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(settings, name='ComputeIQMs'):
"""Workflow that actually computes the IQMs"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'subject_id', 'session_id', 'task_id', 'run_id', 'orig', 'epi_mean',
'brainmask', 'hmc_epi', 'hmc_fd', 'in_tsnr', 'metadata']), name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['out_file', 'out_dvars', 'outliers', 'out_spikes', 'out_fft']),
name='outputnode')
deriv_dir = check_folder(op.abspath(op.join(settings['output_dir'], 'derivatives')))
# Compute DVARS
dvnode = pe.Node(nac.ComputeDVARS(save_plot=False, save_all=True), name='ComputeDVARS')
# AFNI quality measures
fwhm = pe.Node(afni.FWHMx(combine=True, detrend=True), name='smoothness')
# fwhm.inputs.acf = True # add when AFNI >= 16
outliers = pe.Node(afni.OutlierCount(fraction=True, out_file='ouliers.out'),
name='outliers')
quality = pe.Node(afni.QualityIndex(automask=True), out_file='quality.out',
name='quality')
# FFT spikes finder
spikes_fft = pe.Node(niu.Function(
input_names=['in_file'], output_names=['n_spikes', 'out_spikes', 'out_fft'],
function=slice_wise_fft), name='SpikesFinderFFT')
measures = pe.Node(FunctionalQC(), name='measures')
workflow.connect([
(inputnode, dvnode, [('orig', 'in_file'),
('brainmask', 'in_mask')]),
(inputnode, measures, [('epi_mean', 'in_epi'),
('brainmask', 'in_mask'),
('hmc_epi', 'in_hmc'),
('hmc_fd', 'in_fd'),
('in_tsnr', 'in_tsnr')]),
(inputnode, fwhm, [('epi_mean', 'in_file'),
('brainmask', 'mask')]),
(inputnode, spikes_fft, [('orig', 'in_file')]),
(inputnode, quality, [('hmc_epi', 'in_file')]),
(inputnode, outliers, [('hmc_epi', 'in_file'),
('brainmask', 'mask')]),
(dvnode, measures, [('out_all', 'in_dvars')]),
(dvnode, outputnode, [('out_all', 'out_dvars')]),
(outliers, outputnode, [('out_file', 'outliers')]),
(spikes_fft, outputnode, [('out_spikes', 'out_spikes'),
('out_fft', 'out_fft')])
])
# Save to JSON file
datasink = pe.Node(IQMFileSink(
modality='bold', out_dir=deriv_dir), name='datasink')
workflow.connect([
(inputnode, datasink, [('subject_id', 'subject_id'),
('session_id', 'session_id'),
('task_id', 'task_id'),
('run_id', 'run_id'),
('metadata', 'metadata')]),
(outliers, datasink, [(('out_file', _parse_tout), 'aor')]),
(quality, datasink, [(('out_file', _parse_tqual), 'aqi')]),
(measures, datasink, [('out_qc', 'root')]),
(spikes_fft, datasink, [('n_spikes', 'spikes_num')]),
(fwhm, datasink, [(('fwhm', fwhm_dict), 'root0')]),
(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 mriqc.workflows.utils import _tofloat
biggest_file_gb = settings.get("biggest_file_size_gb", 1)
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'subject_id', 'session_id', 'task_id', 'acq_id', 'rec_id', 'run_id',
'orig', 'epi_mean', 'brainmask', 'hmc_epi', 'hmc_fd', 'fd_thres',
'in_tsnr', 'metadata'
]),
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)
deriv_dir = check_folder(
op.abspath(op.join(settings['output_dir'], 'derivatives')))
# Compute DVARS
dvnode = pe.Node(nac.ComputeDVARS(save_plot=False, save_all=True),
name='ComputeDVARS')
dvnode.interface.estimated_memory_gb = biggest_file_gb * 3
# AFNI quality measures
fwhm = pe.Node(afni.FWHMx(combine=True, detrend=True), name='smoothness')
# fwhm.inputs.acf = True # add when AFNI >= 16
outliers = pe.Node(afni.OutlierCount(fraction=True,
out_file='ouliers.out'),
name='outliers')
outliers.interface.estimated_memory_gb = biggest_file_gb * 2.5
quality = pe.Node(afni.QualityIndex(automask=True),
out_file='quality.out',
name='quality')
quality.interface.estimated_memory_gb = biggest_file_gb * 3
measures = pe.Node(FunctionalQC(), name='measures')
measures.interface.estimated_memory_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')]),
(dvnode, measures, [('out_all', 'in_dvars')]),
(fwhm, measures, [(('fwhm', _tofloat), 'in_fwhm')]),
(dvnode, outputnode, [('out_all', 'out_dvars')]),
(outliers, outputnode, [('out_file', 'outliers')])])
# Save to JSON file
datasink = pe.Node(IQMFileSink(modality='bold', out_dir=deriv_dir),
name='datasink')
workflow.connect([
(inputnode, datasink, [('subject_id', 'subject_id'),
('session_id', 'session_id'),
('task_id', 'task_id'), ('acq_id', 'acq_id'),
('rec_id', 'rec_id'), ('run_id', 'run_id'),
('metadata', 'metadata')]),
(outliers, datasink, [(('out_file', _parse_tout), 'aor')]),
(quality, datasink, [(('out_file', _parse_tqual), 'aqi')]),
(measures, datasink, [('out_qc', 'root')]),
(datasink, outputnode, [('out_file', 'out_file')])
])
if settings.get('fft_spikes_detector', False):
# FFT spikes finder
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, [('orig', 'in_file')]),
(spikes_fft, outputnode, [('out_spikes', 'out_spikes'),
('out_fft', 'out_fft')]),
(spikes_fft, datasink, [('n_spikes', 'spikes_num')])
])
return workflow