def avgb0_wf(name='AvgB0Workflow'):
"""
Implements workflow to calculate the average of dwi b0 images.
.. workflow::
from mriqc.workflows.diffusion import avgb0_wf
wf = avgb0_wf
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(
fields=['in_files']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['out_file']),
name='outputnode')
merge = pe.Node(fsl.Merge(dimension='t'), name='Merge')
avg = pe.Node(fsl.maths.MeanImage(), name='AvgImage')
workflow.connect([
(inputnode, merge, [('in_files', 'in_files')]),
(merge, avg, [('merged_file', 'in_file')]),
(avg, outputnode, [('out_file', 'out_file')])
])
return workflow
def init_skullstrip_watershed_wf(debug, name='skullstrip_watershed_wf'):
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(fields=['in_file', 'source_file']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['bias_corrected', 'out_file', 'out_mask']),
name='outputnode')
n4_correct = pe.Node(ants.N4BiasFieldCorrection(dimension=3,
copy_header=True),
name='n4_correct')
t1_skull_strip = pe.Node(fs.WatershedSkullStrip(), name='t1_skull_strip')
create_mask = pe.Node(fs.Binarize(min=0.0, dilate=0, out_type='.nii.gz'),
name='create_mask')
apply_mask = pe.Node(fsl.ApplyMask(), name='apply_mask')
workflow.connect([
(inputnode, n4_correct, [('in_file', 'input_image')]),
(n4_correct, t1_skull_strip, [('bias_corrected', 'in_file')]),
(n4_correct, outputnode, [('bias_corrected', 'bias_corrected')]),
(t1_skull_strip, create_mask, [('out_file', 'in_file')]),
(create_mask, apply_mask, [('binary_file', 'mask_file')]),
(inputnode, apply_mask, [('in_file', 'in_file')]),
(create_mask, outputnode, [('binary_file', 'out_mask')]),
(apply_mask, outputnode, [('out_file', 'out_file')])
])
return workflow
def init_fsl_bbr_wf(bold2t1w_dof, report, name='fsl_bbr_wf'):
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface([
'in_file',
'fs_2_t1_transform',
'subjects_dir',
'subject_id', # BBRegister
't1_seg',
't1_brain'
]), # FLIRT BBR
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
['out_matrix_file', 'out_reg_file', 'out_report', 'final_cost']),
name='outputnode')
wm_mask = pe.Node(niu.Function(function=extract_wm), name='wm_mask')
_FLIRT = FLIRTRPT if report else fsl.FLIRT
flt_bbr_init = pe.Node(fsl.FLIRT(dof=6), name='flt_bbr_init')
flt_bbr = pe.Node(_FLIRT(cost_func='bbr', dof=bold2t1w_dof, save_log=True),
name='flt_bbr')
flt_bbr.inputs.schedule = op.join(os.getenv('FSLDIR'),
'etc/flirtsch/bbr.sch')
def get_final_cost(in_file):
from niworkflows.nipype import logging
with open(in_file, 'r') as fobj:
for line in fobj:
if line.startswith('>> print U:1'):
costs = next(fobj).split()
return float(costs[0])
logger = logging.getLogger('interface')
logger.error('No cost report found in log file. Please report this '
'issue, with contents of {}'.format(in_file))
get_cost = pe.Node(niu.Function(function=get_final_cost),
name='get_cost',
run_without_submitting=True)
workflow.connect([
(inputnode, wm_mask, [('t1_seg', 'in_seg')]),
(inputnode, flt_bbr_init, [('in_file', 'in_file'),
('t1_brain', 'reference')]),
(flt_bbr_init, flt_bbr, [('out_matrix_file', 'in_matrix_file')]),
(inputnode, flt_bbr, [('in_file', 'in_file'),
('t1_brain', 'reference')]),
(wm_mask, flt_bbr, [('out', 'wm_seg')]),
(flt_bbr, outputnode, [('out_matrix_file', 'out_matrix_file')]),
(flt_bbr, get_cost, [('out_log', 'in_file')]),
(get_cost, outputnode, [('out', 'final_cost')]),
])
if report:
flt_bbr.inputs.generate_report = True
workflow.connect([(flt_bbr, outputnode, [('out_report', 'out_report')])
])
return workflow
def headmsk_wf(name='HeadMaskWorkflow', use_bet=True):
"""
Computes a head mask as in [Mortamet2009]_.
.. workflow::
from mriqc.workflows.anatomical import headmsk_wf
wf = headmsk_wf()
"""
has_dipy = False
try:
from dipy.denoise import nlmeans
has_dipy = True
except ImportError:
pass
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['in_file', 'in_segm']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['out_file']), name='outputnode')
if use_bet or not has_dipy:
# Alternative for when dipy is not installed
bet = pe.Node(fsl.BET(surfaces=True), name='fsl_bet')
workflow.connect([
(inputnode, bet, [('in_file', 'in_file')]),
(bet, outputnode, [('outskin_mask_file', 'out_file')])
])
else:
from niworkflows.nipype.interfaces.dipy import Denoise
enhance = pe.Node(niu.Function(
input_names=['in_file'], output_names=['out_file'], function=_enhance), name='Enhance')
estsnr = pe.Node(niu.Function(
input_names=['in_file', 'seg_file'], output_names=['out_snr'],
function=_estimate_snr), name='EstimateSNR')
denoise = pe.Node(Denoise(), name='Denoise')
gradient = pe.Node(niu.Function(
input_names=['in_file', 'snr'], output_names=['out_file'], function=image_gradient), name='Grad')
thresh = pe.Node(niu.Function(
input_names=['in_file', 'in_segm'], output_names=['out_file'], function=gradient_threshold),
name='GradientThreshold')
workflow.connect([
(inputnode, estsnr, [('in_file', 'in_file'),
('in_segm', 'seg_file')]),
(estsnr, denoise, [('out_snr', 'snr')]),
(inputnode, enhance, [('in_file', 'in_file')]),
(enhance, denoise, [('out_file', 'in_file')]),
(estsnr, gradient, [('out_snr', 'snr')]),
(denoise, gradient, [('out_file', 'in_file')]),
(inputnode, thresh, [('in_segm', 'in_segm')]),
(gradient, thresh, [('out_file', 'in_file')]),
(thresh, outputnode, [('out_file', 'out_file')])
])
return workflow
def spatial_normalization(settings,
mod='T1w',
name='SpatialNormalization',
resolution=2.0):
"""
A simple workflow to perform spatial normalization
"""
from niworkflows.data import getters as niwgetters
# Have some settings handy
tpl_id = settings.get('template_id', 'mni_icbm152_nlin_asym_09c')
mni_template = getattr(niwgetters, 'get_{}'.format(tpl_id))()
# Define workflow interface
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(fields=['moving_image', 'moving_mask']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['inverse_composite_transform', 'out_report']),
name='outputnode')
# Spatial normalization
norm = pe.Node(
RobustMNINormalization(
flavor='testing' if settings.get('testing', False) else 'fast',
num_threads=settings.get('ants_nthreads'),
float=settings.get('ants_float', False),
template=tpl_id,
template_resolution=2,
reference=mod[:2],
generate_report=True,
),
name='SpatialNormalization',
# Request all MultiProc processes when ants_nthreads > n_procs
num_threads=min(
settings.get('ants_nthreads', DEFAULTS['ants_nthreads']),
settings.get('n_procs', 1)),
estimated_memory_gb=3)
norm.inputs.reference_mask = op.join(
mni_template, '%dmm_brainmask.nii.gz' % int(resolution))
workflow.connect([
(inputnode, norm, [('moving_image', 'moving_image'),
('moving_mask', 'moving_mask')]),
(norm, outputnode, [('inverse_composite_transform',
'inverse_composite_transform'),
('out_report', 'out_report')]),
])
return workflow
def init_enhance_and_skullstrip_bold_wf(name='enhance_and_skullstrip_bold_wf',
omp_nthreads=1):
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['in_file']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
'mask_file', 'skull_stripped_file', 'bias_corrected_file', 'out_report'
]),
name='outputnode')
n4_correct = pe.Node(ants.N4BiasFieldCorrection(dimension=3,
copy_header=True,
num_threads=omp_nthreads),
name='n4_correct',
n_procs=omp_nthreads)
skullstrip_first_pass = pe.Node(fsl.BET(frac=0.2, mask=True),
name='skullstrip_first_pass')
unifize = pe.Node(afni.Unifize(t2=True,
outputtype='NIFTI_GZ',
args='-clfrac 0.4',
out_file="uni.nii.gz"),
name='unifize')
skullstrip_second_pass = pe.Node(afni.Automask(dilate=1,
outputtype='NIFTI_GZ'),
name='skullstrip_second_pass')
combine_masks = pe.Node(fsl.BinaryMaths(operation='mul'),
name='combine_masks')
apply_mask = pe.Node(fsl.ApplyMask(), name='apply_mask')
mask_reportlet = pe.Node(SimpleShowMaskRPT(), name='mask_reportlet')
workflow.connect([
(inputnode, n4_correct, [('in_file', 'input_image')]),
(n4_correct, skullstrip_first_pass, [('output_image', 'in_file')]),
(skullstrip_first_pass, unifize, [('out_file', 'in_file')]),
(unifize, skullstrip_second_pass, [('out_file', 'in_file')]),
(skullstrip_first_pass, combine_masks, [('mask_file', 'in_file')]),
(skullstrip_second_pass, combine_masks, [('out_file', 'operand_file')
]),
(unifize, apply_mask, [('out_file', 'in_file')]),
(combine_masks, apply_mask, [('out_file', 'mask_file')]),
(n4_correct, mask_reportlet, [('output_image', 'background_file')]),
(combine_masks, mask_reportlet, [('out_file', 'mask_file')]),
(combine_masks, outputnode, [('out_file', 'mask_file')]),
(mask_reportlet, outputnode, [('out_report', 'out_report')]),
(apply_mask, outputnode, [('out_file', 'skull_stripped_file')]),
(n4_correct, outputnode, [('output_image', 'bias_corrected_file')]),
])
return workflow
def assert_inputs_set(self, workflow, additional_inputs={}):
''' Check that all mandatory inputs of nodes in the workflow (at the first level) are
already set. Additionally, check that inputs in additional_inputs are set. An input is
"set" if it is
a) defined explicitly (e.g. in the Interface declaration)
OR
b) connected to another node's output (e.g. using the workflow.connect method)
additional_inputs is a dict:
{'node_name': ['mandatory', 'input', 'fields']}'''
dummy_node = engine.Node(utility.IdentityInterface(fields=['dummy']),
name='DummyNode')
node_names = [
name for name in workflow.list_node_names() if name.count('.') == 0
]
for node_name in set(node_names + list(additional_inputs.keys())):
node = workflow.get_node(node_name)
mandatory_inputs = list(node.inputs.traits(mandatory=True).keys())
other_inputs = additional_inputs[
node_name] if node_name in additional_inputs else []
for field in set(mandatory_inputs + other_inputs):
if field_is_defined(node, field):
pass
else: # not explicitly defined
# maybe it is connected to an output
with self.assertRaises(Exception):
# throws an error if the input is already connected
workflow.connect([(dummy_node, node, [('dummy', field)
])])
def init_anat_reports_wf(reportlets_dir,
skull_strip_ants,
output_spaces,
template,
freesurfer,
name='anat_reports_wf'):
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'source_file', 'summary_report', 't1_seg_report', 't1_2_mni_report',
't1_skull_strip_report', 'recon_report'
]),
name='inputnode')
ds_summary_report = pe.Node(DerivativesDataSink(
base_directory=reportlets_dir, suffix='summary'),
name='ds_summary_report',
run_without_submitting=True)
ds_t1_seg_report = pe.Node(DerivativesDataSink(
base_directory=reportlets_dir, suffix='t1_seg'),
name='ds_t1_seg_report',
run_without_submitting=True)
ds_t1_2_mni_report = pe.Node(DerivativesDataSink(
base_directory=reportlets_dir, suffix='t1_2_mni'),
name='ds_t1_2_mni_report',
run_without_submitting=True)
ds_t1_skull_strip_report = pe.Node(DerivativesDataSink(
base_directory=reportlets_dir, suffix='t1_skull_strip'),
name='ds_t1_skull_strip_report',
run_without_submitting=True)
ds_recon_report = pe.Node(DerivativesDataSink(
base_directory=reportlets_dir, suffix='reconall'),
name='ds_recon_report',
run_without_submitting=True)
workflow.connect([
(inputnode, ds_summary_report, [('source_file', 'source_file'),
('summary_report', 'in_file')]),
(inputnode, ds_t1_seg_report, [('source_file', 'source_file'),
('t1_seg_report', 'in_file')]),
])
if skull_strip_ants:
workflow.connect([(inputnode, ds_t1_skull_strip_report,
[('source_file', 'source_file'),
('t1_skull_strip_report', 'in_file')])])
if freesurfer:
workflow.connect([(inputnode, ds_recon_report,
[('source_file', 'source_file'),
('recon_report', 'in_file')])])
if 'template' in output_spaces:
workflow.connect([(inputnode, ds_t1_2_mni_report,
[('source_file', 'source_file'),
('t1_2_mni_report', 'in_file')])])
return workflow
def airmsk_wf(name='AirMaskWorkflow'):
"""
Implements the Step 1 of [Mortamet2009]_.
.. workflow::
from mriqc.workflows.anatomical import airmsk_wf
wf = airmsk_wf()
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'in_file', 'in_mask', 'head_mask', 'inverse_composite_transform'
]),
name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(fields=['out_file', 'artifact_msk', 'rot_mask']),
name='outputnode')
rotmsk = pe.Node(RotationMask(), name='RotationMask')
invt = pe.Node(ants.ApplyTransforms(dimension=3,
default_value=0,
interpolation='Linear',
float=True),
name='invert_xfm')
invt.inputs.input_image = op.join(get_mni_icbm152_nlin_asym_09c(),
'1mm_headmask.nii.gz')
binarize = pe.Node(niu.Function(function=_binarize), name='Binarize')
qi1 = pe.Node(ArtifactMask(), name='ArtifactMask')
workflow.connect([(inputnode, rotmsk, [('in_file', 'in_file')]),
(inputnode, qi1, [('in_file', 'in_file'),
('head_mask', 'head_mask')]),
(rotmsk, qi1, [('out_file', 'rot_mask')]),
(inputnode, invt, [('in_mask', 'reference_image'),
('inverse_composite_transform',
'transforms')]),
(invt, binarize, [('output_image', 'in_file')]),
(binarize, qi1, [('out', 'nasion_post_mask')]),
(qi1, outputnode, [('out_air_msk', 'out_file'),
('out_art_msk', 'artifact_msk')]),
(rotmsk, outputnode, [('out_file', 'rot_mask')])])
return workflow
def init_gifti_surface_wf(name='gifti_surface_wf'):
"""
Extract surfaces from FreeSurfer derivatives folder and
re-center GIFTI coordinates to align to native T1 space
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(['subjects_dir', 'subject_id']), name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(['surfaces']), name='outputnode')
get_surfaces = pe.Node(nio.FreeSurferSource(), name='get_surfaces')
midthickness = pe.MapNode(
MakeMidthickness(thickness=True, distance=0.5, out_name='midthickness'),
iterfield='in_file',
name='midthickness')
save_midthickness = pe.Node(nio.DataSink(parameterization=False),
name='save_midthickness')
surface_list = pe.Node(niu.Merge(4, ravel_inputs=True),
name='surface_list', run_without_submitting=True)
fs_2_gii = pe.MapNode(fs.MRIsConvert(out_datatype='gii'),
iterfield='in_file', name='fs_2_gii')
fix_surfs = pe.MapNode(NormalizeSurf(), iterfield='in_file', name='fix_surfs')
workflow.connect([
(inputnode, get_surfaces, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(inputnode, save_midthickness, [('subjects_dir', 'base_directory'),
('subject_id', 'container')]),
# Generate midthickness surfaces and save to FreeSurfer derivatives
(get_surfaces, midthickness, [('smoothwm', 'in_file'),
('graymid', 'graymid')]),
(midthickness, save_midthickness, [('out_file', 'surf.@graymid')]),
# Produce valid GIFTI surface files (dense mesh)
(get_surfaces, surface_list, [('smoothwm', 'in1'),
('pial', 'in2'),
('inflated', 'in3')]),
(save_midthickness, surface_list, [('out_file', 'in4')]),
(surface_list, fs_2_gii, [('out', 'in_file')]),
(fs_2_gii, fix_surfs, [('converted', 'in_file')]),
(fix_surfs, outputnode, [('out_file', 'surfaces')]),
])
return workflow
def hmc_mcflirt(settings, name='fMRI_HMC_mcflirt'):
"""
An :abbr:`HMC (head motion correction)` for functional scans
using FSL MCFLIRT
.. workflow::
from mriqc.workflows.functional import hmc_mcflirt
wf = hmc_mcflirt({'biggest_file_size_gb': 1})
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(
fields=['in_file', 'fd_radius', 'start_idx', 'stop_idx']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['out_file', 'out_fd']),
name='outputnode')
gen_ref = pe.Node(nwr.EstimateReferenceImage(mc_method="AFNI"),
name="gen_ref")
mcflirt = pe.Node(fsl.MCFLIRT(save_plots=True, interpolation='sinc'),
name='MCFLIRT',
mem_gb=settings['biggest_file_size_gb'] * 2.5)
fdnode = pe.Node(nac.FramewiseDisplacement(normalize=False,
parameter_source="FSL"),
name='ComputeFD')
workflow.connect([
(inputnode, gen_ref, [('in_file', 'in_file')]),
(gen_ref, mcflirt, [('ref_image', 'ref_file')]),
(inputnode, mcflirt, [('in_file', 'in_file')]),
(inputnode, fdnode, [('fd_radius', 'radius')]),
(mcflirt, fdnode, [('par_file', 'in_file')]),
(mcflirt, outputnode, [('out_file', 'out_file')]),
(fdnode, outputnode, [('out_file', 'out_fd')]),
])
return workflow
def init_skullstrip_ants_wf(debug, omp_nthreads, name='skullstrip_ants_wf'):
from niworkflows.data import get_ants_oasis_template_ras
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(fields=['in_file', 'source_file']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['bias_corrected', 'out_file', 'out_mask', 'out_report']),
name='outputnode')
t1_skull_strip = pe.Node(BrainExtractionRPT(dimension=3,
use_floatingpoint_precision=1,
debug=debug,
generate_report=True,
num_threads=omp_nthreads,
keep_temporary_files=1),
name='t1_skull_strip')
# should not be necesssary byt does not hurt - make sure the multiproc
# scheduler knows the resource limits
t1_skull_strip.interface.num_threads = omp_nthreads
t1_skull_strip.inputs.brain_template = op.join(
get_ants_oasis_template_ras(), 'T_template0.nii.gz')
t1_skull_strip.inputs.brain_probability_mask = op.join(
get_ants_oasis_template_ras(),
'T_template0_BrainCerebellumProbabilityMask.nii.gz')
t1_skull_strip.inputs.extraction_registration_mask = op.join(
get_ants_oasis_template_ras(),
'T_template0_BrainCerebellumRegistrationMask.nii.gz')
workflow.connect([
(inputnode, t1_skull_strip, [('in_file', 'anatomical_image')]),
(t1_skull_strip, outputnode, [('BrainExtractionMask', 'out_mask'),
('BrainExtractionBrain', 'out_file'),
('N4Corrected0', 'bias_corrected'),
('out_report', 'out_report')])
])
return workflow
def fmri_bmsk_workflow(name='fMRIBrainMask', use_bet=False):
"""
Computes a brain mask for the input :abbr:`fMRI (functional MRI)`
dataset
.. workflow::
from mriqc.workflows.functional import fmri_bmsk_workflow
wf = fmri_bmsk_workflow()
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['in_file']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['out_file']),
name='outputnode')
if not use_bet:
afni_msk = pe.Node(afni.Automask(
outputtype='NIFTI_GZ'), name='afni_msk')
# Connect brain mask extraction
workflow.connect([
(inputnode, afni_msk, [('in_file', 'in_file')]),
(afni_msk, outputnode, [('out_file', 'out_file')])
])
else:
bet_msk = pe.Node(fsl.BET(mask=True, functional=True), name='bet_msk')
erode = pe.Node(fsl.ErodeImage(), name='erode')
# Connect brain mask extraction
workflow.connect([
(inputnode, bet_msk, [('in_file', 'in_file')]),
(bet_msk, erode, [('mask_file', 'in_file')]),
(erode, outputnode, [('out_file', 'out_file')])
])
return workflow
def init_skullstrip_epi_wf(name='skullstrip_epi_wf'):
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['in_file']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['mask_file', 'skull_stripped_file', 'out_report']),
name='outputnode')
skullstrip_first_pass = pe.Node(fsl.BET(frac=0.2, mask=True),
name='skullstrip_first_pass')
skullstrip_second_pass = pe.Node(afni.Automask(dilate=1,
outputtype='NIFTI_GZ'),
name='skullstrip_second_pass')
combine_masks = pe.Node(fsl.BinaryMaths(operation='mul'),
name='combine_masks')
apply_mask = pe.Node(fsl.ApplyMask(), name='apply_mask')
mask_reportlet = pe.Node(SimpleShowMaskRPT(), name='mask_reportlet')
workflow.connect([
(inputnode, skullstrip_first_pass, [('in_file', 'in_file')]),
(skullstrip_first_pass, skullstrip_second_pass, [('out_file',
'in_file')]),
(skullstrip_first_pass, combine_masks, [('mask_file', 'in_file')]),
(skullstrip_second_pass, combine_masks, [('out_file', 'operand_file')
]),
(combine_masks, outputnode, [('out_file', 'mask_file')]),
# Masked file
(inputnode, apply_mask, [('in_file', 'in_file')]),
(combine_masks, apply_mask, [('out_file', 'mask_file')]),
(apply_mask, outputnode, [('out_file', 'skull_stripped_file')]),
# Reportlet
(inputnode, mask_reportlet, [('in_file', 'background_file')]),
(combine_masks, mask_reportlet, [('out_file', 'mask_file')]),
(mask_reportlet, outputnode, [('out_report', 'out_report')]),
])
return workflow
def init_anat_reports_wf(reportlets_dir, output_spaces,
template, freesurfer, name='anat_reports_wf'):
"""
Set up a battery of datasinks to store reports in the right location
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(
fields=['source_file', 't1_conform_report', 'seg_report',
't1_2_mni_report', 'recon_report']),
name='inputnode')
ds_t1_conform_report = pe.Node(
DerivativesDataSink(base_directory=reportlets_dir, suffix='conform'),
name='ds_t1_conform_report', run_without_submitting=True)
ds_t1_2_mni_report = pe.Node(
DerivativesDataSink(base_directory=reportlets_dir, suffix='t1_2_mni'),
name='ds_t1_2_mni_report', run_without_submitting=True)
ds_t1_seg_mask_report = pe.Node(
DerivativesDataSink(base_directory=reportlets_dir, suffix='seg_brainmask'),
name='ds_t1_seg_mask_report', run_without_submitting=True)
ds_recon_report = pe.Node(
DerivativesDataSink(base_directory=reportlets_dir, suffix='reconall'),
name='ds_recon_report', run_without_submitting=True)
workflow.connect([
(inputnode, ds_t1_conform_report, [('source_file', 'source_file'),
('t1_conform_report', 'in_file')]),
(inputnode, ds_t1_seg_mask_report, [('source_file', 'source_file'),
('seg_report', 'in_file')]),
])
if freesurfer:
workflow.connect([
(inputnode, ds_recon_report, [('source_file', 'source_file'),
('recon_report', 'in_file')])
])
if 'template' in output_spaces:
workflow.connect([
(inputnode, ds_t1_2_mni_report, [('source_file', 'source_file'),
('t1_2_mni_report', 'in_file')])
])
return workflow
def init_gifti_surface_wf(name='gifti_surface_wf'):
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(['subjects_dir', 'subject_id']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(['surfaces']),
name='outputnode')
get_surfaces = pe.Node(nio.FreeSurferSource(), name='get_surfaces')
midthickness = pe.MapNode(MakeMidthickness(thickness=True,
distance=0.5,
out_name='midthickness'),
iterfield='in_file',
name='midthickness')
save_midthickness = pe.Node(nio.DataSink(parameterization=False),
name='save_midthickness')
surface_list = pe.Node(niu.Merge(4, ravel_inputs=True),
name='surface_list',
run_without_submitting=True)
fs_2_gii = pe.MapNode(fs.MRIsConvert(out_datatype='gii'),
iterfield='in_file',
name='fs_2_gii')
def normalize_surfs(in_file):
""" Re-center GIFTI coordinates to fit align to native T1 space
For midthickness surfaces, add MidThickness metadata
Coordinate update based on:
https://github.com/Washington-University/workbench/blob/1b79e56/src/Algorithms/AlgorithmSurfaceApplyAffine.cxx#L73-L91
and
https://github.com/Washington-University/Pipelines/blob/ae69b9a/PostFreeSurfer/scripts/FreeSurfer2CaretConvertAndRegisterNonlinear.sh#L147
"""
import os
import numpy as np
import nibabel as nib
img = nib.load(in_file)
pointset = img.get_arrays_from_intent('NIFTI_INTENT_POINTSET')[0]
coords = pointset.data
c_ras_keys = ('VolGeomC_R', 'VolGeomC_A', 'VolGeomC_S')
ras = np.array([float(pointset.metadata[key]) for key in c_ras_keys])
# Apply C_RAS translation to coordinates
pointset.data = (coords + ras).astype(coords.dtype)
secondary = nib.gifti.GiftiNVPairs('AnatomicalStructureSecondary',
'MidThickness')
geom_type = nib.gifti.GiftiNVPairs('GeometricType', 'Anatomical')
has_ass = has_geo = False
for nvpair in pointset.meta.data:
# Remove C_RAS translation from metadata to avoid double-dipping in FreeSurfer
if nvpair.name in c_ras_keys:
nvpair.value = '0.000000'
# Check for missing metadata
elif nvpair.name == secondary.name:
has_ass = True
elif nvpair.name == geom_type.name:
has_geo = True
fname = os.path.basename(in_file)
# Update metadata for MidThickness/graymid surfaces
if 'midthickness' in fname.lower() or 'graymid' in fname.lower():
if not has_ass:
pointset.meta.data.insert(1, secondary)
if not has_geo:
pointset.meta.data.insert(2, geom_type)
img.to_filename(fname)
return os.path.abspath(fname)
fix_surfs = pe.MapNode(niu.Function(function=normalize_surfs),
iterfield='in_file',
name='fix_surfs')
workflow.connect([
(inputnode, get_surfaces, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(inputnode, save_midthickness, [('subjects_dir', 'base_directory'),
('subject_id', 'container')]),
# Generate midthickness surfaces and save to FreeSurfer derivatives
(get_surfaces, midthickness, [('smoothwm', 'in_file'),
('graymid', 'graymid')]),
(midthickness, save_midthickness, [('out_file', 'surf.@graymid')]),
# Produce valid GIFTI surface files (dense mesh)
(get_surfaces, surface_list, [('smoothwm', 'in1'), ('pial', 'in2'),
('inflated', 'in3')]),
(save_midthickness, surface_list, [('out_file', 'in4')]),
(surface_list, fs_2_gii, [('out', 'in_file')]),
(fs_2_gii, fix_surfs, [('converted', 'in_file')]),
(fix_surfs, outputnode, [('out', 'surfaces')]),
])
return workflow
def init_autorecon_resume_wf(omp_nthreads, name='autorecon_resume_wf'):
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(fields=['subjects_dir', 'subject_id', 'use_T2']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['subjects_dir', 'subject_id', 'out_report']),
name='outputnode')
autorecon2_vol = pe.Node(fs.ReconAll(directive='autorecon2-volonly',
openmp=omp_nthreads),
n_procs=omp_nthreads,
name='autorecon2_vol')
autorecon_surfs = pe.MapNode(fs.ReconAll(
directive='autorecon-hemi',
flags=[
'-noparcstats', '-nocortparc2', '-noparcstats2', '-nocortparc3',
'-noparcstats3', '-nopctsurfcon', '-nohyporelabel',
'-noaparc2aseg', '-noapas2aseg', '-nosegstats', '-nowmparc',
'-nobalabels'
],
openmp=omp_nthreads),
iterfield='hemi',
n_procs=omp_nthreads,
name='autorecon_surfs')
autorecon_surfs.inputs.hemi = ['lh', 'rh']
autorecon3 = pe.MapNode(fs.ReconAll(directive='autorecon3',
openmp=omp_nthreads),
iterfield='hemi',
n_procs=omp_nthreads,
name='autorecon3')
autorecon3.inputs.hemi = ['lh', 'rh']
# Only generate the report once; should be nothing to do
recon_report = pe.Node(ReconAllRPT(directive='autorecon3',
generate_report=True),
name='recon_report')
def _dedup(in_list):
vals = set(in_list)
if len(vals) > 1:
raise ValueError(
"Non-identical values can't be deduplicated:\n{!r}".format(
in_list))
return vals.pop()
workflow.connect([
(inputnode, autorecon3, [('use_T2', 'use_T2')]),
(inputnode, autorecon2_vol, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(autorecon2_vol, autorecon_surfs, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(autorecon_surfs, autorecon3,
[(('subjects_dir', _dedup), 'subjects_dir'),
(('subject_id', _dedup), 'subject_id')]),
(autorecon3, outputnode, [(('subjects_dir', _dedup), 'subjects_dir'),
(('subject_id', _dedup), 'subject_id')]),
(autorecon3, recon_report, [(('subjects_dir', _dedup), 'subjects_dir'),
(('subject_id', _dedup), 'subject_id')]),
(recon_report, outputnode, [('out_report', 'out_report')]),
])
return workflow
def init_surface_recon_wf(omp_nthreads, hires, name='surface_recon_wf'):
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
't1w', 't2w', 'skullstripped_t1', 'subjects_dir', 'subject_id'
]),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
'subjects_dir', 'subject_id', 'fs_2_t1_transform', 'surfaces',
'out_report'
]),
name='outputnode')
def detect_inputs(t1w_list, t2w_list=[], hires_enabled=True):
from niworkflows.nipype.interfaces.base import isdefined
from niworkflows.nipype.utils.filemanip import filename_to_list
from niworkflows.nipype.interfaces.traits_extension import Undefined
import nibabel as nib
t1w_list = filename_to_list(t1w_list)
t2w_list = filename_to_list(t2w_list) if isdefined(t2w_list) else []
t1w_ref = nib.load(t1w_list[0])
# Use high resolution preprocessing if voxel size < 1.0mm
# Tolerance of 0.05mm requires that rounds down to 0.9mm or lower
hires = hires_enabled and max(t1w_ref.header.get_zooms()) < 1 - 0.05
t2w = Undefined
if t2w_list and max(nib.load(t2w_list[0]).header.get_zooms()) < 1.2:
t2w = t2w_list[0]
# https://surfer.nmr.mgh.harvard.edu/fswiki/SubmillimeterRecon
mris_inflate = '-n 50' if hires else Undefined
return (t2w, isdefined(t2w), hires, mris_inflate)
recon_config = pe.Node(niu.Function(
function=detect_inputs,
output_names=['t2w', 'use_T2', 'hires', 'mris_inflate']),
name='recon_config')
recon_config.inputs.hires_enabled = hires
autorecon1 = pe.Node(fs.ReconAll(directive='autorecon1',
flags='-noskullstrip',
openmp=omp_nthreads),
name='autorecon1')
autorecon1.interface._can_resume = False
autorecon1.interface.num_threads = omp_nthreads
def inject_skullstripped(subjects_dir, subject_id, skullstripped):
import os
import nibabel as nib
from nilearn.image import resample_to_img, new_img_like
from niworkflows.nipype.utils.filemanip import copyfile
mridir = os.path.join(subjects_dir, subject_id, 'mri')
t1 = os.path.join(mridir, 'T1.mgz')
bm_auto = os.path.join(mridir, 'brainmask.auto.mgz')
bm = os.path.join(mridir, 'brainmask.mgz')
if not os.path.exists(bm_auto):
img = nib.load(t1)
mask = nib.load(skullstripped)
bmask = new_img_like(mask, mask.get_data() > 0)
resampled_mask = resample_to_img(bmask, img, 'nearest')
masked_image = new_img_like(
img,
img.get_data() * resampled_mask.get_data())
masked_image.to_filename(bm_auto)
if not os.path.exists(bm):
copyfile(bm_auto, bm, copy=True, use_hardlink=True)
return subjects_dir, subject_id
skull_strip_extern = pe.Node(niu.Function(
function=inject_skullstripped,
output_names=['subjects_dir', 'subject_id']),
name='skull_strip_extern')
fs_transform = pe.Node(fs.Tkregister2(fsl_out='freesurfer2subT1.mat',
reg_header=True),
name='fs_transform')
autorecon_resume_wf = init_autorecon_resume_wf(omp_nthreads=omp_nthreads)
gifti_surface_wf = init_gifti_surface_wf()
workflow.connect([
# Configuration
(inputnode, recon_config, [('t1w', 't1w_list'), ('t2w', 't2w_list')]),
# Passing subjects_dir / subject_id enforces serial order
(inputnode, autorecon1, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(autorecon1, skull_strip_extern, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(skull_strip_extern, autorecon_resume_wf,
[('subjects_dir', 'inputnode.subjects_dir'),
('subject_id', 'inputnode.subject_id')]),
(autorecon_resume_wf, gifti_surface_wf,
[('outputnode.subjects_dir', 'inputnode.subjects_dir'),
('outputnode.subject_id', 'inputnode.subject_id')]),
# Reconstruction phases
(inputnode, autorecon1, [('t1w', 'T1_files')]),
(
recon_config,
autorecon1,
[
('t2w', 'T2_file'),
('hires', 'hires'),
# First run only (recon-all saves expert options)
('mris_inflate', 'mris_inflate')
]),
(inputnode, skull_strip_extern, [('skullstripped_t1', 'skullstripped')
]),
(recon_config, autorecon_resume_wf, [('use_T2', 'inputnode.use_T2')]),
# Construct transform from FreeSurfer conformed image to FMRIPREP
# reoriented image
(inputnode, fs_transform, [('t1w', 'target_image')]),
(autorecon1, fs_transform, [('T1', 'moving_image')]),
# Output
(autorecon_resume_wf, outputnode,
[('outputnode.subjects_dir', 'subjects_dir'),
('outputnode.subject_id', 'subject_id'),
('outputnode.out_report', 'out_report')]),
(gifti_surface_wf, outputnode, [('outputnode.surfaces', 'surfaces')]),
(fs_transform, outputnode, [('fsl_file', 'fs_2_t1_transform')]),
])
return workflow
def init_anat_preproc_wf(skull_strip_ants,
skull_strip_template,
output_spaces,
template,
debug,
freesurfer,
omp_nthreads,
hires,
reportlets_dir,
output_dir,
name='anat_preproc_wf'):
"""T1w images preprocessing pipeline"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(fields=['t1w', 't2w', 'subjects_dir']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
't1_preproc', 't1_brain', 't1_mask', 't1_seg', 't1_tpms', 't1_2_mni',
't1_2_mni_forward_transform', 't1_2_mni_reverse_transform', 'mni_mask',
'mni_seg', 'mni_tpms', 'subjects_dir', 'subject_id',
'fs_2_t1_transform', 'surfaces'
]),
name='outputnode')
def bidsinfo(in_file):
from fmriprep.interfaces.bids import BIDS_NAME
match = BIDS_NAME.search(in_file)
params = match.groupdict() if match is not None else {}
return tuple(
map(params.get, [
'subject_id', 'ses_id', 'task_id', 'acq_id', 'rec_id', 'run_id'
]))
bids_info = pe.Node(niu.Function(function=bidsinfo,
output_names=[
'subject_id', 'ses_id', 'task_id',
'acq_id', 'rec_id', 'run_id'
]),
name='bids_info',
run_without_submitting=True)
summary = pe.Node(AnatomicalSummary(output_spaces=output_spaces,
template=template),
name='summary')
# 0. Reorient T1w image(s) to RAS and resample to common voxel space
t1_conform = pe.Node(ConformSeries(), name='t1_conform')
# 1. Align and merge if several T1w images are provided
t1_merge = pe.Node(
# StructuralReference is fs.RobustTemplate if > 1 volume, copying otherwise
StructuralReference(
auto_detect_sensitivity=True,
initial_timepoint=1,
fixed_timepoint=True, # Align to first image
intensity_scaling=True, # 7-DOF (rigid + intensity)
no_iteration=True,
subsample_threshold=200,
),
name='t1_merge')
# 2. T1 Bias Field Correction
# Bias field correction is handled in skull strip workflows.
# 3. Skull-stripping
#skullstrip_wf = init_skullstrip_afni_wf(name='skullstrip_afni_wf')
skullstrip_wf = init_skullstrip_watershed_wf(
name='skullstrip_watershed_wf')
if skull_strip_ants:
skullstrip_wf = init_skullstrip_ants_wf(
name='skullstrip_ants_wf',
debug=debug,
omp_nthreads=omp_nthreads,
skull_strip_template=skull_strip_template)
# 4. Segmentation
t1_seg = pe.Node(FASTRPT(generate_report=True,
segments=True,
no_bias=True,
probability_maps=True),
name='t1_seg')
# 5. Spatial normalization (T1w to MNI registration)
t1_2_mni = pe.Node(RobustMNINormalizationRPT(
generate_report=True,
num_threads=omp_nthreads,
flavor='testing' if debug else 'precise',
),
name='t1_2_mni')
# should not be necessary but does not hurt - make sure the multiproc
# scheduler knows the resource limits
t1_2_mni.interface.num_threads = omp_nthreads
# Resample the brain mask and the tissue probability maps into mni space
mni_mask = pe.Node(ants.ApplyTransforms(dimension=3,
default_value=0,
float=True,
interpolation='NearestNeighbor'),
name='mni_mask')
mni_seg = pe.Node(ants.ApplyTransforms(dimension=3,
default_value=0,
float=True,
interpolation='NearestNeighbor'),
name='mni_seg')
mni_tpms = pe.MapNode(ants.ApplyTransforms(dimension=3,
default_value=0,
float=True,
interpolation='Linear'),
iterfield=['input_image'],
name='mni_tpms')
workflow.connect([
(inputnode, bids_info, [(('t1w', fix_multi_T1w_source_name), 'in_file')
]),
(inputnode, t1_conform, [('t1w', 't1w_list')]),
(t1_conform, t1_merge, [('t1w_list', 'in_files'),
(('t1w_list', add_suffix, '_template'),
'out_file')]),
(t1_merge, skullstrip_wf, [('out_file', 'inputnode.in_file')]),
(skullstrip_wf, t1_seg, [('outputnode.out_file', 'in_files')]),
(skullstrip_wf, outputnode, [('outputnode.bias_corrected',
't1_preproc'),
('outputnode.out_file', 't1_brain'),
('outputnode.out_mask', 't1_mask')]),
(t1_seg, outputnode, [('tissue_class_map', 't1_seg'),
('probability_maps', 't1_tpms')]),
(inputnode, summary, [('t1w', 't1w')]),
])
if 'template' in output_spaces:
template_str = nid.TEMPLATE_MAP[template]
ref_img = op.join(nid.get_dataset(template_str), '1mm_T1.nii.gz')
t1_2_mni.inputs.template = template_str
mni_mask.inputs.reference_image = ref_img
mni_seg.inputs.reference_image = ref_img
mni_tpms.inputs.reference_image = ref_img
workflow.connect([
(skullstrip_wf, t1_2_mni, [('outputnode.bias_corrected',
'moving_image')]),
(skullstrip_wf, t1_2_mni, [('outputnode.out_mask', 'moving_mask')
]),
(skullstrip_wf, mni_mask, [('outputnode.out_mask', 'input_image')
]),
(t1_2_mni, mni_mask, [('composite_transform', 'transforms')]),
(t1_seg, mni_seg, [('tissue_class_map', 'input_image')]),
(t1_2_mni, mni_seg, [('composite_transform', 'transforms')]),
(t1_seg, mni_tpms, [('probability_maps', 'input_image')]),
(t1_2_mni, mni_tpms, [('composite_transform', 'transforms')]),
(t1_2_mni, outputnode,
[('warped_image', 't1_2_mni'),
('composite_transform', 't1_2_mni_forward_transform'),
('inverse_composite_transform', 't1_2_mni_reverse_transform')]),
(mni_mask, outputnode, [('output_image', 'mni_mask')]),
(mni_seg, outputnode, [('output_image', 'mni_seg')]),
(mni_tpms, outputnode, [('output_image', 'mni_tpms')]),
])
# 6. FreeSurfer reconstruction
if freesurfer:
surface_recon_wf = init_surface_recon_wf(name='surface_recon_wf',
omp_nthreads=omp_nthreads,
hires=hires)
workflow.connect([
(inputnode, summary, [('subjects_dir', 'subjects_dir')]),
(bids_info, summary, [('subject_id', 'subject_id')]),
(inputnode, surface_recon_wf, [('t2w', 'inputnode.t2w'),
('subjects_dir',
'inputnode.subjects_dir')]),
(summary, surface_recon_wf, [('subject_id', 'inputnode.subject_id')
]),
(t1_merge, surface_recon_wf, [('out_file', 'inputnode.t1w')]),
(skullstrip_wf, surface_recon_wf,
[('outputnode.out_file', 'inputnode.skullstripped_t1')]),
(surface_recon_wf, outputnode,
[('outputnode.subjects_dir', 'subjects_dir'),
('outputnode.subject_id', 'subject_id'),
('outputnode.fs_2_t1_transform', 'fs_2_t1_transform'),
('outputnode.surfaces', 'surfaces')]),
])
anat_reports_wf = init_anat_reports_wf(reportlets_dir=reportlets_dir,
skull_strip_ants=skull_strip_ants,
output_spaces=output_spaces,
template=template,
freesurfer=freesurfer)
workflow.connect([
(inputnode, anat_reports_wf, [(('t1w', fix_multi_T1w_source_name),
'inputnode.source_file')]),
(t1_seg, anat_reports_wf, [('out_report', 'inputnode.t1_seg_report')]),
(summary, anat_reports_wf, [('out_report', 'inputnode.summary_report')
]),
])
if skull_strip_ants:
workflow.connect([(skullstrip_wf, anat_reports_wf, [
('outputnode.out_report', 'inputnode.t1_skull_strip_report')
])])
if freesurfer:
workflow.connect([(surface_recon_wf, anat_reports_wf, [
('outputnode.out_report', 'inputnode.recon_report')
])])
if 'template' in output_spaces:
workflow.connect([
(t1_2_mni, anat_reports_wf, [('out_report',
'inputnode.t1_2_mni_report')]),
])
anat_derivatives_wf = init_anat_derivatives_wf(output_dir=output_dir,
output_spaces=output_spaces,
template=template,
freesurfer=freesurfer)
workflow.connect([
(inputnode, anat_derivatives_wf, [(('t1w', fix_multi_T1w_source_name),
'inputnode.source_file')]),
(outputnode, anat_derivatives_wf, [
('t1_preproc', 'inputnode.t1_preproc'),
('t1_mask', 'inputnode.t1_mask'),
('t1_seg', 'inputnode.t1_seg'),
('t1_tpms', 'inputnode.t1_tpms'),
('t1_2_mni_forward_transform',
'inputnode.t1_2_mni_forward_transform'),
('t1_2_mni', 'inputnode.t1_2_mni'),
('mni_mask', 'inputnode.mni_mask'),
('mni_seg', 'inputnode.mni_seg'),
('mni_tpms', 'inputnode.mni_tpms'),
('surfaces', 'inputnode.surfaces'),
]),
])
return workflow
def init_func_derivatives_wf(output_dir, output_spaces, template, freesurfer,
use_aroma, cifti_output, name='func_derivatives_wf'):
"""
Set up a battery of datasinks to store derivatives in the right location
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(
fields=['source_file', 'bold_t1', 'bold_mask_t1', 'bold_mni', 'bold_mask_mni',
'bold_aseg_t1', 'bold_aparc_t1', 'cifti_variant_key',
'confounds', 'surfaces', 'aroma_noise_ics', 'melodic_mix',
'nonaggr_denoised_file', 'bold_cifti', 'cifti_variant']),
name='inputnode')
suffix_fmt = 'space-{}_{}'.format
variant_suffix_fmt = 'space-{}_variant-{}_{}'.format
ds_confounds = pe.Node(DerivativesDataSink(
base_directory=output_dir, suffix='confounds'),
name="ds_confounds", run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, ds_confounds, [('source_file', 'source_file'),
('confounds', 'in_file')]),
])
# Resample to T1w space
if 'T1w' in output_spaces:
ds_bold_t1 = pe.Node(DerivativesDataSink(
base_directory=output_dir, suffix=suffix_fmt('T1w', 'preproc'), compress=True),
name='ds_bold_t1', run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_bold_mask_t1 = pe.Node(DerivativesDataSink(
base_directory=output_dir, suffix=suffix_fmt('T1w', 'brainmask')),
name='ds_bold_mask_t1', run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, ds_bold_t1, [('source_file', 'source_file'),
('bold_t1', 'in_file')]),
(inputnode, ds_bold_mask_t1, [('source_file', 'source_file'),
('bold_mask_t1', 'in_file')]),
])
# Resample to template (default: MNI)
if 'template' in output_spaces:
ds_bold_mni = pe.Node(DerivativesDataSink(
base_directory=output_dir, suffix=suffix_fmt(template, 'preproc')),
name='ds_bold_mni', run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_bold_mask_mni = pe.Node(DerivativesDataSink(
base_directory=output_dir, suffix=suffix_fmt(template, 'brainmask')),
name='ds_bold_mask_mni', run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, ds_bold_mni, [('source_file', 'source_file'),
('bold_mni', 'in_file')]),
(inputnode, ds_bold_mask_mni, [('source_file', 'source_file'),
('bold_mask_mni', 'in_file')]),
])
if freesurfer:
ds_bold_aseg_t1 = pe.Node(DerivativesDataSink(
base_directory=output_dir, suffix='space-T1w_label-aseg_roi'),
name='ds_bold_aseg_t1', run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_bold_aparc_t1 = pe.Node(DerivativesDataSink(
base_directory=output_dir, suffix='space-T1w_label-aparcaseg_roi'),
name='ds_bold_aparc_t1', run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, ds_bold_aseg_t1, [('source_file', 'source_file'),
('bold_aseg_t1', 'in_file')]),
(inputnode, ds_bold_aparc_t1, [('source_file', 'source_file'),
('bold_aparc_t1', 'in_file')]),
])
# fsaverage space
if freesurfer and any(space.startswith('fs') for space in output_spaces):
name_surfs = pe.MapNode(GiftiNameSource(
pattern=r'(?P<LR>[lr])h.(?P<space>\w+).gii', template='space-{space}.{LR}.func'),
iterfield='in_file', name='name_surfs', mem_gb=DEFAULT_MEMORY_MIN_GB,
run_without_submitting=True)
ds_bold_surfs = pe.MapNode(DerivativesDataSink(base_directory=output_dir),
iterfield=['in_file', 'suffix'], name='ds_bold_surfs',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, name_surfs, [('surfaces', 'in_file')]),
(inputnode, ds_bold_surfs, [('source_file', 'source_file'),
('surfaces', 'in_file')]),
(name_surfs, ds_bold_surfs, [('out_name', 'suffix')]),
])
# CIFTI output
if cifti_output and 'template' in output_spaces:
name_cifti = pe.MapNode(
CiftiNameSource(), iterfield=['variant'], name='name_cifti',
mem_gb=DEFAULT_MEMORY_MIN_GB, run_without_submitting=True)
cifti_bolds = pe.MapNode(DerivativesDataSink(
base_directory=output_dir, compress=False),
iterfield=['in_file', 'suffix'], name='cifti_bolds',
run_without_submitting=True, mem_gb=DEFAULT_MEMORY_MIN_GB)
cifti_key = pe.MapNode(DerivativesDataSink(
base_directory=output_dir), iterfield=['in_file', 'suffix'],
name='cifti_key', run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, name_cifti, [('cifti_variant', 'variant')]),
(inputnode, cifti_bolds, [('bold_cifti', 'in_file'),
('source_file', 'source_file')]),
(name_cifti, cifti_bolds, [('out_name', 'suffix')]),
(name_cifti, cifti_key, [('out_name', 'suffix')]),
(inputnode, cifti_key, [('source_file', 'source_file'),
('cifti_variant_key', 'in_file')]),
])
if use_aroma:
ds_aroma_noise_ics = pe.Node(DerivativesDataSink(
base_directory=output_dir, suffix='AROMAnoiseICs'),
name="ds_aroma_noise_ics", run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_melodic_mix = pe.Node(DerivativesDataSink(
base_directory=output_dir, suffix='MELODICmix'),
name="ds_melodic_mix", run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_aroma_mni = pe.Node(DerivativesDataSink(
base_directory=output_dir, suffix=variant_suffix_fmt(
template, 'smoothAROMAnonaggr', 'preproc')),
name='ds_aroma_mni', run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, ds_aroma_noise_ics, [('source_file', 'source_file'),
('aroma_noise_ics', 'in_file')]),
(inputnode, ds_melodic_mix, [('source_file', 'source_file'),
('melodic_mix', 'in_file')]),
(inputnode, ds_aroma_mni, [('source_file', 'source_file'),
('nonaggr_denoised_file', 'in_file')]),
])
return workflow
def init_func_preproc_wf(bold_file, ignore, freesurfer,
use_bbr, t2s_coreg, bold2t1w_dof, reportlets_dir,
output_spaces, template, output_dir, omp_nthreads,
fmap_bspline, fmap_demean, use_syn, force_syn,
use_aroma, ignore_aroma_err, aroma_melodic_dim,
medial_surface_nan, cifti_output,
debug, low_mem, template_out_grid, layout=None):
"""
This workflow controls the functional preprocessing stages of FMRIPREP.
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.bold import init_func_preproc_wf
wf = init_func_preproc_wf('/completely/made/up/path/sub-01_task-nback_bold.nii.gz',
omp_nthreads=1,
ignore=[],
freesurfer=True,
reportlets_dir='.',
output_dir='.',
template='MNI152NLin2009cAsym',
output_spaces=['T1w', 'fsnative',
'template', 'fsaverage5'],
debug=False,
use_bbr=True,
t2s_coreg=False,
bold2t1w_dof=9,
fmap_bspline=True,
fmap_demean=True,
use_syn=True,
force_syn=True,
low_mem=False,
template_out_grid='native',
medial_surface_nan=False,
cifti_output=False,
use_aroma=False,
ignore_aroma_err=False,
aroma_melodic_dim=None)
**Parameters**
bold_file : str
BOLD series NIfTI file
ignore : list
Preprocessing steps to skip (may include "slicetiming", "fieldmaps")
freesurfer : bool
Enable FreeSurfer functional registration (bbregister) and resampling
BOLD series to FreeSurfer surface meshes.
use_bbr : bool or None
Enable/disable boundary-based registration refinement.
If ``None``, test BBR result for distortion before accepting.
t2s_coreg : bool
Use multiple BOLD echos to create T2*-map for T2*-driven coregistration
bold2t1w_dof : 6, 9 or 12
Degrees-of-freedom for BOLD-T1w registration
reportlets_dir : str
Directory in which to save reportlets
output_spaces : list
List of output spaces functional images are to be resampled to.
Some parts of pipeline will only be instantiated for some output spaces.
Valid spaces:
- T1w
- template
- fsnative
- fsaverage (or other pre-existing FreeSurfer templates)
template : str
Name of template targeted by ``template`` output space
output_dir : str
Directory in which to save derivatives
omp_nthreads : int
Maximum number of threads an individual process may use
fmap_bspline : bool
**Experimental**: Fit B-Spline field using least-squares
fmap_demean : bool
Demean voxel-shift map during unwarp
use_syn : bool
**Experimental**: Enable ANTs SyN-based susceptibility distortion correction (SDC).
If fieldmaps are present and enabled, this is not run, by default.
force_syn : bool
**Temporary**: Always run SyN-based SDC
use_aroma : bool
Perform ICA-AROMA on MNI-resampled functional series
ignore_aroma_err : bool
Do not fail on ICA-AROMA errors
medial_surface_nan : bool
Replace medial wall values with NaNs on functional GIFTI files
cifti_output : bool
Generate bold CIFTI file in output spaces
debug : bool
Enable debugging outputs
low_mem : bool
Write uncompressed .nii files in some cases to reduce memory usage
template_out_grid : str
Keyword ('native', '1mm' or '2mm') or path of custom reference
image for normalization
layout : BIDSLayout
BIDSLayout structure to enable metadata retrieval
**Inputs**
bold_file
BOLD series NIfTI file
t1_preproc
Bias-corrected structural template image
t1_brain
Skull-stripped ``t1_preproc``
t1_mask
Mask of the skull-stripped template image
t1_seg
Segmentation of preprocessed structural image, including
gray-matter (GM), white-matter (WM) and cerebrospinal fluid (CSF)
t1_tpms
List of tissue probability maps in T1w space
t1_2_mni_forward_transform
ANTs-compatible affine-and-warp transform file
t1_2_mni_reverse_transform
ANTs-compatible affine-and-warp transform file (inverse)
subjects_dir
FreeSurfer SUBJECTS_DIR
subject_id
FreeSurfer subject ID
t1_2_fsnative_forward_transform
LTA-style affine matrix translating from T1w to FreeSurfer-conformed subject space
t1_2_fsnative_reverse_transform
LTA-style affine matrix translating from FreeSurfer-conformed subject space to T1w
**Outputs**
bold_t1
BOLD series, resampled to T1w space
bold_mask_t1
BOLD series mask in T1w space
bold_mni
BOLD series, resampled to template space
bold_mask_mni
BOLD series mask in template space
confounds
TSV of confounds
surfaces
BOLD series, resampled to FreeSurfer surfaces
aroma_noise_ics
Noise components identified by ICA-AROMA
melodic_mix
FSL MELODIC mixing matrix
bold_cifti
BOLD CIFTI image
cifti_variant
combination of target spaces for `bold_cifti`
**Subworkflows**
* :py:func:`~fmriprep.workflows.bold.util.init_bold_reference_wf`
* :py:func:`~fmriprep.workflows.bold.stc.init_bold_stc_wf`
* :py:func:`~fmriprep.workflows.bold.hmc.init_bold_hmc_wf`
* :py:func:`~fmriprep.workflows.bold.t2s.init_bold_t2s_wf`
* :py:func:`~fmriprep.workflows.bold.registration.init_bold_reg_wf`
* :py:func:`~fmriprep.workflows.bold.confounds.init_bold_confounds_wf`
* :py:func:`~fmriprep.workflows.bold.confounds.init_ica_aroma_wf`
* :py:func:`~fmriprep.workflows.bold.resampling.init_bold_mni_trans_wf`
* :py:func:`~fmriprep.workflows.bold.resampling.init_bold_preproc_trans_wf`
* :py:func:`~fmriprep.workflows.bold.resampling.init_bold_surf_wf`
* :py:func:`~fmriprep.workflows.fieldmap.pepolar.init_pepolar_unwarp_wf`
* :py:func:`~fmriprep.workflows.fieldmap.init_fmap_estimator_wf`
* :py:func:`~fmriprep.workflows.fieldmap.init_sdc_unwarp_wf`
* :py:func:`~fmriprep.workflows.fieldmap.init_nonlinear_sdc_wf`
"""
from ..fieldmap.base import init_sdc_wf # Avoid circular dependency (#1066)
ref_file = bold_file
mem_gb = {'filesize': 1, 'resampled': 1, 'largemem': 1}
bold_tlen = 10
multiecho = isinstance(bold_file, list)
if multiecho:
tes = [layout.get_metadata(echo)['EchoTime'] for echo in bold_file]
ref_file = dict(zip(tes, bold_file))[min(tes)]
if os.path.isfile(ref_file):
bold_tlen, mem_gb = _create_mem_gb(ref_file)
wf_name = _get_wf_name(ref_file)
LOGGER.log(25, ('Creating bold processing workflow for "%s" (%.2f GB / %d TRs). '
'Memory resampled/largemem=%.2f/%.2f GB.'),
ref_file, mem_gb['filesize'], bold_tlen, mem_gb['resampled'], mem_gb['largemem'])
# For doc building purposes
if layout is None or bold_file == 'bold_preprocesing':
LOGGER.log(25, 'No valid layout: building empty workflow.')
metadata = {
'RepetitionTime': 2.0,
'SliceTiming': [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
'PhaseEncodingDirection': 'j',
}
fmaps = [{
'type': 'phasediff',
'phasediff': 'sub-03/ses-2/fmap/sub-03_ses-2_run-1_phasediff.nii.gz',
'magnitude1': 'sub-03/ses-2/fmap/sub-03_ses-2_run-1_magnitude1.nii.gz',
'magnitude2': 'sub-03/ses-2/fmap/sub-03_ses-2_run-1_magnitude2.nii.gz',
}]
run_stc = True
multiecho = False
else:
metadata = layout.get_metadata(ref_file)
# Find fieldmaps. Options: (phase1|phase2|phasediff|epi|fieldmap|syn)
fmaps = []
if 'fieldmaps' not in ignore:
fmaps = layout.get_fieldmap(ref_file, return_list=True)
for fmap in fmaps:
fmap['metadata'] = layout.get_metadata(fmap[fmap['type']])
# Run SyN if forced or in the absence of fieldmap correction
if force_syn or (use_syn and not fmaps):
fmaps.append({'type': 'syn'})
# Short circuits: (True and True and (False or 'TooShort')) == 'TooShort'
run_stc = ("SliceTiming" in metadata and
'slicetiming' not in ignore and
(_get_series_len(ref_file) > 4 or "TooShort"))
# Use T2* as target for ME-EPI in co-registration
if t2s_coreg and not multiecho:
LOGGER.warning("No multiecho BOLD images found for T2* coregistration. "
"Using standard EPI-T1 coregistration.")
t2s_coreg = False
# Switch stc off
if multiecho and run_stc is True:
LOGGER.warning('Slice-timing correction is not available for '
'multiecho BOLD data (not implemented).')
run_stc = False
# Build workflow
workflow = pe.Workflow(name=wf_name)
inputnode = pe.Node(niu.IdentityInterface(
fields=['bold_file', 'subjects_dir', 'subject_id',
't1_preproc', 't1_brain', 't1_mask', 't1_seg', 't1_tpms',
't1_aseg', 't1_aparc',
't1_2_mni_forward_transform', 't1_2_mni_reverse_transform',
't1_2_fsnative_forward_transform', 't1_2_fsnative_reverse_transform']),
name='inputnode')
inputnode.inputs.bold_file = bold_file
outputnode = pe.Node(niu.IdentityInterface(
fields=['bold_t1', 'bold_mask_t1', 'bold_aseg_t1', 'bold_aparc_t1', 'cifti_variant',
'bold_mni', 'bold_mask_mni', 'bold_cifti', 'confounds', 'surfaces',
't2s_map', 'aroma_noise_ics', 'melodic_mix', 'nonaggr_denoised_file',
'cifti_variant_key']),
name='outputnode')
# BOLD buffer: an identity used as a pointer to either the original BOLD
# or the STC'ed one for further use.
boldbuffer = pe.Node(niu.IdentityInterface(fields=['bold_file']), name='boldbuffer')
summary = pe.Node(
FunctionalSummary(output_spaces=output_spaces,
slice_timing=run_stc,
registration='FreeSurfer' if freesurfer else 'FSL',
registration_dof=bold2t1w_dof,
pe_direction=metadata.get("PhaseEncodingDirection")),
name='summary', mem_gb=DEFAULT_MEMORY_MIN_GB, run_without_submitting=True)
func_derivatives_wf = init_func_derivatives_wf(output_dir=output_dir,
output_spaces=output_spaces,
template=template,
freesurfer=freesurfer,
use_aroma=use_aroma,
cifti_output=cifti_output)
workflow.connect([
(inputnode, func_derivatives_wf, [('bold_file', 'inputnode.source_file')]),
(outputnode, func_derivatives_wf, [
('bold_t1', 'inputnode.bold_t1'),
('bold_aseg_t1', 'inputnode.bold_aseg_t1'),
('bold_aparc_t1', 'inputnode.bold_aparc_t1'),
('bold_mask_t1', 'inputnode.bold_mask_t1'),
('bold_mni', 'inputnode.bold_mni'),
('bold_mask_mni', 'inputnode.bold_mask_mni'),
('confounds', 'inputnode.confounds'),
('surfaces', 'inputnode.surfaces'),
('aroma_noise_ics', 'inputnode.aroma_noise_ics'),
('melodic_mix', 'inputnode.melodic_mix'),
('nonaggr_denoised_file', 'inputnode.nonaggr_denoised_file'),
('bold_cifti', 'inputnode.bold_cifti'),
('cifti_variant', 'inputnode.cifti_variant'),
('cifti_variant_key', 'inputnode.cifti_variant_key')
]),
])
# The first reference uses T2 contrast enhancement
bold_reference_wf = init_bold_reference_wf(
omp_nthreads=omp_nthreads, enhance_t2=True)
# Top-level BOLD splitter
bold_split = pe.Node(FSLSplit(dimension='t'), name='bold_split',
mem_gb=mem_gb['filesize'] * 3)
# HMC on the BOLD
bold_hmc_wf = init_bold_hmc_wf(name='bold_hmc_wf',
mem_gb=mem_gb['filesize'],
omp_nthreads=omp_nthreads)
# mean BOLD registration to T1w
bold_reg_wf = init_bold_reg_wf(name='bold_reg_wf',
freesurfer=freesurfer,
use_bbr=use_bbr,
bold2t1w_dof=bold2t1w_dof,
mem_gb=mem_gb['resampled'],
omp_nthreads=omp_nthreads,
use_compression=False,
use_fieldwarp=(fmaps is not None or use_syn))
# get confounds
bold_confounds_wf = init_bold_confs_wf(
mem_gb=mem_gb['largemem'],
metadata=metadata,
name='bold_confounds_wf')
bold_confounds_wf.get_node('inputnode').inputs.t1_transform_flags = [False]
# Apply transforms in 1 shot
# Only use uncompressed output if AROMA is to be run
bold_bold_trans_wf = init_bold_preproc_trans_wf(
mem_gb=mem_gb['resampled'],
omp_nthreads=omp_nthreads,
use_compression=not low_mem,
use_fieldwarp=(fmaps is not None or use_syn),
name='bold_bold_trans_wf'
)
# SLICE-TIME CORRECTION (or bypass) #############################################
if run_stc is True: # bool('TooShort') == True, so check True explicitly
bold_stc_wf = init_bold_stc_wf(name='bold_stc_wf', metadata=metadata)
workflow.connect([
(bold_reference_wf, bold_stc_wf, [('outputnode.bold_file', 'inputnode.bold_file'),
('outputnode.skip_vols', 'inputnode.skip_vols')]),
(bold_stc_wf, boldbuffer, [('outputnode.stc_file', 'bold_file')]),
])
else: # bypass STC from original BOLD to the splitter through boldbuffer
workflow.connect([
(bold_reference_wf, boldbuffer, [
('outputnode.bold_file', 'bold_file')]),
])
# SDC (SUSCEPTIBILITY DISTORTION CORRECTION) or bypass ##########################
bold_sdc_wf = init_sdc_wf(
fmaps, metadata, omp_nthreads=omp_nthreads,
debug=debug, fmap_demean=fmap_demean, fmap_bspline=fmap_bspline)
bold_sdc_wf.inputs.inputnode.template = template
if not fmaps:
LOGGER.warning('SDC: no fieldmaps found or they were ignored (%s).',
ref_file)
elif fmaps[0]['type'] == 'syn':
LOGGER.warning(
'SDC: no fieldmaps found or they were ignored. '
'Using EXPERIMENTAL "fieldmap-less SyN" correction '
'for dataset %s.', ref_file)
else:
LOGGER.log(25, 'SDC: fieldmap estimation of type "%s" intended for %s found.',
fmaps[0]['type'], ref_file)
# MAIN WORKFLOW STRUCTURE #######################################################
workflow.connect([
# Generate early reference
(inputnode, bold_reference_wf, [('bold_file', 'inputnode.bold_file')]),
# BOLD buffer has slice-time corrected if it was run, original otherwise
(boldbuffer, bold_split, [('bold_file', 'in_file')]),
# HMC
(bold_reference_wf, bold_hmc_wf, [
('outputnode.raw_ref_image', 'inputnode.raw_ref_image'),
('outputnode.bold_file', 'inputnode.bold_file')]),
# EPI-T1 registration workflow
(inputnode, bold_reg_wf, [
('bold_file', 'inputnode.name_source'),
('t1_preproc', 'inputnode.t1_preproc'),
('t1_brain', 'inputnode.t1_brain'),
('t1_mask', 'inputnode.t1_mask'),
('t1_seg', 'inputnode.t1_seg'),
('t1_aseg', 'inputnode.t1_aseg'),
('t1_aparc', 'inputnode.t1_aparc'),
# Undefined if --no-freesurfer, but this is safe
('subjects_dir', 'inputnode.subjects_dir'),
('subject_id', 'inputnode.subject_id'),
('t1_2_fsnative_reverse_transform', 'inputnode.t1_2_fsnative_reverse_transform')]),
(bold_split, bold_reg_wf, [('out_files', 'inputnode.bold_split')]),
(bold_hmc_wf, bold_reg_wf, [('outputnode.xforms', 'inputnode.hmc_xforms')]),
(bold_reg_wf, outputnode, [('outputnode.bold_t1', 'bold_t1'),
('outputnode.bold_aseg_t1', 'bold_aseg_t1'),
('outputnode.bold_aparc_t1', 'bold_aparc_t1')]),
(bold_reg_wf, summary, [('outputnode.fallback', 'fallback')]),
# SDC (or pass-through workflow)
(inputnode, bold_sdc_wf, [
('t1_brain', 'inputnode.t1_brain'),
('t1_2_mni_reverse_transform', 'inputnode.t1_2_mni_reverse_transform')]),
(bold_reference_wf, bold_sdc_wf, [
('outputnode.ref_image', 'inputnode.bold_ref'),
('outputnode.ref_image_brain', 'inputnode.bold_ref_brain'),
('outputnode.bold_mask', 'inputnode.bold_mask')]),
(bold_sdc_wf, bold_reg_wf, [
('outputnode.bold_ref_brain', 'inputnode.ref_bold_brain'),
('outputnode.bold_mask', 'inputnode.ref_bold_mask'),
('outputnode.out_warp', 'inputnode.fieldwarp')]),
(bold_sdc_wf, bold_bold_trans_wf, [
('outputnode.out_warp', 'inputnode.fieldwarp'),
('outputnode.bold_mask', 'inputnode.bold_mask')]),
(bold_sdc_wf, summary, [('outputnode.method', 'distortion_correction')]),
# Connect bold_confounds_wf
(inputnode, bold_confounds_wf, [('t1_tpms', 'inputnode.t1_tpms'),
('t1_mask', 'inputnode.t1_mask')]),
(bold_hmc_wf, bold_confounds_wf, [
('outputnode.movpar_file', 'inputnode.movpar_file')]),
(bold_reg_wf, bold_confounds_wf, [
('outputnode.itk_t1_to_bold', 'inputnode.t1_bold_xform')]),
(bold_confounds_wf, outputnode, [
('outputnode.confounds_file', 'confounds'),
]),
# Connect bold_bold_trans_wf
(inputnode, bold_bold_trans_wf, [
('bold_file', 'inputnode.name_source')]),
(bold_split, bold_bold_trans_wf, [
('out_files', 'inputnode.bold_file')]),
(bold_hmc_wf, bold_bold_trans_wf, [
('outputnode.xforms', 'inputnode.hmc_xforms')]),
(bold_bold_trans_wf, bold_confounds_wf, [
('outputnode.bold', 'inputnode.bold'),
('outputnode.bold_mask', 'inputnode.bold_mask')]),
# Summary
(outputnode, summary, [('confounds', 'confounds_file')]),
])
if fmaps:
from ..fieldmap.unwarp import init_fmap_unwarp_report_wf
sdc_type = fmaps[0]['type']
# Report on BOLD correction
fmap_unwarp_report_wf = init_fmap_unwarp_report_wf(
suffix='sdc_%s' % sdc_type)
workflow.connect([
(inputnode, fmap_unwarp_report_wf, [
('t1_seg', 'inputnode.in_seg')]),
(bold_reference_wf, fmap_unwarp_report_wf, [
('outputnode.ref_image', 'inputnode.in_pre')]),
(bold_reg_wf, fmap_unwarp_report_wf, [
('outputnode.itk_t1_to_bold', 'inputnode.in_xfm')]),
(bold_sdc_wf, fmap_unwarp_report_wf, [
('outputnode.bold_ref', 'inputnode.in_post')]),
])
if force_syn and sdc_type != 'syn':
syn_unwarp_report_wf = init_fmap_unwarp_report_wf(
suffix='forcedsyn', name='syn_unwarp_report_wf')
workflow.connect([
(inputnode, syn_unwarp_report_wf, [
('t1_seg', 'inputnode.in_seg')]),
(bold_reference_wf, syn_unwarp_report_wf, [
('outputnode.ref_image', 'inputnode.in_pre')]),
(bold_reg_wf, syn_unwarp_report_wf, [
('outputnode.itk_t1_to_bold', 'inputnode.in_xfm')]),
(bold_sdc_wf, syn_unwarp_report_wf, [
('outputnode.syn_bold_ref', 'inputnode.in_post')]),
])
# if multiecho data, select first echo for hmc correction
if multiecho:
inputnode.iterables = ('bold_file', bold_file)
me_first_echo = pe.Node(FirstEcho(
te_list=tes, in_files=bold_file, ref_imgs=bold_file),
name='me_first_echo')
# Replace reference with the echo selected with FirstEcho
workflow.disconnect([
(inputnode, bold_reference_wf, [
('bold_file', 'inputnode.bold_file')]),
(bold_reference_wf, boldbuffer, [
('outputnode.bold_file', 'bold_file')]),
])
workflow.connect([
(me_first_echo, bold_reference_wf, [
('first_image', 'inputnode.bold_file')]),
(inputnode, boldbuffer, [
('bold_file', 'bold_file')]),
])
if t2s_coreg:
# create a T2* map
bold_t2s_wf = init_bold_t2s_wf(bold_echos=bold_file,
echo_times=tes,
mem_gb=mem_gb['resampled'],
omp_nthreads=omp_nthreads,
name='bold_t2s_wf')
bold_t2s_wf.inputs.inputnode.name_source = ref_file
# Replace EPI-to-T1w registration inputs
workflow.disconnect([
(bold_sdc_wf, bold_reg_wf, [
('outputnode.bold_ref_brain', 'inputnode.ref_bold_brain'),
('outputnode.bold_mask', 'inputnode.ref_bold_mask')]),
])
workflow.connect([
(bold_hmc_wf, bold_t2s_wf, [
('outputnode.xforms', 'inputnode.hmc_xforms')]),
(bold_t2s_wf, bold_reg_wf, [
('outputnode.t2s_map', 'inputnode.ref_bold_brain'),
('outputnode.oc_mask', 'inputnode.ref_bold_mask')]),
])
# Map final BOLD mask into T1w space (if required)
if 'T1w' in output_spaces:
from niworkflows.interfaces.fixes import (
FixHeaderApplyTransforms as ApplyTransforms
)
boldmask_to_t1w = pe.Node(
ApplyTransforms(interpolation='MultiLabel', float=True),
name='boldmask_to_t1w', mem_gb=0.1
)
workflow.connect([
(bold_bold_trans_wf, boldmask_to_t1w, [
('outputnode.bold_mask', 'input_image')]),
(bold_reg_wf, boldmask_to_t1w, [
('outputnode.bold_mask_t1', 'reference_image'),
('outputnode.itk_bold_to_t1', 'transforms')]),
(boldmask_to_t1w, outputnode, [
('output_image', 'bold_mask_t1')]),
])
if 'template' in output_spaces:
# Apply transforms in 1 shot
# Only use uncompressed output if AROMA is to be run
bold_mni_trans_wf = init_bold_mni_trans_wf(
template=template,
mem_gb=mem_gb['resampled'],
omp_nthreads=omp_nthreads,
template_out_grid=template_out_grid,
use_compression=not low_mem,
use_fieldwarp=fmaps is not None,
name='bold_mni_trans_wf'
)
carpetplot_wf = init_carpetplot_wf(
mem_gb=mem_gb['resampled'],
metadata=metadata,
name='carpetplot_wf')
workflow.connect([
(inputnode, bold_mni_trans_wf, [
('bold_file', 'inputnode.name_source'),
('t1_2_mni_forward_transform', 'inputnode.t1_2_mni_forward_transform')]),
(bold_split, bold_mni_trans_wf, [
('out_files', 'inputnode.bold_split')]),
(bold_hmc_wf, bold_mni_trans_wf, [
('outputnode.xforms', 'inputnode.hmc_xforms')]),
(bold_reg_wf, bold_mni_trans_wf, [
('outputnode.itk_bold_to_t1', 'inputnode.itk_bold_to_t1')]),
(bold_bold_trans_wf, bold_mni_trans_wf, [
('outputnode.bold_mask', 'inputnode.bold_mask')]),
(bold_sdc_wf, bold_mni_trans_wf, [
('outputnode.out_warp', 'inputnode.fieldwarp')]),
(bold_mni_trans_wf, outputnode, [('outputnode.bold_mni', 'bold_mni'),
('outputnode.bold_mask_mni', 'bold_mask_mni')]),
(bold_bold_trans_wf, carpetplot_wf, [
('outputnode.bold', 'inputnode.bold'),
('outputnode.bold_mask', 'inputnode.bold_mask')]),
(inputnode, carpetplot_wf, [
('t1_2_mni_reverse_transform', 'inputnode.t1_2_mni_reverse_transform')]),
(bold_reg_wf, carpetplot_wf, [
('outputnode.itk_t1_to_bold', 'inputnode.t1_bold_xform')]),
(bold_confounds_wf, carpetplot_wf, [
('outputnode.confounds_file', 'inputnode.confounds_file')]),
])
if use_aroma:
# ICA-AROMA workflow
# Internally resamples to MNI152 Linear (2006)
from .confounds import init_ica_aroma_wf
from ...interfaces import JoinTSVColumns
ica_aroma_wf = init_ica_aroma_wf(
template=template,
metadata=metadata,
mem_gb=mem_gb['resampled'],
omp_nthreads=omp_nthreads,
use_fieldwarp=fmaps is not None,
ignore_aroma_err=ignore_aroma_err,
aroma_melodic_dim=aroma_melodic_dim,
name='ica_aroma_wf')
join = pe.Node(JoinTSVColumns(), name='aroma_confounds')
workflow.disconnect([
(bold_confounds_wf, outputnode, [
('outputnode.confounds_file', 'confounds'),
]),
])
workflow.connect([
(inputnode, ica_aroma_wf, [
('bold_file', 'inputnode.name_source'),
('t1_2_mni_forward_transform', 'inputnode.t1_2_mni_forward_transform')]),
(bold_split, ica_aroma_wf, [
('out_files', 'inputnode.bold_split')]),
(bold_hmc_wf, ica_aroma_wf, [
('outputnode.movpar_file', 'inputnode.movpar_file'),
('outputnode.xforms', 'inputnode.hmc_xforms')]),
(bold_reg_wf, ica_aroma_wf, [
('outputnode.itk_bold_to_t1', 'inputnode.itk_bold_to_t1')]),
(bold_bold_trans_wf, ica_aroma_wf, [
('outputnode.bold_mask', 'inputnode.bold_mask')]),
(bold_sdc_wf, ica_aroma_wf, [
('outputnode.out_warp', 'inputnode.fieldwarp')]),
(bold_confounds_wf, join, [
('outputnode.confounds_file', 'in_file')]),
(ica_aroma_wf, join,
[('outputnode.aroma_confounds', 'join_file')]),
(ica_aroma_wf, outputnode,
[('outputnode.aroma_noise_ics', 'aroma_noise_ics'),
('outputnode.melodic_mix', 'melodic_mix'),
('outputnode.nonaggr_denoised_file', 'nonaggr_denoised_file')]),
(join, outputnode, [('out_file', 'confounds')]),
])
# SURFACES ##################################################################################
if freesurfer and any(space.startswith('fs') for space in output_spaces):
LOGGER.log(25, 'Creating BOLD surface-sampling workflow.')
bold_surf_wf = init_bold_surf_wf(mem_gb=mem_gb['resampled'],
output_spaces=output_spaces,
medial_surface_nan=medial_surface_nan,
name='bold_surf_wf')
workflow.connect([
(inputnode, bold_surf_wf, [
('t1_preproc', 'inputnode.t1_preproc'),
('subjects_dir', 'inputnode.subjects_dir'),
('subject_id', 'inputnode.subject_id'),
('t1_2_fsnative_forward_transform', 'inputnode.t1_2_fsnative_forward_transform')]),
(bold_reg_wf, bold_surf_wf, [('outputnode.bold_t1', 'inputnode.source_file')]),
(bold_surf_wf, outputnode, [('outputnode.surfaces', 'surfaces')]),
])
# CIFTI output
if cifti_output and 'template' in output_spaces:
gen_cifti = pe.MapNode(GenerateCifti(), iterfield=["surface_target", "gifti_files"],
name="gen_cifti")
gen_cifti.inputs.TR = metadata.get("RepetitionTime")
workflow.connect([
(bold_surf_wf, gen_cifti, [
('targets.out', 'surface_target'),
('outputnode.surfaces', 'gifti_files')]),
(inputnode, gen_cifti, [('subjects_dir', 'subjects_dir')]),
(bold_mni_trans_wf, gen_cifti, [('outputnode.bold_mni', 'bold_file')]),
(gen_cifti, outputnode, [('out_file', 'bold_cifti'),
('variant', 'cifti_variant'),
('variant_key', 'cifti_variant_key')]),
])
# REPORTING ############################################################
ds_report_summary = pe.Node(
DerivativesDataSink(suffix='summary'),
name='ds_report_summary', run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_report_validation = pe.Node(
DerivativesDataSink(base_directory=reportlets_dir,
suffix='validation'),
name='ds_report_validation', run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(summary, ds_report_summary, [('out_report', 'in_file')]),
(bold_reference_wf, ds_report_validation, [
('outputnode.validation_report', 'in_file')]),
])
# Fill-in datasinks of reportlets seen so far
for node in workflow.list_node_names():
if node.split('.')[-1].startswith('ds_report'):
workflow.get_node(node).inputs.base_directory = reportlets_dir
workflow.get_node(node).inputs.source_file = ref_file
return workflow
def init_prepare_epi_wf(ants_nthreads, name="prepare_epi_wf"):
"""
This workflow takes in a set of EPI files with with the same phase
encoding direction and returns a single 3D volume ready to be used in
field distortion estimation.
The procedure involves: estimating a robust template using FreeSurfer's
'mri_robust_template', bias field correction using ANTs N4BiasFieldCorrection
and AFNI 3dUnifize, skullstripping using FSL BET and AFNI 3dAutomask,
and rigid coregistration to the reference using ANTs.
.. workflow ::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.fieldmap.unwarp import init_prepare_epi_wf
wf = init_prepare_epi_wf(ants_nthreads=8)
Inputs
fmaps
list of 3D or 4D NIfTI images
ref_brain
coregistration reference (skullstripped and bias field corrected)
Outputs
out_file
single 3D NIfTI file
"""
inputnode = pe.Node(niu.IdentityInterface(fields=['fmaps', 'ref_brain']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['out_file']),
name='outputnode')
split = pe.MapNode(fsl.Split(dimension='t'),
iterfield='in_file',
name='split')
merge = pe.Node(
StructuralReference(
auto_detect_sensitivity=True,
initial_timepoint=1,
fixed_timepoint=True, # Align to first image
intensity_scaling=True,
# 7-DOF (rigid + intensity)
no_iteration=True,
subsample_threshold=200,
out_file='template.nii.gz'),
name='merge')
enhance_and_skullstrip_epi_wf = init_enhance_and_skullstrip_epi_wf()
ants_settings = pkgr.resource_filename('fmriprep',
'data/translation_rigid.json')
fmap2ref_reg = pe.Node(ants.Registration(from_file=ants_settings,
output_warped_image=True,
num_threads=ants_nthreads),
name='fmap2ref_reg')
fmap2ref_reg.interface.num_threads = ants_nthreads
workflow = pe.Workflow(name=name)
def _flatten(l):
return [item for sublist in l for item in sublist]
workflow.connect([
(inputnode, split, [('fmaps', 'in_file')]),
(split, merge, [(('out_files', _flatten), 'in_files')]),
(merge, enhance_and_skullstrip_epi_wf, [('out_file',
'inputnode.in_file')]),
(enhance_and_skullstrip_epi_wf, fmap2ref_reg,
[('outputnode.skull_stripped_file', 'moving_image')]),
(inputnode, fmap2ref_reg, [('ref_brain', 'fixed_image')]),
(fmap2ref_reg, outputnode, [('warped_image', 'out_file')]),
])
return workflow
def init_bold_surf_wf(mem_gb,
output_spaces,
medial_surface_nan,
name='bold_surf_wf'):
"""
This workflow samples functional images to FreeSurfer surfaces
For each vertex, the cortical ribbon is sampled at six points (spaced 20% of thickness apart)
and averaged.
Outputs are in GIFTI format.
.. workflow::
:graph2use: colored
:simple_form: yes
from fmriprep.workflows.bold import init_bold_surf_wf
wf = init_bold_surf_wf(mem_gb=0.1,
output_spaces=['T1w', 'fsnative',
'template', 'fsaverage5'],
medial_surface_nan=False)
**Parameters**
output_spaces : list
List of output spaces functional images are to be resampled to
Target spaces beginning with ``fs`` will be selected for resampling,
such as ``fsaverage`` or related template spaces
If the list contains ``fsnative``, images will be resampled to the
individual subject's native surface
medial_surface_nan : bool
Replace medial wall values with NaNs on functional GIFTI files
**Inputs**
source_file
Motion-corrected BOLD series in T1 space
t1_preproc
Bias-corrected structural template image
subjects_dir
FreeSurfer SUBJECTS_DIR
subject_id
FreeSurfer subject ID
t1_2_fsnative_forward_transform
LTA-style affine matrix translating from T1w to FreeSurfer-conformed subject space
**Outputs**
surfaces
BOLD series, resampled to FreeSurfer surfaces
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'source_file', 't1_preproc', 'subject_id', 'subjects_dir',
't1_2_fsnative_forward_transform'
]),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['surfaces']),
name='outputnode')
spaces = [space for space in output_spaces if space.startswith('fs')]
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
targets = pe.MapNode(niu.Function(function=select_target),
iterfield=['space'],
name='targets',
mem_gb=DEFAULT_MEMORY_MIN_GB)
targets.inputs.space = spaces
# Rename the source file to the output space to simplify naming later
rename_src = pe.MapNode(niu.Rename(format_string='%(subject)s',
keep_ext=True),
iterfield='subject',
name='rename_src',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
rename_src.inputs.subject = spaces
resampling_xfm = pe.Node(fs.utils.LTAConvert(in_lta='identity.nofile',
out_lta=True),
name='resampling_xfm')
set_xfm_source = pe.Node(ConcatenateLTA(out_type='RAS2RAS'),
name='set_xfm_source')
sampler = pe.MapNode(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'),
iterfield=['source_file', 'target_subject'],
iterables=('hemi', ['lh', 'rh']),
name='sampler',
mem_gb=mem_gb * 3)
medial_nans = pe.MapNode(MedialNaNs(),
iterfield=['in_file', 'target_subject'],
name='medial_nans',
mem_gb=DEFAULT_MEMORY_MIN_GB)
merger = pe.JoinNode(niu.Merge(1, ravel_inputs=True),
name='merger',
joinsource='sampler',
joinfield=['in1'],
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
update_metadata = pe.MapNode(GiftiSetAnatomicalStructure(),
iterfield='in_file',
name='update_metadata',
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, targets, [('subject_id', 'subject_id')]),
(inputnode, rename_src, [('source_file', 'in_file')]),
(inputnode, resampling_xfm, [('source_file', 'source_file'),
('t1_preproc', 'target_file')]),
(inputnode, set_xfm_source, [('t1_2_fsnative_forward_transform',
'in_lta2')]),
(resampling_xfm, set_xfm_source, [('out_lta', 'in_lta1')]),
(inputnode, sampler, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(set_xfm_source, sampler, [('out_file', 'reg_file')]),
(targets, sampler, [('out', 'target_subject')]),
(rename_src, sampler, [('out_file', 'source_file')]),
(merger, update_metadata, [('out', 'in_file')]),
(update_metadata, outputnode, [('out_file', 'surfaces')]),
])
if medial_surface_nan:
workflow.connect([
(inputnode, medial_nans, [('subjects_dir', 'subjects_dir')]),
(sampler, medial_nans, [('out_file', 'in_file')]),
(targets, medial_nans, [('out', 'target_subject')]),
(medial_nans, merger, [('out', 'in1')]),
])
else:
workflow.connect(sampler, 'out_file', merger, 'in1')
return workflow
def init_bold_mni_trans_wf(template,
mem_gb,
omp_nthreads,
name='bold_mni_trans_wf',
template_out_grid='2mm',
use_compression=True,
use_fieldwarp=False):
"""
This workflow samples functional images to the MNI template in a "single shot"
from the original BOLD series.
.. workflow::
:graph2use: colored
:simple_form: yes
from fmriprep.workflows.bold import init_bold_mni_trans_wf
wf = init_bold_mni_trans_wf(template='MNI152NLin2009cAsym',
mem_gb=3,
omp_nthreads=1,
template_out_grid='native')
**Parameters**
template : str
Name of template targeted by ``template`` output space
mem_gb : float
Size of BOLD file in GB
omp_nthreads : int
Maximum number of threads an individual process may use
name : str
Name of workflow (default: ``bold_mni_trans_wf``)
template_out_grid : str
Keyword ('native', '1mm' or '2mm') or path of custom reference
image for normalization.
use_compression : bool
Save registered BOLD series as ``.nii.gz``
use_fieldwarp : bool
Include SDC warp in single-shot transform from BOLD to MNI
**Inputs**
itk_bold_to_t1
Affine transform from ``ref_bold_brain`` to T1 space (ITK format)
t1_2_mni_forward_transform
ANTs-compatible affine-and-warp transform file
bold_split
Individual 3D volumes, not motion corrected
bold_mask
Skull-stripping mask of reference image
name_source
BOLD series NIfTI file
Used to recover original information lost during processing
hmc_xforms
List of affine transforms aligning each volume to ``ref_image`` in ITK format
fieldwarp
a :abbr:`DFM (displacements field map)` in ITK format
**Outputs**
bold_mni
BOLD series, resampled to template space
bold_mask_mni
BOLD series mask in template space
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'itk_bold_to_t1', 't1_2_mni_forward_transform', 'name_source',
'bold_split', 'bold_mask', 'hmc_xforms', 'fieldwarp'
]),
name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(fields=['bold_mni', 'bold_mask_mni']),
name='outputnode')
def _aslist(in_value):
if isinstance(in_value, list):
return in_value
return [in_value]
gen_ref = pe.Node(GenerateSamplingReference(), name='gen_ref',
mem_gb=0.3) # 256x256x256 * 64 / 8 ~ 150MB)
template_str = nid.TEMPLATE_MAP[template]
gen_ref.inputs.fixed_image = op.join(nid.get_dataset(template_str),
'1mm_T1.nii.gz')
mask_mni_tfm = pe.Node(ApplyTransforms(interpolation='MultiLabel',
float=True),
name='mask_mni_tfm',
mem_gb=1)
# Write corrected file in the designated output dir
mask_merge_tfms = pe.Node(niu.Merge(2),
name='mask_merge_tfms',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
nxforms = 4 if use_fieldwarp else 3
merge_xforms = pe.Node(niu.Merge(nxforms),
name='merge_xforms',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([(inputnode, merge_xforms, [('hmc_xforms',
'in%d' % nxforms)])])
if use_fieldwarp:
workflow.connect([(inputnode, merge_xforms, [('fieldwarp', 'in3')])])
workflow.connect([
(inputnode, gen_ref, [(('bold_split', _first), 'moving_image')]),
(inputnode, mask_merge_tfms, [('t1_2_mni_forward_transform', 'in1'),
(('itk_bold_to_t1', _aslist), 'in2')]),
(mask_merge_tfms, mask_mni_tfm, [('out', 'transforms')]),
(mask_mni_tfm, outputnode, [('output_image', 'bold_mask_mni')]),
(inputnode, mask_mni_tfm, [('bold_mask', 'input_image')])
])
bold_to_mni_transform = pe.Node(MultiApplyTransforms(
interpolation="LanczosWindowedSinc", float=True, copy_dtype=True),
name='bold_to_mni_transform',
mem_gb=mem_gb * 3 * omp_nthreads,
n_procs=omp_nthreads)
merge = pe.Node(Merge(compress=use_compression),
name='merge',
mem_gb=mem_gb * 3)
workflow.connect([
(inputnode, merge_xforms, [('t1_2_mni_forward_transform', 'in1'),
(('itk_bold_to_t1', _aslist), 'in2')]),
(merge_xforms, bold_to_mni_transform, [('out', 'transforms')]),
(inputnode, merge, [('name_source', 'header_source')]),
(inputnode, bold_to_mni_transform, [('bold_split', 'input_image')]),
(bold_to_mni_transform, merge, [('out_files', 'in_files')]),
(merge, outputnode, [('out_file', 'bold_mni')]),
])
if template_out_grid == 'native':
workflow.connect([
(gen_ref, mask_mni_tfm, [('out_file', 'reference_image')]),
(gen_ref, bold_to_mni_transform, [('out_file', 'reference_image')
]),
])
elif template_out_grid == '1mm' or template_out_grid == '2mm':
mask_mni_tfm.inputs.reference_image = op.join(
nid.get_dataset(template_str),
'%s_brainmask.nii.gz' % template_out_grid)
bold_to_mni_transform.inputs.reference_image = op.join(
nid.get_dataset(template_str), '%s_T1.nii.gz' % template_out_grid)
else:
mask_mni_tfm.inputs.reference_image = template_out_grid
bold_to_mni_transform.inputs.reference_image = template_out_grid
return workflow
def init_anat_derivatives_wf(output_dir,
output_spaces,
template,
freesurfer,
name='anat_derivatives_wf'):
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'source_file', 't1_preproc', 't1_mask', 't1_seg', 't1_tpms',
't1_2_mni_forward_transform', 't1_2_mni', 'mni_mask', 'mni_seg',
'mni_tpms', 'surfaces'
]),
name='inputnode')
ds_t1_preproc = pe.Node(DerivativesDataSink(base_directory=output_dir,
suffix='preproc'),
name='ds_t1_preproc',
run_without_submitting=True)
ds_t1_mask = pe.Node(DerivativesDataSink(base_directory=output_dir,
suffix='brainmask'),
name='ds_t1_mask',
run_without_submitting=True)
ds_t1_seg = pe.Node(DerivativesDataSink(base_directory=output_dir,
suffix='dtissue'),
name='ds_t1_seg',
run_without_submitting=True)
ds_t1_tpms = pe.Node(DerivativesDataSink(
base_directory=output_dir, suffix='class-{extra_value}_probtissue'),
name='ds_t1_tpms',
run_without_submitting=True)
ds_t1_tpms.inputs.extra_values = ['CSF', 'GM', 'WM']
suffix_fmt = 'space-{}_{}'.format
ds_t1_mni = pe.Node(DerivativesDataSink(base_directory=output_dir,
suffix=suffix_fmt(
template, 'preproc')),
name='ds_t1_mni',
run_without_submitting=True)
ds_mni_mask = pe.Node(DerivativesDataSink(base_directory=output_dir,
suffix=suffix_fmt(
template, 'brainmask')),
name='ds_mni_mask',
run_without_submitting=True)
ds_mni_seg = pe.Node(DerivativesDataSink(base_directory=output_dir,
suffix=suffix_fmt(
template, 'dtissue')),
name='ds_mni_seg',
run_without_submitting=True)
ds_mni_tpms = pe.Node(DerivativesDataSink(
base_directory=output_dir,
suffix=suffix_fmt(template, 'class-{extra_value}_probtissue')),
name='ds_mni_tpms',
run_without_submitting=True)
ds_mni_tpms.inputs.extra_values = ['CSF', 'GM', 'WM']
ds_t1_mni_warp = pe.Node(DerivativesDataSink(base_directory=output_dir,
suffix=suffix_fmt(
template, 'warp')),
name='ds_t1_mni_warp',
run_without_submitting=True)
def get_gifti_name(in_file):
import os
import re
in_format = re.compile(r'(?P<LR>[lr])h.(?P<surf>.+)_converted.gii')
name = os.path.basename(in_file)
info = in_format.match(name).groupdict()
info['LR'] = info['LR'].upper()
return '{surf}.{LR}.surf'.format(**info)
name_surfs = pe.MapNode(niu.Function(function=get_gifti_name),
iterfield='in_file',
name='name_surfs')
ds_surfs = pe.MapNode(DerivativesDataSink(base_directory=output_dir),
iterfield=['in_file', 'suffix'],
name='ds_surfs',
run_without_submitting=True)
workflow.connect([
(inputnode, ds_t1_preproc, [('source_file', 'source_file'),
('t1_preproc', 'in_file')]),
(inputnode, ds_t1_mask, [('source_file', 'source_file'),
('t1_mask', 'in_file')]),
(inputnode, ds_t1_seg, [('source_file', 'source_file'),
('t1_seg', 'in_file')]),
(inputnode, ds_t1_tpms, [('source_file', 'source_file'),
('t1_tpms', 'in_file')]),
])
if freesurfer:
workflow.connect([
(inputnode, name_surfs, [('surfaces', 'in_file')]),
(inputnode, ds_surfs, [('source_file', 'source_file'),
('surfaces', 'in_file')]),
(name_surfs, ds_surfs, [('out', 'suffix')]),
])
if 'template' in output_spaces:
workflow.connect([
(inputnode, ds_t1_mni_warp, [('source_file', 'source_file'),
('t1_2_mni_forward_transform',
'in_file')]),
(inputnode, ds_t1_mni, [('source_file', 'source_file'),
('t1_2_mni', 'in_file')]),
(inputnode, ds_mni_mask, [('source_file', 'source_file'),
('mni_mask', 'in_file')]),
(inputnode, ds_mni_seg, [('source_file', 'source_file'),
('mni_seg', 'in_file')]),
(inputnode, ds_mni_tpms, [('source_file', 'source_file'),
('mni_tpms', 'in_file')]),
])
return workflow
def init_single_subject_wf(subject_id, task_id, name, ignore, debug, anat_only,
omp_nthreads, skull_strip_ants, reportlets_dir,
output_dir, bids_dir, freesurfer, output_spaces,
template, hires, bold2t1w_dof, fmap_bspline,
fmap_demean, use_syn, force_syn, output_grid_ref,
use_aroma, ignore_aroma_err):
"""
The adaptable fMRI preprocessing workflow
"""
if name == 'single_subject_wf':
# for documentation purposes
subject_data = {
'func': ['/completely/made/up/path/sub-01_task-nback_bold.nii.gz']
}
layout = None
else:
layout = BIDSLayout(bids_dir)
subject_data = collect_bids_data(bids_dir, subject_id, task_id)
if not anat_only and subject_data['func'] == []:
raise Exception(
"No BOLD images found for participant {} and task {}. "
"All workflows require BOLD images.".format(
subject_id, task_id if task_id else '<all>'))
if subject_data['t1w'] == []:
raise Exception(
"No T1w images found for participant {}. "
"All workflows require T1w images.".format(subject_id))
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['subjects_dir']),
name='inputnode')
bidssrc = pe.Node(BIDSDataGrabber(subject_data=subject_data,
anat_only=anat_only),
name='bidssrc')
# Preprocessing of T1w (includes registration to MNI)
anat_preproc_wf = init_anat_preproc_wf(name="anat_preproc_wf",
skull_strip_ants=skull_strip_ants,
output_spaces=output_spaces,
template=template,
debug=debug,
omp_nthreads=omp_nthreads,
freesurfer=freesurfer,
hires=hires,
reportlets_dir=reportlets_dir,
output_dir=output_dir)
workflow.connect([
(inputnode, anat_preproc_wf, [('subjects_dir',
'inputnode.subjects_dir')]),
(bidssrc, anat_preproc_wf, [('t1w', 'inputnode.t1w'),
('t2w', 'inputnode.t2w')]),
])
if anat_only:
return workflow
for bold_file in subject_data['func']:
func_preproc_wf = init_func_preproc_wf(
bold_file=bold_file,
layout=layout,
ignore=ignore,
freesurfer=freesurfer,
bold2t1w_dof=bold2t1w_dof,
reportlets_dir=reportlets_dir,
output_spaces=output_spaces,
template=template,
output_dir=output_dir,
omp_nthreads=omp_nthreads,
fmap_bspline=fmap_bspline,
fmap_demean=fmap_demean,
use_syn=use_syn,
force_syn=force_syn,
debug=debug,
output_grid_ref=output_grid_ref,
use_aroma=use_aroma,
ignore_aroma_err=ignore_aroma_err)
workflow.connect([(anat_preproc_wf, func_preproc_wf,
[('outputnode.t1_preproc', 'inputnode.t1_preproc'),
('outputnode.t1_brain', 'inputnode.t1_brain'),
('outputnode.t1_mask', 'inputnode.t1_mask'),
('outputnode.t1_seg', 'inputnode.t1_seg'),
('outputnode.t1_tpms', 'inputnode.t1_tpms'),
('outputnode.t1_2_mni_forward_transform',
'inputnode.t1_2_mni_forward_transform'),
('outputnode.t1_2_mni_reverse_transform',
'inputnode.t1_2_mni_reverse_transform')])])
if freesurfer:
workflow.connect([
(anat_preproc_wf, func_preproc_wf,
[('outputnode.subjects_dir', 'inputnode.subjects_dir'),
('outputnode.subject_id', 'inputnode.subject_id'),
('outputnode.fs_2_t1_transform',
'inputnode.fs_2_t1_transform')]),
])
return workflow
def init_bold_preproc_trans_wf(mem_gb,
omp_nthreads,
name='bold_preproc_trans_wf',
use_compression=True,
use_fieldwarp=False,
split_file=False,
interpolation='LanczosWindowedSinc'):
"""
This workflow resamples the input fMRI in its native (original)
space in a "single shot" from the original BOLD series.
.. workflow::
:graph2use: colored
:simple_form: yes
from fmriprep.workflows.bold import init_bold_preproc_trans_wf
wf = init_bold_preproc_trans_wf(mem_gb=3, omp_nthreads=1)
**Parameters**
mem_gb : float
Size of BOLD file in GB
omp_nthreads : int
Maximum number of threads an individual process may use
name : str
Name of workflow (default: ``bold_mni_trans_wf``)
use_compression : bool
Save registered BOLD series as ``.nii.gz``
use_fieldwarp : bool
Include SDC warp in single-shot transform from BOLD to MNI
split_file : bool
Whether the input file should be splitted (it is a 4D file)
or it is a list of 3D files (default ``False``, do not split)
interpolation : str
Interpolation type to be used by ANTs' ``applyTransforms``
(default ``'LanczosWindowedSinc'``)
**Inputs**
bold_file
Individual 3D volumes, not motion corrected
bold_mask
Skull-stripping mask of reference image
name_source
BOLD series NIfTI file
Used to recover original information lost during processing
hmc_xforms
List of affine transforms aligning each volume to ``ref_image`` in ITK format
fieldwarp
a :abbr:`DFM (displacements field map)` in ITK format
**Outputs**
bold
BOLD series, resampled in native space, including all preprocessing
bold_mask
BOLD series mask calculated with the new time-series
bold_ref
BOLD reference image: an average-like 3D image of the time-series
bold_ref_brain
Same as ``bold_ref``, but once the brain mask has been applied
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'name_source', 'bold_file', 'bold_mask', 'hmc_xforms', 'fieldwarp'
]),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['bold', 'bold_mask', 'bold_ref', 'bold_ref_brain']),
name='outputnode')
bold_transform = pe.Node(MultiApplyTransforms(interpolation=interpolation,
float=True,
copy_dtype=True),
name='bold_transform',
mem_gb=mem_gb * 3 * omp_nthreads,
n_procs=omp_nthreads)
merge = pe.Node(Merge(compress=use_compression),
name='merge',
mem_gb=mem_gb * 3)
# Generate a new BOLD reference
bold_reference_wf = init_bold_reference_wf(omp_nthreads=omp_nthreads)
workflow.connect([
(inputnode, merge, [('name_source', 'header_source')]),
(bold_transform, merge, [('out_files', 'in_files')]),
(merge, bold_reference_wf, [('out_file', 'inputnode.bold_file')]),
(merge, outputnode, [('out_file', 'bold')]),
(bold_reference_wf, outputnode,
[('outputnode.ref_image', 'bold_ref'),
('outputnode.ref_image_brain', 'bold_ref_brain'),
('outputnode.bold_mask', 'bold_mask')]),
])
# Input file is not splitted
if split_file:
bold_split = pe.Node(FSLSplit(dimension='t'),
name='bold_split',
mem_gb=mem_gb * 3)
workflow.connect([(inputnode, bold_split, [('bold_file', 'in_file')]),
(bold_split, bold_transform, [
('out_files', 'input_image'),
(('out_files', _first), 'reference_image'),
])])
else:
workflow.connect([
(inputnode, bold_transform, [('bold_file', 'input_image'),
(('bold_file', _first),
'reference_image')]),
])
if use_fieldwarp:
merge_xforms = pe.Node(niu.Merge(2),
name='merge_xforms',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, merge_xforms, [('fieldwarp', 'in1'),
('hmc_xforms', 'in2')]),
(merge_xforms, bold_transform, [('out', 'transforms')]),
])
else:
def _aslist(val):
return [val]
workflow.connect([
(inputnode, bold_transform, [(('hmc_xforms', _aslist),
'transforms')]),
])
# Code ready to generate a pre/post processing report
# bold_bold_report_wf = init_bold_preproc_report_wf(
# mem_gb=mem_gb['resampled'],
# reportlets_dir=reportlets_dir
# )
# workflow.connect([
# (inputnode, bold_bold_report_wf, [
# ('bold_file', 'inputnode.name_source'),
# ('bold_file', 'inputnode.in_pre')]), # This should be after STC
# (bold_bold_trans_wf, bold_bold_report_wf, [
# ('outputnode.bold', 'inputnode.in_post')]),
# ])
return workflow
def init_pepolar_unwarp_wf(fmaps,
bold_file,
omp_nthreads,
layout=None,
fmaps_pes=None,
bold_file_pe=None,
name="pepolar_unwarp_wf"):
"""
This workflow takes in a set of EPI files with opposite phase encoding
direction than the target file and calculates a displacements field
(in other words, an ANTs-compatible warp file).
This procedure works if there is only one '_epi' file is present
(as long as it has the opposite phase encoding direction to the target
file). The target file will be used to estimate the field distortion.
However, if there is another '_epi' file present with a matching
phase encoding direction to the target it will be used instead.
Currently, different phase encoding dimension in the target file and the
'_epi' file(s) (for example 'i' and 'j') is not supported.
The warp field correcting for the distortions is estimated using AFNI's
3dQwarp, with displacement estimation limited to the target file phase
encoding direction.
It also calculates a new mask for the input dataset that takes into
account the distortions.
.. workflow ::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.fieldmap.unwarp import init_pepolar_unwarp_wf
wf = init_pepolar_unwarp_wf(fmaps=['/dataset/sub-01/fmap/sub-01_epi.nii.gz'],
fmaps_pes=['j-'],
bold_file='/dataset/sub-01/func/sub-01_task-rest_bold.nii.gz',
bold_file_pe='j',
omp_nthreads=8)
Inputs
in_reference
the reference image
in_reference_brain
the reference image skullstripped
in_mask
a brain mask corresponding to ``in_reference``
name_source
not used, kept for signature compatibility with ``init_sdc_unwarp_wf``
Outputs
out_reference
the ``in_reference`` after unwarping
out_reference_brain
the ``in_reference`` after unwarping and skullstripping
out_warp
the corresponding :abbr:`DFM (displacements field map)` compatible with
ANTs
out_mask
mask of the unwarped input file
out_mask_report
reportlet for the skullstripping
"""
if not bold_file_pe:
bold_file_pe = layout.get_metadata(bold_file)["PhaseEncodingDirection"]
usable_fieldmaps_matching_pe = []
usable_fieldmaps_opposite_pe = []
args = '-noXdis -noYdis -noZdis'
rm_arg = {'i': '-noXdis', 'j': '-noYdis', 'k': '-noZdis'}[bold_file_pe[0]]
args = args.replace(rm_arg, '')
for i, fmap in enumerate(fmaps):
if fmaps_pes:
fmap_pe = fmaps_pes[i]
else:
fmap_pe = layout.get_metadata(fmap)["PhaseEncodingDirection"]
if fmap_pe[0] == bold_file_pe[0]:
if len(fmap_pe) != len(bold_file_pe):
add_list = usable_fieldmaps_opposite_pe
else:
add_list = usable_fieldmaps_matching_pe
add_list.append(fmap)
if len(usable_fieldmaps_opposite_pe) == 0:
raise Exception("None of the discovered fieldmaps has the right "
"phase encoding direction. Possibly a problem with "
"metadata. If not, rerun with '--ignore fieldmaps' to "
"skip distortion correction step.")
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'in_reference', 'in_reference_brain', 'in_mask', 'name_source'
]),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
'out_reference', 'out_reference_brain', 'out_warp', 'out_mask',
'out_mask_report'
]),
name='outputnode')
prepare_epi_opposite_wf = init_prepare_epi_wf(
ants_nthreads=omp_nthreads, name="prepare_epi_opposite_wf")
prepare_epi_opposite_wf.inputs.inputnode.fmaps = usable_fieldmaps_opposite_pe
qwarp = pe.Node(afni.QwarpPlusMinus(
pblur=[0.05, 0.05],
blur=[-1, -1],
noweight=True,
minpatch=9,
nopadWARP=True,
environ={'OMP_NUM_THREADS': str(omp_nthreads)},
args=args),
name='qwarp')
qwarp.interface.num_threads = omp_nthreads
workflow.connect([
(inputnode, prepare_epi_opposite_wf, [('in_reference_brain',
'inputnode.ref_brain')]),
(prepare_epi_opposite_wf, qwarp, [('outputnode.out_file', 'base_file')
]),
])
if usable_fieldmaps_matching_pe:
prepare_epi_matching_wf = init_prepare_epi_wf(
ants_nthreads=omp_nthreads, name="prepare_epi_matching_wf")
prepare_epi_matching_wf.inputs.inputnode.fmaps = usable_fieldmaps_matching_pe
workflow.connect([
(inputnode, prepare_epi_matching_wf, [('in_reference_brain',
'inputnode.ref_brain')]),
(prepare_epi_matching_wf, qwarp, [('outputnode.out_file',
'source_file')]),
])
else:
workflow.connect([(inputnode, qwarp, [('in_reference_brain',
'source_file')])])
to_ants = pe.Node(niu.Function(function=_fix_hdr), name='to_ants')
cphdr_warp = pe.Node(CopyHeader(), name='cphdr_warp')
unwarp_reference = pe.Node(ANTSApplyTransformsRPT(
dimension=3,
generate_report=False,
float=True,
interpolation='LanczosWindowedSinc'),
name='unwarp_reference')
enhance_and_skullstrip_epi_wf = init_enhance_and_skullstrip_epi_wf()
workflow.connect([
(inputnode, cphdr_warp, [('in_reference', 'hdr_file')]),
(qwarp, cphdr_warp, [('source_warp', 'in_file')]),
(cphdr_warp, to_ants, [('out_file', 'in_file')]),
(to_ants, unwarp_reference, [('out', 'transforms')]),
(inputnode, unwarp_reference, [('in_reference', 'reference_image'),
('in_reference', 'input_image')]),
(unwarp_reference, enhance_and_skullstrip_epi_wf,
[('output_image', 'inputnode.in_file')]),
(unwarp_reference, outputnode, [('output_image', 'out_reference')]),
(enhance_and_skullstrip_epi_wf, outputnode,
[('outputnode.mask_file', 'out_mask'),
('outputnode.out_report', 'out_report'),
('outputnode.skull_stripped_file', 'out_reference_brain')]),
(to_ants, outputnode, [('out', 'out_warp')]),
])
return workflow
def init_bold_preproc_report_wf(mem_gb,
reportlets_dir,
name='bold_preproc_report_wf'):
"""
This workflow generates and saves a reportlet showing the effect of resampling
the BOLD signal using the standard deviation maps.
.. workflow::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.bold.resampling import init_bold_preproc_report_wf
wf = init_bold_preproc_report_wf(mem_gb=1, reportlets_dir='.')
**Parameters**
mem_gb : float
Size of BOLD file in GB
reportlets_dir : str
Directory in which to save reportlets
name : str, optional
Workflow name (default: bold_preproc_report_wf)
**Inputs**
in_pre
BOLD time-series, before resampling
in_post
BOLD time-series, after resampling
name_source
BOLD series NIfTI file
Used to recover original information lost during processing
"""
from niworkflows.nipype.algorithms.confounds import TSNR
from niworkflows.interfaces import SimpleBeforeAfter
from ...interfaces import DerivativesDataSink
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(fields=['in_pre', 'in_post', 'name_source']),
name='inputnode')
pre_tsnr = pe.Node(TSNR(), name='pre_tsnr', mem_gb=mem_gb * 4.5)
pos_tsnr = pe.Node(TSNR(), name='pos_tsnr', mem_gb=mem_gb * 4.5)
bold_rpt = pe.Node(SimpleBeforeAfter(), name='bold_rpt', mem_gb=0.1)
bold_rpt_ds = pe.Node(DerivativesDataSink(base_directory=reportlets_dir,
suffix='variant-preproc'),
name='bold_rpt_ds',
mem_gb=DEFAULT_MEMORY_MIN_GB,
run_without_submitting=True)
workflow.connect([
(inputnode, bold_rpt_ds, [('name_source', 'source_file')]),
(inputnode, pre_tsnr, [('in_pre', 'in_file')]),
(inputnode, pos_tsnr, [('in_post', 'in_file')]),
(pre_tsnr, bold_rpt, [('stddev_file', 'before')]),
(pos_tsnr, bold_rpt, [('stddev_file', 'after')]),
(bold_rpt, bold_rpt_ds, [('out_report', 'in_file')]),
])
return workflow
def init_sdc_unwarp_wf(reportlets_dir,
omp_nthreads,
fmap_bspline,
fmap_demean,
debug,
name='sdc_unwarp_wf'):
"""
This workflow takes in a displacements fieldmap and calculates the corresponding
displacements field (in other words, an ANTs-compatible warp file).
It also calculates a new mask for the input dataset that takes into account the distortions.
The mask is restricted to the field of view of the fieldmap since outside of it corrections
could not be performed.
.. workflow ::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.fieldmap.unwarp import init_sdc_unwarp_wf
wf = init_sdc_unwarp_wf(reportlets_dir='.', omp_nthreads=8,
fmap_bspline=False, fmap_demean=True,
debug=False)
Inputs
in_reference
the reference image
in_mask
a brain mask corresponding to ``in_reference``
name_source
path to the original _bold file being unwarped
fmap
the fieldmap in Hz
fmap_ref
the reference (anatomical) image corresponding to ``fmap``
fmap_mask
a brain mask corresponding to ``fmap``
Outputs
out_reference
the ``in_reference`` after unwarping
out_reference_brain
the ``in_reference`` after unwarping and skullstripping
out_warp
the corresponding :abbr:`DFM (displacements field map)` compatible with
ANTs
out_jacobian
the jacobian of the field (for drop-out alleviation)
out_mask
mask of the unwarped input file
out_mask_report
reportled for the skullstripping
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'in_reference', 'in_reference_brain', 'in_mask', 'name_source',
'fmap_ref', 'fmap_mask', 'fmap'
]),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
'out_reference', 'out_reference_brain', 'out_warp', 'out_mask',
'out_jacobian', 'out_mask_report'
]),
name='outputnode')
meta = pe.Node(ReadSidecarJSON(), name='meta')
# Register the reference of the fieldmap to the reference
# of the target image (the one that shall be corrected)
ants_settings = pkgr.resource_filename('fmriprep',
'data/fmap-any_registration.json')
if debug:
ants_settings = pkgr.resource_filename(
'fmriprep', 'data/fmap-any_registration_testing.json')
fmap2ref_reg = pe.Node(ANTSRegistrationRPT(
generate_report=True,
from_file=ants_settings,
output_inverse_warped_image=True,
output_warped_image=True,
num_threads=omp_nthreads),
name='fmap2ref_reg')
fmap2ref_reg.interface.num_threads = omp_nthreads
ds_reg = pe.Node(DerivativesDataSink(base_directory=reportlets_dir,
suffix='fmap_reg'),
name='ds_reg')
# Map the VSM into the EPI space
fmap2ref_apply = pe.Node(ANTSApplyTransformsRPT(generate_report=True,
dimension=3,
interpolation='BSpline',
float=True),
name='fmap2ref_apply')
fmap_mask2ref_apply = pe.Node(ANTSApplyTransformsRPT(
generate_report=False,
dimension=3,
interpolation='NearestNeighbor',
float=True),
name='fmap_mask2ref_apply')
ds_reg_vsm = pe.Node(DerivativesDataSink(base_directory=reportlets_dir,
suffix='fmap_reg_vsm'),
name='ds_reg_vsm')
# Fieldmap to rads and then to voxels (VSM - voxel shift map)
torads = pe.Node(niu.Function(function=_hz2rads), name='torads')
gen_vsm = pe.Node(fsl.FUGUE(save_unmasked_shift=True), name='gen_vsm')
# Convert the VSM into a DFM (displacements field map)
# or: FUGUE shift to ANTS warping.
vsm2dfm = pe.Node(itk.FUGUEvsm2ANTSwarp(), name='vsm2dfm')
jac_dfm = pe.Node(ants.CreateJacobianDeterminantImage(
imageDimension=3, outputImage='jacobian.nii.gz'),
name='jac_dfm')
unwarp_reference = pe.Node(ANTSApplyTransformsRPT(
dimension=3,
generate_report=False,
float=True,
interpolation='LanczosWindowedSinc'),
name='unwarp_reference')
fieldmap_fov_mask = pe.Node(niu.Function(function=_fill_with_ones),
name='fieldmap_fov_mask')
fmap_fov2ref_apply = pe.Node(ANTSApplyTransformsRPT(
generate_report=False,
dimension=3,
interpolation='NearestNeighbor',
float=True),
name='fmap_fov2ref_apply')
apply_fov_mask = pe.Node(fsl.ApplyMask(), name="apply_fov_mask")
enhance_and_skullstrip_epi_wf = init_enhance_and_skullstrip_epi_wf()
workflow.connect([
(inputnode, meta, [('name_source', 'in_file')]),
(inputnode, fmap2ref_reg, [('fmap_ref', 'moving_image')]),
(inputnode, fmap2ref_apply, [('in_reference', 'reference_image')]),
(fmap2ref_reg, fmap2ref_apply, [('composite_transform', 'transforms')
]),
(inputnode, fmap_mask2ref_apply, [('in_reference', 'reference_image')
]),
(fmap2ref_reg, fmap_mask2ref_apply, [('composite_transform',
'transforms')]),
(inputnode, ds_reg_vsm, [('name_source', 'source_file')]),
(fmap2ref_apply, ds_reg_vsm, [('out_report', 'in_file')]),
(inputnode, fmap2ref_reg, [('in_reference_brain', 'fixed_image')]),
(inputnode, ds_reg, [('name_source', 'source_file')]),
(fmap2ref_reg, ds_reg, [('out_report', 'in_file')]),
(inputnode, fmap2ref_apply, [('fmap', 'input_image')]),
(inputnode, fmap_mask2ref_apply, [('fmap_mask', 'input_image')]),
(fmap2ref_apply, torads, [('output_image', 'in_file')]),
(meta, gen_vsm, [(('out_dict', _get_ec), 'dwell_time'),
(('out_dict', _get_pedir_fugue), 'unwarp_direction')
]),
(meta, vsm2dfm, [(('out_dict', _get_pedir_bids), 'pe_dir')]),
(torads, gen_vsm, [('out', 'fmap_in_file')]),
(vsm2dfm, unwarp_reference, [('out_file', 'transforms')]),
(inputnode, unwarp_reference, [('in_reference', 'reference_image')]),
(inputnode, unwarp_reference, [('in_reference', 'input_image')]),
(vsm2dfm, outputnode, [('out_file', 'out_warp')]),
(vsm2dfm, jac_dfm, [('out_file', 'deformationField')]),
(inputnode, fieldmap_fov_mask, [('fmap_ref', 'in_file')]),
(fieldmap_fov_mask, fmap_fov2ref_apply, [('out', 'input_image')]),
(inputnode, fmap_fov2ref_apply, [('in_reference', 'reference_image')]),
(fmap2ref_reg, fmap_fov2ref_apply, [('composite_transform',
'transforms')]),
(fmap_fov2ref_apply, apply_fov_mask, [('output_image', 'mask_file')]),
(unwarp_reference, apply_fov_mask, [('output_image', 'in_file')]),
(apply_fov_mask, enhance_and_skullstrip_epi_wf,
[('out_file', 'inputnode.in_file')]),
(apply_fov_mask, outputnode, [('out_file', 'out_reference')]),
(enhance_and_skullstrip_epi_wf, outputnode,
[('outputnode.mask_file', 'out_mask'),
('outputnode.out_report', 'out_mask_report'),
('outputnode.skull_stripped_file', 'out_reference_brain')]),
(jac_dfm, outputnode, [('jacobian_image', 'out_jacobian')]),
])
if not fmap_bspline:
workflow.connect([(fmap_mask2ref_apply, gen_vsm, [('output_image',
'mask_file')])])
if fmap_demean:
# Demean within mask
demean = pe.Node(niu.Function(function=_demean), name='demean')
workflow.connect([
(gen_vsm, demean, [('shift_out_file', 'in_file')]),
(fmap_mask2ref_apply, demean, [('output_image', 'in_mask')]),
(demean, vsm2dfm, [('out', 'in_file')]),
])
else:
workflow.connect([
(gen_vsm, vsm2dfm, [('shift_out_file', 'in_file')]),
])
return workflow
