def test_dvars(tmpdir):
ground_truth = np.loadtxt(example_data('ds003_sub-01_mc.DVARS'))
dvars = ComputeDVARS(in_file=example_data('ds003_sub-01_mc.nii.gz'),
in_mask=example_data('ds003_sub-01_mc_brainmask.nii.gz'),
save_all=True,
intensity_normalization=0)
os.chdir(str(tmpdir))
res = dvars.run()
dv1 = np.loadtxt(res.outputs.out_all, skiprows=1)
assert (np.abs(dv1[:, 0] - ground_truth[:, 0]).sum()/ len(dv1)) < 0.05
assert (np.abs(dv1[:, 1] - ground_truth[:, 1]).sum() / len(dv1)) < 0.05
assert (np.abs(dv1[:, 2] - ground_truth[:, 2]).sum() / len(dv1)) < 0.05
dvars = ComputeDVARS(in_file=example_data('ds003_sub-01_mc.nii.gz'),
in_mask=example_data(
'ds003_sub-01_mc_brainmask.nii.gz'),
save_all=True)
res = dvars.run()
dv1 = np.loadtxt(res.outputs.out_all, skiprows=1)
assert (np.abs(dv1[:, 0] - ground_truth[:, 0]).sum() / len(dv1)) < 0.05
assert (np.abs(dv1[:, 1] - ground_truth[:, 1]).sum() / len(dv1)) > 0.05
assert (np.abs(dv1[:, 2] - ground_truth[:, 2]).sum() / len(dv1)) < 0.05
def test_dvars(tmpdir):
ground_truth = np.loadtxt(example_data('ds003_sub-01_mc.DVARS'))
dvars = ComputeDVARS(in_file=example_data('ds003_sub-01_mc.nii.gz'),
in_mask=example_data('ds003_sub-01_mc_brainmask.nii.gz'),
save_all=True)
os.chdir(str(tmpdir))
res = dvars.run()
dv1 = np.loadtxt(res.outputs.out_std)
assert (np.abs(dv1 - ground_truth).sum()/ len(dv1)) < 0.05
def test_dvars(tmpdir):
ground_truth = np.loadtxt(example_data('ds003_sub-01_mc.DVARS'))
dvars = ComputeDVARS(
in_file=example_data('ds003_sub-01_mc.nii.gz'),
in_mask=example_data('ds003_sub-01_mc_brainmask.nii.gz'),
save_all=True)
os.chdir(str(tmpdir))
res = dvars.run()
dv1 = np.loadtxt(res.outputs.out_std)
assert (np.abs(dv1 - ground_truth).sum() / len(dv1)) < 0.05
def test_dvars():
tempdir = mkdtemp()
ground_truth = np.loadtxt(example_data('ds003_sub-01_mc.DVARS'))
dvars = ComputeDVARS(in_file=example_data('ds003_sub-01_mc.nii.gz'),
in_mask=example_data('ds003_sub-01_mc_brainmask.nii.gz'),
save_all=True)
origdir = os.getcwd()
os.chdir(tempdir)
res = dvars.run()
dv1 = np.loadtxt(res.outputs.out_std)
yield assert_equal, (np.abs(dv1 - ground_truth).sum()/ len(dv1)) < 0.05, True
os.chdir(origdir)
rmtree(tempdir)
def test_dvars(tmpdir):
ground_truth = np.loadtxt(example_data('ds003_sub-01_mc.DVARS'))
dvars = ComputeDVARS(
in_file=example_data('ds003_sub-01_mc.nii.gz'),
in_mask=example_data('ds003_sub-01_mc_brainmask.nii.gz'),
save_all=True,
intensity_normalization=0)
os.chdir(str(tmpdir))
res = dvars.run()
dv1 = np.loadtxt(res.outputs.out_all, skiprows=1)
assert (np.abs(dv1[:, 0] - ground_truth[:, 0]).sum() / len(dv1)) < 0.05
assert (np.abs(dv1[:, 1] - ground_truth[:, 1]).sum() / len(dv1)) < 0.05
assert (np.abs(dv1[:, 2] - ground_truth[:, 2]).sum() / len(dv1)) < 0.05
dvars = ComputeDVARS(
in_file=example_data('ds003_sub-01_mc.nii.gz'),
in_mask=example_data('ds003_sub-01_mc_brainmask.nii.gz'),
save_all=True)
res = dvars.run()
dv1 = np.loadtxt(res.outputs.out_all, skiprows=1)
assert (np.abs(dv1[:, 0] - ground_truth[:, 0]).sum() / len(dv1)) < 0.05
assert (np.abs(dv1[:, 1] - ground_truth[:, 1]).sum() / len(dv1)) > 0.05
assert (np.abs(dv1[:, 2] - ground_truth[:, 2]).sum() / len(dv1)) < 0.05
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 create_confound_workflow(name='confound'):
input_node = pe.Node(interface=IdentityInterface(fields=[
'in_file', 'par_file', 'fast_files', 'highres2epi_mat',
'n_comp_tcompcor', 'n_comp_acompcor', 'output_directory', 'sub_id'
]),
name='inputspec')
output_node = pe.Node(interface=IdentityInterface(fields=[
'all_confounds',
]),
name='outputspec')
datasink = pe.Node(DataSink(), name='sinker')
datasink.inputs.parameterization = False
compute_DVARS = pe.MapNode(ComputeDVARS(save_all=True,
remove_zerovariance=True),
iterfield=['in_file', 'in_mask'],
name='compute_DVARS')
motion_wf = create_motion_confound_workflow(order=2)
confound_wf = pe.Workflow(name=name)
confound_wf.connect(input_node, 'par_file', motion_wf,
'inputspec.par_file')
confound_wf.connect(input_node, 'sub_id', motion_wf, 'inputspec.sub_id')
confound_wf.connect(input_node, 'output_directory', motion_wf,
'inputspec.output_directory')
compcor_wf = create_compcor_workflow()
confound_wf.connect(input_node, 'in_file', compcor_wf, 'inputspec.in_file')
confound_wf.connect(input_node, 'fast_files', compcor_wf,
'inputspec.fast_files')
confound_wf.connect(input_node, 'highres2epi_mat', compcor_wf,
'inputspec.highres2epi_mat')
confound_wf.connect(input_node, 'n_comp_tcompcor', compcor_wf,
'inputspec.n_comp_tcompcor')
confound_wf.connect(input_node, 'n_comp_acompcor', compcor_wf,
'inputspec.n_comp_acompcor')
confound_wf.connect(input_node, 'sub_id', compcor_wf, 'inputspec.sub_id')
confound_wf.connect(input_node, 'output_directory', compcor_wf,
'inputspec.output_directory')
confound_wf.connect(compcor_wf, 'outputspec.epi_mask', compute_DVARS,
'in_mask')
confound_wf.connect(input_node, 'in_file', compute_DVARS, 'in_file')
concat = pe.MapNode(Concat_confound_files,
iterfield=['ext_par_file', 'fd_file', 'dvars_file'],
name='concat')
confound_wf.connect(motion_wf, 'outputspec.out_ext_moco', concat,
'ext_par_file')
confound_wf.connect(motion_wf, 'outputspec.out_fd', concat, 'fd_file')
confound_wf.connect(compcor_wf, 'outputspec.acompcor_file', concat,
'acompcor_file')
#confound_wf.connect(compcor_wf, 'outputspec.tcompcor_file', concat,
# 'tcompcor_file')
confound_wf.connect(compute_DVARS, 'out_all', concat, 'dvars_file')
confound_wf.connect(input_node, 'sub_id', datasink, 'sub_id')
confound_wf.connect(input_node, 'output_directory', datasink,
'base_directory')
confound_wf.connect(concat, 'out_file', datasink, 'confounds')
return confound_wf
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
def main(sourcedata,
derivatives,
tmp_dir,
subject=None,
session=None,
run=None):
print(subject, session, run)
layout = BIDSLayout(sourcedata)
derivatives_layout = BIDSLayout('/derivatives/spynoza', validate=False)
cortex_l = get_derivative(derivatives,
'nighres',
'anat',
subject,
'dseg',
session='anat',
space='average',
description='cortex',
hemi='left')
cortex_r = get_derivative(derivatives,
'nighres',
'anat',
subject,
'dseg',
session='anat',
space='average',
description='cortex',
hemi='left')
mask = derivatives_layout.get(subject=subject,
session=session,
suffix='mask',
return_type='file')
mask = sorted(mask)
assert (len(mask) == 1)
mask = mask[0]
bold = derivatives_layout.get(subject=subject,
session=session,
suffix='preproc',
return_type='file')
bold = sorted(bold)
print('BOLD: {}'.format(bold))
print('MASK: {}'.format(mask))
inputnode = pe.Node(
niu.IdentityInterface(fields=['cortex_l', 'cortex_r', 'bold', 'mask']),
name='inputnode')
inputnode.inputs.cortex_l = cortex_l
inputnode.inputs.cortex_r = cortex_r
inputnode.inputs.bold = bold
inputnode.inputs.mask = mask
get_masks = pe.MapNode(niu.Function(
function=get_brain_regions_cruise,
input_names=['cortex_l', 'cortex_r', 'type'],
output_names=['out']),
iterfield=['type'],
name='get_masks')
get_masks.inputs.type = ['csf', 'wm']
wf = pe.Workflow(name='get_confounds_{}_{}'.format(subject, session),
base_dir='/workflow_folders')
wf.connect(inputnode, 'cortex_l', get_masks, 'cortex_l')
wf.connect(inputnode, 'cortex_r', get_masks, 'cortex_r')
resampler = pe.MapNode(niu.Function(
function=resample_img,
input_names=['input_image', 'ref_image', 'interpolation'],
output_names=['resampled_image'],
),
iterfield=['input_image'],
name='resampler')
wf.connect(inputnode, ('bold', pickfirst), resampler, 'ref_image')
wf.connect(get_masks, 'out', resampler, 'input_image')
compcorr = pe.MapNode(ACompCor(merge_method='union'),
iterfield=['realigned_file'],
name='acompcorr')
wf.connect(resampler, 'resampled_image', compcorr, 'mask_files')
wf.connect(inputnode, 'bold', compcorr, 'realigned_file')
dvars = pe.MapNode(ComputeDVARS(), iterfield=['in_file'], name='dvars')
wf.connect(inputnode, 'mask', dvars, 'in_mask')
wf.connect(inputnode, 'bold', dvars, 'in_file')
add_header = pe.MapNode(AddTSVHeader(columns=["dvars"]),
iterfield=['in_file'],
name="add_header_dvars")
wf.connect(dvars, 'out_std', add_header, 'in_file')
concat = pe.MapNode(GatherConfounds(),
iterfield=['acompcor', 'dvars'],
name="concat")
wf.connect(add_header, 'out_file', concat, 'dvars')
wf.connect(compcorr, 'components_file', concat, 'acompcor')
ds_confounds = pe.MapNode(DerivativesDataSink(out_path_base='spynoza',
suffix='confounds_compcor',
base_directory=derivatives),
iterfield=['in_file', 'source_file'],
name='ds_reg_report')
wf.connect(inputnode, 'bold', ds_confounds, 'source_file')
wf.connect(concat, 'confounds_file', ds_confounds, 'in_file')
wf.run(plugin='MultiProc', plugin_args={'n_procs': 10})
