def t1w_preprocessing(name='t1w_preprocessing', settings=None):
"""T1w images preprocessing pipeline"""
if settings is None:
raise RuntimeError('Workflow settings are missing')
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['t1w']), name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['t1_seg', 't1_tpms', 'bias_corrected_t1', 't1_brain', 't1_mask',
't1_2_mni', 't1_2_mni_forward_transform',
't1_2_mni_reverse_transform']), name='outputnode')
# 0. Align and merge if several T1w images are provided
t1wmrg = pe.Node(IntraModalMerge(), name='MergeT1s')
# 1. Reorient T1
arw = pe.Node(niu.Function(input_names=['in_file'],
output_names=['out_file'],
function=reorient),
name='Reorient')
# 2. T1 Bias Field Correction
inu_n4 = pe.Node(ants.N4BiasFieldCorrection(dimension=3),
name='CorrectINU')
# 3. Skull-stripping
asw = skullstrip_wf()
if settings.get('skull_strip_ants', False):
asw = skullstrip_ants(settings=settings)
# 4. Segmentation
t1_seg = pe.Node(FASTRPT(generate_report=True, segments=True,
no_bias=True, probability_maps=True),
name='Segmentation')
# 5. Spatial normalization (T1w to MNI registration)
t1_2_mni = pe.Node(
RobustMNINormalizationRPT(
generate_report=True,
num_threads=settings['ants_nthreads'],
testing=settings.get('debug', False),
template='mni_icbm152_nlin_asym_09c'
),
name='T1_2_MNI_Registration'
)
# should not be necesssary byt does not hurt - make sure the multiproc
# scheduler knows the resource limits
t1_2_mni.interface.num_threads = settings['ants_nthreads']
# Resample the brain mask and the tissue probability maps into mni space
bmask_mni = pe.Node(
ants.ApplyTransforms(dimension=3, default_value=0,
interpolation='NearestNeighbor'),
name='brain_mni_warp'
)
bmask_mni.inputs.reference_image = op.join(get_mni_icbm152_nlin_asym_09c(),
'1mm_T1.nii.gz')
tpms_mni = pe.MapNode(
ants.ApplyTransforms(dimension=3, default_value=0,
interpolation='Linear'),
iterfield=['input_image'],
name='tpms_mni_warp'
)
tpms_mni.inputs.reference_image = op.join(get_mni_icbm152_nlin_asym_09c(),
'1mm_T1.nii.gz')
ds_t1_seg_report = pe.Node(
DerivativesDataSink(base_directory=settings['output_dir'],
suffix='t1_seg', out_path_base='reports'),
name='DS_T1_Seg_Report'
)
ds_t1_2_mni_report = pe.Node(
DerivativesDataSink(base_directory=settings['output_dir'],
suffix='t1_2_mni', out_path_base='reports'),
name='DS_T1_2_MNI_Report'
)
workflow.connect([
(inputnode, t1wmrg, [('t1w', 'in_files')]),
(t1wmrg, arw, [('out_avg', 'in_file')]),
(arw, inu_n4, [('out_file', 'input_image')]),
(inu_n4, asw, [('output_image', 'inputnode.in_file')]),
(asw, t1_seg, [('outputnode.out_file', 'in_files')]),
(inu_n4, t1_2_mni, [('output_image', 'moving_image')]),
(asw, t1_2_mni, [('outputnode.out_mask', 'moving_mask')]),
(t1_seg, outputnode, [('tissue_class_map', 't1_seg')]),
(inu_n4, outputnode, [('output_image', 'bias_corrected_t1')]),
(t1_seg, outputnode, [('probability_maps', 't1_tpms')]),
(t1_2_mni, outputnode, [
('warped_image', 't1_2_mni'),
('forward_transforms', 't1_2_mni_forward_transform'),
('reverse_transforms', 't1_2_mni_reverse_transform')
]),
(asw, bmask_mni, [('outputnode.out_mask', 'input_image')]),
(t1_2_mni, bmask_mni, [('forward_transforms', 'transforms'),
('forward_invert_flags',
'invert_transform_flags')]),
(t1_seg, tpms_mni, [('probability_maps', 'input_image')]),
(t1_2_mni, tpms_mni, [('forward_transforms', 'transforms'),
('forward_invert_flags', 'invert_transform_flags')]),
(asw, outputnode, [('outputnode.out_file', 't1_brain'),
('outputnode.out_mask', 't1_mask')]),
(inputnode, ds_t1_seg_report, [(('t1w', fix_multi_T1w_source_name), 'source_file')]),
(t1_seg, ds_t1_seg_report, [('out_report', 'in_file')]),
(inputnode, ds_t1_2_mni_report, [(('t1w', fix_multi_T1w_source_name), 'source_file')]),
(t1_2_mni, ds_t1_2_mni_report, [('out_report', 'in_file')])
])
if settings.get('skull_strip_ants', False):
ds_t1_skull_strip_report = pe.Node(
DerivativesDataSink(base_directory=settings['output_dir'],
suffix='t1_skull_strip', out_path_base='reports'),
name='DS_Report'
)
workflow.connect([
(inputnode, ds_t1_skull_strip_report, [(('t1w', fix_multi_T1w_source_name), 'source_file')]),
(asw, ds_t1_skull_strip_report, [('outputnode.out_report', 'in_file')])
])
# Write corrected file in the designated output dir
ds_t1_bias = pe.Node(
DerivativesDataSink(base_directory=settings['output_dir'],
suffix='preproc'),
name='DerivT1_inu'
)
ds_t1_seg = pe.Node(
DerivativesDataSink(base_directory=settings['output_dir'],
suffix='dtissue'),
name='DerivT1_seg'
)
ds_mask = pe.Node(
DerivativesDataSink(base_directory=settings['output_dir'],
suffix='brainmask'),
name='DerivT1_mask'
)
ds_t1_mni = pe.Node(
DerivativesDataSink(base_directory=settings['output_dir'],
suffix='space-MNI152NLin2009cAsym_preproc'),
name='DerivT1w_MNI'
)
ds_t1_mni_aff = pe.Node(
DerivativesDataSink(base_directory=settings['output_dir'],
suffix='target-MNI152NLin2009cAsym_affine'),
name='DerivT1w_MNI_affine'
)
ds_bmask_mni = pe.Node(
DerivativesDataSink(base_directory=settings['output_dir'],
suffix='space-MNI152NLin2009cAsym_brainmask'),
name='DerivT1_Mask_MNI'
)
ds_tpms_mni = pe.Node(
DerivativesDataSink(base_directory=settings['output_dir'],
suffix='space-MNI152NLin2009cAsym_class-{extra_value}_probtissue'),
name='DerivT1_TPMs_MNI'
)
ds_tpms_mni.inputs.extra_values = ['CSF', 'GM', 'WM']
if settings.get('debug', False):
workflow.connect([
(t1_2_mni, ds_t1_mni_aff, [('forward_transforms', 'in_file')])
])
else:
ds_t1_mni_warp = pe.Node(
DerivativesDataSink(base_directory=settings['output_dir'],
suffix='target-MNI152NLin2009cAsym_warp'), name='mni_warp')
def _get_aff(inlist):
return inlist[:-1]
def _get_warp(inlist):
return inlist[-1]
workflow.connect([
(inputnode, ds_t1_mni_warp, [(('t1w', fix_multi_T1w_source_name), 'source_file')]),
(t1_2_mni, ds_t1_mni_aff, [
(('forward_transforms', _get_aff), 'in_file')]),
(t1_2_mni, ds_t1_mni_warp, [
(('forward_transforms', _get_warp), 'in_file')])
])
workflow.connect([
(inputnode, ds_t1_bias, [(('t1w', fix_multi_T1w_source_name), 'source_file')]),
(inputnode, ds_t1_seg, [(('t1w', fix_multi_T1w_source_name), 'source_file')]),
(inputnode, ds_mask, [(('t1w', fix_multi_T1w_source_name), 'source_file')]),
(inputnode, ds_t1_mni, [(('t1w', fix_multi_T1w_source_name), 'source_file')]),
(inputnode, ds_t1_mni_aff, [(('t1w', fix_multi_T1w_source_name), 'source_file')]),
(inputnode, ds_bmask_mni, [(('t1w', fix_multi_T1w_source_name), 'source_file')]),
(inputnode, ds_tpms_mni, [(('t1w', fix_multi_T1w_source_name), 'source_file')]),
(inu_n4, ds_t1_bias, [('output_image', 'in_file')]),
(t1_seg, ds_t1_seg, [('tissue_class_map', 'in_file')]),
(asw, ds_mask, [('outputnode.out_mask', 'in_file')]),
(t1_2_mni, ds_t1_mni, [('warped_image', 'in_file')]),
(bmask_mni, ds_bmask_mni, [('output_image', 'in_file')]),
(tpms_mni, ds_tpms_mni, [('output_image', 'in_file')])
])
return workflow
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 anat_qc_workflow(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
from mriqc.testing import mock_config
with mock_config():
wf = anat_qc_workflow()
"""
from niworkflows.anat.skullstrip import afni_wf as skullstrip_wf
dataset = config.workflow.inputs.get(
"T1w", []) + config.workflow.inputs.get("T2w", [])
message = BUILDING_WORKFLOW.format(dataset=", ".join(dataset))
config.loggers.workflow.info(message)
# Initialize workflow
workflow = pe.Workflow(name=name)
# 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")
# 1. Reorient anatomical image
to_ras = pe.Node(ConformImage(check_dtype=False), name="conform")
# 2. Skull-stripping (afni)
asw = skullstrip_wf(n4_nthreads=config.nipype.omp_nthreads, unifize=False)
# 3. Head mask
hmsk = headmsk_wf()
# 4. Spatial Normalization, using ANTs
norm = spatial_normalization()
# 5. Air mask (with and without artifacts)
amw = airmsk_wf()
# 6. Brain tissue segmentation
segment = pe.Node(
fsl.FAST(segments=True, out_basename="segment"),
name="segmentation",
mem_gb=5,
)
# 7. Compute IQMs
iqmswf = compute_iqms()
# Reports
repwf = individual_reports()
# Connect all nodes
# fmt: off
workflow.connect([
(inputnode, to_ras, [("in_file", "in_file")]),
(inputnode, iqmswf, [("in_file", "inputnode.in_file")]),
(inputnode, norm, [(("in_file", _get_mod), "inputnode.modality")]),
(inputnode, segment, [(("in_file", _get_imgtype), "img_type")]),
(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.in_ras")]),
(asw, iqmswf, [("outputnode.bias_corrected",
"inputnode.inu_corrected"),
("outputnode.bias_image", "inputnode.in_inu"),
("outputnode.out_mask", "inputnode.brainmask")]),
(amw, iqmswf, [("outputnode.air_mask", "inputnode.airmask"),
("outputnode.hat_mask", "inputnode.hatmask"),
("outputnode.art_mask", "inputnode.artmask"),
("outputnode.rot_mask", "inputnode.rotmask")]),
(segment, iqmswf, [("tissue_class_map", "inputnode.segmentation"),
("partial_volume_files", "inputnode.pvms")]),
(hmsk, iqmswf, [("outputnode.out_file", "inputnode.headmask")]),
(to_ras, repwf, [("out_file", "inputnode.in_ras")]),
(asw, repwf, [("outputnode.bias_corrected", "inputnode.inu_corrected"),
("outputnode.out_mask", "inputnode.brainmask")]),
(hmsk, repwf, [("outputnode.out_file", "inputnode.headmask")]),
(amw, repwf, [("outputnode.air_mask", "inputnode.airmask"),
("outputnode.art_mask", "inputnode.artmask"),
("outputnode.rot_mask", "inputnode.rotmask")]),
(segment, repwf, [("tissue_class_map", "inputnode.segmentation")]),
(iqmswf, repwf, [("outputnode.noisefit", "inputnode.noisefit")]),
(iqmswf, repwf, [("outputnode.out_file", "inputnode.in_iqms")]),
(iqmswf, outputnode, [("outputnode.out_file", "out_json")]),
])
# fmt: on
# Upload metrics
if not config.execution.no_sub:
from ..interfaces.webapi import UploadIQMs
upldwf = pe.Node(UploadIQMs(), name="UploadMetrics")
upldwf.inputs.url = config.execution.webapi_url
upldwf.inputs.strict = config.execution.upload_strict
if config.execution.webapi_port:
upldwf.inputs.port = config.execution.webapi_port
# fmt: off
workflow.connect([
(iqmswf, upldwf, [("outputnode.out_file", "in_iqms")]),
(upldwf, repwf, [("api_id", "inputnode.api_id")]),
])
# fmt: on
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,
'no_sub': True})
"""
logging.getLogger('nipype.workflow').info(
'Building anatomical MRIQC workflow, datasets list: %s', [
str(Path(d).relative_to(settings['bids_dir']))
for d in sorted(dataset)
])
# Initialize workflow
workflow = pe.Workflow(name="%s%s" % (name, mod))
# 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')
# 1. Reorient anatomical image
to_ras = pe.Node(ConformImage(check_dtype=False), name='conform')
# 2. Skull-stripping (afni)
asw = skullstrip_wf(n4_nthreads=settings.get('ants_nthreads', 1),
unifize=False)
# 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',
mem_gb=5)
# 7. Compute IQMs
iqmswf = compute_iqms(settings, modality=mod)
# Reports
repwf = individual_reports(settings)
# Connect all nodes
workflow.connect([
(inputnode, to_ras, [('in_file', 'in_file')]),
(inputnode, iqmswf, [('in_file', 'inputnode.in_file')]),
(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.in_ras')]),
(asw, iqmswf, [('outputnode.bias_corrected',
'inputnode.inu_corrected'),
('outputnode.bias_image', 'inputnode.in_inu'),
('outputnode.out_mask', 'inputnode.brainmask')]),
(amw, iqmswf, [('outputnode.air_mask', 'inputnode.airmask'),
('outputnode.hat_mask', 'inputnode.hatmask'),
('outputnode.art_mask', 'inputnode.artmask'),
('outputnode.rot_mask', 'inputnode.rotmask')]),
(segment, iqmswf, [('tissue_class_map', 'inputnode.segmentation'),
('partial_volume_files', 'inputnode.pvms')]),
(hmsk, iqmswf, [('outputnode.out_file', 'inputnode.headmask')]),
(to_ras, repwf, [('out_file', 'inputnode.in_ras')]),
(asw, repwf, [('outputnode.bias_corrected', 'inputnode.inu_corrected'),
('outputnode.out_mask', 'inputnode.brainmask')]),
(hmsk, repwf, [('outputnode.out_file', 'inputnode.headmask')]),
(amw, repwf, [('outputnode.air_mask', 'inputnode.airmask'),
('outputnode.art_mask', 'inputnode.artmask'),
('outputnode.rot_mask', 'inputnode.rotmask')]),
(segment, repwf, [('tissue_class_map', 'inputnode.segmentation')]),
(iqmswf, repwf, [('outputnode.noisefit', 'inputnode.noisefit')]),
(iqmswf, repwf, [('outputnode.out_file', 'inputnode.in_iqms')]),
(iqmswf, outputnode, [('outputnode.out_file', 'out_json')])
])
# Upload metrics
if not settings.get('no_sub', False):
from ..interfaces.webapi import UploadIQMs
upldwf = pe.Node(UploadIQMs(), name='UploadMetrics')
upldwf.inputs.email = settings.get('email', '')
upldwf.inputs.url = settings.get('webapi_url')
if settings.get('webapi_port'):
upldwf.inputs.port = settings.get('webapi_port')
upldwf.inputs.strict = settings.get('upload_strict', False)
workflow.connect([
(iqmswf, upldwf, [('outputnode.out_file', 'in_iqms')]),
(upldwf, repwf, [('api_id', 'inputnode.api_id')]),
])
return workflow
def t1w_preprocessing(name='t1w_preprocessing', settings=None):
"""T1w images preprocessing pipeline"""
if settings is None:
raise RuntimeError('Workflow settings are missing')
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['t1w']), name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['t1_seg', 'bias_corrected_t1', 't1_brain', 't1_2_mni',
't1_2_mni_forward_transform', 't1_2_mni_reverse_transform',
't1_segmentation']), name='outputnode')
# 1. Reorient T1
arw = pe.Node(fs.MRIConvert(out_type='niigz', out_orientation='RAS'), name='Reorient')
# 2. T1 Bias Field Correction
inu_n4 = pe.Node(ants.N4BiasFieldCorrection(dimension=3), name='Bias_Field_Correction')
# 3. Skull-stripping
asw = skullstrip_wf()
if settings.get('skull_strip_ants', False):
asw = skullstrip_ants(settings=settings)
# 4. Segmentation
t1_seg = pe.Node(fsl.FAST(no_bias=True, probability_maps=True), name='T1_Segmentation')
# 5. T1w to MNI registration
t1_2_mni = pe.Node(ants.Registration(), name='T1_2_MNI_Registration')
t1_2_mni.inputs.collapse_output_transforms = False
t1_2_mni.inputs.write_composite_transform = False
t1_2_mni.inputs.output_transform_prefix = 'T1_to_MNI_'
t1_2_mni.inputs.output_warped_image = 't1_to_mni.nii.gz'
t1_2_mni.inputs.num_threads = 4
t1_2_mni.inputs.fixed_image = op.join(get_mni_template_ras(), 'MNI152_T1_1mm.nii.gz')
t1_2_mni.inputs.fixed_image_mask = op.join(
get_mni_template_ras(), 'MNI152_T1_1mm_brain_mask.nii.gz')
# Hack to avoid re-running ANTs all the times
grabber_interface = nio.JSONFileGrabber()
setattr(grabber_interface, '_always_run', False)
t1_2_mni_params = pe.Node(grabber_interface, name='t1_2_mni_params')
t1_2_mni_params.inputs.in_file = (
pkgr.resource_filename('fmriprep', 'data/{}.json'.format(
settings.get('ants_t1-mni_settings', 't1-mni_registration2')))
)
# Resampe the brain mask and the tissue probability maps into mni space
bmask_mni = pe.Node(ants.ApplyTransforms(
dimension=3, default_value=0, interpolation='NearestNeighbor'), name='brain_mni_warp')
bmask_mni.inputs.reference_image = op.join(get_mni_template_ras(), 'MNI152_T1_1mm.nii.gz')
tpms_mni = pe.MapNode(ants.ApplyTransforms(dimension=3, default_value=0, interpolation='Linear'),
iterfield=['input_image'], name='tpms_mni_warp')
tpms_mni.inputs.reference_image = op.join(get_mni_template_ras(), 'MNI152_T1_1mm.nii.gz')
workflow.connect([
(inputnode, arw, [('t1w', 'in_file')]),
(arw, inu_n4, [('out_file', 'input_image')]),
(inu_n4, asw, [('output_image', 'inputnode.in_file')]),
(asw, t1_seg, [('outputnode.out_file', 'in_files')]),
(inu_n4, t1_2_mni, [('output_image', 'moving_image')]),
(asw, t1_2_mni, [('outputnode.out_mask', 'moving_image_mask')]),
(t1_seg, outputnode, [('tissue_class_map', 't1_seg')]),
(inu_n4, outputnode, [('output_image', 'bias_corrected_t1')]),
(t1_seg, outputnode, [('tissue_class_map', 't1_segmentation')]),
(t1_2_mni, outputnode, [
('warped_image', 't1_2_mni'),
('forward_transforms', 't1_2_mni_forward_transform'),
('reverse_transforms', 't1_2_mni_reverse_transform')
]),
(asw, bmask_mni, [('outputnode.out_mask', 'input_image')]),
(t1_2_mni, bmask_mni, [('forward_transforms', 'transforms'),
('forward_invert_flags', 'invert_transform_flags')]),
(t1_seg, tpms_mni, [('probability_maps', 'input_image')]),
(t1_2_mni, tpms_mni, [('forward_transforms', 'transforms'),
('forward_invert_flags', 'invert_transform_flags')]),
(asw, outputnode, [
('outputnode.out_file', 't1_brain')]),
])
# Connect reporting nodes
t1_stripped_overlay = pe.Node(niu.Function(
input_names=['in_file', 'overlay_file', 'out_file'], output_names=['out_file'],
function=stripped_brain_overlay), name='PNG_T1_SkullStrip')
t1_stripped_overlay.inputs.out_file = 't1_stripped_overlay.png'
# The T1-to-MNI will be plotted using the segmentation. That's why we transform it first
seg_2_mni = pe.Node(ants.ApplyTransforms(
dimension=3, default_value=0, interpolation='NearestNeighbor'), name='T1_2_MNI_warp')
seg_2_mni.inputs.reference_image = op.join(get_mni_template_ras(), 'MNI152_T1_1mm.nii.gz')
t1_2_mni_overlay = pe.Node(niu.Function(
input_names=['in_file', 'overlay_file', 'out_file'], output_names=['out_file'],
function=stripped_brain_overlay), name='PNG_T1_to_MNI')
t1_2_mni_overlay.inputs.out_file = 't1_to_mni_overlay.png'
t1_2_mni_overlay.inputs.overlay_file = op.join(get_mni_template_ras(), 'MNI152_T1_1mm.nii.gz')
datasink = pe.Node(
interface=nio.DataSink(
base_directory=op.join(settings['output_dir'], 'images')),
name='datasink',
parameterization=False
)
workflow.connect([
(inu_n4, t1_stripped_overlay, [('output_image', 'overlay_file')]),
(asw, t1_stripped_overlay, [('outputnode.out_mask', 'in_file')]),
(t1_stripped_overlay, datasink, [('out_file', '@t1_stripped_overlay')]),
(t1_seg, seg_2_mni, [('tissue_class_map', 'input_image')]),
(t1_2_mni, seg_2_mni, [('forward_transforms', 'transforms'),
('forward_invert_flags', 'invert_transform_flags')]),
(seg_2_mni, t1_2_mni_overlay, [('output_image', 'in_file')]),
(t1_2_mni_overlay, datasink, [('out_file', '@t1_2_mni_overlay')]),
])
# Write corrected file in the designated output dir
ds_t1_bias = pe.Node(
DerivativesDataSink(base_directory=settings['output_dir'],
suffix='inu'), name='DerivT1_inu')
ds_t1_seg = pe.Node(
DerivativesDataSink(base_directory=settings['output_dir'],
suffix='inu_seg'), name='DerivT1_seg')
ds_mask = pe.Node(
DerivativesDataSink(base_directory=settings['output_dir'],
suffix='bmask'), name='DerivT1_mask')
ds_t1_mni = pe.Node(
DerivativesDataSink(base_directory=settings['output_dir'],
suffix='mni'), name='DerivT1w_MNI')
ds_t1_mni_aff = pe.Node(
DerivativesDataSink(base_directory=settings['output_dir'],
suffix='mni_affine'), name='DerivT1w_MNI_affine')
ds_bmask_mni = pe.Node(
DerivativesDataSink(base_directory=settings['output_dir'],
suffix='bmask_mni'), name='DerivT1_Mask_MNI')
ds_tpms_mni = pe.Node(
DerivativesDataSink(base_directory=settings['output_dir'],
suffix='tpm_mni'), name='DerivT1_TPMs_MNI')
if settings.get('debug', False):
workflow.connect([
(t1_2_mni, ds_t1_mni_aff, [('forward_transforms', 'in_file')])
])
else:
ds_t1_mni_warp = pe.Node(
DerivativesDataSink(base_directory=settings['output_dir'],
suffix='mni_warp'), name='DerivT1w_MNI_warp')
def _get_aff(inlist):
return inlist[:-1]
def _get_warp(inlist):
return inlist[-1]
workflow.connect([
(inputnode, ds_t1_mni_warp, [('t1w', 'source_file')]),
(t1_2_mni, ds_t1_mni_aff, [
(('forward_transforms', _get_aff), 'in_file')]),
(t1_2_mni, ds_t1_mni_warp, [
(('forward_transforms', _get_warp), 'in_file')])
])
workflow.connect([
(inputnode, ds_t1_bias, [('t1w', 'source_file')]),
(inputnode, ds_t1_seg, [('t1w', 'source_file')]),
(inputnode, ds_mask, [('t1w', 'source_file')]),
(inputnode, ds_t1_mni, [('t1w', 'source_file')]),
(inputnode, ds_t1_mni_aff, [('t1w', 'source_file')]),
(inputnode, ds_bmask_mni, [('t1w', 'source_file')]),
(inputnode, ds_tpms_mni, [('t1w', 'source_file')]),
(asw, ds_t1_bias, [('outputnode.out_file', 'in_file')]),
(t1_seg, ds_t1_seg, [('tissue_class_map', 'in_file')]),
(asw, ds_mask, [('outputnode.out_mask', 'in_file')]),
(t1_2_mni, ds_t1_mni, [('warped_image', 'in_file')]),
(bmask_mni, ds_bmask_mni, [('output_image', 'in_file')]),
(tpms_mni, ds_tpms_mni, [('output_image', 'in_file')])
])
# ANTs inputs connected here for clarity
workflow.connect([
(t1_2_mni_params, t1_2_mni, [
('metric', 'metric'),
('metric_weight', 'metric_weight'),
('dimension', 'dimension'),
('write_composite_transform', 'write_composite_transform'),
('radius_or_number_of_bins', 'radius_or_number_of_bins'),
('shrink_factors', 'shrink_factors'),
('smoothing_sigmas', 'smoothing_sigmas'),
('sigma_units', 'sigma_units'),
('output_transform_prefix', 'output_transform_prefix'),
('transforms', 'transforms'),
('transform_parameters', 'transform_parameters'),
('initial_moving_transform_com', 'initial_moving_transform_com'),
('number_of_iterations', 'number_of_iterations'),
('convergence_threshold', 'convergence_threshold'),
('convergence_window_size', 'convergence_window_size'),
('sampling_strategy', 'sampling_strategy'),
('sampling_percentage', 'sampling_percentage'),
('output_warped_image', 'output_warped_image'),
('use_histogram_matching', 'use_histogram_matching'),
('use_estimate_learning_rate_once',
'use_estimate_learning_rate_once'),
('collapse_output_transforms', 'collapse_output_transforms'),
('winsorize_lower_quantile', 'winsorize_lower_quantile'),
('winsorize_upper_quantile', 'winsorize_upper_quantile'),
])
])
return workflow
def anat_qc_workflow(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
from mriqc.testing import mock_config
with mock_config():
wf = anat_qc_workflow()
"""
from niworkflows.anat.skullstrip import afni_wf as skullstrip_wf
dataset = config.workflow.inputs.get("T1w", []) \
+ config.workflow.inputs.get("T2w", [])
config.loggers.workflow.info(f"""\
Building anatomical MRIQC workflow for files: {', '.join(dataset)}.""")
# Initialize workflow
workflow = pe.Workflow(name=name)
# 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')
# 1. Reorient anatomical image
to_ras = pe.Node(ConformImage(check_dtype=False), name='conform')
# 2. Skull-stripping (afni)
asw = skullstrip_wf(n4_nthreads=config.nipype.omp_nthreads, unifize=False)
# 3. Head mask
hmsk = headmsk_wf()
# 4. Spatial Normalization, using ANTs
norm = spatial_normalization()
# 5. Air mask (with and without artifacts)
amw = airmsk_wf()
# 6. Brain tissue segmentation
segment = pe.Node(fsl.FAST(segments=True, out_basename='segment'),
name='segmentation',
mem_gb=5)
# 7. Compute IQMs
iqmswf = compute_iqms()
# Reports
repwf = individual_reports()
# Connect all nodes
workflow.connect([
(inputnode, to_ras, [('in_file', 'in_file')]),
(inputnode, iqmswf, [('in_file', 'inputnode.in_file')]),
(inputnode, norm, [(('in_file', _get_mod), 'inputnode.modality')]),
(inputnode, segment, [(('in_file', _get_imgtype), 'img_type')]),
(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.in_ras')]),
(asw, iqmswf, [('outputnode.bias_corrected',
'inputnode.inu_corrected'),
('outputnode.bias_image', 'inputnode.in_inu'),
('outputnode.out_mask', 'inputnode.brainmask')]),
(amw, iqmswf, [('outputnode.air_mask', 'inputnode.airmask'),
('outputnode.hat_mask', 'inputnode.hatmask'),
('outputnode.art_mask', 'inputnode.artmask'),
('outputnode.rot_mask', 'inputnode.rotmask')]),
(segment, iqmswf, [('tissue_class_map', 'inputnode.segmentation'),
('partial_volume_files', 'inputnode.pvms')]),
(hmsk, iqmswf, [('outputnode.out_file', 'inputnode.headmask')]),
(to_ras, repwf, [('out_file', 'inputnode.in_ras')]),
(asw, repwf, [('outputnode.bias_corrected', 'inputnode.inu_corrected'),
('outputnode.out_mask', 'inputnode.brainmask')]),
(hmsk, repwf, [('outputnode.out_file', 'inputnode.headmask')]),
(amw, repwf, [('outputnode.air_mask', 'inputnode.airmask'),
('outputnode.art_mask', 'inputnode.artmask'),
('outputnode.rot_mask', 'inputnode.rotmask')]),
(segment, repwf, [('tissue_class_map', 'inputnode.segmentation')]),
(iqmswf, repwf, [('outputnode.noisefit', 'inputnode.noisefit')]),
(iqmswf, repwf, [('outputnode.out_file', 'inputnode.in_iqms')]),
(iqmswf, outputnode, [('outputnode.out_file', 'out_json')])
])
# Upload metrics
if not config.execution.no_sub:
from ..interfaces.webapi import UploadIQMs
upldwf = pe.Node(UploadIQMs(), name='UploadMetrics')
upldwf.inputs.url = config.execution.webapi_url
upldwf.inputs.strict = config.execution.upload_strict
if config.execution.webapi_port:
upldwf.inputs.port = config.execution.webapi_port
workflow.connect([
(iqmswf, upldwf, [('outputnode.out_file', 'in_iqms')]),
(upldwf, repwf, [('api_id', 'inputnode.api_id')]),
])
return workflow
def anat_qc_workflow(name='MRIQC_Anat', settings=None):
"""
One-subject-one-session-one-run pipeline to extract the NR-IQMs from
anatomical images
"""
if settings is None:
settings = {}
workflow = pe.Workflow(name=name)
deriv_dir = op.abspath(op.join(settings['output_dir'], 'derivatives'))
if not op.exists(deriv_dir):
os.makedirs(deriv_dir)
# Define workflow, inputs and outputs
inputnode = pe.Node(niu.IdentityInterface(
fields=['bids_dir', 'subject_id', 'session_id', 'run_id']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['out_json']),
name='outputnode')
# 0. Get data
datasource = pe.Node(niu.Function(input_names=[
'bids_dir', 'data_type', 'subject_id', 'session_id', 'run_id'
],
output_names=['anatomical_scan'],
function=bids_getfile),
name='datasource')
datasource.inputs.data_type = 'anat'
meta = pe.Node(ReadSidecarJSON(), name='metadata')
# 1a. Reorient anatomical image
arw = pe.Node(MRIConvert(out_type='niigz', out_orientation='LAS'),
name='Reorient')
# 1b. Estimate bias
n4itk = pe.Node(ants.N4BiasFieldCorrection(dimension=3, save_bias=True),
name='Bias')
# 2. Skull-stripping (afni)
asw = skullstrip_wf()
mask = pe.Node(fsl.ApplyMask(), name='MaskAnatomical')
# 3. Head mask (including nasial-cerebelum mask)
hmsk = headmsk_wf()
# 4. Air mask (with and without artifacts)
amw = airmsk_wf(settings=settings)
# Brain tissue segmentation
segment = pe.Node(fsl.FAST(img_type=1,
segments=True,
out_basename='segment'),
name='segmentation')
# AFNI check smoothing
fwhm = pe.Node(afp.FWHMx(combine=True, detrend=True), name='smoothness')
# fwhm.inputs.acf = True # add when AFNI >= 16
# Compute python-coded measures
measures = pe.Node(StructuralQC(testing=settings.get('testing', False)),
'measures')
# Link images that should be reported
dsreport = pe.Node(nio.DataSink(base_directory=settings['report_dir'],
parameterization=True),
name='dsreport')
dsreport.inputs.container = 'anat'
dsreport.inputs.substitutions = [('_data', ''),
('background_fit', 'plot_bgfit')]
dsreport.inputs.regexp_substitutions = [
('_u?(sub-[\\w\\d]*)\\.([\\w\\d_]*)(?:\\.([\\w\\d_-]*))+',
'\\1_ses-\\2_\\3'),
('anatomical_bgplotsub-[^/.]*_dvars_std', 'plot_dvars'),
('sub-[^/.]*_T1w_out_calc_thresh', 'mask'),
('sub-[^/.]*_T1w_out\\.', 'mosaic_t1w.')
]
# Connect all nodes
workflow.connect([
(inputnode, datasource, [('bids_dir', 'bids_dir'),
('subject_id', 'subject_id'),
('session_id', 'session_id'),
('run_id', 'run_id')]),
(datasource, arw, [('anatomical_scan', 'in_file')]),
(datasource, meta, [('anatomical_scan', 'in_file')]),
(arw, asw, [('out_file', 'inputnode.in_file')]),
(arw, n4itk, [('out_file', 'input_image')]),
# (asw, n4itk, [('outputnode.out_mask', 'mask_image')]),
(n4itk, mask, [('output_image', 'in_file')]),
(asw, mask, [('outputnode.out_mask', 'mask_file')]),
(mask, segment, [('out_file', 'in_files')]),
(n4itk, hmsk, [('output_image', 'inputnode.in_file')]),
(segment, hmsk, [('tissue_class_map', 'inputnode.in_segm')]),
(n4itk, measures, [('output_image', 'in_noinu')]),
(arw, measures, [('out_file', 'in_file')]),
(arw, fwhm, [('out_file', 'in_file')]),
(asw, fwhm, [('outputnode.out_mask', 'mask')]),
(arw, amw, [('out_file', 'inputnode.in_file')]),
(n4itk, amw, [('output_image', 'inputnode.in_noinu')]),
(asw, amw, [('outputnode.out_mask', 'inputnode.in_mask')]),
(hmsk, amw, [('outputnode.out_file', 'inputnode.head_mask')]),
(amw, measures, [('outputnode.out_file', 'air_msk')]),
(amw, measures, [('outputnode.artifact_msk', 'artifact_msk')]),
(segment, measures, [('tissue_class_map', 'in_segm'),
('partial_volume_files', 'in_pvms')]),
(n4itk, measures, [('bias_image', 'in_bias')]),
(measures, dsreport, [('out_noisefit', '@anat_noiseplot')]),
(arw, dsreport, [('out_file', '@anat_t1w')]),
(asw, dsreport, [('outputnode.out_mask', '@anat_t1_mask')])
])
# Format name
out_name = pe.Node(niu.Function(
input_names=['subid', 'sesid', 'runid', 'prefix', 'out_path'],
output_names=['out_file'],
function=bids_path),
name='FormatName')
out_name.inputs.out_path = deriv_dir
out_name.inputs.prefix = 'anat'
# Save to JSON file
jfs_if = nio.JSONFileSink()
setattr(jfs_if, '_always_run', settings.get('force_run', False))
datasink = pe.Node(jfs_if, name='datasink')
datasink.inputs.qc_type = 'anat'
workflow.connect([(inputnode, out_name, [('subject_id', 'subid'),
('session_id', 'sesid'),
('run_id', 'runid')]),
(inputnode, datasink, [('subject_id', 'subject_id'),
('session_id', 'session_id'),
('run_id', 'run_id')]),
(fwhm, datasink, [(('fwhm', fwhm_dict), 'fwhm')]),
(measures, datasink, [('summary', 'summary'),
('spacing', 'spacing'),
('size', 'size'), ('icvs', 'icvs'),
('rpve', 'rpve'), ('inu', 'inu'),
('snr', 'snr'), ('cnr', 'cnr'),
('fber', 'fber'), ('efc', 'efc'),
('qi1', 'qi1'), ('qi2', 'qi2'),
('cjv', 'cjv'),
('wm2max', 'wm2max')]),
(out_name, datasink, [('out_file', 'out_file')]),
(meta, datasink, [('out_dict', 'metadata')]),
(datasink, outputnode, [('out_file', 'out_file')])])
return workflow
def dwi_qc_workflow(dataset, settings, mod='dwi', name='dwiMRIQC'):
"""
One-subject-one-session-one-run pipeline to extract the NR-IQMs from
diffusion weighted images
.. workflow::
import os.path as op
from mriqc.workflows.diffusion import dwi_qc_workflow
datadir = op.abspath('data')
wf = dwi_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,
'no_sub': True})
"""
workflow = pe.Workflow(name=name+mod)
WFLOGGER.info('Building diffusion weighted 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, in_bval']), name='inputnode')
inputnode.iterables = [('in_file', dataset)]
outputnode = pe.Node(niu.IdentityInterface(fields=['out_json']), name='outputnode')
# 1. Reorient anatomical image
to_ras = pe.Node(ConformImage(check_dtype=False), name='conform')
# 2. Extract b0 images
extb0 = pe.Node(ExtractB0, name='ExtractB0s')
# 3. Eddy correction with fsl FLIRT
flirt = pe.MapNode(fsl.FLIRT(out_matrix_file='affine_xfrm'), name='flirt')
# 4. Average b0 images
avgb0 = avgb0_wf()
# 5. Skull-stripping (afni)
asw = skullstrip_wf(n4_nthreads=settings.get('ants_nthreads', 1), unifize=False)
# 6. Head mask
hmsk = headmsk_wf()
# 7. Spatial Normalization, using ANTs
norm = spatial_normalization(settings)
# 8. Air mask (with and without artifacts)
amw = airmsk_wf()
# 9. Brain tissue segmentation
segment = pe.Node(fsl.FAST(segments=True, out_basename='segment', img_type=int(mod[1])),
name='segmentation', estimated_memory_gb=3)
# 10. Compute IQMs
iqmswf = compute_iqms(settings, modality=mod)
# Reports
repwf = individual_reports(settings)
# Connect all nodes
workflow.connect([
(inputnode, to_ras, [('in_file', 'in_file')]),
(inputnode, iqmswf, [('in_file', 'inputnode.in_file')]),
(inputnode, extb0, [('in_bval', 'in_bval')]),
(to_ras, extb0, [('out_file', 'in_dwi')]),
(extb0, flirt, [('ref_img', 'reference')]),
(extb0, flirt, [('out_imgs', 'in_file')]),
(flirt, avgb0, [('out_file', 'inputnode.in_files')]),
(avgb0, 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')])),
(avgb0, amw, [('out_file', 'inputnode.in_file')]),
(asw, amw, [('outputnode.out_mask', 'inputnode.in_mask')]),
(hmsk, amw, [('outputnode.out_file', 'inputnode.head_mask')]),
(avgb0, iqmswf, [('out_file', 'inputnode.in_ras')]),
(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'),
('outputnode.rot_mask', 'inputnode.rotmask')]),
(segment, iqmswf, [('tissue_class_map', 'inputnode.segmentation'),
('partial_volume_files', 'inputnode.pvms')]),
(hmsk, iqmswf, [('outputnode.out_file', 'inputnode.headmask')]),
(avgb0, repwf, [('out_file', 'inputnode.in_ras')]),
(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'),
('outputnode.rot_mask', 'inputnode.rotmask')]),
(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')])
])
# Upload metrics
if not settings.get('no_sub', False):
from ..interfaces.webapi import UploadIQMs
upldwf = pe.Node(UploadIQMs(), name='UploadMetrics')
upldwf.inputs.email = settings.get('email', '')
upldwf.inputs.url = settings.get('webapi_url')
if settings.get('webapi_port'):
upldwf.inputs.port = settings.get('webapi_port')
upldwf.inputs.strict = settings.get('upload_strict', False)
workflow.connect([
(iqmswf, upldwf, [('outputnode.out_file', 'in_iqms')]),
])
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,
'no_sub': True})
"""
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')
# 1. Reorient anatomical image
to_ras = pe.Node(ConformImage(check_dtype=False), name='conform')
# 2. Skull-stripping (afni)
asw = skullstrip_wf(n4_nthreads=settings.get('ants_nthreads', 1), unifize=False)
# 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)
# Connect all nodes
workflow.connect([
(inputnode, to_ras, [('in_file', 'in_file')]),
(inputnode, iqmswf, [('in_file', 'inputnode.in_file')]),
(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.in_ras')]),
(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'),
('outputnode.rot_mask', 'inputnode.rotmask')]),
(segment, iqmswf, [('tissue_class_map', 'inputnode.segmentation'),
('partial_volume_files', 'inputnode.pvms')]),
(hmsk, iqmswf, [('outputnode.out_file', 'inputnode.headmask')]),
(to_ras, repwf, [('out_file', 'inputnode.in_ras')]),
(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'),
('outputnode.rot_mask', 'inputnode.rotmask')]),
(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')])
])
# Upload metrics
if not settings.get('no_sub', False):
from ..interfaces.webapi import UploadIQMs
upldwf = pe.Node(UploadIQMs(), name='UploadMetrics')
upldwf.inputs.email = settings.get('email', '')
upldwf.inputs.url = settings.get('webapi_url')
if settings.get('webapi_port'):
upldwf.inputs.port = settings.get('webapi_port')
upldwf.inputs.strict = settings.get('upload_strict', False)
workflow.connect([
(iqmswf, upldwf, [('outputnode.out_file', 'in_iqms')]),
])
return workflow
def anat_qc_workflow(name='MRIQC_Anat', settings=None):
"""
One-subject-one-session-one-run pipeline to extract the NR-IQMs from
anatomical images
"""
if settings is None:
settings = {}
workflow = pe.Workflow(name=name)
deriv_dir = op.abspath('./derivatives')
if 'work_dir' in settings.keys():
deriv_dir = op.abspath(op.join(settings['work_dir'], 'derivatives'))
if not op.exists(deriv_dir):
os.makedirs(deriv_dir)
# Define workflow, inputs and outputs
inputnode = pe.Node(niu.IdentityInterface(
fields=['bids_root', 'subject_id', 'session_id',
'run_id']), name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['out_json']), name='outputnode')
# 0. Get data
datasource = pe.Node(niu.Function(
input_names=['bids_root', 'data_type', 'subject_id', 'session_id', 'run_id'],
output_names=['anatomical_scan'], function=bids_getfile), name='datasource')
datasource.inputs.data_type = 'anat'
# 1a. Reorient anatomical image
arw = mri_reorient_wf()
# 1b. Estimate bias
n4itk = pe.Node(ants.N4BiasFieldCorrection(dimension=3, save_bias=True), name='Bias')
# 2. Skull-stripping (afni)
asw = skullstrip_wf()
mask = pe.Node(fsl.ApplyMask(), name='MaskAnatomical')
# 3. Head mask (including nasial-cerebelum mask)
hmsk = headmsk_wf()
# 4. Air mask (with and without artifacts)
amw = airmsk_wf(save_memory=settings.get('save_memory', False),
ants_settings=settings.get('ants_settings', None))
# Brain tissue segmentation
segment = pe.Node(fsl.FAST(
img_type=1, segments=True, out_basename='segment'), name='segmentation')
# AFNI check smoothing
fwhm = pe.Node(afp.FWHMx(combine=True, detrend=True), name='smoothness')
# fwhm.inputs.acf = True # add when AFNI >= 16
# Compute python-coded measures
measures = pe.Node(StructuralQC(), 'measures')
# Plot mosaic
plot = pe.Node(PlotMosaic(), name='plot_mosaic')
merg = pe.Node(niu.Merge(3), name='plot_metadata')
# Connect all nodes
workflow.connect([
(inputnode, datasource, [('bids_root', 'bids_root'),
('subject_id', 'subject_id'),
('session_id', 'session_id'),
('run_id', 'run_id')]),
(datasource, arw, [('anatomical_scan', 'inputnode.in_file')]),
(arw, asw, [('outputnode.out_file', 'inputnode.in_file')]),
(arw, n4itk, [('outputnode.out_file', 'input_image')]),
# (asw, n4itk, [('outputnode.out_mask', 'mask_image')]),
(n4itk, mask, [('output_image', 'in_file')]),
(asw, mask, [('outputnode.out_mask', 'mask_file')]),
(mask, segment, [('out_file', 'in_files')]),
(n4itk, hmsk, [('output_image', 'inputnode.in_file')]),
(segment, hmsk, [('tissue_class_map', 'inputnode.in_segm')]),
(n4itk, measures, [('output_image', 'in_noinu')]),
(arw, measures, [('outputnode.out_file', 'in_file')]),
(arw, fwhm, [('outputnode.out_file', 'in_file')]),
(asw, fwhm, [('outputnode.out_mask', 'mask')]),
(arw, amw, [('outputnode.out_file', 'inputnode.in_file')]),
(n4itk, amw, [('output_image', 'inputnode.in_noinu')]),
(asw, amw, [('outputnode.out_mask', 'inputnode.in_mask')]),
(hmsk, amw, [('outputnode.out_file', 'inputnode.head_mask')]),
(amw, measures, [('outputnode.out_file', 'air_msk')]),
(amw, measures, [('outputnode.artifact_msk', 'artifact_msk')]),
(segment, measures, [('tissue_class_map', 'in_segm'),
('partial_volume_files', 'in_pvms')]),
(n4itk, measures, [('bias_image', 'in_bias')]),
(arw, plot, [('outputnode.out_file', 'in_file')]),
(inputnode, plot, [('subject_id', 'subject')]),
(inputnode, merg, [('session_id', 'in1'),
('run_id', 'in2')]),
(merg, plot, [('out', 'metadata')])
])
if settings.get('mask_mosaic', False):
workflow.connect(asw, 'outputnode.out_file', plot, 'in_mask')
# Save mosaic to well-formed path
mvplot = pe.Node(niu.Rename(
format_string='anatomical_%(subject_id)s_%(session_id)s_%(run_id)s',
keep_ext=True), name='rename_plot')
dsplot = pe.Node(nio.DataSink(
base_directory=settings['work_dir'], parameterization=False), name='ds_plot')
workflow.connect([
(inputnode, mvplot, [('subject_id', 'subject_id'),
('session_id', 'session_id'),
('run_id', 'run_id')]),
(plot, mvplot, [('out_file', 'in_file')]),
(mvplot, dsplot, [('out_file', '@mosaic')])
])
# Save background-noise fitting plot
mvbgplot = pe.Node(niu.Rename(
format_string='anatomical_bgplot_%(subject_id)s_%(session_id)s_%(run_id)s',
keep_ext=True), name='rename_bgplot')
dsbgplot = pe.Node(nio.DataSink(
base_directory=settings['work_dir'], parameterization=False), name='ds_bgplot')
workflow.connect([
(inputnode, mvbgplot, [('subject_id', 'subject_id'),
('session_id', 'session_id'),
('run_id', 'run_id')]),
(measures, mvbgplot, [('out_noisefit', 'in_file')]),
(mvbgplot, dsbgplot, [('out_file', '@bg_fitting')])
])
# Format name
out_name = pe.Node(niu.Function(
input_names=['subid', 'sesid', 'runid', 'prefix', 'out_path'], output_names=['out_file'],
function=bids_path), name='FormatName')
out_name.inputs.out_path = deriv_dir
out_name.inputs.prefix = 'anat'
# Save to JSON file
jfs_if = nio.JSONFileSink()
setattr(jfs_if, '_always_run', settings.get('force_run', False))
datasink = pe.Node(jfs_if, name='datasink')
datasink.inputs.qc_type = 'anat'
workflow.connect([
(inputnode, out_name, [('subject_id', 'subid'),
('session_id', 'sesid'),
('run_id', 'runid')]),
(inputnode, datasink, [('subject_id', 'subject_id'),
('session_id', 'session_id'),
('run_id', 'run_id')]),
(plot, datasink, [('out_file', 'mosaic_file')]),
(fwhm, datasink, [(('fwhm', fwhm_dict), 'fwhm')]),
(measures, datasink, [('summary', 'summary'),
('spacing', 'spacing'),
('size', 'size'),
('icvs', 'icvs'),
('rpve', 'rpve'),
('inu', 'inu'),
('snr', 'snr'),
('cnr', 'cnr'),
('fber', 'fber'),
('efc', 'efc'),
('qi1', 'qi1'),
('qi2', 'qi2'),
('cjv', 'cjv')]),
(out_name, datasink, [('out_file', 'out_file')]),
(datasink, outputnode, [('out_file', 'out_file')])
])
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
def anat_qc_workflow(name='MRIQC_Anat', settings=None):
"""
One-subject-one-session-one-run pipeline to extract the NR-IQMs from
anatomical images
"""
if settings is None:
settings = {}
workflow = pe.Workflow(name=name)
# Define workflow, inputs and outputs
inputnode = pe.Node(niu.IdentityInterface(
fields=['bids_dir', 'subject_id', 'session_id',
'run_id']), name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['out_json']), name='outputnode')
# 0. Get data
datasource = pe.Node(niu.Function(
input_names=['bids_dir', 'data_type', 'subject_id', 'session_id', 'run_id'],
output_names=['anatomical_scan'], function=bids_getfile), name='datasource')
datasource.inputs.data_type = 'anat'
meta = pe.Node(ReadSidecarJSON(), name='metadata')
# 1a. Reorient anatomical image
to_ras = pe.Node(niu.Function(input_names=['in_file'],
output_names=["out_file"],
function=reorient), name='Reorient')
# 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)), 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, datasource, [('bids_dir', 'bids_dir'),
('subject_id', 'subject_id'),
('session_id', 'session_id'),
('run_id', 'run_id')]),
(inputnode, iqmswf, [('subject_id', 'inputnode.subject_id'),
('session_id', 'inputnode.session_id'),
('run_id', 'inputnode.run_id')]),
(inputnode, repwf, [('subject_id', 'inputnode.subject_id'),
('session_id', 'inputnode.session_id'),
('run_id', 'inputnode.run_id')]),
(datasource, to_ras, [('anatomical_scan', 'in_file')]),
(datasource, meta, [('anatomical_scan', 'in_file')]),
(to_ras, n4itk, [('out_file', 'input_image')]),
(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')]),
(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 anat_qc_workflow(name='MRIQC_Anat', settings=None):
"""
One-subject-one-session-one-run pipeline to extract the NR-IQMs from
anatomical images
"""
if settings is None:
settings = {}
workflow = pe.Workflow(name=name)
deriv_dir = op.abspath('./derivatives')
if 'work_dir' in settings.keys():
deriv_dir = op.abspath(op.join(settings['work_dir'], 'derivatives'))
if not op.exists(deriv_dir):
os.makedirs(deriv_dir)
# Define workflow, inputs and outputs
inputnode = pe.Node(niu.IdentityInterface(
fields=['bids_root', 'subject_id', 'session_id', 'run_id']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['out_json']),
name='outputnode')
# 0. Get data
datasource = pe.Node(niu.Function(input_names=[
'bids_root', 'data_type', 'subject_id', 'session_id', 'run_id'
],
output_names=['anatomical_scan'],
function=bids_getfile),
name='datasource')
datasource.inputs.data_type = 'anat'
# 1a. Reorient anatomical image
arw = mri_reorient_wf()
# 1b. Estimate bias
n4itk = pe.Node(ants.N4BiasFieldCorrection(dimension=3, save_bias=True),
name='Bias')
# 2. Skull-stripping (afni)
asw = skullstrip_wf()
mask = pe.Node(fsl.ApplyMask(), name='MaskAnatomical')
# 3. Head mask (including nasial-cerebelum mask)
hmsk = headmsk_wf()
# 4. Air mask (with and without artifacts)
amw = airmsk_wf(save_memory=settings.get('save_memory', False),
ants_settings=settings.get('ants_settings', None))
# Brain tissue segmentation
segment = pe.Node(fsl.FAST(img_type=1,
segments=True,
out_basename='segment'),
name='segmentation')
# AFNI check smoothing
fwhm = pe.Node(afp.FWHMx(combine=True, detrend=True), name='smoothness')
# fwhm.inputs.acf = True # add when AFNI >= 16
# Compute python-coded measures
measures = pe.Node(StructuralQC(), 'measures')
# Plot mosaic
plot = pe.Node(PlotMosaic(), name='plot_mosaic')
merg = pe.Node(niu.Merge(3), name='plot_metadata')
# Connect all nodes
workflow.connect([
(inputnode, datasource, [('bids_root', 'bids_root'),
('subject_id', 'subject_id'),
('session_id', 'session_id'),
('run_id', 'run_id')]),
(datasource, arw, [('anatomical_scan', 'inputnode.in_file')]),
(arw, asw, [('outputnode.out_file', 'inputnode.in_file')]),
(arw, n4itk, [('outputnode.out_file', 'input_image')]),
# (asw, n4itk, [('outputnode.out_mask', 'mask_image')]),
(n4itk, mask, [('output_image', 'in_file')]),
(asw, mask, [('outputnode.out_mask', 'mask_file')]),
(mask, segment, [('out_file', 'in_files')]),
(n4itk, hmsk, [('output_image', 'inputnode.in_file')]),
(segment, hmsk, [('tissue_class_map', 'inputnode.in_segm')]),
(n4itk, measures, [('output_image', 'in_noinu')]),
(arw, measures, [('outputnode.out_file', 'in_file')]),
(arw, fwhm, [('outputnode.out_file', 'in_file')]),
(asw, fwhm, [('outputnode.out_mask', 'mask')]),
(arw, amw, [('outputnode.out_file', 'inputnode.in_file')]),
(n4itk, amw, [('output_image', 'inputnode.in_noinu')]),
(asw, amw, [('outputnode.out_mask', 'inputnode.in_mask')]),
(hmsk, amw, [('outputnode.out_file', 'inputnode.head_mask')]),
(amw, measures, [('outputnode.out_file', 'air_msk')]),
(amw, measures, [('outputnode.artifact_msk', 'artifact_msk')]),
(segment, measures, [('tissue_class_map', 'in_segm'),
('partial_volume_files', 'in_pvms')]),
(n4itk, measures, [('bias_image', 'in_bias')]),
(arw, plot, [('outputnode.out_file', 'in_file')]),
(inputnode, plot, [('subject_id', 'subject')]),
(inputnode, merg, [('session_id', 'in1'), ('run_id', 'in2')]),
(merg, plot, [('out', 'metadata')])
])
if settings.get('mask_mosaic', False):
workflow.connect(asw, 'outputnode.out_file', plot, 'in_mask')
# Save mosaic to well-formed path
mvplot = pe.Node(niu.Rename(
format_string='anatomical_%(subject_id)s_%(session_id)s_%(run_id)s',
keep_ext=True),
name='rename_plot')
dsplot = pe.Node(nio.DataSink(base_directory=settings['work_dir'],
parameterization=False),
name='ds_plot')
workflow.connect([(inputnode, mvplot, [('subject_id', 'subject_id'),
('session_id', 'session_id'),
('run_id', 'run_id')]),
(plot, mvplot, [('out_file', 'in_file')]),
(mvplot, dsplot, [('out_file', '@mosaic')])])
# Save background-noise fitting plot
mvbgplot = pe.Node(niu.Rename(
format_string=
'anatomical_bgplot_%(subject_id)s_%(session_id)s_%(run_id)s',
keep_ext=True),
name='rename_bgplot')
dsbgplot = pe.Node(nio.DataSink(base_directory=settings['work_dir'],
parameterization=False),
name='ds_bgplot')
workflow.connect([(inputnode, mvbgplot, [('subject_id', 'subject_id'),
('session_id', 'session_id'),
('run_id', 'run_id')]),
(measures, mvbgplot, [('out_noisefit', 'in_file')]),
(mvbgplot, dsbgplot, [('out_file', '@bg_fitting')])])
# Format name
out_name = pe.Node(niu.Function(
input_names=['subid', 'sesid', 'runid', 'prefix', 'out_path'],
output_names=['out_file'],
function=bids_path),
name='FormatName')
out_name.inputs.out_path = deriv_dir
out_name.inputs.prefix = 'anat'
# Save to JSON file
jfs_if = nio.JSONFileSink()
setattr(jfs_if, '_always_run', settings.get('force_run', False))
datasink = pe.Node(jfs_if, name='datasink')
datasink.inputs.qc_type = 'anat'
workflow.connect([(inputnode, out_name, [('subject_id', 'subid'),
('session_id', 'sesid'),
('run_id', 'runid')]),
(inputnode, datasink, [('subject_id', 'subject_id'),
('session_id', 'session_id'),
('run_id', 'run_id')]),
(plot, datasink, [('out_file', 'mosaic_file')]),
(fwhm, datasink, [(('fwhm', fwhm_dict), 'fwhm')]),
(measures, datasink, [('summary', 'summary'),
('spacing', 'spacing'),
('size', 'size'), ('icvs', 'icvs'),
('rpve', 'rpve'), ('inu', 'inu'),
('snr', 'snr'), ('cnr', 'cnr'),
('fber', 'fber'), ('efc', 'efc'),
('qi1', 'qi1'), ('qi2', 'qi2'),
('cjv', 'cjv')]),
(out_name, datasink, [('out_file', 'out_file')]),
(datasink, outputnode, [('out_file', 'out_file')])])
return workflow
