def __init__(self, in_file='path', format_string='', **options):
from nipype.interfaces.utility import Rename
rename = Rename()
rename.inputs.in_file = in_file
rename.inputs.format_string = format_string
for ef in options:
setattr(rename.inputs, ef, options[ef])
self.res = rename.run()
def rename_into_caps(
in_bids_pet,
fname_pet,
fname_trans,
suvr_reference_region,
uncropped_image,
fname_pet_in_t1w=None,
):
"""
Rename the outputs of the pipelines into CAPS format.
Args:
in_bids_pet (str): Input BIDS PET to extract the <source_file>
fname_pet (str): Preprocessed PET file.
fname_trans (str): Transformation file from PET to MRI space
suvr_reference_region (str): SUVR mask name for file name output
uncropped_image (bool): Pipeline argument for image cropping
fname_pet_in_t1w (bool): Pipeline argument for saving intermediate file
Returns:
The different outputs in CAPS format
"""
import os
from nipype.interfaces.utility import Rename
from nipype.utils.filemanip import split_filename
_, source_file_pet, _ = split_filename(in_bids_pet)
# Rename into CAPS PET:
rename_pet = Rename()
rename_pet.inputs.in_file = fname_pet
if not uncropped_image:
suffix = f"_space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_suvr-{suvr_reference_region}_pet.nii.gz"
rename_pet.inputs.format_string = source_file_pet + suffix
else:
suffix = f"_space-MNI152NLin2009cSym_res-1x1x1_suvr-{suvr_reference_region}_pet.nii.gz"
rename_pet.inputs.format_string = source_file_pet + suffix
out_caps_pet = rename_pet.run().outputs.out_file
# Rename into CAPS transformation file:
rename_trans = Rename()
rename_trans.inputs.in_file = fname_trans
rename_trans.inputs.format_string = source_file_pet + "_space-T1w_rigid.mat"
out_caps_trans = rename_trans.run().outputs.out_file
# Rename intermediate PET in T1w MRI space
if fname_pet_in_t1w is not None:
rename_pet_in_t1w = Rename()
rename_pet_in_t1w.inputs.in_file = fname_pet_in_t1w
rename_pet_in_t1w.inputs.format_string = (source_file_pet +
"_space-T1w_pet.nii.gz")
out_caps_pet_in_t1w = rename_pet_in_t1w.run().outputs.out_file
else:
out_caps_pet_in_t1w = None
return out_caps_pet, out_caps_trans, out_caps_pet_in_t1w
def test_Rename_outputs():
output_map = dict(out_file=dict(), )
outputs = Rename.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_Rename_outputs():
output_map = dict(out_file=dict())
outputs = Rename.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_Rename_inputs():
input_map = dict(
use_fullpath=dict(usedefault=True),
format_string=dict(mandatory=True),
keep_ext=dict(),
in_file=dict(mandatory=True),
parse_string=dict(),
)
inputs = Rename.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def test_Rename_inputs():
input_map = dict(
format_string=dict(mandatory=True, ),
in_file=dict(mandatory=True, ),
keep_ext=dict(),
parse_string=dict(),
use_fullpath=dict(usedefault=True, ),
)
inputs = Rename.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def test_neuro(change_dir, plugin):
# wf = Workflow(name, mem_gb_node=DEFAULT_MEMORY_MIN_GB,
# inputs=['source_file', 't1_preproc', 'subject_id',
# 'subjects_dir', 't1_2_fsnative_forward_transform',
# 'mem_gb', 'output_spaces', 'medial_surface_nan'],
# outputs='surfaces')
#
# dj: why do I need outputs?
wf = Workflow(
name=Name,
inputs=Inputs,
workingdir="test_neuro_{}".format(plugin),
write_state=False,
wf_output_names=[
("sampler", "out_file", "sampler_out"),
("targets", "out", "target_out"),
],
)
# @interface
# def select_target(subject_id, space):
# """ Given a source subject ID and a target space, get the target subject ID """
# return subject_id if space == 'fsnative' else space
# wf.add('targets', select_target(subject_id=wf.inputs.subject_id))
# .split('space', space=[space for space in wf.inputs.output_spaces
# if space.startswith('fs')])
# dj: don't have option in split to connect with wf input
wf.add(
runnable=select_target,
name="targets",
subject_id="subject_id",
input_names=["subject_id", "space"],
output_names=["out"],
write_state=False,
).split_node(
splitter="space",
inputs={
"space": [
space for space in Inputs["output_spaces"]
if space.startswith("fs")
]
},
)
# wf.add('rename_src', Rename(format_string='%(subject)s',
# keep_ext=True,
# in_file=wf.inputs.source_file))
# .split('subject')
wf.add(
name="rename_src",
runnable=Rename(format_string="%(subject)s", keep_ext=True),
in_file="source_file",
output_names=["out_file"],
write_state=False,
).split_node(
"subject",
inputs={
"subject": [
space for space in Inputs["output_spaces"]
if space.startswith("fs")
]
},
) # TODO: now it's only one subject
# wf.add('resampling_xfm',
# fs.utils.LTAConvert(in_lta='identity.nofile',
# out_lta=True,
# source_file=wf.inputs.source_file,
# target_file=wf.inputs.t1_preproc)
# .add('set_xfm_source', ConcatenateLTA(out_type='RAS2RAS',
# in_lta2=wf.inputs.t1_2_fsnative_forward_transform,
# in_lta1=wf.resampling_xfm.out_lta))
wf.add(
name="resampling_xfm",
runnable=fs.utils.LTAConvert(in_lta="identity.nofile", out_lta=True),
source_file="source_file",
target_file="t1_preproc",
output_names=["out_lta"],
write_state=False,
).add(
name="set_xfm_source",
runnable=ConcatenateLTA(out_type="RAS2RAS"),
in_lta2="t1_2_fsnative_forward_transform",
in_lta1="resampling_xfm.out_lta",
output_names=["out_file"],
write_state=False,
)
# wf.add('sampler',
# fs.SampleToSurface(sampling_method='average', sampling_range=(0, 1, 0.2),
# sampling_units='frac', interp_method='trilinear',
# cortex_mask=True, override_reg_subj=True,
# out_type='gii',
# subjects_dir=wf.inputs.subjects_dir,
# subject_id=wf.inputs.subject_id,
# reg_file=wf.set_xfm_source.out_file,
# target_subject=wf.targets.out,
# source_file=wf.rename_src.out_file),
# mem_gb=mem_gb * 3)
# .split([('source_file', 'target_subject'), 'hemi'], hemi=['lh', 'rh'])
wf.add(
name="sampler",
runnable=fs.SampleToSurface(
sampling_method="average",
sampling_range=(0, 1, 0.2),
sampling_units="frac",
interp_method="trilinear",
cortex_mask=True,
override_reg_subj=True,
out_type="gii",
),
write_state=False,
subjects_dir="subjects_dir",
subject_id="subject_id",
reg_file="set_xfm_source.out_file",
target_subject="targets.out",
source_file="rename_src.out_file",
output_names=["out_file"],
).split_node(splitter=[("_targets", "_rename_src"), "hemi"],
inputs={"hemi": ["lh", "rh"]})
# dj: adding combiner to the last node
wf.combine_node(combiner="hemi")
sub = Submitter(plugin=plugin, runnable=wf)
sub.run()
sub.close()
assert "target_out" in wf.output.keys()
assert len(list(wf.output["target_out"].keys())) == 2
assert "targets.space" in list(wf.output["target_out"].keys())[0]
assert "sampler_out" in wf.output.keys()
# length is 2 because of the combiner
assert len(list(wf.output["sampler_out"].keys())) == 2
assert "rename_src.subject" in list(wf.output["sampler_out"].keys())[0]
assert "targets.space" in list(wf.output["sampler_out"].keys())[0]
# hemi is eliminated from the state inputs after combiner
assert "sampler.hemi" not in list(wf.output["sampler_out"].keys())[0]
def rename_into_caps(in_bids_dwi, fname_dwi, fname_bval, fname_bvec,
fname_brainmask):
"""
Rename the outputs of the pipelines into CAPS format namely:
<source_file>_space-T1w_preproc[.nii.gz|bval|bvec]
Args:
in_bids_dwi (str): Input BIDS DWI to extract the <source_file>
fname_dwi (str): Preprocessed DWI.
fname_bval (str): Preprocessed DWI.
fname_bvec (str): Preprocessed DWI.
fname_brainmask (str): B0 mask.
Returns:
The different outputs in CAPS format
"""
import os
from nipype.interfaces.utility import Rename
from nipype.utils.filemanip import split_filename
# Extract <source_file> in format sub-CLNC01_ses-M00_[acq-label]_dwi
_, source_file_dwi, _ = split_filename(in_bids_dwi)
# Extract base path from fname:
base_dir_dwi, _, _ = split_filename(fname_dwi)
base_dir_bval, _, _ = split_filename(fname_bval)
base_dir_bvec, _, _ = split_filename(fname_bvec)
base_dir_brainmask, _, _ = split_filename(fname_brainmask)
# Rename into CAPS DWI :
rename_dwi = Rename()
rename_dwi.inputs.in_file = fname_dwi
rename_dwi.inputs.format_string = os.path.join(
base_dir_dwi, source_file_dwi + "_space-b0_preproc.nii.gz")
out_caps_dwi = rename_dwi.run()
# Rename into CAPS bval :
rename_bval = Rename()
rename_bval.inputs.in_file = fname_bval
rename_bval.inputs.format_string = os.path.join(
base_dir_bval, source_file_dwi + "_space-b0_preproc.bval")
out_caps_bval = rename_bval.run()
# Rename into CAPS DWI :
rename_bvec = Rename()
rename_bvec.inputs.in_file = fname_bvec
rename_bvec.inputs.format_string = os.path.join(
base_dir_bvec, source_file_dwi + "_space-b0_preproc.bvec")
out_caps_bvec = rename_bvec.run()
# Rename into CAPS DWI :
rename_brainmask = Rename()
rename_brainmask.inputs.in_file = fname_brainmask
rename_brainmask.inputs.format_string = os.path.join(
base_dir_brainmask, source_file_dwi + "_space-b0_brainmask.nii.gz")
out_caps_brainmask = rename_brainmask.run()
return out_caps_dwi.outputs.out_file, out_caps_bval.outputs.out_file, \
out_caps_bvec.outputs.out_file, out_caps_brainmask.outputs.out_file
def create_stage(stage_nr, workflow, inputs, inputs_nr_slices, slice_name):
"""
Don't use this directly, see build_workflow() instead.
Create an interpolation stage. Mutates the 'workflow' argument.
"""
# Selectors into 'inputs'
select_inputs = {}
for i in range(inputs_nr_slices):
fi = Function(input_names=['x', 'i'],
output_names=['out_file'],
function=select_function)
select_inputs[i] = pe.Node(interface=fi,
name='select_inputs_%s_%d_%d' % (
slice_name,
stage_nr,
i,
))
select_inputs[i].inputs.i = i
workflow.connect(inputs, 'out_files', select_inputs[i], 'x')
# Interpolations.
interp_nodes = []
for i in range(inputs_nr_slices - 1):
interp_node = pe.Node(interface=InterpolateBetweenSlices(),
name='interp_%s_%d_%08d' % (
slice_name,
stage_nr,
i,
))
select_node = pe.Node(interface=Select(index=[i, i + 1]),
name='select_%s_%d_%d_%d' % (
slice_name,
stage_nr,
i,
i + 1,
))
workflow.connect(inputs, 'out_files', select_node, 'inlist')
workflow.connect(select_node, 'out', interp_node, 'slices')
interp_node.inputs.level = stage_nr
interp_nodes.append(interp_node)
# Rename slices.
renamers = []
k = 0
rename = pe.Node(interface=Rename(),
name='rename_%s_%d_%08d' % (
slice_name,
stage_nr,
k,
))
rename.inputs.format_string = 'slice_%08d.npz' % k
workflow.connect(select_inputs[0], 'out_file', rename, 'in_file')
renamers.append(rename)
k += 1
for i in range(len(interp_nodes)):
rename = pe.Node(interface=Rename(),
name='rename_%s_%d_%08d' % (
slice_name,
stage_nr,
k,
))
rename.inputs.format_string = 'slice_%08d.npz' % k
workflow.connect(interp_nodes[i], 'interpolated_slice', rename,
'in_file')
renamers.append(rename)
k += 1
rename = pe.Node(interface=Rename(),
name='rename_%s_%d_%08d' % (
slice_name,
stage_nr,
k,
))
rename.inputs.format_string = 'slice_%08d.npz' % k
workflow.connect(select_inputs[i + 1], 'out_file', rename, 'in_file')
renamers.append(rename)
k += 1
# Could skip this unless we want to see intermediate steps.
datasink = pe.Node(nio.DataSink(),
name='sinker_%s_%d' % (
slice_name,
stage_nr,
))
for (i, rename) in enumerate(renamers):
workflow.connect(rename, 'out_file', datasink, 'slices.@%d' % i)
# If we want to do another stage, use the out_file's of renamers.
# We need a single node with an output 'out_files' consisting of each of the files.
merge_renamed_files = pe.Node(interface=Merge(len(renamers)),
name='merge_renamed_files_%s_%d' % (
slice_name,
stage_nr,
))
for (i, rename) in enumerate(renamers):
workflow.connect(rename, 'out_file', merge_renamed_files,
'in%d' % (i + 1))
# Now rename the output 'out' to 'out_files' so we can pass it in to a recursive
# call to this function.
out_to_out_files = pe.Node(interface=Function(input_names=['x'],
output_names=['out_files'],
function=identity_function),
name='out_to_out_files_%s_%d' % (
slice_name,
stage_nr,
))
workflow.connect(merge_renamed_files, 'out', out_to_out_files, 'x')
return out_to_out_files
def make_workflow(self):
# Infosource: Iterate through subject names
infosource = Node(interface=IdentityInterface(fields=['subject_str']),
name="infosource")
infosource.iterables = ('subject_str', self.subject_list)
# Infosource: Iterate through subject names
#imgsrc = Node(interface=IdentityInterface(fields=['img']), name="imgsrc")
#imgsrc.iterables = ('img', ['uni'])
parse_scanner_dir = Node(
interface=ParseScannerDir(raw_data_dir=self.raw_data_dir),
name='parse_scanner_dir')
ro = Node(interface=fsl.Reorient2Std(), name='ro')
mv_uni = Node(interface=Rename(format_string='uni_'), name='mv_uni')
mv_uniden = Node(interface=Rename(format_string='uniden'),
name='mv_uniden')
mv_flair = Node(interface=Rename(format_string='flair'),
name='mv_flair')
mv_bold = Node(interface=Rename(format_string='bold_'), name='mv_bold')
mv_boldmag1 = Node(interface=Rename(format_string='boldmag1'),
name='mv_boldmag1')
mv_boldmag2 = Node(interface=Rename(format_string='boldmag2'),
name='mv_boldmag2')
mv_phasediff = Node(interface=Rename(format_string='boldphdiff'),
name='mv_phasediff')
sink = Node(interface=DataSink(), name='sink')
sink.inputs.base_directory = self.bids_root
#sink.inputs.substitutions = [('mp2rage075iso', '{}'.format(str(sink.inputs._outputs.keys()))),
# ('uni', 'uni.nii.gz')]#,
# ('_uniden_DEN', ''),
# ('DEN_mp2rage_orig_reoriented_masked_maths', 'mUNIbrain_DENskull_SPMmasked'),
# ('_mp2rage_orig_reoriented_maths_maths_bin', '_brain_bin')]
sink.inputs.regexp_substitutions = [
(r'_subject_str_2(?P<subid>[0-9][0-9][0-9])T(?P<sesid>[0-9])/uni_',
r'sub-NeuroMET\g<subid>/ses-0\g<sesid>/anat/sub-NeuroMET\g<subid>_ses-0\g<sesid>_T1w.nii.gz'
),
(r'_subject_str_2(?P<subid>[0-9][0-9][0-9])T(?P<sesid>[0-9])/uniden',
r'/derivatives/Siemens/sub-NeuroMET\g<subid>/ses-0\g<sesid>/anat/sub-NeuroMET\g<subid>_ses-0\g<sesid>_desc-UNIDEN.nii.gz'
),
(r'_subject_str_2(?P<subid>[0-9][0-9][0-9])T(?P<sesid>[0-9])/flair',
r'sub-NeuroMET\g<subid>/ses-0\g<sesid>/anat/sub-NeuroMET\g<subid>_ses-0\g<sesid>_FLAIR.nii.gz'
),
(r'_subject_str_2(?P<subid>[0-9][0-9][0-9])T(?P<sesid>[0-9])/bold_',
r'sub-NeuroMET\g<subid>/ses-0\g<sesid>/func/sub-NeuroMET\g<subid>_ses-0\g<sesid>_task-rest_bold.nii.gz'
),
(r'_subject_str_2(?P<subid>[0-9][0-9][0-9])T(?P<sesid>[0-9])/boldmag1',
r'sub-NeuroMET\g<subid>/ses-0\g<sesid>/fmap/sub-NeuroMET\g<subid>_ses-0\g<sesid>_magnitude1.nii.gz'
),
(r'_subject_str_2(?P<subid>[0-9][0-9][0-9])T(?P<sesid>[0-9])/boldmag2',
r'sub-NeuroMET\g<subid>/ses-0\g<sesid>/fmap/sub-NeuroMET\g<subid>_ses-0\g<sesid>_magnitude2.nii.gz'
),
(r'_subject_str_2(?P<subid>[0-9][0-9][0-9])T(?P<sesid>[0-9])/boldphdiff',
r'sub-NeuroMET\g<subid>/ses-0\g<sesid>/fmap/sub-NeuroMET\g<subid>_ses-0\g<sesid>_phasediff.nii.gz'
),
]
# (r'c1{prefix}(.*).UNI_brain_bin.nii.gz'.format(prefix=self.project_prefix),
# r'{prefix}\1.UNI_brain_bin.nii.gz'.format(prefix=self.project_prefix)),
# (r'c1{prefix}(.*).DEN_brain_bin.nii.gz'.format(prefix=self.project_prefix),
# r'{prefix}\1.DEN_brain_bin.nii.gz'.format(prefix=self.project_prefix))]
scanner_to_bids = Workflow(name='scanner_to_bids',
base_dir=self.temp_dir)
#scanner_to_bids.connect(imgsrc, 'img', mv, 'format_string')
scanner_to_bids.connect(infosource, 'subject_str', parse_scanner_dir,
'subject_id')
scanner_to_bids.connect(parse_scanner_dir, 'uni', mv_uni, 'in_file')
scanner_to_bids.connect(parse_scanner_dir, 'uniden', mv_uniden,
'in_file')
scanner_to_bids.connect(parse_scanner_dir, 'flair', mv_flair,
'in_file')
scanner_to_bids.connect(parse_scanner_dir, 'bold', mv_bold, 'in_file')
scanner_to_bids.connect(parse_scanner_dir, 'boldmag1', mv_boldmag1,
'in_file')
scanner_to_bids.connect(parse_scanner_dir, 'boldmag2', mv_boldmag2,
'in_file')
scanner_to_bids.connect(parse_scanner_dir, 'boldphdiff', mv_phasediff,
'in_file')
scanner_to_bids.connect(mv_uni, 'out_file', sink, '@uni')
scanner_to_bids.connect(mv_uniden, 'out_file', sink, '@uniden')
scanner_to_bids.connect(mv_flair, 'out_file', sink, '@flair')
scanner_to_bids.connect(mv_bold, 'out_file', sink, '@bold')
scanner_to_bids.connect(mv_boldmag1, 'out_file', sink, '@boldmag1')
scanner_to_bids.connect(mv_boldmag2, 'out_file', sink, '@boldmag2')
scanner_to_bids.connect(mv_phasediff, 'out_file', sink, '@phasediff')
return scanner_to_bids
def create_motion_correction_workflow(name='moco',
method='AFNI',
extend_moco_params=False):
"""uses sub-workflows to perform different registration steps.
Requires fsl and freesurfer tools
Parameters
----------
name : string
name of workflow
Example
-------
>>> motion_correction_workflow = create_motion_correction_workflow('motion_correction_workflow')
>>> motion_correction_workflow.inputs.inputspec.output_directory = '/data/project/raw/BIDS/sj_1/'
>>> motion_correction_workflow.inputs.inputspec.in_files = ['sub-001.nii.gz','sub-002.nii.gz']
>>> motion_correction_workflow.inputs.inputspec.which_file_is_EPI_space = 'middle'
Inputs::
inputspec.output_directory : directory in which to sink the result files
inputspec.in_files : list of functional files
inputspec.which_file_is_EPI_space : determines which file is the 'standard EPI space'
Outputs::
outputspec.EPI_space_file : standard EPI space file, one timepoint
outputspec.motion_corrected_files : motion corrected files
outputspec.motion_correction_plots : motion correction plots
outputspec.motion_correction_parameters : motion correction parameters
"""
### NODES
input_node = pe.Node(IdentityInterface(fields=[
'in_files', 'output_directory', 'which_file_is_EPI_space', 'sub_id',
'tr'
]),
name='inputspec')
output_node = pe.Node(IdentityInterface(fields=([
'motion_corrected_files', 'EPI_space_file', 'mask_EPI_space_file',
'motion_correction_plots', 'motion_correction_parameters',
'extended_motion_correction_parameters',
'new_motion_correction_parameters'
])),
name='outputspec')
########################################################################################
# Invariant nodes
########################################################################################
EPI_file_selector_node = pe.Node(interface=EPI_file_selector,
name='EPI_file_selector_node')
mean_bold = pe.Node(interface=fsl.maths.MeanImage(dimension='T'),
name='mean_space')
rename_mean_bold = pe.Node(niu.Rename(format_string='session_EPI_space',
keep_ext=True),
name='rename_mean_bold')
########################################################################################
# Workflow
########################################################################################
motion_correction_workflow = pe.Workflow(name=name)
motion_correction_workflow.connect(input_node, 'which_file_is_EPI_space',
EPI_file_selector_node, 'which_file')
motion_correction_workflow.connect(input_node, 'in_files',
EPI_file_selector_node, 'in_files')
########################################################################################
# outputs via datasink
########################################################################################
datasink = pe.Node(nio.DataSink(), name='sinker')
datasink.inputs.parameterization = False
# first link the workflow's output_directory into the datasink.
motion_correction_workflow.connect(input_node, 'output_directory',
datasink, 'base_directory')
motion_correction_workflow.connect(input_node, 'sub_id', datasink,
'container')
########################################################################################
# FSL MCFlirt
########################################################################################
# new approach, which should aid in the joint motion correction of
# multiple sessions together, by pre-registering each run.
# the strategy would be to, for each run, take the first TR
# and FLIRT-align (6dof) it to the EPI_space file.
# then we can use this as an --infile argument to mcflirt.
if method == 'FSL':
rename_motion_files = pe.MapNode(
niu.Rename(keep_ext=False),
name='rename_motion_files',
iterfield=['in_file', 'format_string'])
remove_niigz_ext = pe.MapNode(interface=Remove_extension,
name='remove_niigz_ext',
iterfield=['in_file'])
motion_correct_EPI_space = pe.Node(interface=fsl.MCFLIRT(
cost='normcorr', interpolation='sinc', mean_vol=True),
name='motion_correct_EPI_space')
motion_correct_all = pe.MapNode(interface=fsl.MCFLIRT(
save_mats=True,
save_plots=True,
cost='normcorr',
interpolation='sinc',
stats_imgs=True),
name='motion_correct_all',
iterfield=['in_file'])
plot_motion = pe.MapNode(
interface=fsl.PlotMotionParams(in_source='fsl'),
name='plot_motion',
iterfield=['in_file'])
if extend_moco_params:
# make extend_motion_pars node here
# extend_motion_pars = pe.MapNode(Function(input_names=['moco_par_file', 'tr'], output_names=['new_out_file', 'ext_out_file'],
# function=_extend_motion_parameters), name='extend_motion_pars', iterfield = ['moco_par_file'])
pass
# create reference:
motion_correction_workflow.connect(EPI_file_selector_node, 'out_file',
motion_correct_EPI_space, 'in_file')
motion_correction_workflow.connect(motion_correct_EPI_space,
'out_file', mean_bold, 'in_file')
motion_correction_workflow.connect(mean_bold, 'out_file',
motion_correct_all, 'ref_file')
# motion correction across runs
motion_correction_workflow.connect(input_node, 'in_files',
motion_correct_all, 'in_file')
#motion_correction_workflow.connect(motion_correct_all, 'out_file', output_node, 'motion_corrected_files')
# motion_correction_workflow.connect(motion_correct_all, 'par_file', extend_motion_pars, 'moco_par_file')
# motion_correction_workflow.connect(input_node, 'tr', extend_motion_pars, 'tr')
# motion_correction_workflow.connect(extend_motion_pars, 'ext_out_file', output_node, 'extended_motion_correction_parameters')
# motion_correction_workflow.connect(extend_motion_pars, 'new_out_file', output_node, 'new_motion_correction_parameters')
########################################################################################
# Plot the estimated motion parameters
########################################################################################
# rename:
motion_correction_workflow.connect(mean_bold, 'out_file',
rename_mean_bold, 'in_file')
motion_correction_workflow.connect(motion_correct_all, 'par_file',
rename_motion_files, 'in_file')
motion_correction_workflow.connect(motion_correct_all, 'par_file',
remove_niigz_ext, 'in_file')
motion_correction_workflow.connect(remove_niigz_ext, 'out_file',
rename_motion_files,
'format_string')
# plots:
plot_motion.iterables = ('plot_type', ['rotations', 'translations'])
motion_correction_workflow.connect(rename_motion_files, 'out_file',
plot_motion, 'in_file')
motion_correction_workflow.connect(plot_motion, 'out_file',
output_node,
'motion_correction_plots')
# output node:
motion_correction_workflow.connect(mean_bold, 'out_file', output_node,
'EPI_space_file')
motion_correction_workflow.connect(rename_motion_files, 'out_file',
output_node,
'motion_correction_parameters')
motion_correction_workflow.connect(motion_correct_all, 'out_file',
output_node,
'motion_corrected_files')
# datasink:
motion_correction_workflow.connect(rename_mean_bold, 'out_file',
datasink, 'reg')
motion_correction_workflow.connect(motion_correct_all, 'out_file',
datasink, 'mcf')
motion_correction_workflow.connect(rename_motion_files, 'out_file',
datasink, 'mcf.motion_pars')
motion_correction_workflow.connect(plot_motion, 'out_file', datasink,
'mcf.motion_plots')
# motion_correction_workflow.connect(extend_motion_pars, 'ext_out_file', datasink, 'mcf.ext_motion_pars')
# motion_correction_workflow.connect(extend_motion_pars, 'new_out_file', datasink, 'mcf.new_motion_pars')
########################################################################################
# AFNI 3DVolReg
########################################################################################
# for speed, we use AFNI's 3DVolReg brute-force.
# this loses plotting of motion parameters but increases speed
# we hold on to the same setup, first moco the selected run
# and then moco everything to that image, but without the
# intermediate FLIRT step.
if method == 'AFNI':
motion_correct_EPI_space = pe.Node(
interface=afni.Volreg(
outputtype='NIFTI_GZ',
zpad=5,
args=' -cubic ' # -twopass -Fourier
),
name='motion_correct_EPI_space')
motion_correct_all = pe.MapNode(
interface=afni.Volreg(
outputtype='NIFTI_GZ',
zpad=5,
args=' -cubic ' # -twopass
),
name='motion_correct_all',
iterfield=['in_file'])
# for renaming *_volreg.nii.gz to *_mcf.nii.gz
set_postfix_mcf = pe.MapNode(interface=Set_postfix,
name='set_postfix_mcf',
iterfield=['in_file'])
set_postfix_mcf.inputs.postfix = 'mcf'
rename_volreg = pe.MapNode(interface=Rename(keep_ext=True),
name='rename_volreg',
iterfield=['in_file', 'format_string'])
# curate for moco between sessions
motion_correction_workflow.connect(EPI_file_selector_node, 'out_file',
motion_correct_EPI_space, 'in_file')
motion_correction_workflow.connect(motion_correct_EPI_space,
'out_file', mean_bold, 'in_file')
# motion correction across runs
motion_correction_workflow.connect(input_node, 'in_files',
motion_correct_all, 'in_file')
motion_correction_workflow.connect(mean_bold, 'out_file',
motion_correct_all, 'basefile')
# motion_correction_workflow.connect(mean_bold, 'out_file', motion_correct_all, 'rotparent')
# motion_correction_workflow.connect(mean_bold, 'out_file', motion_correct_all, 'gridparent')
# output node:
motion_correction_workflow.connect(mean_bold, 'out_file', output_node,
'EPI_space_file')
motion_correction_workflow.connect(motion_correct_all, 'md1d_file',
output_node,
'max_displacement_info')
motion_correction_workflow.connect(motion_correct_all, 'oned_file',
output_node,
'motion_correction_parameter_info')
motion_correction_workflow.connect(
motion_correct_all, 'oned_matrix_save', output_node,
'motion_correction_parameter_matrix')
motion_correction_workflow.connect(input_node, 'in_files',
set_postfix_mcf, 'in_file')
motion_correction_workflow.connect(set_postfix_mcf, 'out_file',
rename_volreg, 'format_string')
motion_correction_workflow.connect(motion_correct_all, 'out_file',
rename_volreg, 'in_file')
motion_correction_workflow.connect(rename_volreg, 'out_file',
output_node,
'motion_corrected_files')
# datasink:
motion_correction_workflow.connect(mean_bold, 'out_file',
rename_mean_bold, 'in_file')
motion_correction_workflow.connect(rename_mean_bold, 'out_file',
datasink, 'reg')
motion_correction_workflow.connect(rename_volreg, 'out_file', datasink,
'mcf')
motion_correction_workflow.connect(motion_correct_all, 'md1d_file',
datasink,
'mcf.max_displacement_info')
motion_correction_workflow.connect(motion_correct_all, 'oned_file',
datasink, 'mcf.parameter_info')
motion_correction_workflow.connect(motion_correct_all,
'oned_matrix_save', datasink,
'mcf.motion_pars')
return motion_correction_workflow
def create_registration_workflow(analysis_info, name='reg'):
"""uses sub-workflows to perform different registration steps.
Requires fsl and freesurfer tools
Parameters
----------
name : string
name of workflow
analysis_info : dict
contains session information needed for workflow, such as
whether to use FreeSurfer or FLIRT etc.
Example
-------
>>> registration_workflow = create_registration_workflow(name = 'registration_workflow', analysis_info = {'use_FS':True})
>>> registration_workflow.inputs.inputspec.output_directory = '/data/project/raw/BIDS/sj_1/'
>>> registration_workflow.inputs.inputspec.EPI_space_file = 'example_func.nii.gz'
>>> registration_workflow.inputs.inputspec.T1_file = 'T1.nii.gz' # if using freesurfer, this file will be created instead of used.
>>> registration_workflow.inputs.inputspec.freesurfer_subject_ID = 'sub_01'
>>> registration_workflow.inputs.inputspec.freesurfer_subject_dir = '$SUBJECTS_DIR'
>>> registration_workflow.inputs.inputspec.reference_file = '/usr/local/fsl/data/standard/standard152_T1_2mm_brain.nii.gz'
Inputs::
inputspec.output_directory : directory in which to sink the result files
inputspec.T1_file : T1 anatomy file
inputspec.EPI_space_file : EPI session file
inputspec.freesurfer_subject_ID : FS subject ID
inputspec.freesurfer_subject_dir : $SUBJECTS_DIR
Outputs::
outputspec.out_reg_file : BBRegister registration file that maps EPI space to T1
outputspec.out_matrix_file : FLIRT registration file that maps EPI space to T1
outputspec.out_inv_matrix_file : FLIRT registration file that maps T1 space to EPI
"""
### NODES
input_node = pe.Node(IdentityInterface(fields=[
'EPI_space_file', 'output_directory', 'freesurfer_subject_ID',
'freesurfer_subject_dir', 'T1_file', 'standard_file', 'sub_id'
]),
name='inputspec')
### Workflow to be returned
registration_workflow = pe.Workflow(name=name)
### sub-workflows
epi_2_T1 = create_epi_to_T1_workflow(name='epi',
use_FS=analysis_info['use_FS'],
do_FAST=analysis_info['do_FAST'])
T1_to_standard = create_T1_to_standard_workflow(
name='T1_to_standard',
use_FS=analysis_info['use_FS'],
do_fnirt=analysis_info['do_fnirt'],
use_AFNI_ss=analysis_info['use_AFNI_ss'])
concat_2_feat = create_concat_2_feat_workflow(name='concat_2_feat')
output_node = pe.Node(IdentityInterface(
fields=('EPI_T1_matrix_file', 'T1_EPI_matrix_file',
'EPI_T1_register_file', 'T1_standard_matrix_file',
'standard_T1_matrix_file', 'EPI_T1_matrix_file',
'T1_EPI_matrix_file', 'T1_file', 'standard_file',
'EPI_space_file')),
name='outputspec')
###########################################################################
# EPI to T1
###########################################################################
registration_workflow.connect([(input_node, epi_2_T1, [
('EPI_space_file', 'inputspec.EPI_space_file'),
('output_directory', 'inputspec.output_directory'),
('freesurfer_subject_ID', 'inputspec.freesurfer_subject_ID'),
('freesurfer_subject_dir', 'inputspec.freesurfer_subject_dir'),
('T1_file', 'inputspec.T1_file')
])])
###########################################################################
# T1 to standard
###########################################################################
registration_workflow.connect([(input_node, T1_to_standard, [
('freesurfer_subject_ID', 'inputspec.freesurfer_subject_ID'),
('freesurfer_subject_dir', 'inputspec.freesurfer_subject_dir'),
('T1_file', 'inputspec.T1_file'),
('standard_file', 'inputspec.standard_file')
])])
###########################################################################
# concatenation of all matrices
###########################################################################
# then, the inputs from the previous sub-workflows
registration_workflow.connect([(epi_2_T1, concat_2_feat, [
('outputspec.EPI_T1_matrix_file', 'inputspec.EPI_T1_matrix_file'),
])])
registration_workflow.connect([(T1_to_standard, concat_2_feat, [
('outputspec.T1_standard_matrix_file',
'inputspec.T1_standard_matrix_file'),
])])
###########################################################################
# Rename nodes, for the datasink
###########################################################################
if analysis_info['use_FS']:
rename_register = pe.Node(Rename(format_string='register.dat',
keep_ext=False),
name='rename_register')
registration_workflow.connect(epi_2_T1,
'outputspec.EPI_T1_register_file',
rename_register, 'in_file')
rename_example_func = pe.Node(Rename(format_string='example_func',
keep_ext=True),
name='rename_example_func')
registration_workflow.connect(input_node, 'EPI_space_file',
rename_example_func, 'in_file')
rename_highres = pe.Node(Rename(format_string='highres', keep_ext=True),
name='rename_highres')
registration_workflow.connect(T1_to_standard, 'outputspec.T1_file',
rename_highres, 'in_file')
rename_standard = pe.Node(Rename(format_string='standard', keep_ext=True),
name='rename_standard')
registration_workflow.connect(input_node, 'standard_file', rename_standard,
'in_file')
rename_example_func2standard = pe.Node(Rename(
format_string='example_func2standard.mat', keep_ext=False),
name='rename_example_func2standard')
registration_workflow.connect(concat_2_feat,
'outputspec.EPI_standard_matrix_file',
rename_example_func2standard, 'in_file')
rename_example_func2highres = pe.Node(Rename(
format_string='example_func2highres.mat', keep_ext=False),
name='rename_example_func2highres')
registration_workflow.connect(epi_2_T1, 'outputspec.EPI_T1_matrix_file',
rename_example_func2highres, 'in_file')
rename_highres2standard = pe.Node(Rename(
format_string='highres2standard.mat', keep_ext=False),
name='rename_highres2standard')
registration_workflow.connect(T1_to_standard,
'outputspec.T1_standard_matrix_file',
rename_highres2standard, 'in_file')
rename_standard2example_func = pe.Node(Rename(
format_string='standard2example_func.mat', keep_ext=False),
name='rename_standard2example_func')
registration_workflow.connect(concat_2_feat,
'outputspec.standard_EPI_matrix_file',
rename_standard2example_func, 'in_file')
rename_highres2example_func = pe.Node(Rename(
format_string='highres2example_func.mat', keep_ext=False),
name='rename_highres2example_func')
registration_workflow.connect(epi_2_T1, 'outputspec.T1_EPI_matrix_file',
rename_highres2example_func, 'in_file')
rename_standard2highres = pe.Node(Rename(
format_string='standard2highres.mat', keep_ext=False),
name='rename_standard2highres')
registration_workflow.connect(T1_to_standard,
'outputspec.standard_T1_matrix_file',
rename_standard2highres, 'in_file')
# outputs via datasink
datasink = pe.Node(DataSink(infields=['reg']), name='sinker')
datasink.inputs.parameterization = False
registration_workflow.connect(input_node, 'output_directory', datasink,
'base_directory')
registration_workflow.connect(input_node, 'sub_id', datasink, 'container')
# NEW SETUP WITH RENAME (WITHOUT MERGER)
if analysis_info['use_FS']:
registration_workflow.connect(rename_register, 'out_file', datasink,
'reg.@dat')
registration_workflow.connect(rename_example_func, 'out_file', datasink,
'reg.@example_func')
registration_workflow.connect(rename_standard, 'out_file', datasink,
'reg.@standard')
registration_workflow.connect(rename_highres, 'out_file', datasink,
'reg.@highres')
registration_workflow.connect(rename_example_func2highres, 'out_file',
datasink, 'reg.@example_func2highres')
registration_workflow.connect(rename_highres2example_func, 'out_file',
datasink, 'reg.@highres2example_func')
registration_workflow.connect(rename_highres2standard, 'out_file',
datasink, 'reg.@highres2standard')
registration_workflow.connect(rename_standard2highres, 'out_file',
datasink, 'reg.@standard2highres')
registration_workflow.connect(rename_standard2example_func, 'out_file',
datasink, 'reg.@standard2example_func')
registration_workflow.connect(rename_example_func2standard, 'out_file',
datasink, 'reg.@example_func2standard')
registration_workflow.connect(rename_highres, 'out_file', output_node,
'T1_file')
# put the nifti and mat files, renamed above, in the reg/feat directory.
# don't yet know what's wrong with this merge to datasink
# registration_workflow.connect(merge_for_reg_N, 'out', datasink, 'reg')
return registration_workflow
def create_workflow(files,
target_file,
subject_id,
TR,
slice_times,
norm_threshold=1,
num_components=5,
vol_fwhm=None,
surf_fwhm=None,
lowpass_freq=-1,
highpass_freq=-1,
subjects_dir=None,
sink_directory=os.getcwd(),
target_subject=['fsaverage3', 'fsaverage4'],
name='resting'):
wf = Workflow(name=name)
# Rename files in case they are named identically
name_unique = MapNode(Rename(format_string='rest_%(run)02d'),
iterfield=['in_file', 'run'],
name='rename')
name_unique.inputs.keep_ext = True
name_unique.inputs.run = list(range(1, len(files) + 1))
name_unique.inputs.in_file = files
realign = Node(interface=spm.Realign(), name="realign")
realign.inputs.jobtype = 'estwrite'
num_slices = len(slice_times)
slice_timing = Node(interface=spm.SliceTiming(), name="slice_timing")
slice_timing.inputs.num_slices = num_slices
slice_timing.inputs.time_repetition = TR
slice_timing.inputs.time_acquisition = TR - TR / float(num_slices)
slice_timing.inputs.slice_order = (np.argsort(slice_times) + 1).tolist()
slice_timing.inputs.ref_slice = int(num_slices / 2)
# Comute TSNR on realigned data regressing polynomials upto order 2
tsnr = MapNode(TSNR(regress_poly=2), iterfield=['in_file'], name='tsnr')
wf.connect(slice_timing, 'timecorrected_files', tsnr, 'in_file')
# Compute the median image across runs
calc_median = Node(Function(input_names=['in_files'],
output_names=['median_file'],
function=median,
imports=imports),
name='median')
wf.connect(tsnr, 'detrended_file', calc_median, 'in_files')
"""Segment and Register
"""
registration = create_reg_workflow(name='registration')
wf.connect(calc_median, 'median_file', registration,
'inputspec.mean_image')
registration.inputs.inputspec.subject_id = subject_id
registration.inputs.inputspec.subjects_dir = subjects_dir
registration.inputs.inputspec.target_image = target_file
"""Use :class:`nipype.algorithms.rapidart` to determine which of the
images in the functional series are outliers based on deviations in
intensity or movement.
"""
art = Node(interface=ArtifactDetect(), name="art")
art.inputs.use_differences = [True, True]
art.inputs.use_norm = True
art.inputs.norm_threshold = norm_threshold
art.inputs.zintensity_threshold = 9
art.inputs.mask_type = 'spm_global'
art.inputs.parameter_source = 'SPM'
"""Here we are connecting all the nodes together. Notice that we add the merge node only if you choose
to use 4D. Also `get_vox_dims` function is passed along the input volume of normalise to set the optimal
voxel sizes.
"""
wf.connect([
(name_unique, realign, [('out_file', 'in_files')]),
(realign, slice_timing, [('realigned_files', 'in_files')]),
(slice_timing, art, [('timecorrected_files', 'realigned_files')]),
(realign, art, [('realignment_parameters', 'realignment_parameters')]),
])
def selectindex(files, idx):
import numpy as np
from nipype.utils.filemanip import filename_to_list, list_to_filename
return list_to_filename(
np.array(filename_to_list(files))[idx].tolist())
mask = Node(fsl.BET(), name='getmask')
mask.inputs.mask = True
wf.connect(calc_median, 'median_file', mask, 'in_file')
# get segmentation in normalized functional space
def merge_files(in1, in2):
out_files = filename_to_list(in1)
out_files.extend(filename_to_list(in2))
return out_files
# filter some noise
# Compute motion regressors
motreg = Node(Function(
input_names=['motion_params', 'order', 'derivatives'],
output_names=['out_files'],
function=motion_regressors,
imports=imports),
name='getmotionregress')
wf.connect(realign, 'realignment_parameters', motreg, 'motion_params')
# Create a filter to remove motion and art confounds
createfilter1 = Node(Function(
input_names=['motion_params', 'comp_norm', 'outliers', 'detrend_poly'],
output_names=['out_files'],
function=build_filter1,
imports=imports),
name='makemotionbasedfilter')
createfilter1.inputs.detrend_poly = 2
wf.connect(motreg, 'out_files', createfilter1, 'motion_params')
wf.connect(art, 'norm_files', createfilter1, 'comp_norm')
wf.connect(art, 'outlier_files', createfilter1, 'outliers')
filter1 = MapNode(fsl.GLM(out_f_name='F_mcart.nii',
out_pf_name='pF_mcart.nii',
demean=True),
iterfield=['in_file', 'design', 'out_res_name'],
name='filtermotion')
wf.connect(slice_timing, 'timecorrected_files', filter1, 'in_file')
wf.connect(slice_timing, ('timecorrected_files', rename, '_filtermotart'),
filter1, 'out_res_name')
wf.connect(createfilter1, 'out_files', filter1, 'design')
createfilter2 = MapNode(Function(input_names=[
'realigned_file', 'mask_file', 'num_components', 'extra_regressors'
],
output_names=['out_files'],
function=extract_noise_components,
imports=imports),
iterfield=['realigned_file', 'extra_regressors'],
name='makecompcorrfilter')
createfilter2.inputs.num_components = num_components
wf.connect(createfilter1, 'out_files', createfilter2, 'extra_regressors')
wf.connect(filter1, 'out_res', createfilter2, 'realigned_file')
wf.connect(registration,
('outputspec.segmentation_files', selectindex, [0, 2]),
createfilter2, 'mask_file')
filter2 = MapNode(fsl.GLM(out_f_name='F.nii',
out_pf_name='pF.nii',
demean=True),
iterfield=['in_file', 'design', 'out_res_name'],
name='filter_noise_nosmooth')
wf.connect(filter1, 'out_res', filter2, 'in_file')
wf.connect(filter1, ('out_res', rename, '_cleaned'), filter2,
'out_res_name')
wf.connect(createfilter2, 'out_files', filter2, 'design')
wf.connect(mask, 'mask_file', filter2, 'mask')
bandpass = Node(Function(
input_names=['files', 'lowpass_freq', 'highpass_freq', 'fs'],
output_names=['out_files'],
function=bandpass_filter,
imports=imports),
name='bandpass_unsmooth')
bandpass.inputs.fs = 1. / TR
bandpass.inputs.highpass_freq = highpass_freq
bandpass.inputs.lowpass_freq = lowpass_freq
wf.connect(filter2, 'out_res', bandpass, 'files')
"""Smooth the functional data using
:class:`nipype.interfaces.spm.Smooth`.
"""
smooth = Node(interface=spm.Smooth(), name="smooth")
smooth.inputs.fwhm = vol_fwhm
wf.connect(bandpass, 'out_files', smooth, 'in_files')
collector = Node(Merge(2), name='collect_streams')
wf.connect(smooth, 'smoothed_files', collector, 'in1')
wf.connect(bandpass, 'out_files', collector, 'in2')
"""
Transform the remaining images. First to anatomical and then to target
"""
warpall = MapNode(ants.ApplyTransforms(),
iterfield=['input_image'],
name='warpall')
warpall.inputs.input_image_type = 3
warpall.inputs.interpolation = 'Linear'
warpall.inputs.invert_transform_flags = [False, False]
warpall.inputs.terminal_output = 'file'
warpall.inputs.reference_image = target_file
warpall.inputs.args = '--float'
warpall.inputs.num_threads = 1
# transform to target
wf.connect(collector, 'out', warpall, 'input_image')
wf.connect(registration, 'outputspec.transforms', warpall, 'transforms')
mask_target = Node(fsl.ImageMaths(op_string='-bin'), name='target_mask')
wf.connect(registration, 'outputspec.anat2target', mask_target, 'in_file')
maskts = MapNode(fsl.ApplyMask(), iterfield=['in_file'], name='ts_masker')
wf.connect(warpall, 'output_image', maskts, 'in_file')
wf.connect(mask_target, 'out_file', maskts, 'mask_file')
# map to surface
# extract aparc+aseg ROIs
# extract subcortical ROIs
# extract target space ROIs
# combine subcortical and cortical rois into a single cifti file
#######
# Convert aparc to subject functional space
# Sample the average time series in aparc ROIs
sampleaparc = MapNode(
freesurfer.SegStats(default_color_table=True),
iterfield=['in_file', 'summary_file', 'avgwf_txt_file'],
name='aparc_ts')
sampleaparc.inputs.segment_id = ([8] + list(range(10, 14)) +
[17, 18, 26, 47] + list(range(49, 55)) +
[58] + list(range(1001, 1036)) +
list(range(2001, 2036)))
wf.connect(registration, 'outputspec.aparc', sampleaparc,
'segmentation_file')
wf.connect(collector, 'out', sampleaparc, 'in_file')
def get_names(files, suffix):
"""Generate appropriate names for output files
"""
from nipype.utils.filemanip import (split_filename, filename_to_list,
list_to_filename)
out_names = []
for filename in files:
_, name, _ = split_filename(filename)
out_names.append(name + suffix)
return list_to_filename(out_names)
wf.connect(collector, ('out', get_names, '_avgwf.txt'), sampleaparc,
'avgwf_txt_file')
wf.connect(collector, ('out', get_names, '_summary.stats'), sampleaparc,
'summary_file')
# Sample the time series onto the surface of the target surface. Performs
# sampling into left and right hemisphere
target = Node(IdentityInterface(fields=['target_subject']), name='target')
target.iterables = ('target_subject', filename_to_list(target_subject))
samplerlh = MapNode(freesurfer.SampleToSurface(),
iterfield=['source_file'],
name='sampler_lh')
samplerlh.inputs.sampling_method = "average"
samplerlh.inputs.sampling_range = (0.1, 0.9, 0.1)
samplerlh.inputs.sampling_units = "frac"
samplerlh.inputs.interp_method = "trilinear"
samplerlh.inputs.smooth_surf = surf_fwhm
# samplerlh.inputs.cortex_mask = True
samplerlh.inputs.out_type = 'niigz'
samplerlh.inputs.subjects_dir = subjects_dir
samplerrh = samplerlh.clone('sampler_rh')
samplerlh.inputs.hemi = 'lh'
wf.connect(collector, 'out', samplerlh, 'source_file')
wf.connect(registration, 'outputspec.out_reg_file', samplerlh, 'reg_file')
wf.connect(target, 'target_subject', samplerlh, 'target_subject')
samplerrh.set_input('hemi', 'rh')
wf.connect(collector, 'out', samplerrh, 'source_file')
wf.connect(registration, 'outputspec.out_reg_file', samplerrh, 'reg_file')
wf.connect(target, 'target_subject', samplerrh, 'target_subject')
# Combine left and right hemisphere to text file
combiner = MapNode(Function(input_names=['left', 'right'],
output_names=['out_file'],
function=combine_hemi,
imports=imports),
iterfield=['left', 'right'],
name="combiner")
wf.connect(samplerlh, 'out_file', combiner, 'left')
wf.connect(samplerrh, 'out_file', combiner, 'right')
# Sample the time series file for each subcortical roi
ts2txt = MapNode(Function(
input_names=['timeseries_file', 'label_file', 'indices'],
output_names=['out_file'],
function=extract_subrois,
imports=imports),
iterfield=['timeseries_file'],
name='getsubcortts')
ts2txt.inputs.indices = [8] + list(range(10, 14)) + [17, 18, 26, 47] +\
list(range(49, 55)) + [58]
ts2txt.inputs.label_file = \
os.path.abspath(('OASIS-TRT-20_jointfusion_DKT31_CMA_labels_in_MNI152_'
'2mm_v2.nii.gz'))
wf.connect(maskts, 'out_file', ts2txt, 'timeseries_file')
######
substitutions = [('_target_subject_', ''),
('_filtermotart_cleaned_bp_trans_masked', ''),
('_filtermotart_cleaned_bp', '')]
regex_subs = [
('_ts_masker.*/sar', '/smooth/'),
('_ts_masker.*/ar', '/unsmooth/'),
('_combiner.*/sar', '/smooth/'),
('_combiner.*/ar', '/unsmooth/'),
('_aparc_ts.*/sar', '/smooth/'),
('_aparc_ts.*/ar', '/unsmooth/'),
('_getsubcortts.*/sar', '/smooth/'),
('_getsubcortts.*/ar', '/unsmooth/'),
('series/sar', 'series/smooth/'),
('series/ar', 'series/unsmooth/'),
('_inverse_transform./', ''),
]
# Save the relevant data into an output directory
datasink = Node(interface=DataSink(), name="datasink")
datasink.inputs.base_directory = sink_directory
datasink.inputs.container = subject_id
datasink.inputs.substitutions = substitutions
datasink.inputs.regexp_substitutions = regex_subs # (r'(/_.*(\d+/))', r'/run\2')
wf.connect(realign, 'realignment_parameters', datasink,
'resting.qa.motion')
wf.connect(art, 'norm_files', datasink, 'resting.qa.art.@norm')
wf.connect(art, 'intensity_files', datasink, 'resting.qa.art.@intensity')
wf.connect(art, 'outlier_files', datasink, 'resting.qa.art.@outlier_files')
wf.connect(registration, 'outputspec.segmentation_files', datasink,
'resting.mask_files')
wf.connect(registration, 'outputspec.anat2target', datasink,
'resting.qa.ants')
wf.connect(mask, 'mask_file', datasink, 'resting.mask_files.@brainmask')
wf.connect(mask_target, 'out_file', datasink, 'resting.mask_files.target')
wf.connect(filter1, 'out_f', datasink, 'resting.qa.compmaps.@mc_F')
wf.connect(filter1, 'out_pf', datasink, 'resting.qa.compmaps.@mc_pF')
wf.connect(filter2, 'out_f', datasink, 'resting.qa.compmaps')
wf.connect(filter2, 'out_pf', datasink, 'resting.qa.compmaps.@p')
wf.connect(bandpass, 'out_files', datasink,
'resting.timeseries.@bandpassed')
wf.connect(smooth, 'smoothed_files', datasink,
'resting.timeseries.@smoothed')
wf.connect(createfilter1, 'out_files', datasink,
'resting.regress.@regressors')
wf.connect(createfilter2, 'out_files', datasink,
'resting.regress.@compcorr')
wf.connect(maskts, 'out_file', datasink, 'resting.timeseries.target')
wf.connect(sampleaparc, 'summary_file', datasink,
'resting.parcellations.aparc')
wf.connect(sampleaparc, 'avgwf_txt_file', datasink,
'resting.parcellations.aparc.@avgwf')
wf.connect(ts2txt, 'out_file', datasink,
'resting.parcellations.grayo.@subcortical')
datasink2 = Node(interface=DataSink(), name="datasink2")
datasink2.inputs.base_directory = sink_directory
datasink2.inputs.container = subject_id
datasink2.inputs.substitutions = substitutions
datasink2.inputs.regexp_substitutions = regex_subs # (r'(/_.*(\d+/))', r'/run\2')
wf.connect(combiner, 'out_file', datasink2,
'resting.parcellations.grayo.@surface')
return wf
def mk_w_angio(freesurfer_dir, angiogram, out_dir):
n_input = Node(IdentityInterface(fields=[
'fs_dir',
'fs_subj',
'angiogram',
'out_dir',
]),
name='input')
n_input.inputs.fs_dir = str(freesurfer_dir.parent)
n_input.inputs.fs_subj = freesurfer_dir.name
n_input.inputs.angiogram = str(angiogram)
n_input.inputs.out_dir = str(out_dir)
n_coreg = Node(Registration(), name='antsReg')
n_coreg.inputs.num_threads = 40
n_coreg.inputs.use_histogram_matching = False
n_coreg.inputs.dimension = 3
n_coreg.inputs.winsorize_lower_quantile = 0.001
n_coreg.inputs.winsorize_upper_quantile = 0.999
n_coreg.inputs.float = True
n_coreg.inputs.interpolation = 'Linear'
n_coreg.inputs.transforms = [
'Rigid',
]
n_coreg.inputs.transform_parameters = [
[
0.1,
],
]
n_coreg.inputs.metric = [
'MI',
]
n_coreg.inputs.metric_weight = [
1,
]
n_coreg.inputs.radius_or_number_of_bins = [
32,
]
n_coreg.inputs.sampling_strategy = [
'Regular',
]
n_coreg.inputs.sampling_percentage = [
0.5,
]
n_coreg.inputs.sigma_units = [
'mm',
]
n_coreg.inputs.convergence_threshold = [
1e-6,
]
n_coreg.inputs.smoothing_sigmas = [
[1, 0],
]
n_coreg.inputs.shrink_factors = [
[1, 1],
]
n_coreg.inputs.convergence_window_size = [
10,
]
n_coreg.inputs.number_of_iterations = [
[250, 100],
]
n_coreg.inputs.output_warped_image = True
n_coreg.inputs.output_inverse_warped_image = True
n_coreg.inputs.output_transform_prefix = 'angio_to_struct'
n_apply = Node(ApplyTransforms(), name='ants_apply')
n_apply.inputs.dimension = 3
n_apply.inputs.interpolation = 'Linear'
n_apply.inputs.default_value = 0
n_convert = Node(MRIConvert(), 'convert')
n_convert.inputs.out_type = 'niigz'
n_binarize = Node(Threshold(), 'make_mask')
n_binarize.inputs.thresh = .1
n_binarize.inputs.args = '-bin'
n_mask = Node(BinaryMaths(), 'mask')
n_mask.inputs.operation = 'mul'
n_veins = Node(Rename(), 'rename_veins')
n_veins.inputs.format_string = 'angiogram.nii.gz'
n_sink = Node(DataSink(), 'sink')
n_sink.inputs.base_directory = '/Fridge/users/giovanni/projects/intraop/loenen/angiogram'
n_sink.inputs.remove_dest_dir = True
fs = Node(FreeSurferSource(), 'freesurfer')
n_split = Node(Split(), 'split_pca')
n_split.inputs.dimension = 't'
w = Workflow('tmp_angiogram')
w.base_dir = str(out_dir)
w.connect(n_input, 'fs_dir', fs, 'subjects_dir')
w.connect(n_input, 'fs_subj', fs, 'subject_id')
w.connect(n_input, 'angiogram', n_split, 'in_file')
w.connect(n_split, ('out_files', select_file, 0), n_coreg, 'moving_image')
w.connect(fs, 'T1', n_coreg, 'fixed_image')
w.connect(n_coreg, 'forward_transforms', n_apply, 'transforms')
w.connect(n_split, ('out_files', select_file, 1), n_apply, 'input_image')
w.connect(fs, 'T1', n_apply, 'reference_image')
w.connect(fs, 'brain', n_convert, 'in_file')
w.connect(n_convert, 'out_file', n_binarize, 'in_file')
w.connect(n_apply, 'output_image', n_mask, 'in_file')
w.connect(n_binarize, 'out_file', n_mask, 'operand_file')
w.connect(n_mask, 'out_file', n_veins, 'in_file')
w.connect(n_input, 'out_dir', n_sink, 'base_directory')
w.connect(n_veins, 'out_file', n_sink, '@angiogram')
w.connect(n_convert, 'out_file', n_sink, '@brain')
return w
def writeSeedFiles():
CACHE_DIR = 'seeds_CACHE'
RESULTS_DIR = 'seeds'
REWRITE_DATASINKS = True
nacAtlasFile = "/Shared/paulsen/Experiments/rsFMRI/rs-fMRI-pilot/ReferenceAtlas/template_t1.nii.gz"
nacAtlasLabel = "/Shared/paulsen/Experiments/rsFMRI/rs-fMRI-pilot/ReferenceAtlas/template_nac_labels.nii.gz"
nacResampleResolution = (2.0, 2.0, 2.0)
downsampledNACfilename = 'downsampledNACatlas.nii.gz'
preproc = pipe.Workflow(name=CACHE_DIR)
preproc.base_dir = os.getcwd()
labels, seeds = getAtlasPoints(
'/Shared/paulsen/Experiments/rsFMRI/rs-fMRI-pilot/seeds.fcsv')
seedsIdentity = pipe.Node(interface=IdentityInterface(fields=['index']),
name='seedsIdentity')
seedsIdentity.iterables = ('index', range(len(labels)))
selectSeed = pipe.Node(interface=Select(), name='selectSeed')
selectSeed.inputs.inlist = seeds
preproc.connect(seedsIdentity, 'index', selectSeed, 'index')
selectLabel = pipe.Node(interface=Select(), name='selectLabel')
selectLabel.inputs.inlist = labels
preproc.connect(seedsIdentity, 'index', selectLabel, 'index')
points = pipe.Node(interface=Function(
function=createSphereExpression,
input_names=['coordinates', 'radius'],
output_names=['expression']),
name='createSphereExpression')
preproc.connect(selectSeed, 'out', points, 'coordinates')
downsampleAtlas = pipe.Node(interface=Function(
function=resampleImage,
input_names=['inputVolume', 'outputVolume', 'resolution'],
output_names=['outputVolume']),
name="downsampleAtlas")
downsampleAtlas.inputs.inputVolume = nacAtlasFile
downsampleAtlas.inputs.outputVolume = downsampledNACfilename
downsampleAtlas.inputs.resolution = [int(x) for x in nacResampleResolution]
spheres = pipe.Node(interface=Calc(letters=['a']), name='afni3Dcalc_seeds')
spheres.inputs.outputtype = 'NIFTI_GZ'
preproc.connect(downsampleAtlas, 'outputVolume', spheres, 'in_file_a')
spheres.inputs.args = '-nscale'
preproc.connect(points, 'expression', spheres, 'expr')
renameMasks = pipe.Node(interface=Rename(format_string='%(label)s_mask'),
name='renameMasksAtlas')
renameMasks.inputs.keep_ext = True
preproc.connect(selectLabel, 'out', renameMasks, 'label')
preproc.connect(spheres, 'out_file', renameMasks, 'in_file')
atlas_DataSink = pipe.Node(interface=DataSink(), name="atlas_DataSink")
atlas_DataSink.inputs.base_directory = preproc.base_dir # '/Shared/paulsen/Experiments/20130417_rsfMRI_Results'
atlas_DataSink.inputs.container = RESULTS_DIR
atlas_DataSink.inputs.parameterization = False
atlas_DataSink.overwrite = REWRITE_DATASINKS
preproc.connect(renameMasks, 'out_file', atlas_DataSink, 'Atlas')
preproc.connect(downsampleAtlas, 'outputVolume', atlas_DataSink,
'Atlas.@resampled')
preproc.run()
def create_compcor_workflow(name='compcor'):
""" Creates A/T compcor workflow. """
input_node = pe.Node(interface=IdentityInterface(fields=[
'in_file', 'fast_files', 'highres2epi_mat', 'n_comp_tcompcor',
'n_comp_acompcor', 'output_directory', 'sub_id'
]),
name='inputspec')
output_node = pe.Node(interface=IdentityInterface(
fields=['tcompcor_file', 'acompcor_file', 'epi_mask']),
name='outputspec')
extract_task = pe.MapNode(interface=Extract_task,
iterfield=['in_file'],
name='extract_task')
rename_acompcor = pe.MapNode(interface=Rename(
format_string='task-%(task)s_acompcor.tsv', keepext=True),
iterfield=['task', 'in_file'],
name='rename_acompcor')
datasink = pe.Node(DataSink(), name='sinker')
datasink.inputs.parameterization = False
average_func = pe.MapNode(interface=fsl.maths.MeanImage(dimension='T'),
name='average_func',
iterfield=['in_file'])
epi_mask = pe.MapNode(interface=fsl.BET(frac=.3,
mask=True,
no_output=True,
robust=True),
iterfield=['in_file'],
name='epi_mask')
wm2epi = pe.MapNode(fsl.ApplyXFM(interp='nearestneighbour'),
iterfield=['reference'],
name='wm2epi')
csf2epi = pe.MapNode(fsl.ApplyXFM(interp='nearestneighbour'),
iterfield=['reference'],
name='csf2epi')
erode_csf = pe.MapNode(interface=Erode_mask,
name='erode_csf',
iterfield=['epi_mask', 'in_file'])
erode_csf.inputs.erosion_mm = 0
erode_csf.inputs.epi_mask_erosion_mm = 30
erode_wm = pe.MapNode(interface=Erode_mask,
name='erode_wm',
iterfield=['epi_mask', 'in_file'])
erode_wm.inputs.erosion_mm = 6
erode_wm.inputs.epi_mask_erosion_mm = 10
merge_wm_and_csf_masks = pe.MapNode(Merge(2),
name='merge_wm_and_csf_masks',
iterfield=['in1', 'in2'])
# This should be fit on the 30mm eroded mask from CSF
tcompcor = pe.MapNode(TCompCor(components_file='tcomcor_comps.txt'),
iterfield=['realigned_file', 'mask_files'],
name='tcompcor')
# WM + CSF mask
acompcor = pe.MapNode(ACompCor(components_file='acompcor_comps.txt',
merge_method='union'),
iterfield=['realigned_file', 'mask_files'],
name='acompcor')
compcor_wf = pe.Workflow(name=name)
compcor_wf.connect(input_node, 'in_file', extract_task, 'in_file')
compcor_wf.connect(extract_task, 'task_name', rename_acompcor, 'task')
compcor_wf.connect(acompcor, 'components_file', rename_acompcor, 'in_file')
compcor_wf.connect(input_node, 'sub_id', datasink, 'container')
compcor_wf.connect(input_node, 'output_directory', datasink,
'base_directory')
compcor_wf.connect(input_node, ('fast_files', pick_wm), wm2epi, 'in_file')
compcor_wf.connect(epi_mask, 'mask_file', wm2epi, 'reference')
compcor_wf.connect(input_node, 'highres2epi_mat', wm2epi, 'in_matrix_file')
compcor_wf.connect(input_node, ('fast_files', pick_csf), csf2epi,
'in_file')
compcor_wf.connect(epi_mask, 'mask_file', csf2epi, 'reference')
compcor_wf.connect(input_node, 'highres2epi_mat', csf2epi,
'in_matrix_file')
compcor_wf.connect(input_node, 'n_comp_tcompcor', tcompcor,
'num_components')
compcor_wf.connect(input_node, 'n_comp_acompcor', acompcor,
'num_components')
compcor_wf.connect(input_node, 'in_file', average_func, 'in_file')
compcor_wf.connect(average_func, 'out_file', epi_mask, 'in_file')
compcor_wf.connect(epi_mask, 'mask_file', erode_csf, 'epi_mask')
compcor_wf.connect(epi_mask, 'mask_file', erode_wm, 'epi_mask')
compcor_wf.connect(wm2epi, 'out_file', erode_wm, 'in_file')
compcor_wf.connect(csf2epi, 'out_file', erode_csf, 'in_file')
compcor_wf.connect(erode_wm, 'roi_eroded', merge_wm_and_csf_masks, 'in1')
compcor_wf.connect(erode_csf, 'roi_eroded', merge_wm_and_csf_masks, 'in2')
compcor_wf.connect(merge_wm_and_csf_masks, 'out', acompcor, 'mask_files')
compcor_wf.connect(input_node, 'in_file', acompcor, 'realigned_file')
compcor_wf.connect(input_node, 'in_file', tcompcor, 'realigned_file')
compcor_wf.connect(erode_csf, 'epi_mask_eroded', tcompcor, 'mask_files')
#compcor_wf.connect(tcompcor, 'components_file', output_node, 'acompcor_file')
#compcor_wf.connect(acompcor, 'components_file', output_node, 'tcompcor_file')
compcor_wf.connect(epi_mask, 'mask_file', output_node, 'epi_mask')
compcor_wf.connect(rename_acompcor, 'out_file', datasink, 'acompcor_file')
#compcor_wf.connect(tcompcor, 'components_file', combine_files, 'tcomp')
#compcor_wf.connect(acompcor, 'components_file', combine_files, 'acomp')
#compcor_wf.connect(combine_files, 'out_file', datasink, 'confounds')
return compcor_wf
def rename_into_caps(
in_caps_dwi,
in_norm_fa,
in_norm_md,
in_norm_ad,
in_norm_rd,
in_b_spline_transform,
in_affine_matrix,
):
"""Rename different outputs of the pipelines into CAPS format.
Returns:
The different outputs with CAPS naming convention
"""
from nipype.interfaces.utility import Rename
from clinica.pipelines.dwi_dti.dwi_dti_utils import (
extract_bids_identifier_from_caps_filename, )
bids_identifier = extract_bids_identifier_from_caps_filename(in_caps_dwi)
# CAPS normalized FA
rename_fa = Rename()
rename_fa.inputs.in_file = in_norm_fa
rename_fa.inputs.format_string = (
f"{bids_identifier}_space-MNI152Lin_res-1x1x1_FA.nii.gz")
out_caps_fa = rename_fa.run()
# CAPS normalized MD
rename_md = Rename()
rename_md.inputs.in_file = in_norm_md
rename_md.inputs.format_string = (
f"{bids_identifier}_space-MNI152Lin_res-1x1x1_MD.nii.gz")
out_caps_md = rename_md.run()
# CAPS normalized AD
rename_ad = Rename()
rename_ad.inputs.in_file = in_norm_ad
rename_ad.inputs.format_string = (
f"{bids_identifier}_space-MNI152Lin_res-1x1x1_AD.nii.gz")
out_caps_ad = rename_ad.run()
# CAPS normalized RD
rename_rd = Rename()
rename_rd.inputs.in_file = in_norm_rd
rename_rd.inputs.format_string = (
f"{bids_identifier}_space-MNI152Lin_res-1x1x1_RD.nii.gz")
out_caps_rd = rename_rd.run()
# CAPS B-spline transform
rename_b_spline = Rename()
rename_b_spline.inputs.in_file = in_b_spline_transform
rename_b_spline.inputs.format_string = (
f"{bids_identifier}_space-MNI152Lin_res-1x1x1_deformation.nii.gz")
out_caps_b_spline_transform = rename_b_spline.run()
# CAPS Affine matrix
rename_affine = Rename()
rename_affine.inputs.in_file = in_affine_matrix
rename_affine.inputs.format_string = (
f"{bids_identifier}_space-MNI152Lin_res-1x1x1_affine.mat")
out_caps_affine_matrix = rename_affine.run()
return (
out_caps_fa.outputs.out_file,
out_caps_md.outputs.out_file,
out_caps_ad.outputs.out_file,
out_caps_rd.outputs.out_file,
out_caps_b_spline_transform.outputs.out_file,
out_caps_affine_matrix.outputs.out_file,
)
def rename_into_caps(in_caps_dwi,
in_norm_fa, in_norm_md, in_norm_ad, in_norm_rd,
in_b_spline_transform, in_affine_matrix):
"""
Rename the outputs of the pipelines into CAPS format namely:
<source_file>_space-T1w_preproc[.nii.gz|bval|bvec]
Args:
Returns:
The different outputs in CAPS format
"""
from nipype.utils.filemanip import split_filename
from nipype.interfaces.utility import Rename
import os
from clinica.pipelines.dwi_processing_dti.dwi_processing_dti_utils import extract_bids_identifier_from_caps_filename
bids_identifier = extract_bids_identifier_from_caps_filename(in_caps_dwi)
# Extract base path from fname:
base_dir_norm_fa, _, _ = split_filename(in_norm_fa)
base_dir_norm_md, _, _ = split_filename(in_norm_md)
base_dir_norm_ad, _, _ = split_filename(in_norm_ad)
base_dir_norm_rd, _, _ = split_filename(in_norm_rd)
base_dir_b_spline_transform, _, _ = split_filename(in_b_spline_transform)
base_dir_affine_matrix, _, _ = split_filename(in_affine_matrix)
# Rename into CAPS FA:
rename_fa = Rename()
rename_fa.inputs.in_file = in_norm_fa
rename_fa.inputs.format_string = os.path.join(
base_dir_norm_fa,
bids_identifier + "_space-MNI152Lin_res-1x1x1_fa.nii.gz")
out_caps_fa = rename_fa.run()
# Rename into CAPS MD:
rename_md = Rename()
rename_md.inputs.in_file = in_norm_md
rename_md.inputs.format_string = os.path.join(
base_dir_norm_md,
bids_identifier + "_space-MNI152Lin_res-1x1x1_md.nii.gz")
out_caps_md = rename_md.run()
# Rename into CAPS AD:
rename_ad = Rename()
rename_ad.inputs.in_file = in_norm_ad
rename_ad.inputs.format_string = os.path.join(
base_dir_norm_ad,
bids_identifier + "_space-MNI152Lin_res-1x1x1_ad.nii.gz")
out_caps_ad = rename_ad.run()
# Rename into CAPS RD:
rename_rd = Rename()
rename_rd.inputs.in_file = in_norm_rd
rename_rd.inputs.format_string = os.path.join(
base_dir_norm_rd,
bids_identifier + "_space-MNI152Lin_res-1x1x1_rd.nii.gz")
out_caps_rd = rename_rd.run()
# Rename into CAPS B-spline transform:
rename_b_spline = Rename()
rename_b_spline.inputs.in_file = in_b_spline_transform
rename_b_spline.inputs.format_string = os.path.join(
base_dir_b_spline_transform,
bids_identifier + "_space-MNI152Lin_res-1x1x1_deformation.nii.gz")
out_caps_b_spline_transform = rename_b_spline.run()
# Rename into CAPS Affine Matrix:
rename_affine = Rename()
rename_affine.inputs.in_file = in_affine_matrix
rename_affine.inputs.format_string = os.path.join(
base_dir_affine_matrix,
bids_identifier + "_space-MNI152Lin_res-1x1x1_affine.mat")
out_caps_affine_matrix = rename_affine.run()
from clinica.utils.stream import cprint
cprint("Renamed files:")
cprint(out_caps_fa.outputs.out_file)
cprint(out_caps_md.outputs.out_file)
cprint(out_caps_ad.outputs.out_file)
cprint(out_caps_rd.outputs.out_file)
cprint(out_caps_b_spline_transform.outputs.out_file)
cprint(out_caps_affine_matrix.outputs.out_file)
return out_caps_fa.outputs.out_file, out_caps_md.outputs.out_file,\
out_caps_ad.outputs.out_file, out_caps_rd.outputs.out_file, \
out_caps_b_spline_transform.outputs.out_file, \
out_caps_affine_matrix.outputs.out_file
def rename_into_caps(
in_bids_dwi,
fname_dwi,
fname_bval,
fname_bvec,
fname_brainmask,
fname_magnitude,
fname_fmap,
fname_smoothed_fmap,
):
"""Rename the outputs of the pipelines into CAPS.
Args:
in_bids_dwi (str): Input BIDS DWI to extract the <source_file>
fname_dwi (str): Preprocessed DWI file.
fname_bval (str): Preprocessed bval.
fname_bvec (str): Preprocessed bvec.
fname_brainmask (str): B0 mask.
fname_smoothed_fmap (str): Smoothed (calibrated) fmap on b0 space.
fname_fmap (str): Calibrated fmap on b0 space.
fname_magnitude (str): Magnitude image on b0 space.
Returns:
Tuple[str, str, str, str, str, str, str]: The different outputs in CAPS format.
"""
import os
from nipype.interfaces.utility import Rename
from nipype.utils.filemanip import split_filename
# Extract <source_file> in format sub-CLNC01_ses-M00_[acq-label]_dwi
_, source_file_dwi, _ = split_filename(in_bids_dwi)
# Extract base path from fname:
base_dir_dwi, _, _ = split_filename(fname_dwi)
base_dir_bval, _, _ = split_filename(fname_bval)
base_dir_bvec, _, _ = split_filename(fname_bvec)
base_dir_brainmask, _, _ = split_filename(fname_brainmask)
base_dir_smoothed_fmap, _, _ = split_filename(fname_smoothed_fmap)
base_dir_calibrated_fmap, _, _ = split_filename(fname_fmap)
base_dir_magnitude, _, _ = split_filename(fname_magnitude)
# Rename into CAPS DWI:
rename_dwi = Rename()
rename_dwi.inputs.in_file = fname_dwi
rename_dwi.inputs.format_string = os.path.join(
base_dir_dwi, f"{source_file_dwi}_space-b0_preproc.nii.gz")
out_caps_dwi = rename_dwi.run()
# Rename into CAPS bval:
rename_bval = Rename()
rename_bval.inputs.in_file = fname_bval
rename_bval.inputs.format_string = os.path.join(
base_dir_bval, f"{source_file_dwi}_space-b0_preproc.bval")
out_caps_bval = rename_bval.run()
# Rename into CAPS bvec:
rename_bvec = Rename()
rename_bvec.inputs.in_file = fname_bvec
rename_bvec.inputs.format_string = os.path.join(
base_dir_bvec, f"{source_file_dwi}_space-b0_preproc.bvec")
out_caps_bvec = rename_bvec.run()
# Rename into CAPS brainmask:
rename_brainmask = Rename()
rename_brainmask.inputs.in_file = fname_brainmask
rename_brainmask.inputs.format_string = os.path.join(
base_dir_brainmask, f"{source_file_dwi}_space-b0_brainmask.nii.gz")
out_caps_brainmask = rename_brainmask.run()
# Rename into CAPS magnitude:
rename_magnitude = Rename()
rename_magnitude.inputs.in_file = fname_magnitude
rename_magnitude.inputs.format_string = os.path.join(
base_dir_magnitude, f"{source_file_dwi}_space-b0_magnitude1.nii.gz")
out_caps_magnitude = rename_magnitude.run()
# Rename into CAPS fmap:
rename_calibrated_fmap = Rename()
rename_calibrated_fmap.inputs.in_file = fname_fmap
rename_calibrated_fmap.inputs.format_string = os.path.join(
base_dir_calibrated_fmap, f"{source_file_dwi}_space-b0_fmap.nii.gz")
out_caps_fmap = rename_calibrated_fmap.run()
# Rename into CAPS smoothed fmap:
rename_smoothed_fmap = Rename()
rename_smoothed_fmap.inputs.in_file = fname_smoothed_fmap
rename_smoothed_fmap.inputs.format_string = os.path.join(
base_dir_smoothed_fmap,
f"{source_file_dwi}_space-b0_fwhm-4_fmap.nii.gz")
out_caps_smoothed_fmap = rename_smoothed_fmap.run()
return (
out_caps_dwi.outputs.out_file,
out_caps_bval.outputs.out_file,
out_caps_bvec.outputs.out_file,
out_caps_brainmask.outputs.out_file,
out_caps_magnitude.outputs.out_file,
out_caps_fmap.outputs.out_file,
out_caps_smoothed_fmap.outputs.out_file,
)
def pipeline(args):
if args['debug']:
config.enable_debug_mode()
config.update_config(
{'logging': {
'log_directory': makeSupportDir(args['name'], "logs")
}})
logging.update_logging(config)
# CONSTANTS
sessionID = args['session']
outputType = args['format'].upper()
fOutputType = args['freesurfer']
preprocessOn = args['preprocess']
maskGM = args['maskgm']
maskWholeBrain = args['maskwb']
maskWhiteMatterFromSeeds = args['maskseeds']
# print args['name']
t1_experiment = "20141001_PREDICTHD_long_Results" #"20130729_PREDICT_Results"
atlasFile = os.path.abspath(
os.path.join(os.path.dirname(__file__), "ReferenceAtlas",
"template_t1.nii.gz"))
wholeBrainFile = os.path.abspath(
os.path.join(os.path.dirname(__file__), "ReferenceAtlas",
"template_brain.nii.gz"))
atlasLabel = os.path.abspath(
os.path.join(os.path.dirname(__file__), "ReferenceAtlas",
"template_nac_labels.nii.gz"))
resampleResolution = (2.0, 2.0, 2.0)
downsampledfilename = 'downsampled_atlas.nii.gz'
master = pipe.Workflow(name=args['name'] + "_CACHE")
master.base_dir = os.path.abspath("/Shared/sinapse/CACHE")
sessions = pipe.Node(interface=IdentityInterface(fields=['session_id']),
name='sessionIDs')
sessions.iterables = ('session_id', sessionID)
downsampleAtlas = pipe.Node(interface=Function(
function=resampleImage,
input_names=['inputVolume', 'outputVolume', 'resolution'],
output_names=['outputVolume']),
name="downsampleAtlas")
downsampleAtlas.inputs.inputVolume = atlasFile
downsampleAtlas.inputs.outputVolume = downsampledfilename
downsampleAtlas.inputs.resolution = [int(x) for x in resampleResolution]
# HACK: Remove node from pipeline until Nipype/AFNI file copy issue is resolved
# fmri_DataSink = pipe.Node(interface=DataSink(), name="fmri_DataSink")
# fmri_DataSink.overwrite = REWRITE_DATASINKS
# Output to: /Shared/paulsen/Experiments/YYYYMMDD_<experiment>_Results/fmri
# fmri_DataSink.inputs.base_directory = os.path.join(master.base_dir, RESULTS_DIR, 'fmri')
# fmri_DataSink.inputs.substitutions = [('to_3D_out+orig', 'to3D')]
# fmri_DataSink.inputs.parameterization = False
#
# master.connect([(sessions, fmri_DataSink, [('session_id', 'container')])])
# END HACK
registration = registrationWorkflow.workflow(t1_experiment,
outputType,
name="registration_wkfl")
master.connect([(sessions, registration, [('session_id',
"inputs.session_id")])])
detrend = afninodes.detrendnode(outputType, 'afni3Ddetrend')
# define grabber
site = "*"
subject = "*"
if preprocessOn:
grabber = dataio.iowaGrabber(t1_experiment, site, subject, maskGM,
maskWholeBrain)
master.connect([(sessions, grabber, [('session_id', 'session_id')]),
(grabber, registration, [('t1_File', 'inputs.t1')])])
# Why isn't preprocessWorkflow.workflow() used instead? It would avoid most of the nuisance connections here...
preprocessing = preprocessWorkflow.prepWorkflow(skipCount=6,
outputType=outputType)
name = args.pop(
'name') # HACK: prevent name conflict with nuisance workflow
nuisance = nuisanceWorkflow.workflow(outputType=outputType, **args)
args['name'] = name # END HACK
master.connect([
(grabber, preprocessing, [('fmri_dicom_dir', 'to_3D.infolder'),
('fmri_dicom_dir',
'formatFMRINode.dicomDirectory')]),
(grabber, nuisance, [('whmFile', 'wm.warpWMtoFMRI.input_image')]),
(
preprocessing,
registration,
[
('merge.out_file', 'inputs.fmri'), # 7
('automask.out_file', 'tstat.mask_file')
]), # *optional*
(
registration,
nuisance,
[
('outputs.fmri_reference',
'csf.warpCSFtoFMRI.reference_image'), # CSF
('outputs.nac2fmri_list', 'csf.warpCSFtoFMRI.transforms'),
('outputs.fmri_reference',
'wm.warpWMtoFMRI.reference_image'), # WM
('outputs.t12fmri_list', 'wm.warpWMtoFMRI.transforms')
]),
])
warpCSFtoFMRInode = nuisance.get_node('csf').get_node('warpCSFtoFMRI')
warpCSFtoFMRInode.inputs.input_image = atlasFile
if maskGM:
master.connect([
(grabber, nuisance, [('gryFile', 'gm.warpGMtoFMRI.input_image')
]),
(registration, nuisance, [('outputs.fmri_reference',
'gm.warpGMtoFMRI.reference_image'),
('outputs.t12fmri_list',
'gm.warpGMtoFMRI.transforms')]),
(preprocessing, nuisance,
[('calc.out_file', 'gm.afni3DmaskAve_grm.in_file'),
('volreg.oned_file', 'afni3Ddeconvolve.stim_file_4')])
])
elif maskWholeBrain:
master.connect([
(registration, nuisance,
[('outputs.fmri_reference',
'wb.warpBraintoFMRI.reference_image'),
('outputs.nac2fmri_list', 'wb.warpBraintoFMRI.transforms')]),
(preprocessing, nuisance,
[('calc.out_file', 'wb.afni3DmaskAve_whole.in_file'),
('volreg.oned_file', 'afni3Ddeconvolve.stim_file_4')])
])
warpBraintoFMRInode = nuisance.get_node('wb').get_node(
'warpBraintoFMRI')
warpBraintoFMRInode.inputs.input_image = wholeBrainFile
else:
master.connect([(preprocessing, nuisance, [
('volreg.oned_file', 'afni3Ddeconvolve.stim_file_3')
])])
master.connect([(preprocessing, nuisance,
[('calc.out_file', 'wm.afni3DmaskAve_wm.in_file'),
('calc.out_file', 'csf.afni3DmaskAve_csf.in_file'),
('calc.out_file', 'afni3Ddeconvolve.in_file')]),
(nuisance, detrend, [('afni3Ddeconvolve.out_errts',
'in_file')])]) # 13
else:
cleveland_grabber = dataio.clevelandGrabber()
grabber = dataio.autoworkupGrabber(t1_experiment, site, subject)
converter = pipe.Node(interface=Copy(),
name='converter') # Convert ANALYZE to AFNI
master.connect([
(sessions, grabber, [('session_id', 'session_id')]),
(grabber, registration, [('t1_File', 'inputs.t1')]),
(sessions, cleveland_grabber, [('session_id', 'session_id')]),
(cleveland_grabber, converter, [('fmriHdr', 'in_file')]),
(converter, registration, [('out_file', 'inputs.fmri')]),
(converter, detrend, [('out_file', 'in_file')]), # in fMRI_space
])
t1_wf = registrationWorkflow.t1Workflow()
babc_wf = registrationWorkflow.babcWorkflow()
# HACK: No EPI
# epi_wf = registrationWorkflow.epiWorkflow()
lb_wf = registrationWorkflow.labelWorkflow()
seed_wf = registrationWorkflow.seedWorkflow()
bandpass = afninodes.fouriernode(
outputType, 'fourier'
) # Fourier is the last NIFTI file format in the AFNI pipeline
master.connect([
(detrend, bandpass, [('out_file', 'in_file')
]), # Per Dawei, bandpass after running 3dDetrend
(grabber, t1_wf, [('t1_File', 'warpT1toFMRI.input_image')]),
(
registration,
t1_wf,
[
('outputs.fmri_reference',
'warpT1toFMRI.reference_image'), # T1
('outputs.t12fmri_list', 'warpT1toFMRI.transforms')
]),
(grabber, babc_wf, [('csfFile', 'warpBABCtoFMRI.input_image')]),
(
registration,
babc_wf,
[
('outputs.fmri_reference',
'warpBABCtoFMRI.reference_image'), # Labels
('outputs.t12fmri_list', 'warpBABCtoFMRI.transforms')
]),
# HACK: No EPI
# (downsampleAtlas, epi_wf, [('outputVolume', 'warpEPItoNAC.reference_image')]),
# (registration, epi_wf, [('outputs.fmri2nac_list', 'warpEPItoNAC.transforms')]),
# (bandpass, epi_wf, [('out_file', 'warpEPItoNAC.input_image')]),
# END HACK
(downsampleAtlas, lb_wf, [('outputVolume',
'warpLabeltoNAC.reference_image')]),
(registration, lb_wf, [('outputs.fmri2nac_list',
'warpLabeltoNAC.transforms')]),
(t1_wf, seed_wf, [('warpT1toFMRI.output_image',
'warpSeedtoFMRI.reference_image')]),
(registration, seed_wf, [('outputs.nac2fmri_list',
'warpSeedtoFMRI.transforms')]),
])
renameMasks = pipe.Node(interface=Rename(format_string='%(label)s_mask'),
name='renameMasksAtlas')
renameMasks.inputs.keep_ext = True
atlas_DataSink = dataio.atlasSink(base_directory=master.base_dir, **args)
master.connect([
(renameMasks, atlas_DataSink, [('out_file', 'Atlas')]),
(downsampleAtlas, atlas_DataSink, [('outputVolume', 'Atlas.@resampled')
]),
])
renameMasks2 = pipe.Node(
interface=Rename(format_string='%(session)s_%(label)s_mask'),
name='renameMasksFMRI')
renameMasks2.inputs.keep_ext = True
master.connect(sessions, 'session_id', renameMasks2, 'session')
clipSeedWithVentriclesNode = pipe.Node(interface=Function(
function=clipSeedWithVentricles,
input_names=['seed', 'label', 'outfile'],
output_names=['clipped_seed_fn']),
name='clipSeedWithVentriclesNode')
clipSeedWithVentriclesNode.inputs.outfile = "clipped_seed.nii.gz"
master.connect(seed_wf, 'warpSeedtoFMRI.output_image',
clipSeedWithVentriclesNode, 'seed')
master.connect(babc_wf, 'warpBABCtoFMRI.output_image',
clipSeedWithVentriclesNode, 'label')
if not maskWhiteMatterFromSeeds:
master.connect(clipSeedWithVentriclesNode, 'clipped_seed_fn',
renameMasks2, 'in_file')
else:
clipSeedWithWhiteMatterNode = pipe.Node(
interface=Function(function=clipSeedWithWhiteMatter,
input_names=['seed', 'mask', 'outfile'],
output_names=['outfile']),
name='clipSeedWithWhiteMatterNode')
clipSeedWithWhiteMatterNode.inputs.outfile = 'clipped_wm_seed.nii.gz'
master.connect(babc_wf, 'warpBABCtoFMRI.output_image',
clipSeedWithWhiteMatterNode, 'mask')
master.connect(clipSeedWithVentriclesNode, 'clipped_seed_fn',
clipSeedWithWhiteMatterNode, 'seed')
master.connect(clipSeedWithWhiteMatterNode, 'outfile', renameMasks2,
'in_file')
# Labels are iterated over, so we need a seperate datasink to avoid overwriting any preprocessing
# results when the labels are iterated (e.g. To3d output)
# Write out to: /Shared/sinapse/CACHE/YYYYMMDD_<experiment>_Results/<SESSION>
fmri_label_DataSink = dataio.fmriSink(master.base_dir, **args)
master.connect(sessions, 'session_id', fmri_label_DataSink, 'container')
master.connect(renameMasks2, 'out_file', fmri_label_DataSink, 'masks')
master.connect(bandpass, 'out_file', fmri_label_DataSink,
'masks.@bandpass')
roiMedian = afninodes.maskavenode('AFNI_1D', 'afni_roiMedian',
'-mrange 1 1')
master.connect(renameMasks2, 'out_file', roiMedian, 'mask')
master.connect(bandpass, 'out_file', roiMedian, 'in_file')
correlate = afninodes.fimnode('Correlation', 'afni_correlate')
master.connect(roiMedian, 'out_file', correlate, 'ideal_file')
master.connect(bandpass, 'out_file', correlate, 'in_file')
regionLogCalc = afninodes.logcalcnode(outputType, 'afni_regionLogCalc')
master.connect(correlate, 'out_file', regionLogCalc, 'in_file_a')
renameZscore = pipe.Node(
interface=Rename(format_string="%(session)s_%(label)s_zscore"),
name='renameZscore')
renameZscore.inputs.keep_ext = True
master.connect(sessions, 'session_id', renameZscore, 'session')
master.connect(regionLogCalc, 'out_file', renameZscore, 'in_file')
master.connect(renameZscore, 'out_file', fmri_label_DataSink, 'zscores')
master.connect(t1_wf, 'warpT1toFMRI.output_image', fmri_label_DataSink,
'zscores.@t1Underlay')
# Move z values back into NAC atlas space
# master.connect(downsampleAtlas, 'outputVolume', lb_wf, 'warpLabeltoNAC.reference_image')
master.connect(regionLogCalc, 'out_file', lb_wf,
'warpLabeltoNAC.input_image')
renameZscore2 = pipe.Node(
interface=Rename(format_string="%(session)s_%(label)s_result"),
name='renameZscore2')
renameZscore2.inputs.keep_ext = True
master.connect(sessions, 'session_id', renameZscore2, 'session')
master.connect(lb_wf, 'warpLabeltoNAC.output_image', renameZscore2,
'in_file')
master.connect(renameZscore2, 'out_file', atlas_DataSink, 'Atlas.@zscore')
# Connect seed subworkflow
seedSubflow = seedWorkflow.workflow(args['seeds'],
outputType='NIFTI_GZ',
name='seed_wkfl')
master.connect([
(downsampleAtlas, seedSubflow, [('outputVolume',
'afni3Dcalc_seeds.in_file_a')]),
(seedSubflow, renameMasks, [('afni3Dcalc_seeds.out_file', 'in_file'),
('selectLabel.out', 'label')]),
(seedSubflow, renameMasks2, [('selectLabel.out', 'label')]),
(seedSubflow, renameZscore, [('selectLabel.out', 'label')]),
(seedSubflow, renameZscore2, [('selectLabel.out', 'label')]),
(seedSubflow, seed_wf, [('afni3Dcalc_seeds.out_file',
'warpSeedtoFMRI.input_image')])
])
imageDir = makeSupportDir(args['name'], "images")
if args['plot']:
registration.write_graph(dotfilename=os.path.join(
imageDir, 'register.dot'),
graph2use='orig',
format='png',
simple_form=False)
if preprocessOn:
preprocessing.write_graph(dotfilename=os.path.join(
imageDir, 'preprocess.dot'),
graph2use='orig',
format='png',
simple_form=False)
nuisance.write_graph(dotfilename=os.path.join(
imageDir, 'nuisance.dot'),
graph2use='orig',
format='png',
simple_form=False)
seedSubflow.write_graph(dotfilename=os.path.join(imageDir, 'seed.dot'),
graph2use='orig',
format='png',
simple_form=False)
master.write_graph(dotfilename=os.path.join(imageDir, 'master.dot'),
graph2use="orig",
format='png',
simple_form=False)
elif args['debug']:
try:
master.run(updatehash=True)
# Run restingState on the all threads
# Setup environment for CPU load balancing of ITK based programs.
# --------
# import multiprocessing
# total_CPUS = 10 # multiprocessing.cpu_count()
# master.run(plugin='MultiProc', plugin_args={'n_proc': total_CPUS}) #, updatehash=True)
# --------
# Run restingState on the local cluster
# master.run(plugin='SGE', plugin_args={'template': os.path.join(os.getcwd(), 'ENV/bin/activate'),
# 'qsub_args': '-S /bin/bash -cwd'}) #, updatehash=True)
except:
pass
master.name = "master" # HACK: Bug in Graphviz for nodes beginning with numbers
master.write_graph(dotfilename=os.path.join(imageDir,
'debug_hier.dot'),
graph2use="colored",
format='png')
master.write_graph(dotfilename=os.path.join(imageDir,
'debug_orig.dot'),
graph2use="flat",
format='png')
else:
import multiprocessing
total_CPUS = multiprocessing.cpu_count()
master.run(plugin='MultiProc',
plugin_args={'n_proc': total_CPUS}) #, updatehash=True)
return 0
