def test_ReadSidecarJSON_connection(testdata_dir, field):
"""
This test prevents regressions of #333
"""
from nipype.pipeline import engine as pe
from nipype.interfaces import utility as niu
from niworkflows.interfaces.bids import ReadSidecarJSON
reg_fields = ['RepetitionTime']
n = pe.Node(ReadSidecarJSON(fields=reg_fields), name='node')
n.inputs.in_file = str(testdata_dir / 'ds054' / 'sub-100185' / 'fmap' /
'sub-100185_phasediff.nii.gz')
o = pe.Node(niu.IdentityInterface(fields=['out_port']), name='o')
wf = pe.Workflow(name='json')
if field in reg_fields: # This should work
wf.connect([
(n, o, [(field, 'out_port')]),
])
else:
with pytest.raises(Exception,
match=r'.*Some connections were not found.*'):
wf.connect([
(n, o, [(field, 'out_port')]),
])
def test_ReadSidecarJSON_connection(testdata_dir, field):
"""
This test prevents regressions of #333
"""
from nipype.pipeline import engine as pe
from nipype.interfaces import utility as niu
from niworkflows.interfaces.bids import ReadSidecarJSON
reg_fields = ["RepetitionTime"]
n = pe.Node(ReadSidecarJSON(fields=reg_fields), name="node")
n.inputs.in_file = str(testdata_dir / "ds054" / "sub-100185" / "fmap" /
"sub-100185_phasediff.nii.gz")
o = pe.Node(niu.IdentityInterface(fields=["out_port"]), name="o")
wf = pe.Workflow(name="json")
if field in reg_fields: # This should work
wf.connect([(n, o, [(field, "out_port")])])
else:
with pytest.raises(Exception,
match=r".*Some connections were not found.*"):
wf.connect([(n, o, [(field, "out_port")])])
def init_phdiff_wf(omp_nthreads, phasetype='phasediff', name='phdiff_wf'):
"""
Estimates the fieldmap using a phase-difference image and one or more
magnitude images corresponding to two or more :abbr:`GRE (Gradient Echo sequence)`
acquisitions. The `original code was taken from nipype
<https://github.com/nipy/nipype/blob/master/nipype/workflows/dmri/fsl/artifacts.py#L514>`_.
.. workflow ::
:graph2use: orig
:simple_form: yes
from fmriprep.workflows.fieldmap.phdiff import init_phdiff_wf
wf = init_phdiff_wf(omp_nthreads=1)
Outputs::
outputnode.fmap_ref - The average magnitude image, skull-stripped
outputnode.fmap_mask - The brain mask applied to the fieldmap
outputnode.fmap - The estimated fieldmap in Hz
"""
workflow = Workflow(name=name)
workflow.__desc__ = """\
A deformation field to correct for susceptibility distortions was estimated
based on a field map that was co-registered to the BOLD reference,
using a custom workflow of *fMRIPrep* derived from D. Greve's `epidewarp.fsl`
[script](http://www.nmr.mgh.harvard.edu/~greve/fbirn/b0/epidewarp.fsl) and
further improvements of HCP Pipelines [@hcppipelines].
"""
inputnode = pe.Node(
niu.IdentityInterface(fields=['magnitude', 'phasediff']),
name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(fields=['fmap', 'fmap_ref', 'fmap_mask']),
name='outputnode')
def _pick1st(inlist):
return inlist[0]
# Read phasediff echo times
meta = pe.Node(ReadSidecarJSON(),
name='meta',
mem_gb=0.01,
run_without_submitting=True)
# Merge input magnitude images
magmrg = pe.Node(IntraModalMerge(), name='magmrg')
# de-gradient the fields ("bias/illumination artifact")
n4 = pe.Node(ants.N4BiasFieldCorrection(dimension=3, copy_header=True),
name='n4',
n_procs=omp_nthreads)
bet = pe.Node(BETRPT(generate_report=True, frac=0.6, mask=True),
name='bet')
ds_report_fmap_mask = pe.Node(DerivativesDataSink(desc='brain',
suffix='mask'),
name='ds_report_fmap_mask',
mem_gb=0.01,
run_without_submitting=True)
# uses mask from bet; outputs a mask
# dilate = pe.Node(fsl.maths.MathsCommand(
# nan2zeros=True, args='-kernel sphere 5 -dilM'), name='MskDilate')
# phase diff -> radians
pha2rads = pe.Node(niu.Function(function=siemens2rads), name='pha2rads')
# FSL PRELUDE will perform phase-unwrapping
prelude = pe.Node(fsl.PRELUDE(), name='prelude')
denoise = pe.Node(fsl.SpatialFilter(operation='median',
kernel_shape='sphere',
kernel_size=5),
name='denoise')
demean = pe.Node(niu.Function(function=demean_image), name='demean')
cleanup_wf = cleanup_edge_pipeline(name="cleanup_wf")
compfmap = pe.Node(Phasediff2Fieldmap(), name='compfmap')
# The phdiff2fmap interface is equivalent to:
# rad2rsec (using rads2radsec from nipype.workflows.dmri.fsl.utils)
# pre_fugue = pe.Node(fsl.FUGUE(save_fmap=True), name='ComputeFieldmapFUGUE')
# rsec2hz (divide by 2pi)
if phasetype == "phasediff":
# Read phasediff echo times
meta = pe.Node(ReadSidecarJSON(), name='meta', mem_gb=0.01)
# phase diff -> radians
pha2rads = pe.Node(niu.Function(function=siemens2rads),
name='pha2rads')
# Read phasediff echo times
meta = pe.Node(ReadSidecarJSON(),
name='meta',
mem_gb=0.01,
run_without_submitting=True)
workflow.connect([
(meta, compfmap, [('out_dict', 'metadata')]),
(inputnode, pha2rads, [('phasediff', 'in_file')]),
(pha2rads, prelude, [('out', 'phase_file')]),
(inputnode, ds_report_fmap_mask, [('phasediff', 'source_file')]),
])
elif phasetype == "phase":
workflow.__desc__ += """\
The phase difference used for unwarping was calculated using two separate phase measurements
[@pncprocessing].
"""
# Special case for phase1, phase2 images
meta = pe.MapNode(ReadSidecarJSON(),
name='meta',
mem_gb=0.01,
run_without_submitting=True,
iterfield=['in_file'])
phases2fmap = pe.Node(Phases2Fieldmap(), name='phases2fmap')
workflow.connect([
(meta, phases2fmap, [('out_dict', 'metadatas')]),
(inputnode, phases2fmap, [('phasediff', 'phase_files')]),
(phases2fmap, prelude, [('out_file', 'phase_file')]),
(phases2fmap, compfmap, [('phasediff_metadata', 'metadata')]),
(phases2fmap, ds_report_fmap_mask, [('out_file', 'source_file')])
])
workflow.connect([
(inputnode, meta, [('phasediff', 'in_file')]),
(inputnode, magmrg, [('magnitude', 'in_files')]),
(magmrg, n4, [('out_avg', 'input_image')]),
(n4, prelude, [('output_image', 'magnitude_file')]),
(n4, bet, [('output_image', 'in_file')]),
(bet, prelude, [('mask_file', 'mask_file')]),
(prelude, denoise, [('unwrapped_phase_file', 'in_file')]),
(denoise, demean, [('out_file', 'in_file')]),
(demean, cleanup_wf, [('out', 'inputnode.in_file')]),
(bet, cleanup_wf, [('mask_file', 'inputnode.in_mask')]),
(cleanup_wf, compfmap, [('outputnode.out_file', 'in_file')]),
(compfmap, outputnode, [('out_file', 'fmap')]),
(bet, outputnode, [('mask_file', 'fmap_mask'),
('out_file', 'fmap_ref')]),
(bet, ds_report_fmap_mask, [('out_report', 'in_file')]),
])
return workflow
def compute_iqms(name="ComputeIQMs"):
"""
Setup the workflow that actually computes the IQMs.
.. workflow::
from mriqc.workflows.anatomical import compute_iqms
from mriqc.testing import mock_config
with mock_config():
wf = compute_iqms()
"""
from niworkflows.interfaces.bids import ReadSidecarJSON
from ..interfaces.anatomical import Harmonize
from .utils import _tofloat
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(fields=[
"in_file",
"in_ras",
"brainmask",
"airmask",
"artmask",
"headmask",
"rotmask",
"hatmask",
"segmentation",
"inu_corrected",
"in_inu",
"pvms",
"metadata",
"inverse_composite_transform",
]),
name="inputnode",
)
outputnode = pe.Node(
niu.IdentityInterface(fields=["out_file", "noisefit"]),
name="outputnode",
)
# Extract metadata
meta = pe.Node(ReadSidecarJSON(), name="metadata")
# Add provenance
addprov = pe.Node(AddProvenance(),
name="provenance",
run_without_submitting=True)
# AFNI check smoothing
fwhm_interface = get_fwhmx()
fwhm = pe.Node(fwhm_interface, name="smoothness")
# Harmonize
homog = pe.Node(Harmonize(), name="harmonize")
if config.workflow.species.lower() != "human":
homog.inputs.erodemsk = False
homog.inputs.thresh = 0.8
# Mortamet's QI2
getqi2 = pe.Node(ComputeQI2(), name="ComputeQI2")
# Compute python-coded measures
measures = pe.Node(
StructuralQC(human=config.workflow.species.lower() == "human"),
"measures")
# Project MNI segmentation to T1 space
invt = pe.MapNode(
ants.ApplyTransforms(dimension=3,
default_value=0,
interpolation="Linear",
float=True),
iterfield=["input_image"],
name="MNItpms2t1",
)
if config.workflow.species.lower() == "human":
invt.inputs.input_image = [
str(p) for p in get_template(
config.workflow.template_id,
suffix="probseg",
resolution=1,
label=["CSF", "GM", "WM"],
)
]
else:
invt.inputs.input_image = [
str(p) for p in get_template(
config.workflow.template_id,
suffix="probseg",
label=["CSF", "GM", "WM"],
)
]
datasink = pe.Node(
IQMFileSink(
out_dir=config.execution.output_dir,
dataset=config.execution.dsname,
),
name="datasink",
run_without_submitting=True,
)
def _getwm(inlist):
return inlist[-1]
# fmt: off
workflow.connect([
(inputnode, meta, [("in_file", "in_file")]),
(inputnode, datasink, [("in_file", "in_file"),
(("in_file", _get_mod), "modality")]),
(inputnode, addprov, [(("in_file", _get_mod), "modality")]),
(meta, datasink, [("subject", "subject_id"), ("session", "session_id"),
("task", "task_id"), ("acquisition", "acq_id"),
("reconstruction", "rec_id"), ("run", "run_id"),
("out_dict", "metadata")]),
(inputnode, addprov, [("in_file", "in_file"), ("airmask", "air_msk"),
("rotmask", "rot_msk")]),
(inputnode, getqi2, [("in_ras", "in_file"), ("hatmask", "air_msk")]),
(inputnode, homog, [("inu_corrected", "in_file"),
(("pvms", _getwm), "wm_mask")]),
(inputnode, measures, [("in_inu", "in_bias"), ("in_ras", "in_file"),
("airmask", "air_msk"),
("headmask", "head_msk"),
("artmask", "artifact_msk"),
("rotmask", "rot_msk"),
("segmentation", "in_segm"),
("pvms", "in_pvms")]),
(inputnode, fwhm, [("in_ras", "in_file"), ("brainmask", "mask")]),
(inputnode, invt, [("in_ras", "reference_image"),
("inverse_composite_transform", "transforms")]),
(homog, measures, [("out_file", "in_noinu")]),
(invt, measures, [("output_image", "mni_tpms")]),
(fwhm, measures, [(("fwhm", _tofloat), "in_fwhm")]),
(measures, datasink, [("out_qc", "root")]),
(addprov, datasink, [("out_prov", "provenance")]),
(getqi2, datasink, [("qi2", "qi_2")]),
(getqi2, outputnode, [("out_file", "noisefit")]),
(datasink, outputnode, [("out_file", "out_file")]),
])
# fmt: on
return workflow
def compute_iqms(name='ComputeIQMs'):
"""
Setup the workflow that actually computes the IQMs.
.. workflow::
from mriqc.workflows.anatomical import compute_iqms
from mriqc.testing import mock_config
with mock_config():
wf = compute_iqms()
"""
from niworkflows.interfaces.bids import ReadSidecarJSON
from .utils import _tofloat
from ..interfaces.anatomical import Harmonize
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'in_file', 'in_ras', 'brainmask', 'airmask', 'artmask', 'headmask',
'rotmask', 'hatmask', 'segmentation', 'inu_corrected', 'in_inu',
'pvms', 'metadata', 'inverse_composite_transform'
]),
name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(fields=['out_file', 'noisefit']),
name='outputnode')
# Extract metadata
meta = pe.Node(ReadSidecarJSON(), name='metadata')
# Add provenance
addprov = pe.Node(AddProvenance(),
name='provenance',
run_without_submitting=True)
# AFNI check smoothing
fwhm_interface = get_fwhmx()
fwhm = pe.Node(fwhm_interface, name='smoothness')
# Harmonize
homog = pe.Node(Harmonize(), name='harmonize')
# Mortamet's QI2
getqi2 = pe.Node(ComputeQI2(), name='ComputeQI2')
# Compute python-coded measures
measures = pe.Node(StructuralQC(), 'measures')
# Project MNI segmentation to T1 space
invt = pe.MapNode(ants.ApplyTransforms(dimension=3,
default_value=0,
interpolation='Linear',
float=True),
iterfield=['input_image'],
name='MNItpms2t1')
invt.inputs.input_image = [
str(templatedir + 'NMT_segmentation_CSF.nii.gz'),
str(templatedir + 'NMT_segmentation_GM.nii.gz'),
str(templatedir + 'NMT_segmentation_WM.nii.gz')
]
datasink = pe.Node(IQMFileSink(out_dir=config.execution.output_dir,
dataset=config.execution.dsname),
name='datasink',
run_without_submitting=True)
def _getwm(inlist):
return inlist[-1]
workflow.connect([
(inputnode, meta, [('in_file', 'in_file')]),
(inputnode, datasink, [('in_file', 'in_file'),
(('in_file', _get_mod), 'modality')]),
(inputnode, addprov, [(('in_file', _get_mod), 'modality')]),
(meta, datasink, [('subject', 'subject_id'), ('session', 'session_id'),
('task', 'task_id'), ('acquisition', 'acq_id'),
('reconstruction', 'rec_id'), ('run', 'run_id'),
('out_dict', 'metadata')]),
(inputnode, addprov, [('in_file', 'in_file'), ('airmask', 'air_msk'),
('rotmask', 'rot_msk')]),
(inputnode, getqi2, [('in_ras', 'in_file'), ('hatmask', 'air_msk')]),
(inputnode, homog, [('inu_corrected', 'in_file'),
(('pvms', _getwm), 'wm_mask')]),
(inputnode, measures, [('in_inu', 'in_bias'), ('in_ras', 'in_file'),
('airmask', 'air_msk'),
('headmask', 'head_msk'),
('artmask', 'artifact_msk'),
('rotmask', 'rot_msk'),
('segmentation', 'in_segm'),
('pvms', 'in_pvms')]),
(inputnode, fwhm, [('in_ras', 'in_file'), ('brainmask', 'mask')]),
(inputnode, invt, [('in_ras', 'reference_image'),
('inverse_composite_transform', 'transforms')]),
(homog, measures, [('out_file', 'in_noinu')]),
(invt, measures, [('output_image', 'mni_tpms')]),
(fwhm, measures, [(('fwhm', _tofloat), 'in_fwhm')]),
(measures, datasink, [('out_qc', 'root')]),
(addprov, datasink, [('out_prov', 'provenance')]),
(getqi2, datasink, [('qi2', 'qi_2')]),
(getqi2, outputnode, [('out_file', 'noisefit')]),
(datasink, outputnode, [('out_file', 'out_file')]),
])
return workflow
def compute_iqms(name='ComputeIQMs'):
"""
Initialize the workflow that actually computes the IQMs.
.. workflow::
from mriqc.workflows.functional import compute_iqms
from mriqc.testing import mock_config
with mock_config():
wf = compute_iqms()
"""
from nipype.algorithms.confounds import ComputeDVARS
from nipype.interfaces.afni import QualityIndex, OutlierCount
from niworkflows.interfaces.bids import ReadSidecarJSON
from .utils import get_fwhmx, _tofloat
from ..interfaces.transitional import GCOR
from ..interfaces import FunctionalQC, IQMFileSink
from ..interfaces.reports import AddProvenance
mem_gb = config.workflow.biggest_file_gb
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'in_file', 'in_ras', 'epi_mean', 'brainmask', 'hmc_epi', 'hmc_fd',
'fd_thres', 'in_tsnr', 'metadata', 'exclude_index'
]),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['out_file', 'out_dvars', 'outliers', 'out_spikes', 'out_fft']),
name='outputnode')
# Set FD threshold
inputnode.inputs.fd_thres = config.workflow.fd_thres
# Compute DVARS
dvnode = pe.Node(ComputeDVARS(save_plot=False, save_all=True),
name='ComputeDVARS',
mem_gb=mem_gb * 3)
# AFNI quality measures
fwhm_interface = get_fwhmx()
fwhm = pe.Node(fwhm_interface, name='smoothness')
# fwhm.inputs.acf = True # add when AFNI >= 16
outliers = pe.Node(OutlierCount(fraction=True, out_file='outliers.out'),
name='outliers',
mem_gb=mem_gb * 2.5)
quality = pe.Node(QualityIndex(automask=True),
out_file='quality.out',
name='quality',
mem_gb=mem_gb * 3)
gcor = pe.Node(GCOR(), name='gcor', mem_gb=mem_gb * 2)
measures = pe.Node(FunctionalQC(), name='measures', mem_gb=mem_gb * 3)
workflow.connect([(inputnode, dvnode, [('hmc_epi', 'in_file'),
('brainmask', 'in_mask')]),
(inputnode, measures, [('epi_mean', 'in_epi'),
('brainmask', 'in_mask'),
('hmc_epi', 'in_hmc'),
('hmc_fd', 'in_fd'),
('fd_thres', 'fd_thres'),
('in_tsnr', 'in_tsnr')]),
(inputnode, fwhm, [('epi_mean', 'in_file'),
('brainmask', 'mask')]),
(inputnode, quality, [('hmc_epi', 'in_file')]),
(inputnode, outliers, [('hmc_epi', 'in_file'),
('brainmask', 'mask')]),
(inputnode, gcor, [('hmc_epi', 'in_file'),
('brainmask', 'mask')]),
(dvnode, measures, [('out_all', 'in_dvars')]),
(fwhm, measures, [(('fwhm', _tofloat), 'in_fwhm')]),
(dvnode, outputnode, [('out_all', 'out_dvars')]),
(outliers, outputnode, [('out_file', 'outliers')])])
# Add metadata
meta = pe.Node(ReadSidecarJSON(),
name='metadata',
run_without_submitting=True)
addprov = pe.Node(AddProvenance(modality="bold"),
name='provenance',
run_without_submitting=True)
# Save to JSON file
datasink = pe.Node(IQMFileSink(modality='bold',
out_dir=str(config.execution.output_dir),
dataset=config.execution.dsname),
name='datasink',
run_without_submitting=True)
workflow.connect([
(inputnode, datasink, [('in_file', 'in_file'),
('exclude_index', 'dummy_trs')]),
(inputnode, meta, [('in_file', 'in_file')]),
(inputnode, addprov, [('in_file', 'in_file')]),
(meta, datasink, [('subject', 'subject_id'), ('session', 'session_id'),
('task', 'task_id'), ('acquisition', 'acq_id'),
('reconstruction', 'rec_id'), ('run', 'run_id'),
('out_dict', 'metadata')]),
(addprov, datasink, [('out_prov', 'provenance')]),
(outliers, datasink, [(('out_file', _parse_tout), 'aor')]),
(gcor, datasink, [(('out', _tofloat), 'gcor')]),
(quality, datasink, [(('out_file', _parse_tqual), 'aqi')]),
(measures, datasink, [('out_qc', 'root')]),
(datasink, outputnode, [('out_file', 'out_file')])
])
# FFT spikes finder
if config.workflow.fft_spikes_detector:
from .utils import slice_wise_fft
spikes_fft = pe.Node(niu.Function(
input_names=['in_file'],
output_names=['n_spikes', 'out_spikes', 'out_fft'],
function=slice_wise_fft),
name='SpikesFinderFFT')
workflow.connect([
(inputnode, spikes_fft, [('in_ras', 'in_file')]),
(spikes_fft, outputnode, [('out_spikes', 'out_spikes'),
('out_fft', 'out_fft')]),
(spikes_fft, datasink, [('n_spikes', 'spikes_num')])
])
return workflow
def compute_iqms(settings, modality='T1w', name='ComputeIQMs'):
"""
Workflow that actually computes the IQMs
.. workflow::
from mriqc.workflows.anatomical import compute_iqms
wf = compute_iqms(settings={'output_dir': 'out'})
"""
from .utils import _tofloat
from ..interfaces.anatomical import Harmonize
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'in_file', 'in_ras', 'brainmask', 'airmask', 'artmask', 'headmask',
'rotmask', 'hatmask', 'segmentation', 'inu_corrected', 'in_inu',
'pvms', 'metadata', 'inverse_composite_transform'
]),
name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(fields=['out_file', 'noisefit']),
name='outputnode')
# Extract metadata
meta = pe.Node(ReadSidecarJSON(), name='metadata')
# Add provenance
addprov = pe.Node(niu.Function(function=_add_provenance),
name='provenance')
addprov.inputs.settings = {
'testing': settings.get('testing', False),
'webapi_url': settings.get('webapi_url'),
'webapi_port': settings.get('webapi_port')
}
# AFNI check smoothing
fwhm_interface = get_fwhmx()
fwhm = pe.Node(fwhm_interface, name='smoothness')
# Harmonize
homog = pe.Node(Harmonize(), name='harmonize')
# Mortamet's QI2
getqi2 = pe.Node(ComputeQI2(), name='ComputeQI2')
# Compute python-coded measures
measures = pe.Node(StructuralQC(), 'measures')
# Project MNI segmentation to T1 space
invt = pe.MapNode(ants.ApplyTransforms(dimension=3,
default_value=0,
interpolation='Linear',
float=True),
iterfield=['input_image'],
name='MNItpms2t1')
invt.inputs.input_image = [
str(p) for p in get_template('MNI152NLin2009cAsym',
suffix='probseg',
resolution=1,
label=['CSF', 'GM', 'WM'])
]
datasink = pe.Node(IQMFileSink(modality=modality,
out_dir=str(settings['output_dir']),
dataset=settings.get(
'dataset_name', 'unknown')),
name='datasink',
run_without_submitting=True)
datasink.inputs.modality = modality
def _getwm(inlist):
return inlist[-1]
workflow.connect([
(inputnode, meta, [('in_file', 'in_file')]),
(inputnode, datasink, [('in_file', 'in_file')]),
(meta, datasink, [('subject', 'subject_id'), ('session', 'session_id'),
('task', 'task_id'), ('acquisition', 'acq_id'),
('reconstruction', 'rec_id'), ('run', 'run_id'),
('out_dict', 'metadata')]),
(inputnode, addprov, [('in_file', 'in_file'), ('airmask', 'air_msk'),
('rotmask', 'rot_msk')]),
(inputnode, getqi2, [('in_ras', 'in_file'), ('hatmask', 'air_msk')]),
(inputnode, homog, [('inu_corrected', 'in_file'),
(('pvms', _getwm), 'wm_mask')]),
(inputnode, measures, [('in_inu', 'in_bias'), ('in_ras', 'in_file'),
('airmask', 'air_msk'),
('headmask', 'head_msk'),
('artmask', 'artifact_msk'),
('rotmask', 'rot_msk'),
('segmentation', 'in_segm'),
('pvms', 'in_pvms')]),
(inputnode, fwhm, [('in_ras', 'in_file'), ('brainmask', 'mask')]),
(inputnode, invt, [('in_ras', 'reference_image'),
('inverse_composite_transform', 'transforms')]),
(homog, measures, [('out_file', 'in_noinu')]),
(invt, measures, [('output_image', 'mni_tpms')]),
(fwhm, measures, [(('fwhm', _tofloat), 'in_fwhm')]),
(measures, datasink, [('out_qc', 'root')]),
(addprov, datasink, [('out', 'provenance')]),
(getqi2, datasink, [('qi2', 'qi_2')]),
(getqi2, outputnode, [('out_file', 'noisefit')]),
(datasink, outputnode, [('out_file', 'out_file')]),
])
return workflow
def compute_iqms(settings, name='ComputeIQMs'):
"""
Workflow that actually computes the IQMs
.. workflow::
from mriqc.workflows.functional import compute_iqms
wf = compute_iqms(settings={'output_dir': 'out'})
"""
from .utils import _tofloat
from ..interfaces.transitional import GCOR
biggest_file_gb = settings.get("biggest_file_size_gb", 1)
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'in_file', 'in_ras',
'epi_mean', 'brainmask', 'hmc_epi', 'hmc_fd', 'fd_thres', 'in_tsnr', 'metadata',
'exclude_index']), name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['out_file', 'out_dvars', 'outliers', 'out_spikes', 'out_fft']),
name='outputnode')
# Set FD threshold
inputnode.inputs.fd_thres = settings.get('fd_thres', 0.2)
# Compute DVARS
dvnode = pe.Node(nac.ComputeDVARS(save_plot=False, save_all=True), name='ComputeDVARS',
mem_gb=biggest_file_gb * 3)
# AFNI quality measures
fwhm_interface = get_fwhmx()
fwhm = pe.Node(fwhm_interface, name='smoothness')
# fwhm.inputs.acf = True # add when AFNI >= 16
outliers = pe.Node(afni.OutlierCount(fraction=True, out_file='outliers.out'),
name='outliers', mem_gb=biggest_file_gb * 2.5)
quality = pe.Node(afni.QualityIndex(automask=True), out_file='quality.out',
name='quality', mem_gb=biggest_file_gb * 3)
gcor = pe.Node(GCOR(), name='gcor', mem_gb=biggest_file_gb * 2)
measures = pe.Node(FunctionalQC(), name='measures', mem_gb=biggest_file_gb * 3)
workflow.connect([
(inputnode, dvnode, [('hmc_epi', 'in_file'),
('brainmask', 'in_mask')]),
(inputnode, measures, [('epi_mean', 'in_epi'),
('brainmask', 'in_mask'),
('hmc_epi', 'in_hmc'),
('hmc_fd', 'in_fd'),
('fd_thres', 'fd_thres'),
('in_tsnr', 'in_tsnr')]),
(inputnode, fwhm, [('epi_mean', 'in_file'),
('brainmask', 'mask')]),
(inputnode, quality, [('hmc_epi', 'in_file')]),
(inputnode, outliers, [('hmc_epi', 'in_file'),
('brainmask', 'mask')]),
(inputnode, gcor, [('hmc_epi', 'in_file'),
('brainmask', 'mask')]),
(dvnode, measures, [('out_all', 'in_dvars')]),
(fwhm, measures, [(('fwhm', _tofloat), 'in_fwhm')]),
(dvnode, outputnode, [('out_all', 'out_dvars')]),
(outliers, outputnode, [('out_file', 'outliers')])
])
# Add metadata
meta = pe.Node(ReadSidecarJSON(), name='metadata',
run_without_submitting=True)
addprov = pe.Node(niu.Function(function=_add_provenance), name='provenance',
run_without_submitting=True)
addprov.inputs.settings = {
'fd_thres': settings.get('fd_thres', 0.2),
'hmc_fsl': settings.get('hmc_fsl', True),
'webapi_url': settings.get('webapi_url'),
'webapi_port': settings.get('webapi_port'),
}
# Save to JSON file
datasink = pe.Node(IQMFileSink(
modality='bold', out_dir=str(settings['output_dir']),
dataset=settings.get('dataset_name', 'unknown')),
name='datasink', run_without_submitting=True)
workflow.connect([
(inputnode, datasink, [('in_file', 'in_file'),
('exclude_index', 'dummy_trs')]),
(inputnode, meta, [('in_file', 'in_file')]),
(inputnode, addprov, [('in_file', 'in_file')]),
(meta, datasink, [('subject', 'subject_id'),
('session', 'session_id'),
('task', 'task_id'),
('acquisition', 'acq_id'),
('reconstruction', 'rec_id'),
('run', 'run_id'),
('out_dict', 'metadata')]),
(addprov, datasink, [('out', 'provenance')]),
(outliers, datasink, [(('out_file', _parse_tout), 'aor')]),
(gcor, datasink, [(('out', _tofloat), 'gcor')]),
(quality, datasink, [(('out_file', _parse_tqual), 'aqi')]),
(measures, datasink, [('out_qc', 'root')]),
(datasink, outputnode, [('out_file', 'out_file')])
])
# FFT spikes finder
if settings.get('fft_spikes_detector', False):
from .utils import slice_wise_fft
spikes_fft = pe.Node(niu.Function(
input_names=['in_file'],
output_names=['n_spikes', 'out_spikes', 'out_fft'],
function=slice_wise_fft), name='SpikesFinderFFT')
workflow.connect([
(inputnode, spikes_fft, [('in_ras', 'in_file')]),
(spikes_fft, outputnode, [('out_spikes', 'out_spikes'),
('out_fft', 'out_fft')]),
(spikes_fft, datasink, [('n_spikes', 'spikes_num')])
])
return workflow
def compute_iqms(name="ComputeIQMs"):
"""
Initialize the workflow that actually computes the IQMs.
.. workflow::
from mriqc.workflows.functional import compute_iqms
from mriqc.testing import mock_config
with mock_config():
wf = compute_iqms()
"""
from nipype.algorithms.confounds import ComputeDVARS
from nipype.interfaces.afni import OutlierCount, QualityIndex
from niworkflows.interfaces.bids import ReadSidecarJSON
from mriqc.interfaces import FunctionalQC, IQMFileSink
from mriqc.interfaces.reports import AddProvenance
from mriqc.interfaces.transitional import GCOR
from mriqc.workflows.utils import _tofloat, get_fwhmx
mem_gb = config.workflow.biggest_file_gb
workflow = pe.Workflow(name=name)
inputnode = pe.Node(
niu.IdentityInterface(fields=[
"in_file",
"in_ras",
"epi_mean",
"brainmask",
"hmc_epi",
"hmc_fd",
"fd_thres",
"in_tsnr",
"metadata",
"exclude_index",
]),
name="inputnode",
)
outputnode = pe.Node(
niu.IdentityInterface(fields=[
"out_file",
"out_dvars",
"outliers",
"out_spikes",
"out_fft",
]),
name="outputnode",
)
# Set FD threshold
inputnode.inputs.fd_thres = config.workflow.fd_thres
# Compute DVARS
dvnode = pe.Node(
ComputeDVARS(save_plot=False, save_all=True),
name="ComputeDVARS",
mem_gb=mem_gb * 3,
)
# AFNI quality measures
fwhm_interface = get_fwhmx()
fwhm = pe.Node(fwhm_interface, name="smoothness")
# fwhm.inputs.acf = True # add when AFNI >= 16
outliers = pe.Node(
OutlierCount(fraction=True, out_file="outliers.out"),
name="outliers",
mem_gb=mem_gb * 2.5,
)
quality = pe.Node(
QualityIndex(automask=True),
out_file="quality.out",
name="quality",
mem_gb=mem_gb * 3,
)
gcor = pe.Node(GCOR(), name="gcor", mem_gb=mem_gb * 2)
measures = pe.Node(FunctionalQC(), name="measures", mem_gb=mem_gb * 3)
# fmt: off
workflow.connect([(inputnode, dvnode, [("hmc_epi", "in_file"),
("brainmask", "in_mask")]),
(inputnode, measures, [("epi_mean", "in_epi"),
("brainmask", "in_mask"),
("hmc_epi", "in_hmc"),
("hmc_fd", "in_fd"),
("fd_thres", "fd_thres"),
("in_tsnr", "in_tsnr")]),
(inputnode, fwhm, [("epi_mean", "in_file"),
("brainmask", "mask")]),
(inputnode, quality, [("hmc_epi", "in_file")]),
(inputnode, outliers, [("hmc_epi", "in_file"),
("brainmask", "mask")]),
(inputnode, gcor, [("hmc_epi", "in_file"),
("brainmask", "mask")]),
(dvnode, measures, [("out_all", "in_dvars")]),
(fwhm, measures, [(("fwhm", _tofloat), "in_fwhm")]),
(dvnode, outputnode, [("out_all", "out_dvars")]),
(outliers, outputnode, [("out_file", "outliers")])])
# fmt: on
# Add metadata
meta = pe.Node(ReadSidecarJSON(),
name="metadata",
run_without_submitting=True)
addprov = pe.Node(
AddProvenance(modality="bold"),
name="provenance",
run_without_submitting=True,
)
# Save to JSON file
datasink = pe.Node(
IQMFileSink(
modality="bold",
out_dir=str(config.execution.output_dir),
dataset=config.execution.dsname,
),
name="datasink",
run_without_submitting=True,
)
# fmt: off
workflow.connect([
(inputnode, datasink, [("in_file", "in_file"),
("exclude_index", "dummy_trs")]),
(inputnode, meta, [("in_file", "in_file")]),
(inputnode, addprov, [("in_file", "in_file")]),
(meta, datasink, [("subject", "subject_id"), ("session", "session_id"),
("task", "task_id"), ("acquisition", "acq_id"),
("reconstruction", "rec_id"), ("run", "run_id"),
("out_dict", "metadata")]),
(addprov, datasink, [("out_prov", "provenance")]),
(outliers, datasink, [(("out_file", _parse_tout), "aor")]),
(gcor, datasink, [(("out", _tofloat), "gcor")]),
(quality, datasink, [(("out_file", _parse_tqual), "aqi")]),
(measures, datasink, [("out_qc", "root")]),
(datasink, outputnode, [("out_file", "out_file")])
])
# fmt: on
# FFT spikes finder
if config.workflow.fft_spikes_detector:
from .utils import slice_wise_fft
spikes_fft = pe.Node(
niu.Function(
input_names=["in_file"],
output_names=["n_spikes", "out_spikes", "out_fft"],
function=slice_wise_fft,
),
name="SpikesFinderFFT",
)
# fmt: off
workflow.connect([
(inputnode, spikes_fft, [("in_ras", "in_file")]),
(spikes_fft, outputnode, [("out_spikes", "out_spikes"),
("out_fft", "out_fft")]),
(spikes_fft, datasink, [("n_spikes", "spikes_num")])
])
# fmt: on
return workflow
