def anat_qc_workflow(dataset, settings, name='anatMRIQC'):
"""
One-subject-one-session-one-run pipeline to extract the NR-IQMs from
anatomical images
"""
workflow = pe.Workflow(name=name)
WFLOGGER.info(
'Building anatomical MRI QC workflow, datasets list: %s',
sorted([d.replace(settings['bids_dir'] + '/', '') for d in dataset]))
# Define workflow, inputs and outputs
# 0. Get data
inputnode = pe.Node(niu.IdentityInterface(fields=['in_file']),
name='inputnode')
inputnode.iterables = [('in_file', dataset)]
outputnode = pe.Node(niu.IdentityInterface(fields=['out_json']),
name='outputnode')
meta = pe.Node(ReadSidecarJSON(), name='metadata')
# 1a. Reorient anatomical image
to_ras = pe.Node(ConformImage(), name='conform')
# 1b. Estimate bias
n4itk = pe.Node(ants.N4BiasFieldCorrection(dimension=3, save_bias=True),
name='Bias')
# 2. Skull-stripping (afni)
asw = skullstrip_wf()
# 3. Head mask (including nasial-cerebelum mask)
hmsk = headmsk_wf()
# 4. Spatial Normalization, using ANTs
norm = pe.Node(RobustMNINormalization(
num_threads=settings.get('ants_nthreads', 6),
template='mni_icbm152_nlin_asym_09c',
testing=settings.get('testing', False),
generate_report=True),
name='SpatialNormalization')
# 5. Air mask (with and without artifacts)
amw = airmsk_wf()
# 6. Brain tissue segmentation
segment = pe.Node(fsl.FAST(img_type=1,
segments=True,
out_basename='segment'),
name='segmentation')
# 7. Compute IQMs
iqmswf = compute_iqms(settings)
# Reports
repwf = individual_reports(settings)
# Connect all nodes
workflow.connect([
(inputnode, to_ras, [('in_file', 'in_file')]),
(inputnode, meta, [('in_file', 'in_file')]),
(to_ras, n4itk, [('out_file', 'input_image')]),
(meta, iqmswf, [('subject_id', 'inputnode.subject_id'),
('session_id', 'inputnode.session_id'),
('run_id', 'inputnode.run_id')]),
(n4itk, asw, [('output_image', 'inputnode.in_file')]),
(asw, segment, [('outputnode.out_file', 'in_files')]),
(n4itk, hmsk, [('output_image', 'inputnode.in_file')]),
(segment, hmsk, [('tissue_class_map', 'inputnode.in_segm')]),
(n4itk, norm, [('output_image', 'moving_image')]),
(asw, norm, [('outputnode.out_mask', 'moving_mask')]),
(to_ras, amw, [('out_file', 'inputnode.in_file')]),
(norm, amw, [('reverse_transforms', 'inputnode.reverse_transforms'),
('reverse_invert_flags', 'inputnode.reverse_invert_flags')
]),
(norm, iqmswf, [('reverse_transforms', 'inputnode.reverse_transforms'),
('reverse_invert_flags',
'inputnode.reverse_invert_flags')]),
(norm, repwf, ([('out_report', 'inputnode.mni_report')])),
(asw, amw, [('outputnode.out_mask', 'inputnode.in_mask')]),
(hmsk, amw, [('outputnode.out_file', 'inputnode.head_mask')]),
(to_ras, iqmswf, [('out_file', 'inputnode.orig')]),
(n4itk, iqmswf, [('output_image', 'inputnode.inu_corrected'),
('bias_image', 'inputnode.in_inu')]),
(asw, iqmswf, [('outputnode.out_mask', 'inputnode.brainmask')]),
(amw, iqmswf, [('outputnode.out_file', 'inputnode.airmask'),
('outputnode.artifact_msk', 'inputnode.artmask')]),
(segment, iqmswf, [('tissue_class_map', 'inputnode.segmentation'),
('partial_volume_files', 'inputnode.pvms')]),
(meta, iqmswf, [('out_dict', 'inputnode.metadata')]),
(hmsk, iqmswf, [('outputnode.out_file', 'inputnode.headmask')]),
(to_ras, repwf, [('out_file', 'inputnode.orig')]),
(n4itk, repwf, [('output_image', 'inputnode.inu_corrected')]),
(asw, repwf, [('outputnode.out_mask', 'inputnode.brainmask')]),
(hmsk, repwf, [('outputnode.out_file', 'inputnode.headmask')]),
(amw, repwf, [('outputnode.out_file', 'inputnode.airmask'),
('outputnode.artifact_msk', 'inputnode.artmask')]),
(segment, repwf, [('tissue_class_map', 'inputnode.segmentation')]),
(iqmswf, repwf, [('outputnode.out_noisefit', 'inputnode.noisefit')]),
(iqmswf, repwf, [('outputnode.out_file', 'inputnode.in_iqms')]),
(iqmswf, outputnode, [('outputnode.out_file', 'out_json')])
])
return workflow
def fmri_qc_workflow(dataset, settings, name='funcMRIQC'):
"""
The fMRI qc workflow
.. workflow::
import os.path as op
from mriqc.workflows.functional import fmri_qc_workflow
datadir = op.abspath('data')
wf = fmri_qc_workflow([op.join(datadir, 'sub-001/func/sub-001_task-rest_bold.nii.gz')],
settings={'bids_dir': datadir,
'output_dir': op.abspath('out')})
"""
workflow = pe.Workflow(name=name)
biggest_file_gb = settings.get("biggest_file_size_gb", 1)
# Define workflow, inputs and outputs
# 0. Get data, put it in RAS orientation
inputnode = pe.Node(niu.IdentityInterface(fields=['in_file']),
name='inputnode')
WFLOGGER.info(
'Building fMRI QC workflow, datasets list: %s',
sorted([d.replace(settings['bids_dir'] + '/', '') for d in dataset]))
inputnode.iterables = [('in_file', dataset)]
meta = pe.Node(ReadSidecarJSON(), name='metadata')
outputnode = pe.Node(niu.IdentityInterface(
fields=['qc', 'mosaic', 'out_group', 'out_dvars', 'out_fd']),
name='outputnode')
reorient_and_discard = pe.Node(niu.Function(
input_names=['in_file', 'float32'],
output_names=['exclude_index', 'out_file'],
function=reorient_and_discard_non_steady),
name='reorient_and_discard')
reorient_and_discard.inputs.float32 = settings.get("float32",
DEFAULTS['float32'])
reorient_and_discard.interface.estimated_memory_gb = 4.0 * biggest_file_gb
# Workflow --------------------------------------------------------
# 1. HMC: head motion correct
if settings.get('hmc_fsl', False):
hmcwf = hmc_mcflirt(settings)
else:
hmcwf = hmc_afni(settings,
st_correct=settings.get('correct_slice_timing',
False),
despike=settings.get('despike', False),
deoblique=settings.get('deoblique', False),
start_idx=settings.get('start_idx', None),
stop_idx=settings.get('stop_idx', None))
# Set HMC settings
hmcwf.inputs.inputnode.fd_radius = settings.get('fd_radius',
DEFAULT_FD_RADIUS)
mean = pe.Node(
afni.TStat( # 2. Compute mean fmri
options='-mean', outputtype='NIFTI_GZ'),
name='mean')
mean.interface.estimated_memory_gb = biggest_file_gb * 1.5
skullstrip_epi = fmri_bmsk_workflow(use_bet=True)
# EPI to MNI registration
ema = epi_mni_align(settings)
# Compute TSNR using nipype implementation
tsnr = pe.Node(nac.TSNR(), name='compute_tsnr')
tsnr.interface.estimated_memory_gb = biggest_file_gb * 4.5
# 7. Compute IQMs
iqmswf = compute_iqms(settings)
# Reports
repwf = individual_reports(settings)
# Upload metrics
upldwf = upload_wf(settings)
workflow.connect([
(inputnode, meta, [('in_file', 'in_file')]),
(inputnode, reorient_and_discard, [('in_file', 'in_file')]),
(reorient_and_discard, hmcwf, [('out_file', 'inputnode.in_file')]),
(mean, skullstrip_epi, [('out_file', 'inputnode.in_file')]),
(hmcwf, mean, [('outputnode.out_file', 'in_file')]),
(hmcwf, tsnr, [('outputnode.out_file', 'in_file')]),
(mean, ema, [('out_file', 'inputnode.epi_mean')]),
(skullstrip_epi, ema, [('outputnode.out_file', 'inputnode.epi_mask')]),
(meta, iqmswf, [('subject_id', 'inputnode.subject_id'),
('session_id', 'inputnode.session_id'),
('task_id', 'inputnode.task_id'),
('acq_id', 'inputnode.acq_id'),
('rec_id', 'inputnode.rec_id'),
('run_id', 'inputnode.run_id'),
('out_dict', 'inputnode.metadata')]),
(reorient_and_discard, iqmswf, [('out_file', 'inputnode.orig')]),
(mean, iqmswf, [('out_file', 'inputnode.epi_mean')]),
(hmcwf, iqmswf, [('outputnode.out_file', 'inputnode.hmc_epi'),
('outputnode.out_fd', 'inputnode.hmc_fd')]),
(skullstrip_epi, iqmswf, [('outputnode.out_file',
'inputnode.brainmask')]),
(tsnr, iqmswf, [('tsnr_file', 'inputnode.in_tsnr')]),
(reorient_and_discard, repwf, [('out_file', 'inputnode.orig')]),
(mean, repwf, [('out_file', 'inputnode.epi_mean')]),
(tsnr, repwf, [('stddev_file', 'inputnode.in_stddev')]),
(skullstrip_epi, repwf, [('outputnode.out_file', 'inputnode.brainmask')
]),
(hmcwf, repwf, [('outputnode.out_fd', 'inputnode.hmc_fd'),
('outputnode.out_file', 'inputnode.hmc_epi')]),
(ema, repwf, [('outputnode.epi_parc', 'inputnode.epi_parc'),
('outputnode.report', 'inputnode.mni_report')]),
(reorient_and_discard, repwf, [('exclude_index',
'inputnode.exclude_index')]),
(iqmswf, repwf, [('outputnode.out_file', 'inputnode.in_iqms'),
('outputnode.out_dvars', 'inputnode.in_dvars'),
('outputnode.outliers', 'inputnode.outliers')]),
(iqmswf, upldwf, [('outputnode.out_file', 'inputnode.in_iqms')]),
(hmcwf, outputnode, [('outputnode.out_fd', 'out_fd')]),
])
if settings.get('fft_spikes_detector', False):
workflow.connect([
(iqmswf, repwf, [('outputnode.out_spikes', 'inputnode.in_spikes'),
('outputnode.out_fft', 'inputnode.in_fft')]),
])
if settings.get('ica', False):
melodic = pe.Node(nws.MELODICRPT(no_bet=True,
no_mask=True,
no_mm=True,
generate_report=True),
name="ICA")
melodic.interface.estimated_memory_gb = biggest_file_gb * 5
workflow.connect([
(reorient_and_discard, melodic, [('out_file', 'in_files')]),
(skullstrip_epi, melodic, [('outputnode.out_file', 'report_mask')
]),
(melodic, repwf, [('out_report', 'inputnode.ica_report')])
])
return workflow
def fmri_qc_workflow(dataset, settings, name='funcMRIQC'):
""" The fMRI qc workflow """
workflow = pe.Workflow(name=name)
# Define workflow, inputs and outputs
# 0. Get data, put it in RAS orientation
inputnode = pe.Node(niu.IdentityInterface(fields=['in_file']), name='inputnode')
WFLOGGER.info('Building fMRI QC workflow, datasets list: %s',
sorted([d.replace(settings['bids_dir'] + '/', '') for d in dataset]))
inputnode.iterables = [('in_file', dataset)]
meta = pe.Node(ReadSidecarJSON(), name='metadata')
outputnode = pe.Node(niu.IdentityInterface(
fields=['qc', 'mosaic', 'out_group', 'out_dvars',
'out_fd']), name='outputnode')
reorient_and_discard = pe.Node(niu.Function(input_names=['in_file'],
output_names=['exclude_index',
'out_file'],
function=reorient_and_discard_non_steady),
name='reorient_and_discard')
# Workflow --------------------------------------------------------
# 1. HMC: head motion correct
hmcwf = hmc_mcflirt()
if settings.get('hmc_afni', False):
hmcwf = hmc_afni(st_correct=settings.get('correct_slice_timing', False),
despike=settings.get('despike', False),
deoblique=settings.get('deoblique', False))
# Set HMC settings
hmcwf.inputs.inputnode.fd_radius = settings.get('fd_radius', DEFAULT_FD_RADIUS)
if settings.get('start_idx'):
hmcwf.inputs.inputnode.start_idx = settings['start_idx']
if settings.get('stop_idx'):
hmcwf.inputs.inputnode.stop_idx = settings['stop_idx']
mean = pe.Node(afni.TStat( # 2. Compute mean fmri
options='-mean', outputtype='NIFTI_GZ'), name='mean')
bmw = fmri_bmsk_workflow( # 3. Compute brain mask
use_bet=settings.get('use_bet', False))
# EPI to MNI registration
ema = epi_mni_align(ants_nthreads=settings['ants_nthreads'],
testing=settings.get('testing', False))
# Compute TSNR using nipype implementation
tsnr = pe.Node(nac.TSNR(), name='compute_tsnr')
# 7. Compute IQMs
iqmswf = compute_iqms(settings)
# Reports
repwf = individual_reports(settings)
workflow.connect([
(inputnode, meta, [('in_file', 'in_file')]),
(inputnode, reorient_and_discard, [('in_file', 'in_file')]),
(reorient_and_discard, hmcwf, [('out_file', 'inputnode.in_file')]),
(hmcwf, bmw, [('outputnode.out_file', 'inputnode.in_file')]),
(hmcwf, mean, [('outputnode.out_file', 'in_file')]),
(hmcwf, tsnr, [('outputnode.out_file', 'in_file')]),
(mean, ema, [('out_file', 'inputnode.epi_mean')]),
(bmw, ema, [('outputnode.out_file', 'inputnode.epi_mask')]),
(meta, iqmswf, [('subject_id', 'inputnode.subject_id'),
('session_id', 'inputnode.session_id'),
('task_id', 'inputnode.task_id'),
('run_id', 'inputnode.run_id'),
('out_dict', 'inputnode.metadata')]),
(reorient_and_discard, iqmswf, [('out_file', 'inputnode.orig')]),
(mean, iqmswf, [('out_file', 'inputnode.epi_mean')]),
(hmcwf, iqmswf, [('outputnode.out_file', 'inputnode.hmc_epi'),
('outputnode.out_fd', 'inputnode.hmc_fd')]),
(bmw, iqmswf, [('outputnode.out_file', 'inputnode.brainmask')]),
(tsnr, iqmswf, [('tsnr_file', 'inputnode.in_tsnr')]),
(reorient_and_discard, repwf, [('out_file', 'inputnode.orig')]),
(mean, repwf, [('out_file', 'inputnode.epi_mean')]),
(tsnr, repwf, [('stddev_file', 'inputnode.in_stddev')]),
(bmw, repwf, [('outputnode.out_file', 'inputnode.brainmask')]),
(hmcwf, repwf, [('outputnode.out_fd', 'inputnode.hmc_fd')]),
(ema, repwf, [('outputnode.epi_parc', 'inputnode.epi_parc'),
('outputnode.report', 'inputnode.mni_report')]),
(reorient_and_discard, repwf, [('exclude_index', 'inputnode.exclude_index')]),
(iqmswf, repwf, [('outputnode.out_file', 'inputnode.in_iqms'),
('outputnode.out_dvars', 'inputnode.in_dvars'),
('outputnode.out_spikes', 'inputnode.in_spikes'),
('outputnode.outliers', 'inputnode.outliers')]),
(hmcwf, outputnode, [('outputnode.out_fd', 'out_fd')]),
])
return workflow
def anat_qc_workflow(dataset, settings, mod='T1w', name='anatMRIQC'):
"""
One-subject-one-session-one-run pipeline to extract the NR-IQMs from
anatomical images
.. workflow::
import os.path as op
from mriqc.workflows.anatomical import anat_qc_workflow
datadir = op.abspath('data')
wf = anat_qc_workflow([op.join(datadir, 'sub-001/anat/sub-001_T1w.nii.gz')],
settings={'bids_dir': datadir,
'output_dir': op.abspath('out'),
'ants_nthreads': 1})
"""
workflow = pe.Workflow(name=name + mod)
WFLOGGER.info(
'Building anatomical MRI QC workflow, datasets list: %s',
sorted([d.replace(settings['bids_dir'] + '/', '') for d in dataset]))
# Define workflow, inputs and outputs
# 0. Get data
inputnode = pe.Node(niu.IdentityInterface(fields=['in_file']),
name='inputnode')
inputnode.iterables = [('in_file', dataset)]
outputnode = pe.Node(niu.IdentityInterface(fields=['out_json']),
name='outputnode')
meta = pe.Node(ReadSidecarJSON(), name='metadata')
# 1. Reorient anatomical image
to_ras = pe.Node(ConformImage(), name='conform')
# 2. Skull-stripping (afni)
asw = skullstrip_wf(n4_nthreads=settings.get('ants_nthreads', 1))
# 3. Head mask
hmsk = headmsk_wf()
# 4. Spatial Normalization, using ANTs
norm = spatial_normalization(settings)
# 5. Air mask (with and without artifacts)
amw = airmsk_wf()
# 6. Brain tissue segmentation
segment = pe.Node(fsl.FAST(segments=True,
out_basename='segment',
img_type=int(mod[1])),
name='segmentation',
estimated_memory_gb=3)
# 7. Compute IQMs
iqmswf = compute_iqms(settings, modality=mod)
# Reports
repwf = individual_reports(settings)
# Upload metrics
upldwf = upload_wf(settings)
# Connect all nodes
workflow.connect([
(inputnode, to_ras, [('in_file', 'in_file')]),
(inputnode, meta, [('in_file', 'in_file')]),
(meta, iqmswf, [('subject_id', 'inputnode.subject_id'),
('session_id', 'inputnode.session_id'),
('acq_id', 'inputnode.acq_id'),
('rec_id', 'inputnode.rec_id'),
('run_id', 'inputnode.run_id')]),
(to_ras, asw, [('out_file', 'inputnode.in_file')]),
(asw, segment, [('outputnode.out_file', 'in_files')]),
(asw, hmsk, [('outputnode.bias_corrected', 'inputnode.in_file')]),
(segment, hmsk, [('tissue_class_map', 'inputnode.in_segm')]),
(asw, norm, [('outputnode.bias_corrected', 'inputnode.moving_image'),
('outputnode.out_mask', 'inputnode.moving_mask')]),
(norm, amw, [('outputnode.inverse_composite_transform',
'inputnode.inverse_composite_transform')]),
(norm, iqmswf, [('outputnode.inverse_composite_transform',
'inputnode.inverse_composite_transform')]),
(norm, repwf, ([('outputnode.out_report', 'inputnode.mni_report')])),
(to_ras, amw, [('out_file', 'inputnode.in_file')]),
(asw, amw, [('outputnode.out_mask', 'inputnode.in_mask')]),
(hmsk, amw, [('outputnode.out_file', 'inputnode.head_mask')]),
(to_ras, iqmswf, [('out_file', 'inputnode.orig')]),
(asw, iqmswf, [('outputnode.bias_corrected',
'inputnode.inu_corrected'),
('outputnode.bias_image', 'inputnode.in_inu'),
('outputnode.out_mask', 'inputnode.brainmask')]),
(amw, iqmswf, [('outputnode.out_file', 'inputnode.airmask'),
('outputnode.artifact_msk', 'inputnode.artmask')]),
(segment, iqmswf, [('tissue_class_map', 'inputnode.segmentation'),
('partial_volume_files', 'inputnode.pvms')]),
(meta, iqmswf, [('out_dict', 'inputnode.metadata')]),
(hmsk, iqmswf, [('outputnode.out_file', 'inputnode.headmask')]),
(to_ras, repwf, [('out_file', 'inputnode.orig')]),
(asw, repwf, [('outputnode.bias_corrected', 'inputnode.inu_corrected'),
('outputnode.out_mask', 'inputnode.brainmask')]),
(hmsk, repwf, [('outputnode.out_file', 'inputnode.headmask')]),
(amw, repwf, [('outputnode.out_file', 'inputnode.airmask'),
('outputnode.artifact_msk', 'inputnode.artmask')]),
(segment, repwf, [('tissue_class_map', 'inputnode.segmentation')]),
(iqmswf, repwf, [('outputnode.out_noisefit', 'inputnode.noisefit')]),
(iqmswf, repwf, [('outputnode.out_file', 'inputnode.in_iqms')]),
(iqmswf, upldwf, [('outputnode.out_file', 'inputnode.in_iqms')]),
(iqmswf, outputnode, [('outputnode.out_file', 'out_json')])
])
return workflow