correction, before registration.
pet_output.atlas_pet: existing file
Atlas image warped to PET space.
If the `atlas_file` option is an existing file and `normalize_atlas` is True.
Returns
-------
wf: nipype Workflow
"""
# specify input and output fields
in_fields = ["in_file", "anat", "tissues", "pet_template"]
out_fields = [
"brain_mask", "coreg_others", "coreg_ref", "pvc_warped", "pet_warped",
"warp_field", "pvc_out", "pvc_mask", "gm_norm"
]
do_atlas, _ = check_atlas_file()
if do_atlas:
in_fields += ["atlas_anat"]
out_fields += ["atlas_pet"]
# input
pet_input = setup_node(IdentityInterface(fields=in_fields,
mandatory_inputs=True),
name="pet_input")
# workflow to perform partial volume correction
petpvc = petpvc_workflow(wf_name="petpvc")
unzip_mrg = setup_node(Merge(4), name='merge_for_unzip')
gunzipper = pe.MapNode(Gunzip(), name="gunzip", iterfield=['in_file'])
# warp each subject to the group template
gunzip_template = setup_node(Gunzip(), name="gunzip_template")
gunzip_pet = setup_node(Gunzip(), name="gunzip_pet")
warp_mrg = setup_node(Merge(2), name='merge_for_warp')
warp2template = setup_node(spm.Normalize(jobtype="estwrite",
out_prefix="wgrptemplate_"),
name="warp2template")
get_bbox = setup_node(Function(function=get_bounding_box,
input_names=["in_file"],
output_names=["bbox"]),
name="get_bbox")
# output
pet_output = setup_node(IdentityInterface(fields=out_fields),
name="pet_output")
# Create the workflow object
wf = pe.Workflow(name=wf_name)
wf.connect([
# inputs
(pet_input, petpvc, [("in_file", "pvc_input.in_file"),
("anat", "pvc_input.reference_file"),
("tissues", "pvc_input.tissues")]),
# get template bounding box to apply to results
(pet_input, get_bbox, [("pet_template", "in_file")]),
# gunzip some inputs
(pet_input, gunzip_pet, [("in_file", "in_file")]),
(pet_input, gunzip_template, [("pet_template", "in_file")]),
# gunzip some files for SPM Normalize
(petpvc, unzip_mrg, [("pvc_output.pvc_out", "in1"),
("pvc_output.brain_mask", "in2"),
("pvc_output.gm_norm", "in3")]),
(pet_input, unzip_mrg, [("in_file", "in4")]),
(unzip_mrg, gunzipper, [("out", "in_file")]),
(gunzipper, warp_mrg, [("out_file", "in1")]),
(warp_mrg, warp2template, [(("out", flatten_list), "apply_to_files")]),
# prepare the target parameters of the warp to template
(gunzip_pet, warp2template, [("out_file", "source")]),
(gunzip_template, warp2template, [("out_file", "template")]),
(get_bbox, warp2template, [("bbox", "write_bounding_box")]),
# output
(warp2template, pet_output, [
("normalization_parameters", "warp_field"),
("normalized_files", "pvc_warped"),
("normalized_source", "pet_warped"),
]),
# output
(petpvc, pet_output, [("pvc_output.pvc_out", "pvc_out"),
("pvc_output.brain_mask", "brain_mask"),
("pvc_output.coreg_ref", "coreg_ref"),
("pvc_output.coreg_others", "coreg_others"),
("pvc_output.gm_norm", "gm_norm")]),
])
if do_atlas:
coreg_atlas = setup_node(spm_coregister(cost_function="mi"),
name="coreg_atlas")
# set the registration interpolation to nearest neighbour.
coreg_atlas.inputs.write_interp = 0
wf.connect([
(pet_input, coreg_atlas, [("anat", "source")]),
(petpvc, coreg_atlas, [("pvc_output.coreg_ref", "target")]),
(pet_input, coreg_atlas, [("atlas_anat", "apply_to_files")]),
(coreg_atlas, pet_output, [("coregistered_files", "atlas_pet")]),
# warp the atlas to the template space as well
(coreg_atlas, warp_mrg, [("coregistered_files", "in2")])
])
return wf
def spm_anat_preprocessing(wf_name="spm_anat_preproc"):
""" Run the T1 pre-processing workflow against the anat_hc
files in `data_dir`.
It does:
- N4BiasFieldCorrection
- SPM12 New Segment
- SPM12 Warp of MPRAGE to MNI
[Optional: from config]
- Atlas file warping to MPRAGE
- Cortical thickness (SPM+DiReCT)
Nipype Inputs
-------------
anat_input.in_file: traits.File
Path to the anatomical image.
anat_input.atlas_file: traits.File
Path to an atlas file in MNI space to be
warped to the anatomical space.
Can also be set through the configuration
setting `atlas_file`.
Nipype Outputs
--------------
anat_output.anat_mni: traits.File
The bias-field normalized to MNI anatomical image.
anat_output.tissues_warped: traits.File
The tissue segmentation in MNI space from SPM.
anat_output.tissues_native: traits.File
The tissue segmentation in native space from SPM.
anat_output.affine_transform: traits.File
The affine transformation file.
anat_output.warp_forward: traits.File
The forward (anat to MNI) warp field from SPM.
anat_output.warp_inverse: traits.File
The inverse (MNI to anat) warp field from SPM.
anat_output.anat_biascorr: traits.File
The bias-field corrected anatomical image.
anat_output.brain_mask: traits.File
A brain mask file in anatomical space.
This is calculated by summing up the maps of
segmented tissues (CSF, WM, GM) and then binarised.
anat_output.atlas_anat: traits.File
If `atlas_file` is an existing file in MNI space.
The atlas file warped to anatomical space,
if do_atlas and the atlas file is set in configuration.
anat_output.cortical_thickness: traits.File
If `anat_preproc.do_cortical_thickness` is True.
The cortical thickness estimations calculated with the
SPM+DiReCT method (KellyKapowski).
anat_output.warped_white_matter: warped_white_matter
If `anat_preproc.do_cortical_thickness` is True.
The warped white matter image calculated with the
SPM+DiReCT method (KellyKapowski).
Returns
-------
wf: nipype Workflow
"""
# Create the workflow object
wf = pe.Workflow(name=wf_name)
# specify input and output fields
in_fields = ["in_file"]
out_fields = [
"anat_mni",
"tissues_warped",
"tissues_native",
"affine_transform",
"warp_forward",
"warp_inverse",
"anat_biascorr",
"brain_mask",
]
# check if we have to warp an atlas files too.
do_atlas, atlas_file = check_atlas_file()
if do_atlas:
in_fields += ["atlas_file"]
out_fields += ["atlas_anat"]
# check if we have to do cortical thickness (SPM+DiReCT) method.
do_cortical_thickness = get_config_setting(
'anat_preproc.do_cortical_thickness', False)
if do_cortical_thickness:
out_fields += [
"cortical_thickness",
"warped_white_matter",
]
# input node
anat_input = pe.Node(IdentityInterface(fields=in_fields,
mandatory_inputs=True),
name="anat_input")
# atlas registration
if do_atlas and not isdefined(anat_input.inputs.atlas_file):
anat_input.inputs.set(atlas_file=atlas_file)
# T1 preprocessing nodes
biascor = setup_node(biasfield_correct(), name="bias_correction")
gunzip_anat = setup_node(Gunzip(), name="gunzip_anat")
segment = setup_node(spm_segment(), name="new_segment")
warp_anat = setup_node(spm_apply_deformations(), name="warp_anat")
tpm_bbox = setup_node(Function(function=get_bounding_box,
input_names=["in_file"],
output_names=["bbox"]),
name="tpm_bbox")
tpm_bbox.inputs.in_file = spm_tpm_priors_path()
# calculate brain mask from tissue maps
tissues = setup_node(IdentityInterface(fields=["gm", "wm", "csf"],
mandatory_inputs=True),
name="tissues")
brain_mask = setup_node(Function(
function=math_img,
input_names=["formula", "out_file", "gm", "wm", "csf"],
output_names=["out_file"],
imports=['from neuro_pypes.interfaces.nilearn import ni2file']),
name='brain_mask')
brain_mask.inputs.out_file = "tissues_brain_mask.nii.gz"
brain_mask.inputs.formula = "np.abs(gm + wm + csf) > 0"
# output node
anat_output = pe.Node(IdentityInterface(fields=out_fields),
name="anat_output")
# Connect the nodes
wf.connect([
# input to biasfieldcorrection
(anat_input, biascor, [("in_file", "input_image")]),
# new segment
(biascor, gunzip_anat, [("output_image", "in_file")]),
(gunzip_anat, segment, [("out_file", "channel_files")]),
# Normalize12
(segment, warp_anat, [("forward_deformation_field", "deformation_file")
]),
(segment, warp_anat, [("bias_corrected_images", "apply_to_files")]),
(tpm_bbox, warp_anat, [("bbox", "write_bounding_box")]),
# brain mask from tissues
(segment, tissues, [
(("native_class_images", selectindex, 0), "gm"),
(("native_class_images", selectindex, 1), "wm"),
(("native_class_images", selectindex, 2), "csf"),
]),
(tissues, brain_mask, [
("gm", "gm"),
("wm", "wm"),
("csf", "csf"),
]),
# output
(warp_anat, anat_output, [("normalized_files", "anat_mni")]),
(segment, anat_output, [("modulated_class_images", "tissues_warped"),
("native_class_images", "tissues_native"),
("transformation_mat", "affine_transform"),
("forward_deformation_field", "warp_forward"),
("inverse_deformation_field", "warp_inverse"),
("bias_corrected_images", "anat_biascorr")]),
(brain_mask, anat_output, [("out_file", "brain_mask")]),
])
# atlas warping nodes
if do_atlas:
gunzip_atlas = pe.Node(Gunzip(), name="gunzip_atlas")
warp_atlas = setup_node(spm_apply_deformations(), name="warp_atlas")
anat_bbox = setup_node(Function(function=get_bounding_box,
input_names=["in_file"],
output_names=["bbox"]),
name="anat_bbox")
# set the warping interpolation to nearest neighbour.
warp_atlas.inputs.write_interp = 0
# connect the atlas registration nodes
wf.connect([
(anat_input, gunzip_atlas, [("atlas_file", "in_file")]),
(gunzip_anat, anat_bbox, [("out_file", "in_file")]),
(gunzip_atlas, warp_atlas, [("out_file", "apply_to_files")]),
(segment, warp_atlas, [("inverse_deformation_field",
"deformation_file")]),
(anat_bbox, warp_atlas, [("bbox", "write_bounding_box")]),
(warp_atlas, anat_output, [("normalized_files", "atlas_anat")]),
])
# cortical thickness (SPM+DiReCT) method
if do_cortical_thickness:
from ..interfaces.ants import KellyKapowski
segm_img = setup_node(Function(
function=math_img,
input_names=["formula", "out_file", "gm", "wm"],
output_names=["out_file"],
imports=['from neuro_pypes.interfaces.nilearn import ni2file']),
name='gm-wm_image')
segm_img.inputs.out_file = "gm_wm.nii.gz"
segm_img.inputs.formula = '((gm >= 0.5)*2 + (wm > 0.5)*3).astype(np.uint8)'
# copy the header from the GM tissue image to the result from `gm-wm_image`.
# this is necessary because the `gm-wm_image` operation sometimes modifies the
# offset of the image, which will provoke an ANTs exception due to
# ITK tolerance in ImageToImageFilter
# https://github.com/stnava/ANTs/issues/74
cp_hdr = setup_node(Function(
function=copy_header,
input_names=["in_file", "data_file"],
output_names=["out_file"],
imports=['from neuro_pypes.interfaces.nilearn import ni2file']),
name='copy_header')
kk = setup_node(KellyKapowski(), name='direct')
kk.inputs.cortical_thickness = 'direct_cortical_thickness.nii.gz'
kk.inputs.warped_white_matter = 'direct_warped_white_matter.nii.gz'
# connect the cortical thickness (SPM+DiReCT) method
wf.connect([
# create segmentation GM+WM file
(tissues, segm_img, [("gm", "gm"), ("wm", "wm")]),
(segm_img, cp_hdr, [("out_file", "data_file")]),
(tissues, cp_hdr, [("gm", "in_file")]),
# kellykapowski
(cp_hdr, kk, [("out_file", "segmentation_image")]),
(tissues, kk, [("gm", "gray_matter_prob_image"),
("wm", "white_matter_prob_image")]),
(kk, anat_output, [("cortical_thickness", "cortical_thickness"),
("warped_white_matter", "warped_white_matter")
]),
])
return wf
def attach_camino_tractography(main_wf, wf_name="camino_tract"):
""" Attach the Camino-based tractography workflow to the `main_wf`.
Parameters
----------
main_wf: nipype Workflow
atlas_file: str
Path to the anatomical atlas.
wf_name: str
Name of the preprocessing workflow
Nipype Inputs for `main_wf`
---------------------------
Note: The `main_wf` workflow is expected to have an `input_files` and a `datasink` nodes.
input_files.select.diff: input node
datasink: nipype Node
Nipype Workflow Dependencies
----------------------------
This workflow depends on:
- spm_anat_preproc
- spm_fsl_dti_preprocessing
Returns
-------
main_wf: nipype Workflow
"""
in_files = get_input_node(main_wf)
datasink = get_datasink(main_wf)
dti_coreg_output = get_interface_node(main_wf, 'dti_co_output')
dti_artif_output = get_interface_node(main_wf, 'dti_art_output')
# The workflow box
tract_wf = camino_tractography(wf_name=wf_name)
# input and output diffusion MRI workflow to main workflow connections
main_wf.connect([
(in_files, tract_wf, [("bval", "tract_input.bval")]),
(dti_coreg_output, tract_wf, [("brain_mask_diff", "tract_input.mask")]),
(dti_artif_output, tract_wf, [
("eddy_corr_file", "tract_input.diff"),
("bvec_rotated", "tract_input.bvec"),
]),
# output
(tract_wf, datasink, [
("tract_output.tensor", "tract.@tensor"),
("tract_output.tracks", "tract.@tracks"),
("tract_output.connectivity", "tract.@connectivity"),
("tract_output.mean_fa", "tract.@mean_fa"),
("tract_output.fa", "tract.@fa"),
])
])
# pass the atlas if it's the case
do_atlas, _ = check_atlas_file()
if do_atlas:
main_wf.connect([(dti_coreg_output, tract_wf, [("atlas_diff", "tract_input.atlas")])])
return main_wf
def attach_spm_fsl_dti_preprocessing(main_wf,
wf_name="spm_fsl_dti_preprocessing"):
""" Attach a set of pipelines to the `main_wf` for Diffusion MR (`diff`) image processing.
- dti_artifact_correction
- spm_anat_to_diff_coregistration
- dti_tensor_fitting
Parameters
----------
main_wf: nipype Workflow
wf_name: str
Name of the preprocessing workflow
Nipype Inputs for `main_wf`
---------------------------
Note: The `main_wf` workflow is expected to have an `input_files` and a `datasink` nodes.
input_files.select.diff: input node
datasink: nipype Node
Returns
-------
main_wf: nipype Workflow
"""
in_files = get_input_node(main_wf)
datasink = get_datasink(main_wf)
anat_output = get_interface_node(main_wf, 'anat_output')
# attach the artifact detection and correction pipeline
main_wf = attach_dti_artifact_correction(main_wf)
dti_art_output = get_interface_node(main_wf, 'dti_art_output')
# The workflow boxes
coreg_dti_wf = spm_anat_to_diff_coregistration(wf_name=wf_name)
# dataSink output substitutions
## The base name of the 'diff' file for the substitutions
diff_fbasename = remove_ext(
os.path.basename(get_input_file_name(in_files, 'diff')))
anat_fbasename = remove_ext(
os.path.basename(get_input_file_name(in_files, 'anat')))
regexp_subst = [
(r"/brain_mask_{diff}_space\.nii$", "/brain_mask.nii"),
(r"/eddy_corrected\.nii$", "/{diff}_eddycor.nii"),
(r"/rc1anat_hc_corrected\.nii$", "/gm_diff.nii"),
(r"/rc2anat_hc_corrected\.nii$", "/wm_diff.nii"),
(r"/rc3anat_hc_corrected\.nii$", "/csf_diff.nii"),
(r"/rmanat_hc_corrected\.nii$", "/{anat}_diff.nii"),
]
regexp_subst = format_pair_list(regexp_subst,
diff=diff_fbasename,
anat=anat_fbasename)
# prepare substitution for atlas_file, if any
do_atlas, atlas_file = check_atlas_file()
if do_atlas:
atlas_basename = remove_ext(os.path.basename(atlas_file))
regexp_subst.extend([
(r"/[\w]*{atlas}.*\.nii$", "/{atlas}_{diff}_space.nii"),
])
regexp_subst = format_pair_list(regexp_subst,
atlas=atlas_basename,
diff=diff_fbasename)
regexp_subst += extension_duplicates(regexp_subst)
regexp_subst = concat_to_pair_list(regexp_subst, prefix='/diff')
datasink.inputs.regexp_substitutions = extend_trait_list(
datasink.inputs.regexp_substitutions, regexp_subst)
# input and output diffusion MRI workflow to main workflow connections
main_wf.connect([
(dti_art_output, coreg_dti_wf, [
("avg_b0", "dti_co_input.avg_b0"),
]),
(anat_output, coreg_dti_wf, [("tissues_native",
"dti_co_input.tissues"),
("anat_biascorr", "dti_co_input.anat")]),
(coreg_dti_wf, datasink, [
("dti_co_output.anat_diff", "diff.@anat_diff"),
("dti_co_output.tissues_diff", "diff.tissues.@tissues_diff"),
("dti_co_output.brain_mask_diff", "diff.@brain_mask"),
]),
])
if do_atlas:
main_wf.connect([
(anat_output, coreg_dti_wf, [("atlas_anat",
"dti_co_input.atlas_anat")]),
(coreg_dti_wf, datasink, [("dti_co_output.atlas_diff",
"diff.@atlas")]),
])
return main_wf
def attach_spm_warp_fmri_wf(main_wf,
registration_wf_name="spm_warp_fmri",
do_group_template=False):
""" Attach the fMRI inter-subject spatial normalization workflow to the `main_wf`.
Parameters
----------
main_wf: nipype Workflow
registration_wf_name: str
Name of the registration workflow.
do_group_template: bool
If True will attach the group template creation and pre-processing pipeline.
Nipype Inputs for `main_wf`
---------------------------
Note: The `main_wf` workflow is expected to have an `input_files` and a `datasink` nodes.
input_files.select.anat: input node
datasink: nipype Node
Workflow Dependencies
---------------------
fmri_cleanup, the cleanup and preprocessing of the fMRI data
spm_anat_preproc, for the anatomical to MNI space transformation
spm_fmri_template, if do_group_template is True
Returns
-------
main_wf: nipype Workflow
"""
# Dependency workflows
anat_wf = get_subworkflow(main_wf, 'spm_anat_preproc')
cleanup_wf = get_subworkflow(main_wf, 'fmri_cleanup')
in_files = get_input_node(main_wf)
datasink = get_datasink(main_wf)
if do_group_template:
template_name = 'grptemplate'
else:
template_name = 'stdtemplate'
warp_wf_name = "{}_{}".format(registration_wf_name, template_name)
warp_fmri_wf = spm_warp_fmri_wf(warp_wf_name,
register_to_grptemplate=do_group_template)
# dataSink output substitutions
# The base name of the 'rest' file for the substitutions
rest_fbasename = remove_ext(
os.path.basename(get_input_file_name(in_files, 'rest')))
anat_fbasename = remove_ext(
os.path.basename(get_input_file_name(in_files, 'anat')))
regexp_subst = [
(r"/corr_stc{fmri}_trim_mean_sn.mat$",
"/{fmri}_grptemplate_params.mat"),
(r"/y_corr_stc{fmri}_trim_mean\.nii$", "/{fmri}_to_mni_warpfield.nii"),
(r"/rcorr_stc{fmri}_trim_mean.nii$", "/avg_epi_anat.nii"),
(r"/wgrptmpl_corr_stc{fmri}_trim_mean\.nii$",
"/avg_epi_grptemplate.nii"),
(r"/wgrptmpl_corr_stc{fmri}_trim\.nii$",
"/{fmri}_trimmed_grptemplate.nii"),
(r"/wgrptmpl_corr_stc{fmri}_trim_filtermotart[\w_]*_cleaned\.nii$",
"/{fmri}_nuisance_corrected_grptemplate.nii"),
(r"/wgrptmpl_corr_stc{fmri}_trim_filtermotart[\w_]*_gsr\.nii$",
"/{fmri}_nuisance_corrected_grptemplate.nii"),
(r"/wgrptmpl_corr_stc{fmri}_trim_filtermotart[\w_]*_bandpassed\.nii$",
"/{fmri}_time_filtered_grptemplate.nii"),
(r"/wgrptmpl_corr_stc{fmri}_trim_filtermotart[\w_]*_smooth\.nii$",
"/{fmri}_smooth_grptemplate.nii"),
(r"/w[r]?corr_stc{fmri}_trim_mean\.nii$", "/avg_epi_mni.nii"),
(r"/w[r]?corr_stc{fmri}_trim\.nii$", "/{fmri}_trimmed_mni.nii"),
(r"/w[r]?corr_stc{fmri}_trim_filtermotart[\w_]*_cleaned\.nii$",
"/{fmri}_nuisance_corrected_mni.nii"),
(r"/w[r]?corr_stc{fmri}_trim_filtermotart[\w_]*_gsr\.nii$",
"/{fmri}_nuisance_corrected_mni.nii"),
(r"/w[r]?corr_stc{fmri}_trim_filtermotart[\w_]*_bandpassed\.nii$",
"/{fmri}_time_filtered_mni.nii"),
(r"/w[r]?corr_stc{fmri}_trim_filtermotart[\w_]*_smooth\.nii$",
"/{fmri}_smooth_mni.nii"),
(r"/w[r]?corr_stc{fmri}_trim[\w_]*_smooth\.nii$",
"/{fmri}_nofilt_smooth_mni.nii"),
(r"/w[r]?corr_stc{fmri}_trim[\w_]*_cleaned\.nii$",
"/{fmri}_nofilt_nuisance_corrected_mni.nii"),
(r"/w[r]?corr_stc{fmri}_trim[\w_]*_gsr\.nii$",
"/{fmri}_nofilt_nuisance_corrected_mni.nii"),
(r"/w[r]?corr_stc{fmri}_trim[\w_]*_bandpassed\.nii$",
"/{fmri}_nofilt_time_filtered_mni.nii"),
(r"/w[r]?corr_stc{fmri}_trim[\w_]*_smooth\.nii$",
"/{fmri}_nofilt_smooth_mni.nii"),
]
regexp_subst = format_pair_list(regexp_subst,
fmri=rest_fbasename,
anat=anat_fbasename)
# prepare substitution for atlas_file, if any
do_atlas, atlas_file = check_atlas_file()
if do_atlas:
atlas_basename = remove_ext(os.path.basename(atlas_file))
regexp_subst.extend([
(r"/[\w]*{atlas}.*\.nii$", "/{atlas}_{fmri}_space.nii"),
])
regexp_subst = format_pair_list(regexp_subst,
atlas=atlas_basename,
fmri=rest_fbasename)
regexp_subst += extension_duplicates(regexp_subst)
regexp_subst = concat_to_pair_list(regexp_subst, prefix='/rest')
datasink.inputs.regexp_substitutions = extend_trait_list(
datasink.inputs.regexp_substitutions, regexp_subst)
# input and output anat workflow to main workflow connections
main_wf.connect([
# clean_up_wf to registration_wf
(cleanup_wf, warp_fmri_wf, [
("rest_output.motion_corrected", "wfmri_input.in_file"),
("rest_output.anat", "wfmri_input.anat_fmri"),
("rest_output.time_filtered", "wfmri_input.time_filtered"),
("rest_output.avg_epi", "wfmri_input.avg_epi"),
("rest_output.tissues_brain_mask", "wfmri_input.brain_mask"),
]),
# output
(warp_fmri_wf, datasink, [
("wfmri_output.warped_fmri",
"rest.{}.@warped_fmri".format(template_name)),
("wfmri_output.wtime_filtered",
"rest.{}.@time_filtered".format(template_name)),
("wfmri_output.smooth", "rest.{}.@smooth".format(template_name)),
("wfmri_output.wavg_epi",
"rest.{}.@avg_epi".format(template_name)),
("wfmri_output.warp_field",
"rest.{}.@warp_field".format(template_name)),
]),
])
if not do_group_template:
main_wf.connect([
(anat_wf, warp_fmri_wf, [
("anat_output.anat_biascorr", "wfmri_input.reference_file"),
("anat_output.warp_forward", "wfmri_input.anat_to_mni_warp"),
]),
# output
(warp_fmri_wf, datasink, [
("wfmri_output.coreg_avg_epi", "rest.@coreg_fmri_anat"),
("wfmri_output.coreg_others", "rest.@coreg_others"),
]),
])
if do_atlas:
main_wf.connect([
(anat_wf, warp_fmri_wf, [("anat_output.atlas_anat",
"wfmri_input.atlas_anat")]),
(warp_fmri_wf, datasink, [("wfmri_output.atlas_fmri",
"rest.@atlas")]),
])
return main_wf
def spm_warp_fmri_wf(wf_name="spm_warp_fmri", register_to_grptemplate=False):
""" Run SPM to warp resting-state fMRI pre-processed data to MNI or a given
template.
Tasks:
- Warping the inputs to MNI or a template, if `do_group_template` is True
Parameters
----------
wf_name: str
register_to_grptemplate: bool
If True will expect the wfmri_input.epi_template input and use it as a group template
for inter-subject registratio.
Nipype Inputs
-------------
wfmri_input.in_file: traits.File
The slice time and motion corrected fMRI file.
wfmri_input.reference_file: traits.File
The anatomical image in its native space
for registration reference.
wfmri_input.anat_fmri: traits.File
The anatomical image in fMRI space.
wfmri_input.anat_to_mni_warp: traits.File
The warp field from the transformation of the
anatomical image to the standard MNI space.
wfmri_input.time_filtered: traits.File
The bandpass time filtered fMRI file.
wfmri_input.avg_epi: traits.File
The average EPI from the fMRI file.
wfmri_input.epi_template: traits.File
Reference EPI template file for inter subject registration.
If `do_group_template` is True you must specify this input.
wfmri_input.brain_mask: traits.File
Brain mask in fMRI space.
wfmri_input.atlas_anat: traits.File
Atlas in subject anatomical space.
Nipype Outputs
--------------
wfmri_output.warped_fmri: traits.File
The slice time, motion, and nuisance corrected fMRI
file registered to the template.
wfmri_output.wtime_filtered: traits.File
The bandpass time filtered fMRI file
registered to the template.
wfmri_output.smooth: traits.File
The smooth bandpass time filtered fMRI file
registered to the template.
wfmri_output.wavg_epi: traits.File
The average EPI from the fMRI file
registered to the template.
wfmri_output.warp_field: traits.File
The fMRI to template warp field.
wfmri_output.coreg_avg_epi: traits.File
The average EPI image in anatomical space.
Only if registration.fmri2mni is false.
wfmri_output.coreg_others: traits.File
Other mid-preprocessing fmri images registered to
anatomical space:
- wfmri_input.in_file,
- wfmri_input.brain_mask,
- wfmri_input.time_filtered.
Only if registration.fmri2mni is false
wfmri_output.wbrain_mask: traits.File
Brain mask in fMRI space warped to MNI.
Returns
-------
wf: nipype Workflow
"""
# Create the workflow object
wf = pe.Workflow(name=wf_name)
# specify input and output fields
in_fields = [
"in_file",
"anat_fmri",
"anat_to_mni_warp",
"brain_mask",
"reference_file",
"time_filtered",
"avg_epi",
]
out_fields = [
"warped_fmri", "wtime_filtered", "smooth", "wavg_epi", "wbrain_mask",
"warp_field", "coreg_avg_epi", "coreg_others"
]
if register_to_grptemplate:
in_fields += ['epi_template']
do_atlas, _ = check_atlas_file()
if do_atlas:
in_fields += ["atlas_anat"]
out_fields += ["atlas_fmri"]
# input identities
wfmri_input = setup_node(IdentityInterface(fields=in_fields,
mandatory_inputs=True),
name="wfmri_input")
# in file unzipper
in_gunzip = pe.Node(Gunzip(), name="in_gunzip")
# merge list for normalization input
merge_list = pe.Node(Merge(2), name='merge_for_warp')
gunzipper = pe.MapNode(Gunzip(), name="gunzip", iterfield=['in_file'])
# the template bounding box
tpm_bbox = setup_node(Function(function=get_bounding_box,
input_names=["in_file"],
output_names=["bbox"]),
name="tpm_bbox")
# smooth the final result
smooth = setup_node(fsl.IsotropicSmooth(fwhm=8, output_type='NIFTI'),
name="smooth_fmri")
# output identities
rest_output = setup_node(IdentityInterface(fields=out_fields),
name="wfmri_output")
# check how to perform the registration, to decide how to build the pipeline
fmri2mni = get_config_setting('registration.fmri2mni', False)
# register to group template
if register_to_grptemplate:
gunzip_template = pe.Node(
Gunzip(),
name="gunzip_template",
)
warp = setup_node(spm.Normalize(jobtype="estwrite",
out_prefix="wgrptmpl_"),
name="fmri_grptemplate_warp")
warp_source_arg = "source"
warp_outsource_arg = "normalized_source"
warp_field_arg = "normalization_parameters"
elif fmri2mni:
# register to standard template
warp = setup_node(spm_normalize(), name="fmri_warp")
tpm_bbox.inputs.in_file = spm_tpm_priors_path()
warp_source_arg = "image_to_align"
warp_outsource_arg = "normalized_image"
warp_field_arg = "deformation_field"
else: # fmri2mni is False
coreg = setup_node(spm_coregister(cost_function="mi"),
name="coreg_fmri")
warp = setup_node(spm_apply_deformations(), name="fmri_warp")
coreg_files = pe.Node(Merge(3), name='merge_for_coreg')
warp_files = pe.Node(Merge(2), name='merge_for_warp')
tpm_bbox.inputs.in_file = spm_tpm_priors_path()
# make the connections
if register_to_grptemplate:
wf.connect([
# get template bounding box to apply to results
(wfmri_input, tpm_bbox, [("epi_template", "in_file")]),
# unzip and forward the template file
(wfmri_input, gunzip_template, [("epi_template", "in_file")]),
(gunzip_template, warp, [("out_file", "template")]),
# get template bounding box to apply to results
(wfmri_input, tpm_bbox, [("epi_template", "in_file")]),
])
if fmri2mni or register_to_grptemplate:
# prepare the inputs
wf.connect([
# unzip the in_file input file
(wfmri_input, in_gunzip, [("avg_epi", "in_file")]),
# warp source file
(in_gunzip, warp, [("out_file", warp_source_arg)]),
# bounding box
(tpm_bbox, warp, [("bbox", "write_bounding_box")]),
# merge the other input files into a list
(wfmri_input, merge_list, [
("in_file", "in1"),
("time_filtered", "in2"),
]),
# gunzip them for SPM
(merge_list, gunzipper, [("out", "in_file")]),
# apply to files
(gunzipper, warp, [("out_file", "apply_to_files")]),
# outputs
(warp, rest_output, [
(warp_field_arg, "warp_field"),
(warp_outsource_arg, "wavg_epi"),
]),
])
else: # FMRI to ANAT
wf.connect([
(wfmri_input, coreg, [("reference_file", "target")]),
# unzip the in_file input file
(wfmri_input, in_gunzip, [("avg_epi", "in_file")]),
(in_gunzip, coreg, [("out_file", "source")]),
# merge the other input files into a list
(wfmri_input, coreg_files, [
("in_file", "in1"),
("time_filtered", "in2"),
("brain_mask", "in3"),
]),
# gunzip them for SPM
(coreg_files, gunzipper, [("out", "in_file")]),
# coregister fmri to anat
(gunzipper, coreg, [("out_file", "apply_to_files")]),
# anat to mni warp field
(wfmri_input, warp, [("anat_to_mni_warp", "deformation_file")]),
# bounding box
(tpm_bbox, warp, [("bbox", "write_bounding_box")]),
# apply to files
(coreg, warp_files, [("coregistered_source", "in1")]),
(coreg, warp_files, [("coregistered_files", "in2")]),
(warp_files, warp, [("out", "apply_to_files")]),
# outputs
(warp, rest_output, [
("normalized_files", "warped_files"),
]),
(warp, rest_output, [
(("normalized_files", selectindex, 0), "wavg_epi"),
]),
(coreg, rest_output, [("coregistered_source", "coreg_avg_epi")]),
(coreg, rest_output, [("coregistered_files", "coreg_others")]),
])
# atlas file in fMRI space
if fmri2mni:
coreg_atlas = setup_node(spm_coregister(cost_function="mi"),
name="coreg_atlas2fmri")
# set the registration interpolation to nearest neighbour.
coreg_atlas.inputs.write_interp = 0
wf.connect([
(wfmri_input, coreg_atlas, [
("reference_file", "source"),
("atlas_anat", "apply_to_files"),
]),
(in_gunzip, coreg_atlas, [("out_file", "target")]),
(coreg_atlas, rest_output, [("coregistered_files", "atlas_fmri")]),
])
# smooth and sink
wf.connect([
# smooth the final bandpassed image
(warp, smooth, [(("normalized_files", selectindex, 1), "in_file")]),
# output
(smooth, rest_output, [("out_file", "smooth")]),
(warp, rest_output, [
(("normalized_files", selectindex, 0), "warped_fmri"),
(("normalized_files", selectindex, 1), "wtime_filtered"),
]),
])
return wf