def init_gifti_surface_wf(name='gifti_surface_wf'):
r"""
Prepare GIFTI surfaces from a FreeSurfer subjects directory.
If midthickness (or graymid) surfaces do not exist, they are generated and
saved to the subject directory as ``lh/rh.midthickness``.
These, along with the gray/white matter boundary (``lh/rh.smoothwm``), pial
sufaces (``lh/rh.pial``) and inflated surfaces (``lh/rh.inflated``) are
converted to GIFTI files.
Additionally, the vertex coordinates are :py:class:`recentered
<smriprep.interfaces.NormalizeSurf>` to align with native T1w space.
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from smriprep.workflows.surfaces import init_gifti_surface_wf
wf = init_gifti_surface_wf()
Inputs
------
subjects_dir
FreeSurfer SUBJECTS_DIR
subject_id
FreeSurfer subject ID
fsnative2t1w_xfm
LTA formatted affine transform file (inverse)
Outputs
-------
surfaces
GIFTI surfaces for gray/white matter boundary, pial surface,
midthickness (or graymid) surface, and inflated surfaces
"""
workflow = Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(
['subjects_dir', 'subject_id', 'fsnative2t1w_xfm']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(['surfaces']),
name='outputnode')
get_surfaces = pe.Node(nio.FreeSurferSource(), name='get_surfaces')
midthickness = pe.MapNode(MakeMidthickness(thickness=True,
distance=0.5,
out_name='midthickness'),
iterfield='in_file',
name='midthickness')
save_midthickness = pe.Node(nio.DataSink(parameterization=False),
name='save_midthickness')
surface_list = pe.Node(niu.Merge(4, ravel_inputs=True),
name='surface_list',
run_without_submitting=True)
fs2gii = pe.MapNode(fs.MRIsConvert(out_datatype='gii'),
iterfield='in_file',
name='fs2gii')
fix_surfs = pe.MapNode(NormalizeSurf(),
iterfield='in_file',
name='fix_surfs')
workflow.connect([
(inputnode, get_surfaces, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(inputnode, save_midthickness, [('subjects_dir', 'base_directory'),
('subject_id', 'container')]),
# Generate midthickness surfaces and save to FreeSurfer derivatives
(get_surfaces, midthickness, [('smoothwm', 'in_file'),
('graymid', 'graymid')]),
(midthickness, save_midthickness, [('out_file', 'surf.@graymid')]),
# Produce valid GIFTI surface files (dense mesh)
(get_surfaces, surface_list, [('smoothwm', 'in1'), ('pial', 'in2'),
('inflated', 'in3')]),
(save_midthickness, surface_list, [('out_file', 'in4')]),
(surface_list, fs2gii, [('out', 'in_file')]),
(fs2gii, fix_surfs, [('converted', 'in_file')]),
(inputnode, fix_surfs, [('fsnative2t1w_xfm', 'transform_file')]),
(fix_surfs, outputnode, [('out_file', 'surfaces')]),
])
return workflow
def create_tbss_1_preproc(name='tbss_1_preproc'):
"""Preprocess FA data for TBSS: erodes a little and zero end slicers and
creates masks(for use in FLIRT & FNIRT from FSL).
A pipeline that does the same as tbss_1_preproc script in FSL
Example
-------
>>> from nipype.workflows.dmri.fsl import tbss
>>> tbss1 = tbss.create_tbss_1_preproc()
>>> tbss1.inputs.inputnode.fa_list = ['s1_FA.nii', 's2_FA.nii', 's3_FA.nii']
Inputs::
inputnode.fa_list
Outputs::
outputnode.fa_list
outputnode.mask_list
outputnode.slices
"""
# Define the inputnode
inputnode = pe.Node(interface=util.IdentityInterface(fields=["fa_list"]),
name="inputnode")
# Prep the FA images
prepfa = pe.MapNode(fsl.ImageMaths(suffix="_prep"),
name="prepfa",
iterfield=['in_file', 'op_string'])
# Slicer
slicer = pe.MapNode(fsl.Slicer(all_axial=True, image_width=1280),
name='slicer',
iterfield=['in_file'])
# Create a mask
getmask1 = pe.MapNode(fsl.ImageMaths(op_string="-bin", suffix="_mask"),
name="getmask1",
iterfield=['in_file'])
getmask2 = pe.MapNode(fsl.MultiImageMaths(op_string="-dilD -dilD -sub 1 -abs -add %s"),
name="getmask2",
iterfield=['in_file', 'operand_files'])
# $FSLDIR/bin/fslmaths FA/${f}_FA_mask -dilD -dilD -sub 1 -abs -add FA/${f}_FA_mask FA/${f}_FA_mask -odt char
# Define the tbss1 workflow
tbss1 = pe.Workflow(name=name)
tbss1.connect([
(inputnode, prepfa, [("fa_list", "in_file")]),
(inputnode, prepfa, [(("fa_list", tbss1_op_string), "op_string")]),
(prepfa, getmask1, [("out_file", "in_file")]),
(getmask1, getmask2, [("out_file", "in_file"),
("out_file", "operand_files")]),
(prepfa, slicer, [('out_file', 'in_file')]),
])
# Define the outputnode
outputnode = pe.Node(interface=util.IdentityInterface(fields=["fa_list",
"mask_list",
"slices"]),
name="outputnode")
tbss1.connect([
(prepfa, outputnode, [("out_file", "fa_list")]),
(getmask2, outputnode, [("out_file", "mask_list")]),
(slicer, outputnode, [('out_file', 'slices')])
])
return tbss1
def create_tbss_2_reg(name="tbss_2_reg"):
"""TBSS nonlinear registration:
A pipeline that does the same as 'tbss_2_reg -t' script in FSL. '-n' option
is not supported at the moment.
Example
-------
>>> from nipype.workflows.dmri.fsl import tbss
>>> tbss2 = create_tbss_2_reg(name="tbss2")
>>> tbss2.inputs.inputnode.target = fsl.Info.standard_image("FMRIB58_FA_1mm.nii.gz") # doctest: +SKIP
>>> tbss2.inputs.inputnode.fa_list = ['s1_FA.nii', 's2_FA.nii', 's3_FA.nii']
>>> tbss2.inputs.inputnode.mask_list = ['s1_mask.nii', 's2_mask.nii', 's3_mask.nii']
Inputs::
inputnode.fa_list
inputnode.mask_list
inputnode.target
Outputs::
outputnode.field_list
"""
# Define the inputnode
inputnode = pe.Node(interface=util.IdentityInterface(fields=["fa_list",
"mask_list",
"target"]),
name="inputnode")
# Flirt the FA image to the target
flirt = pe.MapNode(interface=fsl.FLIRT(dof=12),
iterfield=['in_file', 'in_weight'],
name="flirt")
fnirt = pe.MapNode(interface=fsl.FNIRT(fieldcoeff_file=True),
iterfield=['in_file', 'inmask_file', 'affine_file'],
name="fnirt")
# Fnirt the FA image to the target
if fsl.no_fsl():
warn('NO FSL found')
else:
config_file = os.path.join(os.environ["FSLDIR"],
"etc/flirtsch/FA_2_FMRIB58_1mm.cnf")
fnirt.inputs.config_file=config_file
# Define the registration workflow
tbss2 = pe.Workflow(name=name)
# Connect up the registration workflow
tbss2.connect([
(inputnode, flirt, [("fa_list", "in_file"),
("target", "reference"),
("mask_list", "in_weight")]),
(inputnode, fnirt, [("fa_list", "in_file"),
("mask_list", "inmask_file"),
("target", "ref_file")]),
(flirt, fnirt, [("out_matrix_file", "affine_file")]),
])
# Define the outputnode
outputnode = pe.Node(interface=util.IdentityInterface(fields=['field_list']),
name="outputnode")
tbss2.connect([
(fnirt, outputnode, [('fieldcoeff_file', 'field_list')])
])
return tbss2
def generate_single_session_template_WF(projectid,
subjectid,
sessionid,
onlyT1,
master_config,
phase,
interpMode,
pipeline_name,
doDenoise=True):
"""
Run autoworkup on a single sessionid
This is the main function to call when processing a data set with T1 & T2
data. ExperimentBaseDirectoryPrefix is the base of the directory to place results, T1Images & T2Images
are the lists of images to be used in the auto-workup. atlas_fname_wpath is
the path and filename of the atlas to use.
"""
#if not 'landmark' in master_config['components'] or not 'auxlmk' in master_config['components'] or not 'tissue_classify' in master_config['components']:
# print "Baseline DataSink requires 'AUXLMK' and/or 'TISSUE_CLASSIFY'!!!"
# raise NotImplementedError
# master_config['components'].append('auxlmk')
# master_config['components'].append('tissue_classify')
assert phase in [
'atlas-based-reference', 'subject-based-reference'
], "Unknown phase! Valid entries: 'atlas-based-reference', 'subject-based-reference'"
if 'tissue_classify' in master_config['components']:
assert ('landmark' in master_config['components']
), "tissue_classify Requires landmark step!"
if 'landmark' in master_config['components']:
assert 'denoise' in master_config[
'components'], "landmark Requires denoise step!"
from workflows.atlasNode import MakeAtlasNode
baw201 = pe.Workflow(name=pipeline_name)
inputsSpec = pe.Node(interface=IdentityInterface(fields=[
'atlasLandmarkFilename', 'atlasWeightFilename', 'LLSModel',
'inputTemplateModel', 'template_t1', 'atlasDefinition', 'T1s', 'T2s',
'PDs', 'FLs', 'OTHERs', 'hncma_atlas', 'template_rightHemisphere',
'template_leftHemisphere', 'template_WMPM2_labels',
'template_nac_labels', 'template_ventricles'
]),
run_without_submitting=True,
name='inputspec')
outputsSpec = pe.Node(
interface=IdentityInterface(fields=[
't1_average',
't2_average',
'pd_average',
'fl_average',
'posteriorImages',
'outputLabels',
'outputHeadLabels',
'atlasToSubjectTransform',
'atlasToSubjectInverseTransform',
'atlasToSubjectRegistrationState',
'BCD_ACPC_T1_CROPPED',
'outputLandmarksInACPCAlignedSpace',
'outputLandmarksInInputSpace',
'output_tx',
'LMIatlasToSubject_tx',
'writeBranded2DImage',
'brainStemMask',
'UpdatedPosteriorsList' # Longitudinal
]),
run_without_submitting=True,
name='outputspec')
dsName = "{0}_ds_{1}".format(phase, sessionid)
DataSink = pe.Node(name=dsName, interface=nio.DataSink())
DataSink.overwrite = master_config['ds_overwrite']
DataSink.inputs.container = '{0}/{1}/{2}'.format(projectid, subjectid,
sessionid)
DataSink.inputs.base_directory = master_config['resultdir']
atlas_static_directory = master_config['atlascache']
if master_config['workflow_phase'] == 'atlas-based-reference':
atlas_warped_directory = master_config['atlascache']
atlasABCNode_XML = MakeAtlasNode(atlas_warped_directory,
'BABCXMLAtlas_{0}'.format(sessionid),
['W_BRAINSABCSupport'])
baw201.connect(atlasABCNode_XML, 'ExtendedAtlasDefinition_xml',
inputsSpec, 'atlasDefinition')
atlasABCNode_W = MakeAtlasNode(
atlas_warped_directory, 'BABCAtlas_W{0}'.format(sessionid),
['W_BRAINSABCSupport', 'W_LabelMapsSupport'])
baw201.connect([(atlasABCNode_W, inputsSpec, [
('hncma_atlas', 'hncma_atlas'),
('template_leftHemisphere', 'template_leftHemisphere'),
('template_rightHemisphere', 'template_rightHemisphere'),
('template_WMPM2_labels', 'template_WMPM2_labels'),
('template_nac_labels', 'template_nac_labels'),
('template_ventricles', 'template_ventricles')
])])
## These landmarks are only relevant for the atlas-based-reference case
atlasBCDNode_W = MakeAtlasNode(atlas_warped_directory,
'BBCDAtlas_W{0}'.format(sessionid),
['W_BCDSupport'])
baw201.connect([
(atlasBCDNode_W, inputsSpec, [
('template_t1', 'template_t1'),
('template_landmarks_50Lmks_fcsv', 'atlasLandmarkFilename'),
]),
])
## Needed for both segmentation and template building prep
atlasBCUTNode_W = MakeAtlasNode(atlas_warped_directory,
'BBCUTAtlas_W{0}'.format(sessionid),
['W_BRAINSCutSupport'])
elif master_config['workflow_phase'] == 'subject-based-reference':
print master_config['previousresult']
atlas_warped_directory = os.path.join(master_config['previousresult'],
subjectid, 'Atlas')
template_DG = pe.Node(interface=nio.DataGrabber(
infields=['subject'],
outfields=[
'outAtlasXMLFullPath', 'hncma_atlas',
'template_leftHemisphere', 'template_rightHemisphere',
'template_WMPM2_labels', 'template_nac_labels',
'template_ventricles', 'template_t1',
'template_landmarks_50Lmks_fcsv'
]),
name='Template_DG')
template_DG.inputs.base_directory = master_config['previousresult']
template_DG.inputs.subject = subjectid
template_DG.inputs.field_template = {
'outAtlasXMLFullPath': '%s/Atlas/AtlasDefinition_%s.xml',
'hncma_atlas': '%s/Atlas/AVG_hncma_atlas.nii.gz',
'template_leftHemisphere':
'%s/Atlas/AVG_template_leftHemisphere.nii.gz',
'template_rightHemisphere':
'%s/Atlas/AVG_template_rightHemisphere.nii.gz',
'template_WMPM2_labels':
'%s/Atlas/AVG_template_WMPM2_labels.nii.gz',
'template_nac_labels': '%s/Atlas/AVG_template_nac_labels.nii.gz',
'template_ventricles': '%s/Atlas/AVG_template_ventricles.nii.gz',
'template_t1': '%s/Atlas/AVG_T1.nii.gz',
'template_landmarks_50Lmks_fcsv': '%s/Atlas/AVG_LMKS.fcsv',
}
template_DG.inputs.template_args = {
'outAtlasXMLFullPath': [['subject', 'subject']],
'hncma_atlas': [['subject']],
'template_leftHemisphere': [['subject']],
'template_rightHemisphere': [['subject']],
'template_WMPM2_labels': [['subject']],
'template_nac_labels': [['subject']],
'template_ventricles': [['subject']],
'template_t1': [['subject']],
'template_landmarks_50Lmks_fcsv': [['subject']]
}
template_DG.inputs.template = '*'
template_DG.inputs.sort_filelist = True
template_DG.inputs.raise_on_empty = True
baw201.connect(template_DG, 'outAtlasXMLFullPath', inputsSpec,
'atlasDefinition')
baw201.connect([(
template_DG,
inputsSpec,
[
## Already connected ('template_t1','template_t1'),
('hncma_atlas', 'hncma_atlas'),
('template_leftHemisphere', 'template_leftHemisphere'),
('template_rightHemisphere', 'template_rightHemisphere'),
('template_WMPM2_labels', 'template_WMPM2_labels'),
('template_nac_labels', 'template_nac_labels'),
('template_ventricles', 'template_ventricles')
])])
## These landmarks are only relevant for the atlas-based-reference case
baw201.connect([
(template_DG, inputsSpec, [
('template_t1', 'template_t1'),
('template_landmarks_50Lmks_fcsv', 'atlasLandmarkFilename'),
]),
])
else:
assert 0 == 1, "Invalid workflow type specified for singleSession"
atlasBCDNode_S = MakeAtlasNode(atlas_static_directory,
'BBCDAtlas_S{0}'.format(sessionid),
['S_BCDSupport'])
baw201.connect([
(atlasBCDNode_S, inputsSpec,
[('template_weights_50Lmks_wts', 'atlasWeightFilename'),
('LLSModel_50Lmks_h5', 'LLSModel'),
('T1_50Lmks_mdl', 'inputTemplateModel')]),
])
if doDenoise:
print("\ndenoise image filter\n")
makeDenoiseInImageList = pe.Node(Function(
function=MakeOutFileList,
input_names=[
'T1List', 'T2List', 'PDList', 'FLList', 'OtherList', 'postfix',
'PrimaryT1'
],
output_names=['inImageList', 'outImageList', 'imageTypeList']),
run_without_submitting=True,
name="99_makeDenoiseInImageList")
baw201.connect(inputsSpec, 'T1s', makeDenoiseInImageList, 'T1List')
baw201.connect(inputsSpec, 'T2s', makeDenoiseInImageList, 'T2List')
baw201.connect(inputsSpec, 'PDs', makeDenoiseInImageList, 'PDList')
makeDenoiseInImageList.inputs.FLList = [] # an emptyList HACK
makeDenoiseInImageList.inputs.PrimaryT1 = None # an emptyList HACK
makeDenoiseInImageList.inputs.postfix = "_UNM_denoised.nii.gz"
# HACK baw201.connect( inputsSpec, 'FLList', makeDenoiseInImageList, 'FLList' )
baw201.connect(inputsSpec, 'OTHERs', makeDenoiseInImageList,
'OtherList')
print("\nDenoise:\n")
DenoiseInputImgs = pe.MapNode(
interface=UnbiasedNonLocalMeans(),
name='denoiseInputImgs',
iterfield=['inputVolume', 'outputVolume'])
DenoiseInputImgs.inputs.rc = [1, 1, 1]
DenoiseInputImgs.inputs.rs = [4, 4, 4]
DenoiseInputImgs.plugin_args = {
'qsub_args': modify_qsub_args(master_config['queue'], .2, 1, 1),
'overwrite': True
}
baw201.connect([(makeDenoiseInImageList, DenoiseInputImgs,
[('inImageList', 'inputVolume')]),
(makeDenoiseInImageList, DenoiseInputImgs,
[('outImageList', 'outputVolume')])])
print("\nMerge all T1 and T2 List\n")
makePreprocessingOutList = pe.Node(Function(
function=GenerateSeparateImageTypeList,
input_names=['inFileList', 'inTypeList'],
output_names=['T1s', 'T2s', 'PDs', 'FLs', 'OtherList']),
run_without_submitting=True,
name="99_makePreprocessingOutList")
baw201.connect(DenoiseInputImgs, 'outputVolume',
makePreprocessingOutList, 'inFileList')
baw201.connect(makeDenoiseInImageList, 'imageTypeList',
makePreprocessingOutList, 'inTypeList')
else:
makePreprocessingOutList = inputsSpec
if 'landmark' in master_config['components']:
DoReverseMapping = False # Set to true for debugging outputs
if 'auxlmk' in master_config['components']:
DoReverseMapping = True
myLocalLMIWF = CreateLandmarkInitializeWorkflow(
"LandmarkInitialize", interpMode, DoReverseMapping)
baw201.connect([
(makePreprocessingOutList, myLocalLMIWF,
[(('T1s', get_list_element, 0), 'inputspec.inputVolume')]),
(inputsSpec, myLocalLMIWF,
[('atlasLandmarkFilename', 'inputspec.atlasLandmarkFilename'),
('atlasWeightFilename', 'inputspec.atlasWeightFilename'),
('LLSModel', 'inputspec.LLSModel'),
('inputTemplateModel', 'inputspec.inputTemplateModel'),
('template_t1', 'inputspec.atlasVolume')]),
(myLocalLMIWF, outputsSpec,
[('outputspec.outputResampledCroppedVolume',
'BCD_ACPC_T1_CROPPED'),
('outputspec.outputLandmarksInACPCAlignedSpace',
'outputLandmarksInACPCAlignedSpace'),
('outputspec.outputLandmarksInInputSpace',
'outputLandmarksInInputSpace'),
('outputspec.outputTransform', 'output_tx'),
('outputspec.atlasToSubjectTransform', 'LMIatlasToSubject_tx'),
('outputspec.writeBranded2DImage', 'writeBranded2DImage')])
])
baw201.connect([(
outputsSpec,
DataSink, # TODO: change to myLocalLMIWF -> DataSink
[
('outputLandmarksInACPCAlignedSpace',
'ACPCAlign.@outputLandmarks_ACPC'),
('writeBranded2DImage', 'ACPCAlign.@writeBranded2DImage'),
('BCD_ACPC_T1_CROPPED', 'ACPCAlign.@BCD_ACPC_T1_CROPPED'),
('outputLandmarksInInputSpace',
'ACPCAlign.@outputLandmarks_Input'),
('output_tx', 'ACPCAlign.@output_tx'),
('LMIatlasToSubject_tx', 'ACPCAlign.@LMIatlasToSubject_tx'),
])])
if 'tissue_classify' in master_config['components']:
useRegistrationMask = master_config['use_registration_masking']
myLocalTCWF = CreateTissueClassifyWorkflow("TissueClassify",
master_config, interpMode,
useRegistrationMask)
baw201.connect([
(makePreprocessingOutList, myLocalTCWF, [('T1s',
'inputspec.T1List')]),
(makePreprocessingOutList, myLocalTCWF, [('T2s',
'inputspec.T2List')]),
(inputsSpec, myLocalTCWF,
[('atlasDefinition', 'inputspec.atlasDefinition'),
('template_t1', 'inputspec.atlasVolume'),
(('T1s', getAllT1sLength), 'inputspec.T1_count'),
('PDs', 'inputspec.PDList'), ('FLs', 'inputspec.FLList'),
('OTHERs', 'inputspec.OtherList')]),
(myLocalLMIWF, myLocalTCWF,
[('outputspec.outputResampledCroppedVolume',
'inputspec.PrimaryT1'),
('outputspec.atlasToSubjectTransform',
'inputspec.atlasToSubjectInitialTransform')]),
(myLocalTCWF, outputsSpec,
[('outputspec.t1_average', 't1_average'),
('outputspec.t2_average', 't2_average'),
('outputspec.pd_average', 'pd_average'),
('outputspec.fl_average', 'fl_average'),
('outputspec.posteriorImages', 'posteriorImages'),
('outputspec.outputLabels', 'outputLabels'),
('outputspec.outputHeadLabels', 'outputHeadLabels'),
('outputspec.atlasToSubjectTransform',
'atlasToSubjectTransform'),
('outputspec.atlasToSubjectInverseTransform',
'atlasToSubjectInverseTransform'),
('outputspec.atlasToSubjectRegistrationState',
'atlasToSubjectRegistrationState')]),
])
baw201.connect([(
outputsSpec,
DataSink, # TODO: change to myLocalTCWF -> DataSink
[(('t1_average', convertToList), 'TissueClassify.@t1'),
(('t2_average', convertToList), 'TissueClassify.@t2'),
(('pd_average', convertToList), 'TissueClassify.@pd'),
(('fl_average', convertToList), 'TissueClassify.@fl')])])
currentFixWMPartitioningName = "_".join(
['FixWMPartitioning',
str(subjectid),
str(sessionid)])
FixWMNode = pe.Node(interface=Function(
function=FixWMPartitioning,
input_names=['brainMask', 'PosteriorsList'],
output_names=[
'UpdatedPosteriorsList', 'MatchingFGCodeList',
'MatchingLabelList', 'nonAirRegionMask'
]),
name=currentFixWMPartitioningName)
baw201.connect([
(myLocalTCWF, FixWMNode, [('outputspec.outputLabels', 'brainMask'),
(('outputspec.posteriorImages',
flattenDict), 'PosteriorsList')]),
(FixWMNode, outputsSpec, [('UpdatedPosteriorsList',
'UpdatedPosteriorsList')]),
])
currentBRAINSCreateLabelMapName = 'BRAINSCreateLabelMapFromProbabilityMaps_' + str(
subjectid) + "_" + str(sessionid)
BRAINSCreateLabelMapNode = pe.Node(
interface=BRAINSCreateLabelMapFromProbabilityMaps(),
name=currentBRAINSCreateLabelMapName)
## TODO: Fix the file names
BRAINSCreateLabelMapNode.inputs.dirtyLabelVolume = 'fixed_headlabels_seg.nii.gz'
BRAINSCreateLabelMapNode.inputs.cleanLabelVolume = 'fixed_brainlabels_seg.nii.gz'
baw201.connect([
(FixWMNode, BRAINSCreateLabelMapNode,
[('UpdatedPosteriorsList', 'inputProbabilityVolume'),
('MatchingFGCodeList', 'foregroundPriors'),
('MatchingLabelList', 'priorLabelCodes'),
('nonAirRegionMask', 'nonAirRegionMask')]),
(
BRAINSCreateLabelMapNode,
DataSink,
[ # brainstem code below replaces this ('cleanLabelVolume', 'TissueClassify.@outputLabels'),
('dirtyLabelVolume', 'TissueClassify.@outputHeadLabels')
]),
(myLocalTCWF, DataSink,
[('outputspec.atlasToSubjectTransform',
'TissueClassify.@atlas2session_tx'),
('outputspec.atlasToSubjectInverseTransform',
'TissueClassify.@atlas2sessionInverse_tx')]),
(FixWMNode, DataSink, [('UpdatedPosteriorsList',
'TissueClassify.@posteriors')]),
])
currentAccumulateLikeTissuePosteriorsName = 'AccumulateLikeTissuePosteriors_' + str(
subjectid) + "_" + str(sessionid)
AccumulateLikeTissuePosteriorsNode = pe.Node(
interface=Function(
function=AccumulateLikeTissuePosteriors,
input_names=['posteriorImages'],
output_names=['AccumulatePriorsList',
'AccumulatePriorsNames']),
name=currentAccumulateLikeTissuePosteriorsName)
baw201.connect([
(FixWMNode, AccumulateLikeTissuePosteriorsNode,
[('UpdatedPosteriorsList', 'posteriorImages')]),
(AccumulateLikeTissuePosteriorsNode, DataSink,
[('AccumulatePriorsList',
'ACCUMULATED_POSTERIORS.@AccumulateLikeTissuePosteriorsOutputDir'
)])
])
"""
brain stem adds on feature
inputs:
- landmark (fcsv) file
- fixed brainlabels seg.nii.gz
output:
- complete_brainlabels_seg.nii.gz Segmentation
"""
myLocalBrainStemWF = CreateBrainstemWorkflow(
"BrainStem", master_config['queue'],
"complete_brainlabels_seg.nii.gz")
baw201.connect([(myLocalLMIWF, myLocalBrainStemWF,
[('outputspec.outputLandmarksInACPCAlignedSpace',
'inputspec.inputLandmarkFilename')]),
(BRAINSCreateLabelMapNode, myLocalBrainStemWF,
[('cleanLabelVolume',
'inputspec.inputTissueLabelFilename')])])
baw201.connect(myLocalBrainStemWF,
'outputspec.ouputTissuelLabelFilename', DataSink,
'TissueClassify.@complete_brainlabels_seg')
###########################
do_BRAINSCut_Segmentation = DetermineIfSegmentationShouldBeDone(
master_config)
if do_BRAINSCut_Segmentation:
from workflows.segmentation import segmentation
from workflows.WorkupT1T2BRAINSCut import GenerateWFName
sname = 'segmentation'
segWF = segmentation(projectid,
subjectid,
sessionid,
master_config,
onlyT1,
pipeline_name=sname)
baw201.connect([(inputsSpec, segWF, [('template_t1',
'inputspec.template_t1')])])
atlasBCUTNode_W = pe.Node(interface=nio.DataGrabber(
infields=['subject'],
outfields=[
"l_accumben_ProbabilityMap", "r_accumben_ProbabilityMap",
"l_caudate_ProbabilityMap", "r_caudate_ProbabilityMap",
"l_globus_ProbabilityMap", "r_globus_ProbabilityMap",
"l_hippocampus_ProbabilityMap", "r_hippocampus_ProbabilityMap",
"l_putamen_ProbabilityMap", "r_putamen_ProbabilityMap",
"l_thalamus_ProbabilityMap", "r_thalamus_ProbabilityMap",
"phi", "rho", "theta"
]),
name='PerSubject_atlasBCUTNode_W')
atlasBCUTNode_W.inputs.base_directory = master_config['previousresult']
atlasBCUTNode_W.inputs.subject = subjectid
atlasBCUTNode_W.inputs.field_template = {
'l_accumben_ProbabilityMap':
'%s/Atlas/AVG_l_accumben_ProbabilityMap.nii.gz',
'r_accumben_ProbabilityMap':
'%s/Atlas/AVG_r_accumben_ProbabilityMap.nii.gz',
'l_caudate_ProbabilityMap':
'%s/Atlas/AVG_l_caudate_ProbabilityMap.nii.gz',
'r_caudate_ProbabilityMap':
'%s/Atlas/AVG_r_caudate_ProbabilityMap.nii.gz',
'l_globus_ProbabilityMap':
'%s/Atlas/AVG_l_globus_ProbabilityMap.nii.gz',
'r_globus_ProbabilityMap':
'%s/Atlas/AVG_r_globus_ProbabilityMap.nii.gz',
'l_hippocampus_ProbabilityMap':
'%s/Atlas/AVG_l_hippocampus_ProbabilityMap.nii.gz',
'r_hippocampus_ProbabilityMap':
'%s/Atlas/AVG_r_hippocampus_ProbabilityMap.nii.gz',
'l_putamen_ProbabilityMap':
'%s/Atlas/AVG_l_putamen_ProbabilityMap.nii.gz',
'r_putamen_ProbabilityMap':
'%s/Atlas/AVG_r_putamen_ProbabilityMap.nii.gz',
'l_thalamus_ProbabilityMap':
'%s/Atlas/AVG_l_thalamus_ProbabilityMap.nii.gz',
'r_thalamus_ProbabilityMap':
'%s/Atlas/AVG_r_thalamus_ProbabilityMap.nii.gz',
'phi': '%s/Atlas/AVG_phi.nii.gz',
'rho': '%s/Atlas/AVG_rho.nii.gz',
'theta': '%s/Atlas/AVG_theta.nii.gz'
}
atlasBCUTNode_W.inputs.template_args = {
'l_accumben_ProbabilityMap': [['subject']],
'r_accumben_ProbabilityMap': [['subject']],
'l_caudate_ProbabilityMap': [['subject']],
'r_caudate_ProbabilityMap': [['subject']],
'l_globus_ProbabilityMap': [['subject']],
'r_globus_ProbabilityMap': [['subject']],
'l_hippocampus_ProbabilityMap': [['subject']],
'r_hippocampus_ProbabilityMap': [['subject']],
'l_putamen_ProbabilityMap': [['subject']],
'r_putamen_ProbabilityMap': [['subject']],
'l_thalamus_ProbabilityMap': [['subject']],
'r_thalamus_ProbabilityMap': [['subject']],
'phi': [['subject']],
'rho': [['subject']],
'theta': [['subject']]
}
atlasBCUTNode_W.inputs.template = '*'
atlasBCUTNode_W.inputs.sort_filelist = True
atlasBCUTNode_W.inputs.raise_on_empty = True
baw201.connect([(atlasBCUTNode_W, segWF, [
('rho', 'inputspec.rho'), ('phi', 'inputspec.phi'),
('theta', 'inputspec.theta'),
('l_caudate_ProbabilityMap', 'inputspec.l_caudate_ProbabilityMap'),
('r_caudate_ProbabilityMap', 'inputspec.r_caudate_ProbabilityMap'),
('l_hippocampus_ProbabilityMap',
'inputspec.l_hippocampus_ProbabilityMap'),
('r_hippocampus_ProbabilityMap',
'inputspec.r_hippocampus_ProbabilityMap'),
('l_putamen_ProbabilityMap', 'inputspec.l_putamen_ProbabilityMap'),
('r_putamen_ProbabilityMap', 'inputspec.r_putamen_ProbabilityMap'),
('l_thalamus_ProbabilityMap',
'inputspec.l_thalamus_ProbabilityMap'),
('r_thalamus_ProbabilityMap',
'inputspec.r_thalamus_ProbabilityMap'),
('l_accumben_ProbabilityMap',
'inputspec.l_accumben_ProbabilityMap'),
('r_accumben_ProbabilityMap',
'inputspec.r_accumben_ProbabilityMap'),
('l_globus_ProbabilityMap', 'inputspec.l_globus_ProbabilityMap'),
('r_globus_ProbabilityMap', 'inputspec.r_globus_ProbabilityMap')
])])
atlasBCUTNode_S = MakeAtlasNode(atlas_static_directory,
'BBCUTAtlas_S{0}'.format(sessionid),
['S_BRAINSCutSupport'])
baw201.connect(atlasBCUTNode_S, 'trainModelFile_txtD0060NT0060_gz',
segWF, 'inputspec.trainModelFile_txtD0060NT0060_gz')
## baw201_outputspec = baw201.get_node('outputspec')
baw201.connect([
(myLocalTCWF, segWF,
[('outputspec.t1_average', 'inputspec.t1_average'),
('outputspec.atlasToSubjectRegistrationState',
'inputspec.atlasToSubjectRegistrationState'),
('outputspec.outputLabels', 'inputspec.inputLabels'),
('outputspec.posteriorImages', 'inputspec.posteriorImages'),
('outputspec.outputHeadLabels', 'inputspec.inputHeadLabels')]),
(myLocalLMIWF, segWF, [('outputspec.atlasToSubjectTransform',
'inputspec.LMIatlasToSubject_tx')]),
(FixWMNode, segWF, [('UpdatedPosteriorsList',
'inputspec.UpdatedPosteriorsList')]),
])
if not onlyT1:
baw201.connect([(myLocalTCWF, segWF, [('outputspec.t2_average',
'inputspec.t2_average')])])
if 'warp_atlas_to_subject' in master_config['components']:
##
##~/src/NEP-build/bin/BRAINSResample
# --warpTransform AtlasToSubjectPreBABC_Composite.h5
# --inputVolume /Shared/sinapse/CACHE/x20141001_KIDTEST_base_CACHE/Atlas/hncma-atlas.nii.gz
# --referenceVolume /Shared/sinapse/CACHE/x20141001_KIDTEST_base_CACHE/singleSession_KID1_KT1/LandmarkInitialize/BROIAuto_cropped/Cropped_BCD_ACPC_Aligned.nii.gz
# !--outputVolume hncma.nii.gz
# !--interpolationMode NearestNeighbor
# !--pixelType short
##
##
## TODO : SHOULD USE BRAINSCut transform that was refined even further!
BResample = dict()
AtlasLabelMapsToResample = [
'hncma_atlas',
'template_WMPM2_labels',
'template_nac_labels',
]
for atlasImage in AtlasLabelMapsToResample:
BResample[atlasImage] = pe.Node(interface=BRAINSResample(),
name="BRAINSResample_" +
atlasImage)
BResample[atlasImage].plugin_args = {
'qsub_args': modify_qsub_args(master_config['queue'], 1, 1, 1),
'overwrite': True
}
BResample[atlasImage].inputs.pixelType = 'short'
BResample[atlasImage].inputs.interpolationMode = 'NearestNeighbor'
BResample[atlasImage].inputs.outputVolume = atlasImage + ".nii.gz"
baw201.connect(myLocalTCWF, 'outputspec.t1_average',
BResample[atlasImage], 'referenceVolume')
baw201.connect(inputsSpec, atlasImage, BResample[atlasImage],
'inputVolume')
baw201.connect(myLocalTCWF, 'outputspec.atlasToSubjectTransform',
BResample[atlasImage], 'warpTransform')
baw201.connect(BResample[atlasImage], 'outputVolume', DataSink,
'WarpedAtlas2Subject.@' + atlasImage)
AtlasBinaryMapsToResample = [
'template_rightHemisphere', 'template_leftHemisphere',
'template_ventricles'
]
for atlasImage in AtlasBinaryMapsToResample:
BResample[atlasImage] = pe.Node(interface=BRAINSResample(),
name="BRAINSResample_" +
atlasImage)
BResample[atlasImage].plugin_args = {
'qsub_args': modify_qsub_args(master_config['queue'], 1, 1, 1),
'overwrite': True
}
BResample[atlasImage].inputs.pixelType = 'binary'
BResample[
atlasImage].inputs.interpolationMode = 'Linear' ## Conversion to distance map, so use linear to resample distance map
BResample[atlasImage].inputs.outputVolume = atlasImage + ".nii.gz"
baw201.connect(myLocalTCWF, 'outputspec.t1_average',
BResample[atlasImage], 'referenceVolume')
baw201.connect(inputsSpec, atlasImage, BResample[atlasImage],
'inputVolume')
baw201.connect(myLocalTCWF, 'outputspec.atlasToSubjectTransform',
BResample[atlasImage], 'warpTransform')
baw201.connect(BResample[atlasImage], 'outputVolume', DataSink,
'WarpedAtlas2Subject.@' + atlasImage)
BRAINSCutAtlasImages = [
'rho', 'phi', 'theta', 'l_caudate_ProbabilityMap',
'r_caudate_ProbabilityMap', 'l_hippocampus_ProbabilityMap',
'r_hippocampus_ProbabilityMap', 'l_putamen_ProbabilityMap',
'r_putamen_ProbabilityMap', 'l_thalamus_ProbabilityMap',
'r_thalamus_ProbabilityMap', 'l_accumben_ProbabilityMap',
'r_accumben_ProbabilityMap', 'l_globus_ProbabilityMap',
'r_globus_ProbabilityMap'
]
for atlasImage in BRAINSCutAtlasImages:
BResample[atlasImage] = pe.Node(interface=BRAINSResample(),
name="BCUTBRAINSResample_" +
atlasImage)
BResample[atlasImage].plugin_args = {
'qsub_args': modify_qsub_args(master_config['queue'], 1, 1, 1),
'overwrite': True
}
BResample[atlasImage].inputs.pixelType = 'float'
BResample[
atlasImage].inputs.interpolationMode = 'Linear' ## Conversion to distance map, so use linear to resample distance map
BResample[atlasImage].inputs.outputVolume = atlasImage + ".nii.gz"
baw201.connect(myLocalTCWF, 'outputspec.t1_average',
BResample[atlasImage], 'referenceVolume')
baw201.connect(atlasBCUTNode_W, atlasImage, BResample[atlasImage],
'inputVolume')
baw201.connect(myLocalTCWF, 'outputspec.atlasToSubjectTransform',
BResample[atlasImage], 'warpTransform')
baw201.connect(BResample[atlasImage], 'outputVolume', DataSink,
'WarpedAtlas2Subject.@' + atlasImage)
WhiteMatterHemisphereNode = pe.Node(interface=Function(
function=CreateLeftRightWMHemispheres,
input_names=[
'BRAINLABELSFile', 'HDCMARegisteredVentricleMaskFN',
'LeftHemisphereMaskName', 'RightHemisphereMaskName',
'WM_LeftHemisphereFileName', 'WM_RightHemisphereFileName'
],
output_names=[
'WM_LeftHemisphereFileName', 'WM_RightHemisphereFileName'
]),
name="WhiteMatterHemisphere")
WhiteMatterHemisphereNode.inputs.WM_LeftHemisphereFileName = "left_hemisphere_wm.nii.gz"
WhiteMatterHemisphereNode.inputs.WM_RightHemisphereFileName = "right_hemisphere_wm.nii.gz"
baw201.connect(myLocalBrainStemWF,
'outputspec.ouputTissuelLabelFilename',
WhiteMatterHemisphereNode, 'BRAINLABELSFile')
baw201.connect(BResample['hncma_atlas'], 'outputVolume',
WhiteMatterHemisphereNode,
'HDCMARegisteredVentricleMaskFN')
baw201.connect(BResample['template_leftHemisphere'], 'outputVolume',
WhiteMatterHemisphereNode, 'LeftHemisphereMaskName')
baw201.connect(BResample['template_rightHemisphere'], 'outputVolume',
WhiteMatterHemisphereNode, 'RightHemisphereMaskName')
baw201.connect(WhiteMatterHemisphereNode, 'WM_LeftHemisphereFileName',
DataSink, 'WarpedAtlas2Subject.@LeftHemisphereWM')
baw201.connect(WhiteMatterHemisphereNode, 'WM_RightHemisphereFileName',
DataSink, 'WarpedAtlas2Subject.@RightHemisphereWM')
if 'malf_2012_neuro' in master_config[
'components']: ## HACK Do MALF labeling
good_subjects = [
'1001', '1004', '1005', '1011', '1012', '1018', '1019', '1102',
'1103', '1104', '1120', '1129', '1009', '1010', '1013', '1014',
'1036', '1109', '1117', '1122'
]
## HACK FOR NOW SHOULD BE MORE ELEGANT FROM THE .config file
BASE_DATA_GRABBER_DIR = '/Shared/johnsonhj/HDNI/Neuromorphometrics/20141116_Neuromorphometrics_base_Results/Neuromorphometrics/2012Subscription'
myLocalMALF = CreateMALFWorkflow("MALF", master_config, good_subjects,
BASE_DATA_GRABBER_DIR)
baw201.connect(myLocalTCWF, 'outputspec.t1_average', myLocalMALF,
'inputspec.subj_t1_image')
baw201.connect(myLocalLMIWF,
'outputspec.outputLandmarksInACPCAlignedSpace',
myLocalMALF, 'inputspec.subj_lmks')
baw201.connect(atlasBCDNode_S, 'template_weights_50Lmks_wts',
myLocalMALF, 'inputspec.atlasWeightFilename')
baw201.connect(myLocalMALF, 'outputspec.MALF_neuro2012_labelmap',
DataSink, 'TissueClassify.@MALF_neuro2012_labelmap')
return baw201
def temporal_variance_mask(threshold, by_slice=False):
threshold_method = "VAR"
if isinstance(threshold, str):
regex_match = {
"SD": r"([0-9]+(\.[0-9]+)?)\s*SD",
"PCT": r"([0-9]+(\.[0-9]+)?)\s*PCT",
}
for method, regex in regex_match.items():
matched = re.match(regex, threshold)
if matched:
threshold_method = method
threshold_value = matched.groups()[0]
try:
threshold_value = float(threshold_value)
except:
raise ValueError(
"Error converting threshold value {0} from {1} to a "
"floating point number. The threshold value can "
"contain SD or PCT for selecting a threshold based on "
"the variance distribution, otherwise it should be a "
"floating point number.".format(threshold_value, threshold))
if threshold_value < 0:
raise ValueError(
"Threshold value should be positive, instead of {0}.".format(
threshold_value))
if threshold_method is "PCT" and threshold_value >= 100.0:
raise ValueError(
"Percentile should be less than 100, received {0}.".format(
threshold_value))
threshold = threshold_value
wf = pe.Workflow(name='tcompcor')
input_node = pe.Node(util.IdentityInterface(
fields=['functional_file_path', 'mask_file_path']),
name='inputspec')
output_node = pe.Node(util.IdentityInterface(fields=['mask']),
name='outputspec')
detrend = pe.Node(afni.Detrend(args='-polort 1', outputtype='NIFTI'),
name='detrend')
wf.connect(input_node, 'functional_file_path', detrend, 'in_file')
std = pe.Node(afni.TStat(args='-nzstdev', outputtype='NIFTI'), name='std')
wf.connect(input_node, 'mask_file_path', std, 'mask')
wf.connect(detrend, 'out_file', std, 'in_file')
var = pe.Node(afni.Calc(expr='a*a', outputtype='NIFTI'), name='var')
wf.connect(std, 'out_file', var, 'in_file_a')
if by_slice:
slices = pe.Node(fsl.Slice(), name='slicer')
wf.connect(var, 'out_file', slices, 'in_file')
mask_slices = pe.Node(fsl.Slice(), name='mask_slicer')
wf.connect(input_node, 'mask_file_path', mask_slices, 'in_file')
mapper = pe.MapNode(
util.IdentityInterface(fields=['out_file', 'mask_file']),
name='slice_mapper',
iterfield=['out_file', 'mask_file'])
wf.connect(slices, 'out_files', mapper, 'out_file')
wf.connect(mask_slices, 'out_files', mapper, 'mask_file')
else:
mapper_list = pe.Node(util.Merge(1), name='slice_mapper_list')
wf.connect(var, 'out_file', mapper_list, 'in1')
mask_mapper_list = pe.Node(util.Merge(1),
name='slice_mask_mapper_list')
wf.connect(input_node, 'mask_file_path', mask_mapper_list, 'in1')
mapper = pe.Node(
util.IdentityInterface(fields=['out_file', 'mask_file']),
name='slice_mapper')
wf.connect(mapper_list, 'out', mapper, 'out_file')
wf.connect(mask_mapper_list, 'out', mapper, 'mask_file')
if threshold_method is "PCT":
threshold_node = pe.MapNode(Function(
input_names=['in_file', 'mask', 'threshold_pct'],
output_names=['threshold'],
function=compute_pct_threshold,
as_module=True),
name='threshold_value',
iterfield=['in_file', 'mask'])
threshold_node.inputs.threshold_pct = threshold_value
wf.connect(mapper, 'out_file', threshold_node, 'in_file')
wf.connect(mapper, 'mask_file', threshold_node, 'mask')
elif threshold_method is "SD":
threshold_node = pe.MapNode(Function(
input_names=['in_file', 'mask', 'threshold_sd'],
output_names=['threshold'],
function=compute_sd_threshold,
as_module=True),
name='threshold_value',
iterfield=['in_file', 'mask'])
threshold_node.inputs.threshold_sd = threshold_value
wf.connect(mapper, 'out_file', threshold_node, 'in_file')
wf.connect(mapper, 'mask_file', threshold_node, 'mask')
else:
threshold_node = pe.MapNode(Function(
input_names=['in_file', 'mask', 'threshold'],
output_names=['threshold'],
function=compute_threshold,
as_module=True),
name='threshold_value',
iterfield=['in_file', 'mask'])
threshold_node.inputs.threshold = threshold_value
wf.connect(mapper, 'out_file', threshold_node, 'in_file')
wf.connect(mapper, 'mask_file', threshold_node, 'mask')
threshold_mask = pe.MapNode(interface=fsl.maths.Threshold(),
name='threshold',
iterfield=['in_file', 'thresh'])
threshold_mask.inputs.args = '-bin'
wf.connect(mapper, 'out_file', threshold_mask, 'in_file')
wf.connect(threshold_node, 'threshold', threshold_mask, 'thresh')
merge_slice_masks = pe.Node(interface=fsl.Merge(),
name='merge_slice_masks')
merge_slice_masks.inputs.dimension = 'z'
wf.connect(threshold_mask, 'out_file', merge_slice_masks, 'in_files')
wf.connect(merge_slice_masks, 'merged_file', output_node, 'mask')
return wf
def create_get_stats_flow(name='getstats', withreg=False):
"""Retrieves stats from labels
Parameters
----------
name : string
name of workflow
withreg : boolean
indicates whether to register source to label
Example
-------
Inputs::
inputspec.source_file : reference image for mask generation
inputspec.label_file : label file from which to get ROIs
(optionally with registration)
inputspec.reg_file : bbreg file (assumes reg from source to label
inputspec.inverse : boolean whether to invert the registration
inputspec.subjects_dir : freesurfer subjects directory
Outputs::
outputspec.stats_file : stats file
"""
"""
Initialize the workflow
"""
getstats = pe.Workflow(name=name)
"""
Define the inputs to the workflow.
"""
if withreg:
inputnode = pe.Node(niu.IdentityInterface(
fields=['source_file', 'label_file', 'reg_file', 'subjects_dir']),
name='inputspec')
else:
inputnode = pe.Node(
niu.IdentityInterface(fields=['source_file', 'label_file']),
name='inputspec')
statnode = pe.MapNode(fs.SegStats(),
iterfield=['segmentation_file', 'in_file'],
name='segstats')
"""
Convert between source and label spaces if registration info is provided
"""
if withreg:
voltransform = pe.MapNode(fs.ApplyVolTransform(inverse=True),
iterfield=['source_file', 'reg_file'],
name='transform')
getstats.connect(inputnode, 'reg_file', voltransform, 'reg_file')
getstats.connect(inputnode, 'source_file', voltransform, 'source_file')
getstats.connect(inputnode, 'label_file', voltransform, 'target_file')
getstats.connect(inputnode, 'subjects_dir', voltransform,
'subjects_dir')
def switch_labels(inverse, transform_output, source_file, label_file):
if inverse:
return transform_output, source_file
else:
return label_file, transform_output
chooser = pe.MapNode(niu.Function(
input_names=[
'inverse', 'transform_output', 'source_file', 'label_file'
],
output_names=['label_file', 'source_file'],
function=switch_labels),
iterfield=['transform_output', 'source_file'],
name='chooser')
getstats.connect(inputnode, 'source_file', chooser, 'source_file')
getstats.connect(inputnode, 'label_file', chooser, 'label_file')
getstats.connect(inputnode, 'inverse', chooser, 'inverse')
getstats.connect(voltransform, 'transformed_file', chooser,
'transform_output')
getstats.connect(chooser, 'label_file', statnode, 'segmentation_file')
getstats.connect(chooser, 'source_file', statnode, 'in_file')
else:
getstats.connect(inputnode, 'label_file', statnode,
'segmentation_file')
getstats.connect(inputnode, 'source_file', statnode, 'in_file')
"""
Setup an outputnode that defines relevant inputs of the workflow.
"""
outputnode = pe.Node(niu.IdentityInterface(fields=["stats_file"]),
name="outputspec")
getstats.connect([
(statnode, outputnode, [("summary_file", "stats_file")]),
])
return getstats
def create_getmask_flow(name='getmask', dilate_mask=True):
"""Registers a source file to freesurfer space and create a brain mask in
source space
Requires fsl tools for initializing registration
Parameters
----------
name : string
name of workflow
dilate_mask : boolean
indicates whether to dilate mask or not
Example
-------
>>> getmask = create_getmask_flow()
>>> getmask.inputs.inputspec.source_file = 'mean.nii'
>>> getmask.inputs.inputspec.subject_id = 's1'
>>> getmask.inputs.inputspec.subjects_dir = '.'
>>> getmask.inputs.inputspec.contrast_type = 't2'
Inputs::
inputspec.source_file : reference image for mask generation
inputspec.subject_id : freesurfer subject id
inputspec.subjects_dir : freesurfer subjects directory
inputspec.contrast_type : MR contrast of reference image
Outputs::
outputspec.mask_file : binary mask file in reference image space
outputspec.reg_file : registration file that maps reference image to
freesurfer space
outputspec.reg_cost : cost of registration (useful for detecting misalignment)
"""
"""
Initialize the workflow
"""
getmask = pe.Workflow(name=name)
"""
Define the inputs to the workflow.
"""
inputnode = pe.Node(niu.IdentityInterface(
fields=['source_file', 'subject_id', 'subjects_dir', 'contrast_type']),
name='inputspec')
"""
Define all the nodes of the workflow:
fssource: used to retrieve aseg.mgz
threshold : binarize aseg
register : coregister source file to freesurfer space
voltransform: convert binarized aseg to source file space
"""
fssource = pe.Node(nio.FreeSurferSource(), name='fssource')
threshold = pe.Node(fs.Binarize(min=0.5, out_type='nii'), name='threshold')
register = pe.MapNode(fs.BBRegister(init='fsl'),
iterfield=['source_file'],
name='register')
voltransform = pe.MapNode(fs.ApplyVolTransform(inverse=True),
iterfield=['source_file', 'reg_file'],
name='transform')
"""
Connect the nodes
"""
getmask.connect([
(inputnode, fssource, [('subject_id', 'subject_id'),
('subjects_dir', 'subjects_dir')]),
(inputnode, register, [('source_file', 'source_file'),
('subject_id', 'subject_id'),
('subjects_dir', 'subjects_dir'),
('contrast_type', 'contrast_type')]),
(inputnode, voltransform, [('subjects_dir', 'subjects_dir'),
('source_file', 'source_file')]),
(fssource, threshold, [(('aparc_aseg', get_aparc_aseg), 'in_file')]),
(register, voltransform, [('out_reg_file', 'reg_file')]),
(threshold, voltransform, [('binary_file', 'target_file')])
])
"""
Add remaining nodes and connections
dilate : dilate the transformed file in source space
threshold2 : binarize transformed file
"""
threshold2 = pe.MapNode(fs.Binarize(min=0.5, out_type='nii'),
iterfield=['in_file'],
name='threshold2')
if dilate_mask:
threshold2.inputs.dilate = 1
getmask.connect([(voltransform, threshold2, [('transformed_file',
'in_file')])])
"""
Setup an outputnode that defines relevant inputs of the workflow.
"""
outputnode = pe.Node(
niu.IdentityInterface(fields=["mask_file", "reg_file", "reg_cost"]),
name="outputspec")
getmask.connect([
(register, outputnode, [("out_reg_file", "reg_file")]),
(register, outputnode, [("min_cost_file", "reg_cost")]),
(threshold2, outputnode, [("binary_file", "mask_file")]),
])
return getmask
def baw_ants_registration_template_build_single_iteration_wf(
iterationPhasePrefix, CLUSTER_QUEUE, CLUSTER_QUEUE_LONG
):
"""
Inputs::
inputspec.images :
inputspec.fixed_image :
inputspec.ListOfPassiveImagesDictionaries :
inputspec.interpolationMapping :
Outputs::
outputspec.template :
outputspec.transforms_list :
outputspec.passive_deformed_templates :
"""
TemplateBuildSingleIterationWF = pe.Workflow(
name="antsRegistrationTemplateBuildSingleIterationWF_"
+ str(iterationPhasePrefix)
)
inputSpec = pe.Node(
interface=util.IdentityInterface(
fields=[
"ListOfImagesDictionaries",
"registrationImageTypes",
# 'maskRegistrationImageType',
"interpolationMapping",
"fixed_image",
]
),
run_without_submitting=True,
name="inputspec",
)
## HACK: INFO: We need to have the AVG_AIR.nii.gz be warped with a default voxel value of 1.0
## HACK: INFO: Need to move all local functions to a common untility file, or at the top of the file so that
## they do not change due to re-indenting. Otherwise re-indenting for flow control will trigger
## their hash to change.
## HACK: INFO: REMOVE 'transforms_list' it is not used. That will change all the hashes
## HACK: INFO: Need to run all python files through the code beutifiers. It has gotten pretty ugly.
outputSpec = pe.Node(
interface=util.IdentityInterface(
fields=["template", "transforms_list", "passive_deformed_templates"]
),
run_without_submitting=True,
name="outputspec",
)
### NOTE MAP NODE! warp each of the original images to the provided fixed_image as the template
BeginANTS = pe.MapNode(
interface=Registration(), name="BeginANTS", iterfield=["moving_image"]
)
# SEE template.py many_cpu_BeginANTS_options_dictionary = {'qsub_args': modify_qsub_args(CLUSTER_QUEUE,4,2,8), 'overwrite': True}
## This is set in the template.py file BeginANTS.plugin_args = BeginANTS_cpu_sge_options_dictionary
common_ants_registration_settings(
antsRegistrationNode=BeginANTS,
registrationTypeDescription="SixStageAntsRegistrationT1Only",
output_transform_prefix=str(iterationPhasePrefix) + "_tfm",
output_warped_image="atlas2subject.nii.gz",
output_inverse_warped_image="subject2atlas.nii.gz",
save_state="SavedantsRegistrationNodeSyNState.h5",
invert_initial_moving_transform=False,
initial_moving_transform=None,
)
GetMovingImagesNode = pe.Node(
interface=util.Function(
function=get_moving_images,
input_names=[
"ListOfImagesDictionaries",
"registrationImageTypes",
"interpolationMapping",
],
output_names=["moving_images", "moving_interpolation_type"],
),
run_without_submitting=True,
name="99_GetMovingImagesNode",
)
TemplateBuildSingleIterationWF.connect(
inputSpec,
"ListOfImagesDictionaries",
GetMovingImagesNode,
"ListOfImagesDictionaries",
)
TemplateBuildSingleIterationWF.connect(
inputSpec,
"registrationImageTypes",
GetMovingImagesNode,
"registrationImageTypes",
)
TemplateBuildSingleIterationWF.connect(
inputSpec, "interpolationMapping", GetMovingImagesNode, "interpolationMapping"
)
TemplateBuildSingleIterationWF.connect(
GetMovingImagesNode, "moving_images", BeginANTS, "moving_image"
)
TemplateBuildSingleIterationWF.connect(
GetMovingImagesNode, "moving_interpolation_type", BeginANTS, "interpolation"
)
TemplateBuildSingleIterationWF.connect(
inputSpec, "fixed_image", BeginANTS, "fixed_image"
)
## Now warp all the input_images images
wimtdeformed = pe.MapNode(
interface=ApplyTransforms(),
iterfield=["transforms", "input_image"],
# iterfield=['transforms', 'invert_transform_flags', 'input_image'],
name="wimtdeformed",
)
wimtdeformed.inputs.interpolation = "Linear"
wimtdeformed.default_value = 0
# HACK: Should try using forward_composite_transform
##PREVIOUS TemplateBuildSingleIterationWF.connect(BeginANTS, 'forward_transform', wimtdeformed, 'transforms')
TemplateBuildSingleIterationWF.connect(
BeginANTS, "composite_transform", wimtdeformed, "transforms"
)
##PREVIOUS TemplateBuildSingleIterationWF.connect(BeginANTS, 'forward_invert_flags', wimtdeformed, 'invert_transform_flags')
## NOTE: forward_invert_flags:: List of flags corresponding to the forward transforms
# wimtdeformed.inputs.invert_transform_flags = [False,False,False,False,False]
TemplateBuildSingleIterationWF.connect(
GetMovingImagesNode, "moving_images", wimtdeformed, "input_image"
)
TemplateBuildSingleIterationWF.connect(
inputSpec, "fixed_image", wimtdeformed, "reference_image"
)
## Shape Update Next =====
## Now Average All input_images deformed images together to create an updated template average
AvgDeformedImages = pe.Node(interface=AverageImages(), name="AvgDeformedImages")
AvgDeformedImages.inputs.dimension = 3
AvgDeformedImages.inputs.output_average_image = (
str(iterationPhasePrefix) + ".nii.gz"
)
AvgDeformedImages.inputs.normalize = True
TemplateBuildSingleIterationWF.connect(
wimtdeformed, "output_image", AvgDeformedImages, "images"
)
## Now average all affine transforms together
AvgAffineTransform = pe.Node(
interface=AverageAffineTransform(), name="AvgAffineTransform"
)
AvgAffineTransform.inputs.dimension = 3
AvgAffineTransform.inputs.output_affine_transform = (
"Avererage_" + str(iterationPhasePrefix) + "_Affine.h5"
)
SplitCompositeTransform = pe.MapNode(
interface=util.Function(
function=split_composite_to_component_transform,
input_names=["transformFilename"],
output_names=["affine_component_list", "warp_component_list"],
),
iterfield=["transformFilename"],
run_without_submitting=True,
name="99_SplitCompositeTransform",
)
TemplateBuildSingleIterationWF.connect(
BeginANTS, "composite_transform", SplitCompositeTransform, "transformFilename"
)
## PREVIOUS TemplateBuildSingleIterationWF.connect(BeginANTS, 'forward_transforms', SplitCompositeTransform, 'transformFilename')
TemplateBuildSingleIterationWF.connect(
SplitCompositeTransform,
"affine_component_list",
AvgAffineTransform,
"transforms",
)
## Now average the warp fields togther
AvgWarpImages = pe.Node(interface=AverageImages(), name="AvgWarpImages")
AvgWarpImages.inputs.dimension = 3
AvgWarpImages.inputs.output_average_image = (
str(iterationPhasePrefix) + "warp.nii.gz"
)
AvgWarpImages.inputs.normalize = True
TemplateBuildSingleIterationWF.connect(
SplitCompositeTransform, "warp_component_list", AvgWarpImages, "images"
)
## Now average the images together
## INFO: For now GradientStep is set to 0.25 as a hard coded default value.
GradientStep = 0.25
GradientStepWarpImage = pe.Node(
interface=MultiplyImages(), name="GradientStepWarpImage"
)
GradientStepWarpImage.inputs.dimension = 3
GradientStepWarpImage.inputs.second_input = -1.0 * GradientStep
GradientStepWarpImage.inputs.output_product_image = (
"GradientStep0.25_" + str(iterationPhasePrefix) + "_warp.nii.gz"
)
TemplateBuildSingleIterationWF.connect(
AvgWarpImages, "output_average_image", GradientStepWarpImage, "first_input"
)
## Now create the new template shape based on the average of all deformed images
UpdateTemplateShape = pe.Node(
interface=ApplyTransforms(), name="UpdateTemplateShape"
)
UpdateTemplateShape.inputs.invert_transform_flags = [True]
UpdateTemplateShape.inputs.interpolation = "Linear"
UpdateTemplateShape.default_value = 0
TemplateBuildSingleIterationWF.connect(
AvgDeformedImages,
"output_average_image",
UpdateTemplateShape,
"reference_image",
)
TemplateBuildSingleIterationWF.connect(
[
(
AvgAffineTransform,
UpdateTemplateShape,
[(("affine_transform", make_list_of_one_element), "transforms")],
)
]
)
TemplateBuildSingleIterationWF.connect(
GradientStepWarpImage,
"output_product_image",
UpdateTemplateShape,
"input_image",
)
ApplyInvAverageAndFourTimesGradientStepWarpImage = pe.Node(
interface=util.Function(
function=make_transform_list_with_gradient_warps,
input_names=["averageAffineTranform", "gradientStepWarp"],
output_names=["TransformListWithGradientWarps"],
),
run_without_submitting=True,
name="99_MakeTransformListWithGradientWarps",
)
# ApplyInvAverageAndFourTimesGradientStepWarpImage.inputs.ignore_exception = True
TemplateBuildSingleIterationWF.connect(
AvgAffineTransform,
"affine_transform",
ApplyInvAverageAndFourTimesGradientStepWarpImage,
"averageAffineTranform",
)
TemplateBuildSingleIterationWF.connect(
UpdateTemplateShape,
"output_image",
ApplyInvAverageAndFourTimesGradientStepWarpImage,
"gradientStepWarp",
)
ReshapeAverageImageWithShapeUpdate = pe.Node(
interface=ApplyTransforms(), name="ReshapeAverageImageWithShapeUpdate"
)
ReshapeAverageImageWithShapeUpdate.inputs.invert_transform_flags = [
True,
False,
False,
False,
False,
]
ReshapeAverageImageWithShapeUpdate.inputs.interpolation = "Linear"
ReshapeAverageImageWithShapeUpdate.default_value = 0
ReshapeAverageImageWithShapeUpdate.inputs.output_image = (
"ReshapeAverageImageWithShapeUpdate.nii.gz"
)
TemplateBuildSingleIterationWF.connect(
AvgDeformedImages,
"output_average_image",
ReshapeAverageImageWithShapeUpdate,
"input_image",
)
TemplateBuildSingleIterationWF.connect(
AvgDeformedImages,
"output_average_image",
ReshapeAverageImageWithShapeUpdate,
"reference_image",
)
TemplateBuildSingleIterationWF.connect(
ApplyInvAverageAndFourTimesGradientStepWarpImage,
"TransformListWithGradientWarps",
ReshapeAverageImageWithShapeUpdate,
"transforms",
)
TemplateBuildSingleIterationWF.connect(
ReshapeAverageImageWithShapeUpdate, "output_image", outputSpec, "template"
)
######
######
###### Process all the passive deformed images in a way similar to the main image used for registration
######
######
######
##############################################
## Now warp all the ListOfPassiveImagesDictionaries images
FlattenTransformAndImagesListNode = pe.Node(
Function(
function=flatten_transform_and_images_list,
input_names=[
"ListOfPassiveImagesDictionaries",
"transforms",
"interpolationMapping",
"invert_transform_flags",
],
output_names=[
"flattened_images",
"flattened_transforms",
"flattened_invert_transform_flags",
"flattened_image_nametypes",
"flattened_interpolation_type",
],
),
run_without_submitting=True,
name="99_FlattenTransformAndImagesList",
)
GetPassiveImagesNode = pe.Node(
interface=util.Function(
function=get_passive_images,
input_names=["ListOfImagesDictionaries", "registrationImageTypes"],
output_names=["ListOfPassiveImagesDictionaries"],
),
run_without_submitting=True,
name="99_GetPassiveImagesNode",
)
TemplateBuildSingleIterationWF.connect(
inputSpec,
"ListOfImagesDictionaries",
GetPassiveImagesNode,
"ListOfImagesDictionaries",
)
TemplateBuildSingleIterationWF.connect(
inputSpec,
"registrationImageTypes",
GetPassiveImagesNode,
"registrationImageTypes",
)
TemplateBuildSingleIterationWF.connect(
GetPassiveImagesNode,
"ListOfPassiveImagesDictionaries",
FlattenTransformAndImagesListNode,
"ListOfPassiveImagesDictionaries",
)
TemplateBuildSingleIterationWF.connect(
inputSpec,
"interpolationMapping",
FlattenTransformAndImagesListNode,
"interpolationMapping",
)
TemplateBuildSingleIterationWF.connect(
BeginANTS,
"composite_transform",
FlattenTransformAndImagesListNode,
"transforms",
)
## FlattenTransformAndImagesListNode.inputs.invert_transform_flags = [False,False,False,False,False,False]
## INFO: Please check of invert_transform_flags has a fixed number.
## PREVIOUS TemplateBuildSingleIterationWF.connect(BeginANTS, 'forward_invert_flags', FlattenTransformAndImagesListNode, 'invert_transform_flags')
wimtPassivedeformed = pe.MapNode(
interface=ApplyTransforms(),
iterfield=[
"transforms",
"invert_transform_flags",
"input_image",
"interpolation",
],
name="wimtPassivedeformed",
)
wimtPassivedeformed.default_value = 0
TemplateBuildSingleIterationWF.connect(
AvgDeformedImages,
"output_average_image",
wimtPassivedeformed,
"reference_image",
)
TemplateBuildSingleIterationWF.connect(
FlattenTransformAndImagesListNode,
"flattened_interpolation_type",
wimtPassivedeformed,
"interpolation",
)
TemplateBuildSingleIterationWF.connect(
FlattenTransformAndImagesListNode,
"flattened_images",
wimtPassivedeformed,
"input_image",
)
TemplateBuildSingleIterationWF.connect(
FlattenTransformAndImagesListNode,
"flattened_transforms",
wimtPassivedeformed,
"transforms",
)
TemplateBuildSingleIterationWF.connect(
FlattenTransformAndImagesListNode,
"flattened_invert_transform_flags",
wimtPassivedeformed,
"invert_transform_flags",
)
RenestDeformedPassiveImagesNode = pe.Node(
Function(
function=renest_deformed_passive_images,
input_names=[
"deformedPassiveImages",
"flattened_image_nametypes",
"interpolationMapping",
],
output_names=[
"nested_imagetype_list",
"outputAverageImageName_list",
"image_type_list",
"nested_interpolation_type",
],
),
run_without_submitting=True,
name="99_RenestDeformedPassiveImages",
)
TemplateBuildSingleIterationWF.connect(
inputSpec,
"interpolationMapping",
RenestDeformedPassiveImagesNode,
"interpolationMapping",
)
TemplateBuildSingleIterationWF.connect(
wimtPassivedeformed,
"output_image",
RenestDeformedPassiveImagesNode,
"deformedPassiveImages",
)
TemplateBuildSingleIterationWF.connect(
FlattenTransformAndImagesListNode,
"flattened_image_nametypes",
RenestDeformedPassiveImagesNode,
"flattened_image_nametypes",
)
## Now Average All passive input_images deformed images together to create an updated template average
AvgDeformedPassiveImages = pe.MapNode(
interface=AverageImages(),
iterfield=["images", "output_average_image"],
name="AvgDeformedPassiveImages",
)
AvgDeformedPassiveImages.inputs.dimension = 3
AvgDeformedPassiveImages.inputs.normalize = False
TemplateBuildSingleIterationWF.connect(
RenestDeformedPassiveImagesNode,
"nested_imagetype_list",
AvgDeformedPassiveImages,
"images",
)
TemplateBuildSingleIterationWF.connect(
RenestDeformedPassiveImagesNode,
"outputAverageImageName_list",
AvgDeformedPassiveImages,
"output_average_image",
)
## -- INFO: Now neeed to reshape all the passive images as well
ReshapeAveragePassiveImageWithShapeUpdate = pe.MapNode(
interface=ApplyTransforms(),
iterfield=["input_image", "reference_image", "output_image", "interpolation"],
name="ReshapeAveragePassiveImageWithShapeUpdate",
)
ReshapeAveragePassiveImageWithShapeUpdate.inputs.invert_transform_flags = [
True,
False,
False,
False,
False,
]
ReshapeAveragePassiveImageWithShapeUpdate.default_value = 0
TemplateBuildSingleIterationWF.connect(
RenestDeformedPassiveImagesNode,
"nested_interpolation_type",
ReshapeAveragePassiveImageWithShapeUpdate,
"interpolation",
)
TemplateBuildSingleIterationWF.connect(
RenestDeformedPassiveImagesNode,
"outputAverageImageName_list",
ReshapeAveragePassiveImageWithShapeUpdate,
"output_image",
)
TemplateBuildSingleIterationWF.connect(
AvgDeformedPassiveImages,
"output_average_image",
ReshapeAveragePassiveImageWithShapeUpdate,
"input_image",
)
TemplateBuildSingleIterationWF.connect(
AvgDeformedPassiveImages,
"output_average_image",
ReshapeAveragePassiveImageWithShapeUpdate,
"reference_image",
)
TemplateBuildSingleIterationWF.connect(
ApplyInvAverageAndFourTimesGradientStepWarpImage,
"TransformListWithGradientWarps",
ReshapeAveragePassiveImageWithShapeUpdate,
"transforms",
)
TemplateBuildSingleIterationWF.connect(
ReshapeAveragePassiveImageWithShapeUpdate,
"output_image",
outputSpec,
"passive_deformed_templates",
)
return TemplateBuildSingleIterationWF
def create_workflow():
featpreproc = pe.Workflow(name="featpreproc")
featpreproc.base_dir = os.path.join(ds_root, 'workingdirs')
# ===================================================================
# _____ _
# |_ _| | |
# | | _ __ _ __ _ _| |_
# | | | '_ \| '_ \| | | | __|
# _| |_| | | | |_) | |_| | |_
# |_____|_| |_| .__/ \__,_|\__|
# | |
# |_|
# ===================================================================
# ------------------ Specify variables
inputnode = pe.Node(niu.IdentityInterface(fields=[
'funcs',
'subject_id',
'session_id',
'fwhm', # smoothing
'highpass'
]), name="inputspec")
# SelectFiles
templates = {
'ref_manual_fmapmask': # was: manual_fmapmask
'derivatives/manual-masks/sub-eddy/ses-20170511/fmap/'
'sub-eddy_ses-20170511_magnitude1_res-1x1x1_manualmask.nii.gz',
'ref_fmap_magnitude':
'derivatives/manual-masks/sub-eddy/ses-20170511/fmap/'
'sub-eddy_ses-20170511_magnitude1_res-1x1x1_reference.nii.gz',
'ref_fmap_phasediff':
'derivatives/resampled-isotropic-1mm/sub-eddy/ses-20170511/fmap/'
'sub-eddy_ses-20170511_phasediff_res-1x1x1_preproc'
'.nii.gz',
# 'manualweights':
# 'manual-masks/sub-eddy/ses-20170511/func/'
# 'sub-eddy_ses-20170511_task-curvetracing_run-01_frame-50_bold'
# '_res-1x1x1_manualweights.nii.gz',
'ref_func': # was: manualmask_func_ref
'derivatives/manual-masks/sub-eddy/ses-20170607/func/'
'sub-eddy_ses-20170607_task-RestingPRF_run-02_bold_'
'res-1x1x1_fnirt_reference.nii.gz',
'ref_funcmask': # was: manualmask
'derivatives/manual-masks/sub-eddy/ses-20170607/func/'
'sub-eddy_ses-20170607_task-RestingPRF_run-02_bold_'
'res-1x1x1_fnirt_mask.nii.gz',
'ref_t1':
'derivatives/manual-masks/sub-eddy/ses-20170511/anat/'
'sub-eddy_ses-20170511_T1w_res-1x1x1_reference.nii.gz',
'ref_t1mask':
'derivatives/manual-masks/sub-eddy/ses-20170511/anat/'
'sub-eddy_ses-20170511_T1w_res-1x1x1_manualmask.nii.gz',
# 'funcs':
# 'resampled-isotropic-1mm/sub-{subject_id}/ses-{session_id}/func/'
# # 'sub-{subject_id}_ses-{session_id}*_bold_res-1x1x1_preproc'
# 'sub-{subject_id}_ses-{session_id}*run-01_bold_res-1x1x1_preproc'
# # '.nii.gz',
# '_nvol10.nii.gz',
'fmap_phasediff':
'derivatives/resampled-isotropic-1mm/sub-{subject_id}/ses-{session_id}/fmap/'
'sub-{subject_id}_ses-{session_id}_phasediff_res-1x1x1_preproc'
'.nii.gz',
'fmap_magnitude':
'derivatives/resampled-isotropic-1mm/sub-{subject_id}/ses-{session_id}/fmap/'
'sub-{subject_id}_ses-{session_id}_magnitude1_res-1x1x1_preproc'
'.nii.gz',
# 'fmap_mask':
# 'transformed-manual-fmap-mask/sub-{subject_id}/ses-{session_id}/fmap/'
# 'sub-{subject_id}_ses-{session_id}_'
# 'magnitude1_res-1x1x1_preproc.nii.gz',
}
inputfiles = pe.Node(
nio.SelectFiles(templates,
base_directory=data_dir), name="input_files")
featpreproc.connect(
[(inputnode, inputfiles,
[('subject_id', 'subject_id'),
('session_id', 'session_id'),
])])
# ===================================================================
# ____ _ _
# / __ \ | | | |
# | | | |_ _| |_ _ __ _ _| |_
# | | | | | | | __| '_ \| | | | __|
# | |__| | |_| | |_| |_) | |_| | |_
# \____/ \__,_|\__| .__/ \__,_|\__|
# | |
# |_|
# ===================================================================
# ------------------ Output Files
# Datasink
outputfiles = pe.Node(nio.DataSink(
base_directory=ds_root,
container='derivatives/featpreproc',
parameterization=True),
name="output_files")
# Use the following DataSink output substitutions
# each tuple is only matched once per file
outputfiles.inputs.substitutions = [
('/_mc_method_afni3dAllinSlices/', '/'),
('/_mc_method_afni3dAllinSlices/', '/'), # needs to appear twice
('/oned_file/', '/'),
('/out_file/', '/'),
('/oned_matrix_save/', '/'),
('subject_id_', 'sub-'),
('session_id_', 'ses-'),
]
# Put result into a BIDS-like format
outputfiles.inputs.regexp_substitutions = [
(r'_ses-([a-zA-Z0-9]+)_sub-([a-zA-Z0-9]+)', r'sub-\2/ses-\1'),
(r'/_addmean[0-9]+/', r'/func/'),
(r'/_funcbrains[0-9]+/', r'/func/'),
(r'/_maskfunc[0-9]+/', r'/func/'),
(r'/_mc[0-9]+/', r'/func/'),
(r'/_meanfunc[0-9]+/', r'/func/'),
(r'/_outliers[0-9]+/', r'/func/'),
(r'_run_id_[0-9][0-9]', r''),
]
outputnode = pe.Node(interface=util.IdentityInterface(
fields=['motion_parameters',
'motion_corrected',
'motion_plots',
'motion_outlier_files',
'mask',
'smoothed_files',
'highpassed_files',
'mean',
'func_unwarp',
'ref_func',
'ref_funcmask',
'ref_t1',
'ref_t1mask',
]),
name='outputspec')
# ===================================================================
# _____ _ _ _
# | __ (_) | (_)
# | |__) | _ __ ___| |_ _ __ ___
# | ___/ | '_ \ / _ \ | | '_ \ / _ \
# | | | | |_) | __/ | | | | | __/
# |_| |_| .__/ \___|_|_|_| |_|\___|
# | |
# |_|
# ===================================================================
# ~|~ _ _ _ _ |` _ _ _ _ _ _ _ _| _
# | | (_|| |_\~|~(_)| | | | | | |(_|_\|<_\
#
# Transform manual skull-stripped masks to multiple images
# --------------------------------------------------------
# should just be used as input to motion correction,
# after mc, all functionals should be aligned to reference
transmanmask_mc = transform_manualmask.create_workflow()
# - - - - - - Connections - - - - - - -
featpreproc.connect(
[(inputfiles, transmanmask_mc,
[('subject_id', 'in.subject_id'),
('session_id', 'in.session_id'),
])])
featpreproc.connect(inputfiles, 'ref_funcmask',
transmanmask_mc, 'in.manualmask')
featpreproc.connect(inputnode, 'funcs',
transmanmask_mc, 'in.funcs')
featpreproc.connect(inputfiles, 'ref_func',
transmanmask_mc, 'in.manualmask_func_ref')
# fieldmaps not being used
if False:
trans_fmapmask = transmanmask_mc.clone('trans_fmapmask')
featpreproc.connect(inputfiles, 'ref_manual_fmapmask',
trans_fmapmask, 'in.manualmask')
featpreproc.connect(inputfiles, 'fmap_magnitude',
trans_fmapmask, 'in.funcs')
featpreproc.connect(inputfiles, 'ref_func',
trans_fmapmask, 'in.manualmask_func_ref')
# |\/| _ _|_. _ _ _ _ _ _ _ __|_. _ _
# | |(_) | |(_)| | (_(_)| | (/_(_ | |(_)| |
#
# Perform motion correction, using some pipeline
# --------------------------------------------------------
# mc = motioncorrection_workflow.create_workflow_afni()
# Register an image from the functionals to the reference image
median_func = pe.MapNode(
interface=fsl.maths.MedianImage(dimension="T"),
name='median_func',
iterfield=('in_file'),
)
pre_mc = motioncorrection_workflow.create_workflow_allin_slices(
name='premotioncorrection')
featpreproc.connect(
[
(inputnode, median_func,
[
('funcs', 'in_file'),
]),
(median_func, pre_mc,
[
('out_file', 'in.funcs'),
]),
(inputfiles, pre_mc,
[
# median func image will be used a reference / base
('ref_func', 'in.ref_func'),
('ref_funcmask', 'in.ref_func_weights'),
]),
(transmanmask_mc, pre_mc,
[
('funcreg.out_file', 'in.funcs_masks'), # use mask as weights
]),
(pre_mc, outputnode,
[
('mc.out_file', 'pre_motion_corrected'),
('mc.oned_file', 'pre_motion_parameters.oned_file'),
('mc.oned_matrix_save', 'pre_motion_parameters.oned_matrix_save'),
]),
(outputnode, outputfiles,
[
('pre_motion_corrected', 'pre_motion_corrected.out_file'),
('pre_motion_parameters.oned_file', 'pre_motion_corrected.oned_file'), # warp parameters in ASCII (.1D)
('pre_motion_parameters.oned_matrix_save', 'pre_motion_corrected.oned_matrix_save'), # transformation matrices for each sub-brick
]),
])
mc = motioncorrection_workflow.create_workflow_allin_slices(
name='motioncorrection',
iterfield=('in_file', 'ref_file', 'in_weight_file'))
# - - - - - - Connections - - - - - - -
featpreproc.connect(
[(inputnode, mc,
[
('funcs', 'in.funcs'),
]),
(pre_mc, mc, [
# the median image realigned to the reference functional will serve as reference
# this way motion correction is done to an image more similar to the functionals
('mc.out_file', 'in.ref_func'),
]),
(inputfiles, mc, [
# Check and make sure the ref func mask is close enough to the registered median
# image.
('ref_funcmask', 'in.ref_func_weights'),
]),
(transmanmask_mc, mc, [
('funcreg.out_file', 'in.funcs_masks'), # use mask as weights
]),
(mc, outputnode, [
('mc.out_file', 'motion_corrected'),
('mc.oned_file', 'motion_parameters.oned_file'),
('mc.oned_matrix_save', 'motion_parameters.oned_matrix_save'),
]),
(outputnode, outputfiles, [
('motion_corrected', 'motion_corrected.out_file'),
('motion_parameters.oned_file', 'motion_corrected.oned_file'), # warp parameters in ASCII (.1D)
('motion_parameters.oned_matrix_save', 'motion_corrected.oned_matrix_save'), # transformation matrices for each sub-brick
]),
])
# |~. _ | _| _ _ _ _ _ _ _ _ _ __|_. _ _
# |~|(/_|(_|| | |(_||_) (_(_)| | (/_(_ | |(_)| |
# |
# Unwarp EPI distortions
# --------------------------------------------------------
# Performing motion correction to a reference that is undistorted,
# so b0_unwarp is currently not needed
if False:
b0_unwarp = undistort_workflow.create_workflow()
featpreproc.connect(
[(inputfiles, b0_unwarp,
[ # ('subject_id', 'in.subject_id'),
# ('session_id', 'in.session_id'),
('fmap_phasediff', 'in.fmap_phasediff'),
('fmap_magnitude', 'in.fmap_magnitude'),
]),
(mc, b0_unwarp,
[('mc.out_file', 'in.funcs'),
]),
(transmanmask_mc, b0_unwarp,
[('funcreg.out_file', 'in.funcmasks'),
]),
(trans_fmapmask, b0_unwarp,
[('funcreg.out_file', 'in.fmap_mask')]),
(b0_unwarp, outputfiles,
[('out.funcs', 'func_unwarp.funcs'),
('out.funcmasks', 'func_unwarp.funcmasks'),
]),
(b0_unwarp, outputnode,
[('out.funcs', 'func_unwarp.funcs'),
('out.funcmasks', 'mask'),
]),
])
# undistort the reference images
if False:
b0_unwarp_ref = b0_unwarp.clone('b0_unwarp_ref')
featpreproc.connect(
[(inputfiles, b0_unwarp_ref,
[ # ('subject_id', 'in.subject_id'),
# ('session_id', 'in.session_id'),
('ref_fmap_phasediff', 'in.fmap_phasediff'),
('ref_fmap_magnitude', 'in.fmap_magnitude'),
('ref_manual_fmapmask', 'in.fmap_mask'),
('ref_func', 'in.funcs'),
('ref_funcmask', 'in.funcmasks'),
]),
(b0_unwarp_ref, outputfiles,
[('out.funcs', 'func_unwarp_ref.func'),
('out.funcmasks', 'func_unwarp_ref.funcmask'),
]),
(b0_unwarp_ref, outputnode,
[('out.funcs', 'ref_func'),
('out.funcmasks', 'ref_mask'),
]),
])
else:
featpreproc.connect(
[(inputfiles, outputfiles,
[('ref_func', 'reference/func'),
('ref_funcmask', 'reference/func_mask'),
]),
(inputfiles, outputnode,
[('ref_func', 'ref_func'),
('ref_funcmask', 'ref_funcmask'),
]),
])
# |~) _ _ . __|_ _ _ _|_ _ |~) _ |` _ _ _ _ _ _
# |~\(/_(_||_\ | (/_| | (_) |~\(/_~|~(/_| (/_| |(_(/_
# _|
# Register all functionals to common reference
# --------------------------------------------------------
if False: # this is now done during motion correction
# FLIRT cost: intermodal: corratio, intramodal: least squares and normcorr
reg_to_ref = pe.MapNode( # intra-modal
# some runs need to be scaled along the anterior-posterior direction
interface=fsl.FLIRT(dof=12, cost='normcorr'),
name='reg_to_ref',
iterfield=('in_file', 'in_weight'),
)
refEPI_to_refT1 = pe.Node(
# some runs need to be scaled along the anterior-posterior direction
interface=fsl.FLIRT(dof=12, cost='corratio'),
name='refEPI_to_refT1',
)
# combine func -> ref_func and ref_func -> ref_T1
reg_to_refT1 = pe.MapNode(
interface=fsl.ConvertXFM(concat_xfm=True),
name='reg_to_refT1',
iterfield=('in_file'),
)
reg_funcs = pe.MapNode(
interface=fsl.preprocess.ApplyXFM(),
name='reg_funcs',
iterfield=('in_file', 'in_matrix_file'),
)
reg_funcmasks = pe.MapNode(
interface=fsl.preprocess.ApplyXFM(),
name='reg_funcmasks',
iterfield=('in_file', 'in_matrix_file')
)
def deref_list(x):
assert len(x)==1
return x[0]
featpreproc.connect(
[
(b0_unwarp, reg_to_ref, # --> reg_to_ref, (A)
[
('out.funcs', 'in_file'),
('out.funcmasks', 'in_weight'),
]),
(b0_unwarp_ref, reg_to_ref,
[
(('out.funcs', deref_list), 'reference'),
(('out.funcmasks', deref_list), 'ref_weight'),
]),
(b0_unwarp_ref, refEPI_to_refT1, # --> refEPI_to_refT1 (B)
[
(('out.funcs', deref_list), 'in_file'),
(('out.funcmasks', deref_list), 'in_weight'),
]),
(inputfiles, refEPI_to_refT1,
[
('ref_t1', 'reference'),
('ref_t1mask', 'ref_weight'),
]),
(reg_to_ref, reg_to_refT1, # --> reg_to_refT1 (A*B)
[
('out_matrix_file', 'in_file'),
]),
(refEPI_to_refT1, reg_to_refT1,
[
('out_matrix_file', 'in_file2'),
]),
(reg_to_refT1, reg_funcs, # --> reg_funcs
[
# ('out_matrix_file', 'in_matrix_file'),
('out_file', 'in_matrix_file'),
]),
(b0_unwarp, reg_funcs,
[
('out.funcs', 'in_file'),
]),
(b0_unwarp_ref, reg_funcs,
[
(('out.funcs', deref_list), 'reference'),
]),
(reg_to_refT1, reg_funcmasks, # --> reg_funcmasks
[
# ('out_matrix_file', 'in_matrix_file'),
('out_file', 'in_matrix_file'),
]),
(b0_unwarp, reg_funcmasks,
[
('out.funcmasks', 'in_file'),
]),
(b0_unwarp_ref, reg_funcmasks,
[
(('out.funcs', deref_list), 'reference'),
]),
(reg_funcs, outputfiles,
[
('out_file', 'common_ref.func'),
]),
(reg_funcmasks, outputfiles,
[
('out_file', 'common_ref.funcmask'),
]),
])
# |\/| _ _|_. _ _ _ _|_|. _ _ _
# | |(_) | |(_)| | (_)|_|| ||(/_| _\
#
# --------------------------------------------------------
# Apply brain masks to functionals
# --------------------------------------------------------
# Dilate mask
"""
Dilate the mask
"""
if False:
dilatemask = pe.MapNode(interface=fsl.ImageMaths(suffix='_dil',
op_string='-dilF'),
iterfield=['in_file'],
name='dilatemask')
featpreproc.connect(reg_funcmasks, 'out_file', dilatemask, 'in_file')
else:
dilatemask = pe.Node(
interface=fsl.ImageMaths(suffix='_dil', op_string='-dilF'),
name='dilatemask')
featpreproc.connect(inputfiles, 'ref_funcmask', dilatemask, 'in_file')
featpreproc.connect(dilatemask, 'out_file', outputfiles, 'dilate_mask')
funcbrains = pe.MapNode(
fsl.BinaryMaths(operation='mul'),
iterfield=('in_file', 'operand_file'),
name='funcbrains'
)
featpreproc.connect(
[(mc, funcbrains,
[('mc.out_file', 'in_file'),
]),
(dilatemask, funcbrains,
[('out_file', 'operand_file'),
]),
(funcbrains, outputfiles,
[('out_file', 'funcbrains'),
]),
])
# Detect motion outliers
# --------------------------------------------------------
import nipype.algorithms.rapidart as ra
outliers = pe.MapNode(
ra.ArtifactDetect(
mask_type='file',
# trying to "disable" `norm_threshold`:
use_norm=True,
norm_threshold=10.0, # combines translations in mm and rotations
# use_norm=Undefined,
# translation_threshold=1.0, # translation in mm
# rotation_threshold=0.02, # rotation in radians
zintensity_threshold=3.0, # z-score
parameter_source='AFNI',
save_plot=True),
iterfield=('realigned_files', 'realignment_parameters', 'mask_file'),
name='outliers')
featpreproc.connect([
(mc, outliers,
[ # ('mc.par_file', 'realignment_parameters'),
('mc.oned_file', 'realignment_parameters'),
]),
(funcbrains, outliers,
[('out_file', 'realigned_files'),
]),
(dilatemask, outliers,
[('out_file', 'mask_file'),
]),
(outliers, outputfiles,
[('outlier_files', 'motion_outliers.@outlier_files'),
('plot_files', 'motion_outliers.@plot_files'),
('displacement_files', 'motion_outliers.@displacement_files'),
('intensity_files', 'motion_outliers.@intensity_files'),
('mask_files', 'motion_outliers.@mask_files'),
('statistic_files', 'motion_outliers.@statistic_files'),
# ('norm_files', 'outliers.@norm_files'),
]),
(mc, outputnode,
[('mc.oned_file', 'motion_parameters'),
]),
(outliers, outputnode,
[('outlier_files', 'motion_outlier_files'),
('plot_files', 'motion_plots.@plot_files'),
('displacement_files', 'motion_outliers.@displacement_files'),
('intensity_files', 'motion_outliers.@intensity_files'),
('mask_files', 'motion_outliers.@mask_files'),
('statistic_files', 'motion_outliers.@statistic_files'),
# ('norm_files', 'outliers.@norm_files'),
])
])
"""
Determine the 2nd and 98th percentile intensities of each functional run
"""
getthresh = pe.MapNode(interface=fsl.ImageStats(op_string='-p 2 -p 98'),
iterfield=['in_file'],
name='getthreshold')
if False:
featpreproc.connect(b0_unwarp, 'out.funcs', getthresh, 'in_file')
else:
featpreproc.connect(mc, 'mc.out_file', getthresh, 'in_file')
"""
Threshold the first run of functional data at 10% of the 98th percentile
"""
threshold = pe.MapNode(interface=fsl.ImageMaths(out_data_type='char',
suffix='_thresh'),
iterfield=['in_file', 'op_string'],
name='threshold')
if False:
featpreproc.connect(b0_unwarp, 'out.funcs', threshold, 'in_file')
else:
featpreproc.connect(mc, 'mc.out_file', threshold, 'in_file')
"""
Define a function to get 10% of the intensity
"""
def getthreshop(thresh):
return ['-thr %.10f -Tmin -bin' % (0.1 * val[1]) for val in thresh]
featpreproc.connect(
getthresh, ('out_stat', getthreshop),
threshold, 'op_string')
"""
Determine the median value of the functional runs using the mask
"""
medianval = pe.MapNode(interface=fsl.ImageStats(op_string='-k %s -p 50'),
iterfield=['in_file', 'mask_file'],
name='medianval')
if False:
featpreproc.connect(b0_unwarp, 'out.funcs', medianval, 'in_file')
else:
featpreproc.connect(mc, 'mc.out_file', medianval, 'in_file')
featpreproc.connect(threshold, 'out_file', medianval, 'mask_file')
# (~ _ _ _|_. _ | (~ _ _ _ _ _|_|_ . _ _
# _)|_)(_| | |(_|| _)| | |(_)(_) | | ||| |(_|
# | _|
# Spatial smoothing (SUSAN)
# --------------------------------------------------------
# create_susan_smooth takes care of calculating the mean and median
# functional, applying mask to functional, and running the smoothing
smooth = create_susan_smooth(separate_masks=False)
featpreproc.connect(inputnode, 'fwhm', smooth, 'inputnode.fwhm')
# featpreproc.connect(b0_unwarp, 'out.funcs', smooth, 'inputnode.in_files')
if False:
featpreproc.connect(reg_funcs, 'out_file', smooth, 'inputnode.in_files')
else:
featpreproc.connect(mc, 'mc.out_file', smooth, 'inputnode.in_files')
featpreproc.connect(dilatemask, 'out_file',
smooth, 'inputnode.mask_file')
# -------------------------------------------------------
# The below is from workflows/fmri/fsl/preprocess.py
"""
Mask the smoothed data with the dilated mask
"""
maskfunc3 = pe.MapNode(interface=fsl.ImageMaths(suffix='_mask',
op_string='-mas'),
iterfield=['in_file', 'in_file2'],
name='maskfunc3')
featpreproc.connect(
smooth, 'outputnode.smoothed_files', maskfunc3, 'in_file')
featpreproc.connect(dilatemask, 'out_file', maskfunc3, 'in_file2')
concatnode = pe.Node(interface=util.Merge(2),
name='concat')
tolist = lambda x: [x]
def chooseindex(fwhm):
if fwhm < 1:
return [0]
else:
return [1]
# maskfunc2 is the functional data before SUSAN
if False:
featpreproc.connect(b0_unwarp, ('out.funcs', tolist), concatnode, 'in1')
else:
featpreproc.connect(mc, ('mc.out_file', tolist), concatnode, 'in1')
# maskfunc3 is the functional data after SUSAN
featpreproc.connect(maskfunc3, ('out_file', tolist), concatnode, 'in2')
"""
The following nodes select smooth or unsmoothed data depending on the
fwhm. This is because SUSAN defaults to smoothing the data with about the
voxel size of the input data if the fwhm parameter is less than 1/3 of the
voxel size.
"""
selectnode = pe.Node(interface=util.Select(), name='select')
featpreproc.connect(concatnode, 'out', selectnode, 'inlist')
featpreproc.connect(inputnode, ('fwhm', chooseindex), selectnode, 'index')
featpreproc.connect(selectnode, 'out', outputfiles, 'smoothed_files')
"""
Scale the median value of the run is set to 10000.
"""
meanscale = pe.MapNode(interface=fsl.ImageMaths(suffix='_gms'),
iterfield=['in_file', 'op_string'],
name='meanscale')
featpreproc.connect(selectnode, 'out', meanscale, 'in_file')
"""
Define a function to get the scaling factor for intensity normalization
"""
featpreproc.connect(
medianval, ('out_stat', getmeanscale),
meanscale, 'op_string')
# |_|. _ |_ _ _ _ _
# | ||(_|| ||_)(_|_\_\
# _| |
# Temporal filtering
# --------------------------------------------------------
highpass = pe.MapNode(interface=fsl.ImageMaths(suffix='_tempfilt'),
iterfield=['in_file'],
name='highpass')
highpass_operand = lambda x: '-bptf %.10f -1' % x
featpreproc.connect(
inputnode, ('highpass', highpass_operand),
highpass, 'op_string')
featpreproc.connect(meanscale, 'out_file', highpass, 'in_file')
version = 0
if fsl.Info.version() and \
LooseVersion(fsl.Info.version()) > LooseVersion('5.0.6'):
version = 507
if version < 507:
featpreproc.connect(
highpass, 'out_file', outputnode, 'highpassed_files')
else:
"""
Add back the mean removed by the highpass filter operation as
of FSL 5.0.7
"""
meanfunc4 = pe.MapNode(interface=fsl.ImageMaths(op_string='-Tmean',
suffix='_mean'),
iterfield=['in_file'],
name='meanfunc4')
featpreproc.connect(meanscale, 'out_file', meanfunc4, 'in_file')
addmean = pe.MapNode(interface=fsl.BinaryMaths(operation='add'),
iterfield=['in_file', 'operand_file'],
name='addmean')
featpreproc.connect(highpass, 'out_file', addmean, 'in_file')
featpreproc.connect(meanfunc4, 'out_file', addmean, 'operand_file')
featpreproc.connect(
addmean, 'out_file', outputnode, 'highpassed_files')
"""
Generate a mean functional image from the first run
"""
meanfunc3 = pe.MapNode(interface=fsl.ImageMaths(op_string='-Tmean',
suffix='_mean'),
iterfield=['in_file'],
name='meanfunc3')
featpreproc.connect(meanscale, 'out_file', meanfunc3, 'in_file')
featpreproc.connect(meanfunc3, 'out_file', outputfiles, 'mean')
featpreproc.connect(meanfunc3, 'out_file', outputnode, 'mean_highpassed')
featpreproc.connect(outputnode, 'highpassed_files', outputfiles, 'highpassed_files')
return(featpreproc)
def analyze_openfmri_dataset(data_dir,
subject=None,
model_id=None,
task_id=None,
output_dir=None,
subj_prefix='*',
hpcutoff=120.,
use_derivatives=True,
fwhm=6.0,
subjects_dir=None,
target=None):
"""Analyzes an open fmri dataset
Parameters
----------
data_dir : str
Path to the base data directory
work_dir : str
Nipype working directory (defaults to cwd)
"""
"""
Load nipype workflows
"""
preproc = create_featreg_preproc(whichvol='first')
modelfit = create_modelfit_workflow()
fixed_fx = create_fixed_effects_flow()
if subjects_dir:
registration = create_fs_reg_workflow()
else:
registration = create_reg_workflow()
"""
Remove the plotting connection so that plot iterables don't propagate
to the model stage
"""
preproc.disconnect(preproc.get_node('plot_motion'), 'out_file',
preproc.get_node('outputspec'), 'motion_plots')
"""
Set up openfmri data specific components
"""
subjects = sorted([
path.split(os.path.sep)[-1]
for path in glob(os.path.join(data_dir, subj_prefix))
])
infosource = pe.Node(
niu.IdentityInterface(fields=['subject_id', 'model_id', 'task_id']),
name='infosource')
if len(subject) == 0:
infosource.iterables = [('subject_id', subjects),
('model_id', [model_id]), ('task_id', task_id)]
else:
infosource.iterables = [
('subject_id',
[subjects[subjects.index(subj)] for subj in subject]),
('model_id', [model_id]), ('task_id', task_id)
]
subjinfo = pe.Node(niu.Function(
input_names=['subject_id', 'base_dir', 'task_id', 'model_id'],
output_names=['run_id', 'conds', 'TR'],
function=get_subjectinfo),
name='subjectinfo')
subjinfo.inputs.base_dir = data_dir
"""
Return data components as anat, bold and behav
"""
contrast_file = os.path.join(data_dir, 'models', 'model%03d' % model_id,
'task_contrasts.txt')
has_contrast = os.path.exists(contrast_file)
if has_contrast:
datasource = pe.Node(nio.DataGrabber(
infields=['subject_id', 'run_id', 'task_id', 'model_id'],
outfields=['anat', 'bold', 'behav', 'contrasts']),
name='datasource')
else:
datasource = pe.Node(nio.DataGrabber(
infields=['subject_id', 'run_id', 'task_id', 'model_id'],
outfields=['anat', 'bold', 'behav']),
name='datasource')
datasource.inputs.base_directory = data_dir
datasource.inputs.template = '*'
if has_contrast:
datasource.inputs.field_template = {
'anat': '%s/anatomy/T1_001.nii.gz',
'bold': '%s/BOLD/task%03d_r*/bold.nii.gz',
'behav': ('%s/model/model%03d/onsets/task%03d_'
'run%03d/cond*.txt'),
'contrasts': ('models/model%03d/'
'task_contrasts.txt')
}
datasource.inputs.template_args = {
'anat': [['subject_id']],
'bold': [['subject_id', 'task_id']],
'behav': [['subject_id', 'model_id', 'task_id', 'run_id']],
'contrasts': [['model_id']]
}
else:
datasource.inputs.field_template = {
'anat': '%s/anatomy/T1_001.nii.gz',
'bold': '%s/BOLD/task%03d_r*/bold.nii.gz',
'behav': ('%s/model/model%03d/onsets/task%03d_'
'run%03d/cond*.txt')
}
datasource.inputs.template_args = {
'anat': [['subject_id']],
'bold': [['subject_id', 'task_id']],
'behav': [['subject_id', 'model_id', 'task_id', 'run_id']]
}
datasource.inputs.sort_filelist = True
"""
Create meta workflow
"""
wf = pe.Workflow(name='openfmri')
wf.connect(infosource, 'subject_id', subjinfo, 'subject_id')
wf.connect(infosource, 'model_id', subjinfo, 'model_id')
wf.connect(infosource, 'task_id', subjinfo, 'task_id')
wf.connect(infosource, 'subject_id', datasource, 'subject_id')
wf.connect(infosource, 'model_id', datasource, 'model_id')
wf.connect(infosource, 'task_id', datasource, 'task_id')
wf.connect(subjinfo, 'run_id', datasource, 'run_id')
wf.connect([
(datasource, preproc, [('bold', 'inputspec.func')]),
])
def get_highpass(TR, hpcutoff):
return hpcutoff / (2. * TR)
gethighpass = pe.Node(niu.Function(input_names=['TR', 'hpcutoff'],
output_names=['highpass'],
function=get_highpass),
name='gethighpass')
wf.connect(subjinfo, 'TR', gethighpass, 'TR')
wf.connect(gethighpass, 'highpass', preproc, 'inputspec.highpass')
"""
Setup a basic set of contrasts, a t-test per condition
"""
def get_contrasts(contrast_file, task_id, conds):
import numpy as np
import os
contrast_def = []
if os.path.exists(contrast_file):
with open(contrast_file, 'rt') as fp:
contrast_def.extend([
np.array(row.split()) for row in fp.readlines()
if row.strip()
])
contrasts = []
for row in contrast_def:
if row[0] != 'task%03d' % task_id:
continue
con = [
row[1], 'T', ['cond%03d' % (i + 1) for i in range(len(conds))],
row[2:].astype(float).tolist()
]
contrasts.append(con)
# add auto contrasts for each column
for i, cond in enumerate(conds):
con = [cond, 'T', ['cond%03d' % (i + 1)], [1]]
contrasts.append(con)
return contrasts
contrastgen = pe.Node(niu.Function(
input_names=['contrast_file', 'task_id', 'conds'],
output_names=['contrasts'],
function=get_contrasts),
name='contrastgen')
art = pe.MapNode(
interface=ra.ArtifactDetect(use_differences=[True, False],
use_norm=True,
norm_threshold=1,
zintensity_threshold=3,
parameter_source='FSL',
mask_type='file'),
iterfield=['realigned_files', 'realignment_parameters', 'mask_file'],
name="art")
modelspec = pe.Node(interface=model.SpecifyModel(), name="modelspec")
modelspec.inputs.input_units = 'secs'
def check_behav_list(behav, run_id, conds):
import numpy as np
num_conds = len(conds)
if isinstance(behav, (str, bytes)):
behav = [behav]
behav_array = np.array(behav).flatten()
num_elements = behav_array.shape[0]
return behav_array.reshape(int(num_elements / num_conds),
num_conds).tolist()
reshape_behav = pe.Node(niu.Function(
input_names=['behav', 'run_id', 'conds'],
output_names=['behav'],
function=check_behav_list),
name='reshape_behav')
wf.connect(subjinfo, 'TR', modelspec, 'time_repetition')
wf.connect(datasource, 'behav', reshape_behav, 'behav')
wf.connect(subjinfo, 'run_id', reshape_behav, 'run_id')
wf.connect(subjinfo, 'conds', reshape_behav, 'conds')
wf.connect(reshape_behav, 'behav', modelspec, 'event_files')
wf.connect(subjinfo, 'TR', modelfit, 'inputspec.interscan_interval')
wf.connect(subjinfo, 'conds', contrastgen, 'conds')
if has_contrast:
wf.connect(datasource, 'contrasts', contrastgen, 'contrast_file')
else:
contrastgen.inputs.contrast_file = ''
wf.connect(infosource, 'task_id', contrastgen, 'task_id')
wf.connect(contrastgen, 'contrasts', modelfit, 'inputspec.contrasts')
wf.connect([(preproc, art,
[('outputspec.motion_parameters', 'realignment_parameters'),
('outputspec.realigned_files', 'realigned_files'),
('outputspec.mask', 'mask_file')]),
(preproc, modelspec,
[('outputspec.highpassed_files', 'functional_runs'),
('outputspec.motion_parameters', 'realignment_parameters')]),
(art, modelspec, [('outlier_files', 'outlier_files')]),
(modelspec, modelfit, [('session_info',
'inputspec.session_info')]),
(preproc, modelfit, [('outputspec.highpassed_files',
'inputspec.functional_data')])])
# Comute TSNR on realigned data regressing polynomials upto order 2
tsnr = MapNode(TSNR(regress_poly=2), iterfield=['in_file'], name='tsnr')
wf.connect(preproc, "outputspec.realigned_files", tsnr, "in_file")
# Compute the median image across runs
calc_median = Node(CalculateMedian(), name='median')
wf.connect(tsnr, 'detrended_file', calc_median, 'in_files')
"""
Reorder the copes so that now it combines across runs
"""
def sort_copes(copes, varcopes, contrasts):
import numpy as np
if not isinstance(copes, list):
copes = [copes]
varcopes = [varcopes]
num_copes = len(contrasts)
n_runs = len(copes)
all_copes = np.array(copes).flatten()
all_varcopes = np.array(varcopes).flatten()
outcopes = all_copes.reshape(int(len(all_copes) / num_copes),
num_copes).T.tolist()
outvarcopes = all_varcopes.reshape(int(len(all_varcopes) / num_copes),
num_copes).T.tolist()
return outcopes, outvarcopes, n_runs
cope_sorter = pe.Node(niu.Function(
input_names=['copes', 'varcopes', 'contrasts'],
output_names=['copes', 'varcopes', 'n_runs'],
function=sort_copes),
name='cope_sorter')
pickfirst = lambda x: x[0]
wf.connect(contrastgen, 'contrasts', cope_sorter, 'contrasts')
wf.connect([(preproc, fixed_fx, [(('outputspec.mask', pickfirst),
'flameo.mask_file')]),
(modelfit, cope_sorter, [('outputspec.copes', 'copes')]),
(modelfit, cope_sorter, [('outputspec.varcopes', 'varcopes')]),
(cope_sorter, fixed_fx, [('copes', 'inputspec.copes'),
('varcopes', 'inputspec.varcopes'),
('n_runs', 'l2model.num_copes')]),
(modelfit, fixed_fx, [
('outputspec.dof_file', 'inputspec.dof_files'),
])])
wf.connect(calc_median, 'median_file', registration,
'inputspec.mean_image')
if subjects_dir:
wf.connect(infosource, 'subject_id', registration,
'inputspec.subject_id')
registration.inputs.inputspec.subjects_dir = subjects_dir
registration.inputs.inputspec.target_image = fsl.Info.standard_image(
'MNI152_T1_2mm_brain.nii.gz')
if target:
registration.inputs.inputspec.target_image = target
else:
wf.connect(datasource, 'anat', registration,
'inputspec.anatomical_image')
registration.inputs.inputspec.target_image = fsl.Info.standard_image(
'MNI152_T1_2mm.nii.gz')
registration.inputs.inputspec.target_image_brain = fsl.Info.standard_image(
'MNI152_T1_2mm_brain.nii.gz')
registration.inputs.inputspec.config_file = 'T1_2_MNI152_2mm'
def merge_files(copes, varcopes, zstats):
out_files = []
splits = []
out_files.extend(copes)
splits.append(len(copes))
out_files.extend(varcopes)
splits.append(len(varcopes))
out_files.extend(zstats)
splits.append(len(zstats))
return out_files, splits
mergefunc = pe.Node(niu.Function(
input_names=['copes', 'varcopes', 'zstats'],
output_names=['out_files', 'splits'],
function=merge_files),
name='merge_files')
wf.connect([(fixed_fx.get_node('outputspec'), mergefunc, [
('copes', 'copes'),
('varcopes', 'varcopes'),
('zstats', 'zstats'),
])])
wf.connect(mergefunc, 'out_files', registration, 'inputspec.source_files')
def split_files(in_files, splits):
copes = in_files[:splits[0]]
varcopes = in_files[splits[0]:(splits[0] + splits[1])]
zstats = in_files[(splits[0] + splits[1]):]
return copes, varcopes, zstats
splitfunc = pe.Node(niu.Function(
input_names=['in_files', 'splits'],
output_names=['copes', 'varcopes', 'zstats'],
function=split_files),
name='split_files')
wf.connect(mergefunc, 'splits', splitfunc, 'splits')
wf.connect(registration, 'outputspec.transformed_files', splitfunc,
'in_files')
if subjects_dir:
get_roi_mean = pe.MapNode(fs.SegStats(default_color_table=True),
iterfield=['in_file'],
name='get_aparc_means')
get_roi_mean.inputs.avgwf_txt_file = True
wf.connect(fixed_fx.get_node('outputspec'), 'copes', get_roi_mean,
'in_file')
wf.connect(registration, 'outputspec.aparc', get_roi_mean,
'segmentation_file')
get_roi_tsnr = pe.MapNode(fs.SegStats(default_color_table=True),
iterfield=['in_file'],
name='get_aparc_tsnr')
get_roi_tsnr.inputs.avgwf_txt_file = True
wf.connect(tsnr, 'tsnr_file', get_roi_tsnr, 'in_file')
wf.connect(registration, 'outputspec.aparc', get_roi_tsnr,
'segmentation_file')
"""
Connect to a datasink
"""
def get_subs(subject_id, conds, run_id, model_id, task_id):
subs = [('_subject_id_%s_' % subject_id, '')]
subs.append(('_model_id_%d' % model_id, 'model%03d' % model_id))
subs.append(('task_id_%d/' % task_id, '/task%03d_' % task_id))
subs.append(
('bold_dtype_mcf_mask_smooth_mask_gms_tempfilt_mean_warp', 'mean'))
subs.append(('bold_dtype_mcf_mask_smooth_mask_gms_tempfilt_mean_flirt',
'affine'))
for i in range(len(conds)):
subs.append(('_flameo%d/cope1.' % i, 'cope%02d.' % (i + 1)))
subs.append(('_flameo%d/varcope1.' % i, 'varcope%02d.' % (i + 1)))
subs.append(('_flameo%d/zstat1.' % i, 'zstat%02d.' % (i + 1)))
subs.append(('_flameo%d/tstat1.' % i, 'tstat%02d.' % (i + 1)))
subs.append(('_flameo%d/res4d.' % i, 'res4d%02d.' % (i + 1)))
subs.append(('_warpall%d/cope1_warp.' % i, 'cope%02d.' % (i + 1)))
subs.append(('_warpall%d/varcope1_warp.' % (len(conds) + i),
'varcope%02d.' % (i + 1)))
subs.append(('_warpall%d/zstat1_warp.' % (2 * len(conds) + i),
'zstat%02d.' % (i + 1)))
subs.append(('_warpall%d/cope1_trans.' % i, 'cope%02d.' % (i + 1)))
subs.append(('_warpall%d/varcope1_trans.' % (len(conds) + i),
'varcope%02d.' % (i + 1)))
subs.append(('_warpall%d/zstat1_trans.' % (2 * len(conds) + i),
'zstat%02d.' % (i + 1)))
subs.append(('__get_aparc_means%d/' % i, '/cope%02d_' % (i + 1)))
for i, run_num in enumerate(run_id):
subs.append(('__get_aparc_tsnr%d/' % i, '/run%02d_' % run_num))
subs.append(('__art%d/' % i, '/run%02d_' % run_num))
subs.append(('__dilatemask%d/' % i, '/run%02d_' % run_num))
subs.append(('__realign%d/' % i, '/run%02d_' % run_num))
subs.append(('__modelgen%d/' % i, '/run%02d_' % run_num))
subs.append(('/model%03d/task%03d/' % (model_id, task_id), '/'))
subs.append(('/model%03d/task%03d_' % (model_id, task_id), '/'))
subs.append(('_bold_dtype_mcf_bet_thresh_dil', '_mask'))
subs.append(('_output_warped_image', '_anat2target'))
subs.append(('median_flirt_brain_mask', 'median_brain_mask'))
subs.append(('median_bbreg_brain_mask', 'median_brain_mask'))
return subs
subsgen = pe.Node(niu.Function(
input_names=['subject_id', 'conds', 'run_id', 'model_id', 'task_id'],
output_names=['substitutions'],
function=get_subs),
name='subsgen')
wf.connect(subjinfo, 'run_id', subsgen, 'run_id')
datasink = pe.Node(interface=nio.DataSink(), name="datasink")
wf.connect(infosource, 'subject_id', datasink, 'container')
wf.connect(infosource, 'subject_id', subsgen, 'subject_id')
wf.connect(infosource, 'model_id', subsgen, 'model_id')
wf.connect(infosource, 'task_id', subsgen, 'task_id')
wf.connect(contrastgen, 'contrasts', subsgen, 'conds')
wf.connect(subsgen, 'substitutions', datasink, 'substitutions')
wf.connect([(fixed_fx.get_node('outputspec'), datasink,
[('res4d', 'res4d'), ('copes', 'copes'),
('varcopes', 'varcopes'), ('zstats', 'zstats'),
('tstats', 'tstats')])])
wf.connect([(modelfit.get_node('modelgen'), datasink, [
('design_cov', 'qa.model'),
('design_image', 'qa.model.@matrix_image'),
('design_file', 'qa.model.@matrix'),
])])
wf.connect([(preproc, datasink,
[('outputspec.motion_parameters', 'qa.motion'),
('outputspec.motion_plots', 'qa.motion.plots'),
('outputspec.mask', 'qa.mask')])])
wf.connect(registration, 'outputspec.mean2anat_mask', datasink,
'qa.mask.mean2anat')
wf.connect(art, 'norm_files', datasink, 'qa.art.@norm')
wf.connect(art, 'intensity_files', datasink, 'qa.art.@intensity')
wf.connect(art, 'outlier_files', datasink, 'qa.art.@outlier_files')
wf.connect(registration, 'outputspec.anat2target', datasink,
'qa.anat2target')
wf.connect(tsnr, 'tsnr_file', datasink, 'qa.tsnr.@map')
if subjects_dir:
wf.connect(registration, 'outputspec.min_cost_file', datasink,
'qa.mincost')
wf.connect([(get_roi_tsnr, datasink, [('avgwf_txt_file', 'qa.tsnr'),
('summary_file',
'qa.tsnr.@summary')])])
wf.connect([(get_roi_mean, datasink, [('avgwf_txt_file', 'copes.roi'),
('summary_file',
'copes.roi.@summary')])])
wf.connect([(splitfunc, datasink, [
('copes', 'copes.mni'),
('varcopes', 'varcopes.mni'),
('zstats', 'zstats.mni'),
])])
wf.connect(calc_median, 'median_file', datasink, 'mean')
wf.connect(registration, 'outputspec.transformed_mean', datasink,
'mean.mni')
wf.connect(registration, 'outputspec.func2anat_transform', datasink,
'xfm.mean2anat')
wf.connect(registration, 'outputspec.anat2target_transform', datasink,
'xfm.anat2target')
"""
Set processing parameters
"""
preproc.inputs.inputspec.fwhm = fwhm
gethighpass.inputs.hpcutoff = hpcutoff
modelspec.inputs.high_pass_filter_cutoff = hpcutoff
modelfit.inputs.inputspec.bases = {'dgamma': {'derivs': use_derivatives}}
modelfit.inputs.inputspec.model_serial_correlations = True
modelfit.inputs.inputspec.film_threshold = 1000
datasink.inputs.base_directory = output_dir
return wf
def create_reg_workflow(name='registration'):
"""Create a FEAT preprocessing workflow together with freesurfer
Parameters
----------
name : name of workflow (default: 'registration')
Inputs:
inputspec.source_files : files (filename or list of filenames to register)
inputspec.mean_image : reference image to use
inputspec.anatomical_image : anatomical image to coregister to
inputspec.target_image : registration target
Outputs:
outputspec.func2anat_transform : FLIRT transform
outputspec.anat2target_transform : FLIRT+FNIRT transform
outputspec.transformed_files : transformed files in target space
outputspec.transformed_mean : mean image in target space
"""
register = pe.Workflow(name=name)
inputnode = pe.Node(interface=niu.IdentityInterface(fields=[
'source_files', 'mean_image', 'anatomical_image', 'target_image',
'target_image_brain', 'config_file'
]),
name='inputspec')
outputnode = pe.Node(interface=niu.IdentityInterface(fields=[
'func2anat_transform', 'anat2target_transform', 'transformed_files',
'transformed_mean', 'anat2target', 'mean2anat_mask'
]),
name='outputspec')
"""
Estimate the tissue classes from the anatomical image. But use spm's segment
as FSL appears to be breaking.
"""
stripper = pe.Node(fsl.BET(), name='stripper')
register.connect(inputnode, 'anatomical_image', stripper, 'in_file')
fast = pe.Node(fsl.FAST(), name='fast')
register.connect(stripper, 'out_file', fast, 'in_files')
"""
Binarize the segmentation
"""
binarize = pe.Node(fsl.ImageMaths(op_string='-nan -thr 0.5 -bin'),
name='binarize')
pickindex = lambda x, i: x[i]
register.connect(fast, ('partial_volume_files', pickindex, 2), binarize,
'in_file')
"""
Calculate rigid transform from mean image to anatomical image
"""
mean2anat = pe.Node(fsl.FLIRT(), name='mean2anat')
mean2anat.inputs.dof = 6
register.connect(inputnode, 'mean_image', mean2anat, 'in_file')
register.connect(stripper, 'out_file', mean2anat, 'reference')
"""
Now use bbr cost function to improve the transform
"""
mean2anatbbr = pe.Node(fsl.FLIRT(), name='mean2anatbbr')
mean2anatbbr.inputs.dof = 6
mean2anatbbr.inputs.cost = 'bbr'
mean2anatbbr.inputs.schedule = os.path.join(os.getenv('FSLDIR'),
'etc/flirtsch/bbr.sch')
register.connect(inputnode, 'mean_image', mean2anatbbr, 'in_file')
register.connect(binarize, 'out_file', mean2anatbbr, 'wm_seg')
register.connect(inputnode, 'anatomical_image', mean2anatbbr, 'reference')
register.connect(mean2anat, 'out_matrix_file', mean2anatbbr,
'in_matrix_file')
"""
Create a mask of the median image coregistered to the anatomical image
"""
mean2anat_mask = Node(fsl.BET(mask=True), name='mean2anat_mask')
register.connect(mean2anatbbr, 'out_file', mean2anat_mask, 'in_file')
"""
Convert the BBRegister transformation to ANTS ITK format
"""
convert2itk = pe.Node(C3dAffineTool(), name='convert2itk')
convert2itk.inputs.fsl2ras = True
convert2itk.inputs.itk_transform = True
register.connect(mean2anatbbr, 'out_matrix_file', convert2itk,
'transform_file')
register.connect(inputnode, 'mean_image', convert2itk, 'source_file')
register.connect(stripper, 'out_file', convert2itk, 'reference_file')
"""
Compute registration between the subject's structural and MNI template
This is currently set to perform a very quick registration. However, the
registration can be made significantly more accurate for cortical
structures by increasing the number of iterations
All parameters are set using the example from:
#https://github.com/stnava/ANTs/blob/master/Scripts/newAntsExample.sh
"""
reg = pe.Node(ants.Registration(), name='antsRegister')
reg.inputs.output_transform_prefix = "output_"
reg.inputs.transforms = ['Rigid', 'Affine', 'SyN']
reg.inputs.transform_parameters = [(0.1, ), (0.1, ), (0.2, 3.0, 0.0)]
reg.inputs.number_of_iterations = [[10000, 11110, 11110]] * 2 + [[
100, 30, 20
]]
reg.inputs.dimension = 3
reg.inputs.write_composite_transform = True
reg.inputs.collapse_output_transforms = True
reg.inputs.initial_moving_transform_com = True
reg.inputs.metric = ['Mattes'] * 2 + [['Mattes', 'CC']]
reg.inputs.metric_weight = [1] * 2 + [[0.5, 0.5]]
reg.inputs.radius_or_number_of_bins = [32] * 2 + [[32, 4]]
reg.inputs.sampling_strategy = ['Regular'] * 2 + [[None, None]]
reg.inputs.sampling_percentage = [0.3] * 2 + [[None, None]]
reg.inputs.convergence_threshold = [1.e-8] * 2 + [-0.01]
reg.inputs.convergence_window_size = [20] * 2 + [5]
reg.inputs.smoothing_sigmas = [[4, 2, 1]] * 2 + [[1, 0.5, 0]]
reg.inputs.sigma_units = ['vox'] * 3
reg.inputs.shrink_factors = [[3, 2, 1]] * 2 + [[4, 2, 1]]
reg.inputs.use_estimate_learning_rate_once = [True] * 3
reg.inputs.use_histogram_matching = [False] * 2 + [True]
reg.inputs.winsorize_lower_quantile = 0.005
reg.inputs.winsorize_upper_quantile = 0.995
reg.inputs.args = '--float'
reg.inputs.output_warped_image = 'output_warped_image.nii.gz'
reg.inputs.num_threads = 4
reg.plugin_args = {
'qsub_args': '-pe orte 4',
'sbatch_args': '--mem=6G -c 4'
}
register.connect(stripper, 'out_file', reg, 'moving_image')
register.connect(inputnode, 'target_image_brain', reg, 'fixed_image')
"""
Concatenate the affine and ants transforms into a list
"""
pickfirst = lambda x: x[0]
merge = pe.Node(niu.Merge(2), iterfield=['in2'], name='mergexfm')
register.connect(convert2itk, 'itk_transform', merge, 'in2')
register.connect(reg, 'composite_transform', merge, 'in1')
"""
Transform the mean image. First to anatomical and then to target
"""
warpmean = pe.Node(ants.ApplyTransforms(), name='warpmean')
warpmean.inputs.input_image_type = 0
warpmean.inputs.interpolation = 'Linear'
warpmean.inputs.invert_transform_flags = [False, False]
warpmean.terminal_output = 'file'
register.connect(inputnode, 'target_image_brain', warpmean,
'reference_image')
register.connect(inputnode, 'mean_image', warpmean, 'input_image')
register.connect(merge, 'out', warpmean, 'transforms')
"""
Transform the remaining images. First to anatomical and then to target
"""
warpall = pe.MapNode(ants.ApplyTransforms(),
iterfield=['input_image'],
name='warpall')
warpall.inputs.input_image_type = 0
warpall.inputs.interpolation = 'Linear'
warpall.inputs.invert_transform_flags = [False, False]
warpall.terminal_output = 'file'
register.connect(inputnode, 'target_image_brain', warpall,
'reference_image')
register.connect(inputnode, 'source_files', warpall, 'input_image')
register.connect(merge, 'out', warpall, 'transforms')
"""
Assign all the output files
"""
register.connect(reg, 'warped_image', outputnode, 'anat2target')
register.connect(warpmean, 'output_image', outputnode, 'transformed_mean')
register.connect(warpall, 'output_image', outputnode, 'transformed_files')
register.connect(mean2anatbbr, 'out_matrix_file', outputnode,
'func2anat_transform')
register.connect(mean2anat_mask, 'mask_file', outputnode, 'mean2anat_mask')
register.connect(reg, 'composite_transform', outputnode,
'anat2target_transform')
return register
def create_fs_reg_workflow(name='registration'):
"""Create a FEAT preprocessing workflow together with freesurfer
Parameters
----------
name : name of workflow (default: 'registration')
Inputs::
inputspec.source_files : files (filename or list of filenames to register)
inputspec.mean_image : reference image to use
inputspec.target_image : registration target
Outputs::
outputspec.func2anat_transform : FLIRT transform
outputspec.anat2target_transform : FLIRT+FNIRT transform
outputspec.transformed_files : transformed files in target space
outputspec.transformed_mean : mean image in target space
"""
register = Workflow(name=name)
inputnode = Node(interface=IdentityInterface(fields=[
'source_files', 'mean_image', 'subject_id', 'subjects_dir',
'target_image'
]),
name='inputspec')
outputnode = Node(interface=IdentityInterface(fields=[
'func2anat_transform', 'out_reg_file', 'anat2target_transform',
'transforms', 'transformed_mean', 'transformed_files', 'min_cost_file',
'anat2target', 'aparc', 'mean2anat_mask'
]),
name='outputspec')
# Get the subject's freesurfer source directory
fssource = Node(FreeSurferSource(), name='fssource')
fssource.run_without_submitting = True
register.connect(inputnode, 'subject_id', fssource, 'subject_id')
register.connect(inputnode, 'subjects_dir', fssource, 'subjects_dir')
convert = Node(freesurfer.MRIConvert(out_type='nii'), name="convert")
register.connect(fssource, 'T1', convert, 'in_file')
# Coregister the median to the surface
bbregister = Node(freesurfer.BBRegister(registered_file=True),
name='bbregister')
bbregister.inputs.init = 'fsl'
bbregister.inputs.contrast_type = 't2'
bbregister.inputs.out_fsl_file = True
bbregister.inputs.epi_mask = True
register.connect(inputnode, 'subject_id', bbregister, 'subject_id')
register.connect(inputnode, 'mean_image', bbregister, 'source_file')
register.connect(inputnode, 'subjects_dir', bbregister, 'subjects_dir')
# Create a mask of the median coregistered to the anatomical image
mean2anat_mask = Node(fsl.BET(mask=True), name='mean2anat_mask')
register.connect(bbregister, 'registered_file', mean2anat_mask, 'in_file')
"""
use aparc+aseg's brain mask
"""
binarize = Node(fs.Binarize(min=0.5, out_type="nii.gz", dilate=1),
name="binarize_aparc")
register.connect(fssource, ("aparc_aseg", get_aparc_aseg), binarize,
"in_file")
stripper = Node(fsl.ApplyMask(), name='stripper')
register.connect(binarize, "binary_file", stripper, "mask_file")
register.connect(convert, 'out_file', stripper, 'in_file')
"""
Apply inverse transform to aparc file
"""
aparcxfm = Node(freesurfer.ApplyVolTransform(inverse=True,
interp='nearest'),
name='aparc_inverse_transform')
register.connect(inputnode, 'subjects_dir', aparcxfm, 'subjects_dir')
register.connect(bbregister, 'out_reg_file', aparcxfm, 'reg_file')
register.connect(fssource, ('aparc_aseg', get_aparc_aseg), aparcxfm,
'target_file')
register.connect(inputnode, 'mean_image', aparcxfm, 'source_file')
"""
Convert the BBRegister transformation to ANTS ITK format
"""
convert2itk = Node(C3dAffineTool(), name='convert2itk')
convert2itk.inputs.fsl2ras = True
convert2itk.inputs.itk_transform = True
register.connect(bbregister, 'out_fsl_file', convert2itk, 'transform_file')
register.connect(inputnode, 'mean_image', convert2itk, 'source_file')
register.connect(stripper, 'out_file', convert2itk, 'reference_file')
"""
Compute registration between the subject's structural and MNI template
This is currently set to perform a very quick registration. However, the
registration can be made significantly more accurate for cortical
structures by increasing the number of iterations
All parameters are set using the example from:
#https://github.com/stnava/ANTs/blob/master/Scripts/newAntsExample.sh
"""
reg = Node(ants.Registration(), name='antsRegister')
reg.inputs.output_transform_prefix = "output_"
reg.inputs.transforms = ['Rigid', 'Affine', 'SyN']
reg.inputs.transform_parameters = [(0.1, ), (0.1, ), (0.2, 3.0, 0.0)]
reg.inputs.number_of_iterations = [[10000, 11110, 11110]] * 2 + [[
100, 30, 20
]]
reg.inputs.dimension = 3
reg.inputs.write_composite_transform = True
reg.inputs.collapse_output_transforms = True
reg.inputs.initial_moving_transform_com = True
reg.inputs.metric = ['Mattes'] * 2 + [['Mattes', 'CC']]
reg.inputs.metric_weight = [1] * 2 + [[0.5, 0.5]]
reg.inputs.radius_or_number_of_bins = [32] * 2 + [[32, 4]]
reg.inputs.sampling_strategy = ['Regular'] * 2 + [[None, None]]
reg.inputs.sampling_percentage = [0.3] * 2 + [[None, None]]
reg.inputs.convergence_threshold = [1.e-8] * 2 + [-0.01]
reg.inputs.convergence_window_size = [20] * 2 + [5]
reg.inputs.smoothing_sigmas = [[4, 2, 1]] * 2 + [[1, 0.5, 0]]
reg.inputs.sigma_units = ['vox'] * 3
reg.inputs.shrink_factors = [[3, 2, 1]] * 2 + [[4, 2, 1]]
reg.inputs.use_estimate_learning_rate_once = [True] * 3
reg.inputs.use_histogram_matching = [False] * 2 + [True]
reg.inputs.winsorize_lower_quantile = 0.005
reg.inputs.winsorize_upper_quantile = 0.995
reg.inputs.float = True
reg.inputs.output_warped_image = 'output_warped_image.nii.gz'
reg.inputs.num_threads = 4
reg.plugin_args = {
'qsub_args': '-pe orte 4',
'sbatch_args': '--mem=6G -c 4'
}
register.connect(stripper, 'out_file', reg, 'moving_image')
register.connect(inputnode, 'target_image', reg, 'fixed_image')
"""
Concatenate the affine and ants transforms into a list
"""
pickfirst = lambda x: x[0]
merge = Node(Merge(2), iterfield=['in2'], name='mergexfm')
register.connect(convert2itk, 'itk_transform', merge, 'in2')
register.connect(reg, 'composite_transform', merge, 'in1')
"""
Transform the mean image. First to anatomical and then to target
"""
warpmean = Node(ants.ApplyTransforms(), name='warpmean')
warpmean.inputs.input_image_type = 0
warpmean.inputs.interpolation = 'Linear'
warpmean.inputs.invert_transform_flags = [False, False]
warpmean.terminal_output = 'file'
warpmean.inputs.args = '--float'
# warpmean.inputs.num_threads = 4
# warpmean.plugin_args = {'sbatch_args': '--mem=4G -c 4'}
"""
Transform the remaining images. First to anatomical and then to target
"""
warpall = pe.MapNode(ants.ApplyTransforms(),
iterfield=['input_image'],
name='warpall')
warpall.inputs.input_image_type = 0
warpall.inputs.interpolation = 'Linear'
warpall.inputs.invert_transform_flags = [False, False]
warpall.terminal_output = 'file'
warpall.inputs.args = '--float'
warpall.inputs.num_threads = 2
warpall.plugin_args = {'sbatch_args': '--mem=6G -c 2'}
"""
Assign all the output files
"""
register.connect(warpmean, 'output_image', outputnode, 'transformed_mean')
register.connect(warpall, 'output_image', outputnode, 'transformed_files')
register.connect(inputnode, 'target_image', warpmean, 'reference_image')
register.connect(inputnode, 'mean_image', warpmean, 'input_image')
register.connect(merge, 'out', warpmean, 'transforms')
register.connect(inputnode, 'target_image', warpall, 'reference_image')
register.connect(inputnode, 'source_files', warpall, 'input_image')
register.connect(merge, 'out', warpall, 'transforms')
"""
Assign all the output files
"""
register.connect(reg, 'warped_image', outputnode, 'anat2target')
register.connect(aparcxfm, 'transformed_file', outputnode, 'aparc')
register.connect(bbregister, 'out_fsl_file', outputnode,
'func2anat_transform')
register.connect(bbregister, 'out_reg_file', outputnode, 'out_reg_file')
register.connect(bbregister, 'min_cost_file', outputnode, 'min_cost_file')
register.connect(mean2anat_mask, 'mask_file', outputnode, 'mean2anat_mask')
register.connect(reg, 'composite_transform', outputnode,
'anat2target_transform')
register.connect(merge, 'out', outputnode, 'transforms')
return register
def create_wf_c3d_fsl_to_itk(map_node,
input_image_type=0,
name='create_wf_c3d_fsl_to_itk'):
"""
Converts an FSL-format output matrix to an ITK-format (ANTS) matrix
for use with ANTS registration tools.
Parameters
----------
name : string, optional
Name of the workflow.
Returns
-------
fsl_to_itk_conversion : nipype.pipeline.engine.Workflow
Notes
-----
Workflow Inputs::
inputspec.affine_file : string (nifti file)
Output matrix of FSL-based functional to anatomical registration
inputspec.reference_file : string (nifti file)
File of skull-stripped anatomical brain to be used in affine
conversion
inputspec.source_file : string (nifti file)
Should match the input of the apply warp (in_file) unless you are
applying the warp to a 4-d file, in which case this file should
be a mean_functional file
Workflow Outputs::
outputspec.itk_transform : string (nifti file)
Converted affine transform in ITK format usable with ANTS
"""
import nipype.interfaces.c3 as c3
from nipype.interfaces.utility import Function
from CPAC.registration.utils import change_itk_transform_type
from nipype.interfaces.afni import preprocess
fsl_to_itk_conversion = pe.Workflow(name=name)
inputspec = pe.Node(util.IdentityInterface(
fields=['affine_file', 'reference_file', 'source_file']),
name='inputspec')
# converts FSL-format .mat affine xfm into ANTS-format .txt
# .mat affine comes from Func->Anat registration
if map_node == 0:
fsl_reg_2_itk = pe.Node(c3.C3dAffineTool(), name='fsl_reg_2_itk')
elif map_node == 1:
fsl_reg_2_itk = pe.MapNode(c3.C3dAffineTool(),
name='fsl_reg_2_itk_mapnode',
iterfield=['source_file'])
fsl_reg_2_itk.inputs.itk_transform = True
fsl_reg_2_itk.inputs.fsl2ras = True
itk_imports = ['import os']
if map_node == 0:
change_transform = pe.Node(util.Function(
input_names=['input_affine_file'],
output_names=['updated_affine_file'],
function=change_itk_transform_type,
imports=itk_imports),
name='change_transform_type')
elif map_node == 1:
change_transform = pe.MapNode(util.Function(
input_names=['input_affine_file'],
output_names=['updated_affine_file'],
function=change_itk_transform_type,
imports=itk_imports),
name='change_transform_type',
iterfield=['input_affine_file'])
outputspec = pe.Node(util.IdentityInterface(fields=['itk_transform']),
name='outputspec')
fsl_to_itk_conversion.connect(inputspec, 'affine_file', fsl_reg_2_itk,
'transform_file')
fsl_to_itk_conversion.connect(inputspec, 'reference_file', fsl_reg_2_itk,
'reference_file')
# source_file input of the conversion must be a 3D file, so if the source
# file is 4D (input_image_type=3), average it into a 3D file first
if input_image_type == 0:
fsl_to_itk_conversion.connect(inputspec, 'source_file', fsl_reg_2_itk,
'source_file')
elif input_image_type == 3:
try:
tstat_source = pe.Node(interface=preprocess.TStat(),
name='fsl_to_itk_tcat_source')
except AttributeError:
from nipype.interfaces.afni import utils as afni_utils
tstat_source = pe.Node(interface=afni_utils.TStat(),
name='fsl_to_itk_tcat_source')
tstat_source.inputs.outputtype = 'NIFTI_GZ'
tstat_source.inputs.options = '-mean'
fsl_to_itk_conversion.connect(inputspec, 'source_file', tstat_source,
'in_file')
fsl_to_itk_conversion.connect(tstat_source, 'out_file', fsl_reg_2_itk,
'source_file')
fsl_to_itk_conversion.connect(fsl_reg_2_itk, 'itk_transform',
change_transform, 'input_affine_file')
fsl_to_itk_conversion.connect(change_transform, 'updated_affine_file',
outputspec, 'itk_transform')
return fsl_to_itk_conversion
def create_wf_apply_ants_warp(map_node,
name='create_wf_apply_ants_warp',
ants_threads=1):
"""
Applies previously calculated ANTS registration transforms to input
images. This workflow employs the antsApplyTransforms tool:
http://stnava.github.io/ANTs/
Parameters
----------
name : string, optional
Name of the workflow.
Returns
-------
apply_ants_warp_wf : nipype.pipeline.engine.Workflow
Notes
-----
Workflow Inputs::
inputspec.input_image : string (nifti file)
Image file of brain to be registered to reference
inputspec.reference_image : string (nifti file)
Image file of brain or template being used as a reference
inputspec.transforms : list of filepaths (nifti, .mat, .txt)
List of transforms and warps to be applied to the input image
inputspec.dimension : integer
Dimension value of image being registered (2, 3, or 4)
inputspec.interpolation : string
Type of interpolation to be used. See antsApplyTransforms
documentation or Nipype interface documentation for options
Workflow Outputs::
outputspec.output_image : string (nifti file)
Normalized output file
Workflow Graph:
.. image::
:width: 500
Detailed Workflow Graph:
.. image::
:width: 500
"""
import nipype.interfaces.ants as ants
apply_ants_warp_wf = pe.Workflow(name=name)
inputspec = pe.Node(util.IdentityInterface(fields=[
'input_image', 'reference_image', 'transforms', 'dimension',
'input_image_type', 'interpolation'
]),
name='inputspec')
if map_node == 0:
apply_ants_warp = pe.Node(interface=ants.ApplyTransforms(),
name='apply_ants_warp')
elif map_node == 1:
apply_ants_warp = pe.MapNode(interface=ants.ApplyTransforms(),
name='apply_ants_warp_mapnode', iterfield=['input_image', \
'transforms'])
apply_ants_warp.inputs.out_postfix = '_antswarp'
apply_ants_warp.interface.num_threads = ants_threads
apply_ants_warp.interface.estimated_memory_gb = 1.5
outputspec = pe.Node(util.IdentityInterface(fields=['output_image']),
name='outputspec')
# connections from inputspec
apply_ants_warp_wf.connect(inputspec, 'input_image', apply_ants_warp,
'input_image')
apply_ants_warp_wf.connect(inputspec, 'reference_image', apply_ants_warp,
'reference_image')
apply_ants_warp_wf.connect(inputspec, 'transforms', apply_ants_warp,
'transforms')
apply_ants_warp_wf.connect(inputspec, 'dimension', apply_ants_warp,
'dimension')
apply_ants_warp_wf.connect(inputspec, 'input_image_type', apply_ants_warp,
'input_image_type')
apply_ants_warp_wf.connect(inputspec, 'interpolation', apply_ants_warp,
'interpolation')
# connections to outputspec
apply_ants_warp_wf.connect(apply_ants_warp, 'output_image', outputspec,
'output_image')
return apply_ants_warp_wf
def epi_pipeline(name='susceptibility_distortion_correction_using_t1'):
"""
This workflow allows to correct for echo-planareinduced susceptibility artifacts without fieldmap
(e.g. ADNI Database) by elastically register DWIs to their respective baseline T1-weighted
structural scans using an inverse consistent registration algorithm with a mutual information cost
function (SyN algorithm). This workflow allows also a coregistration of DWIs with their respective
baseline T1-weighted structural scans in order to latter combine tracks and cortex parcelation.
.. warning:: This workflow rotates the `b`-vectors'
.. References
.. Nir et al. (Neurobiology of Aging 2015)- Connectivity network measures predict volumetric atrophy in mild cognitive impairment
Leow et al. (IEEE Trans Med Imaging 2007)- Statistical Properties of Jacobian Maps and the Realization of Unbiased Large Deformation Nonlinear Image Registration
Example
-------
>>> epi = epi_pipeline()
>>> epi.inputs.inputnode.DWI = 'DWI.nii'
>>> epi.inputs.inputnode.bvec = 'bvec.txt'
>>> epi.inputs.inputnode.T1 = 'T1.nii'
>>> epi.run() # doctest: +SKIP
"""
from clinica.pipelines.dwi_preprocessing_using_t1.dwi_preprocessing_using_t1_utils import create_jacobian_determinant_image, change_itk_transform_type, expend_matrix_list, rotate_bvecs, ants_registration_syn_quick, ants_warp_image_multi_transform, ants_combin_transform
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as niu
import nipype.interfaces.fsl as fsl
import nipype.interfaces.c3 as c3
inputnode = pe.Node(niu.IdentityInterface(fields=['T1', 'DWI', 'bvec']), name='inputnode')
split = pe.Node(fsl.Split(dimension='t'), name='SplitDWIs')
pick_ref = pe.Node(niu.Select(), name='Pick_b0')
pick_ref.inputs.index = [0]
flirt_b0_2_T1 = pe.Node(interface=fsl.FLIRT(dof=6), name='flirt_B0_2_T1')
flirt_b0_2_T1.inputs.interp = "spline"
flirt_b0_2_T1.inputs.cost = 'normmi'
flirt_b0_2_T1.inputs.cost_func = 'normmi'
apply_xfm = pe.Node(interface=fsl.preprocess.ApplyXFM(), name='apply_xfm')
apply_xfm.inputs.apply_xfm = True
expend_matrix = pe.Node(
interface=niu.Function(
input_names=['in_matrix', 'in_bvec'],
output_names=['out_matrix_list'],
function=expend_matrix_list),
name='expend_matrix')
rot_bvec = pe.Node(
niu.Function(
input_names=['in_matrix', 'in_bvec'],
output_names=['out_file'],
function=rotate_bvecs),
name='Rotate_Bvec')
antsRegistrationSyNQuick = pe.Node(
interface=niu.Function(
input_names=['fix_image', 'moving_image'],
output_names=['image_warped',
'affine_matrix',
'warp',
'inverse_warped',
'inverse_warp'],
function=ants_registration_syn_quick),
name='antsRegistrationSyNQuick')
c3d_flirt2ants = pe.Node(c3.C3dAffineTool(), name='fsl_reg_2_itk')
c3d_flirt2ants.inputs.itk_transform = True
c3d_flirt2ants.inputs.fsl2ras = True
change_transform = pe.Node(niu.Function(
input_names=['input_affine_file'],
output_names=['updated_affine_file'],
function=change_itk_transform_type),
name='change_transform_type')
merge_transform = pe.Node(niu.Merge(3), name='MergeTransforms')
apply_transform = pe.MapNode(interface=niu.Function(input_names=['fix_image', 'moving_image', 'ants_warp_affine'],
output_names=['out_warp_field', 'out_warped'],
function=ants_combin_transform),
iterfield=['moving_image'],
name='warp_filed')
jacobian = pe.MapNode(interface=niu.Function(input_names=['imageDimension', 'deformationField', 'outputImage'],
output_names=['outputImage'],
function=create_jacobian_determinant_image),
iterfield=['deformationField'],
name='jacobian')
jacobian.inputs.imageDimension = 3
jacobian.inputs.outputImage = 'Jacobian_image.nii.gz'
jacmult = pe.MapNode(fsl.MultiImageMaths(op_string='-mul %s'),
iterfield=['in_file', 'operand_files'],
name='ModulateDWIs')
thres = pe.MapNode(fsl.Threshold(thresh=0.0), iterfield=['in_file'],
name='RemoveNegative')
merge = pe.Node(fsl.Merge(dimension='t'), name='MergeDWIs')
outputnode = pe.Node(niu.IdentityInterface(fields=['DWI_2_T1_Coregistration_matrix',
'epi_correction_deformation_field',
'epi_correction_affine_transform',
'epi_correction_image_warped',
'DWIs_epicorrected',
'warp_epi',
'out_bvec'
]), name='outputnode')
wf = pe.Workflow(name='epi_pipeline')
wf.connect([(inputnode, split, [('DWI', 'in_file')])])
wf.connect([(split, pick_ref, [('out_files', 'inlist')])])
wf.connect([(pick_ref, flirt_b0_2_T1, [('out', 'in_file')])])
wf.connect([(inputnode, flirt_b0_2_T1, [('T1', 'reference')])])
wf.connect([(inputnode, rot_bvec, [('bvec', 'in_bvec')])])
wf.connect([(flirt_b0_2_T1, expend_matrix, [('out_matrix_file', 'in_matrix')])])
wf.connect([(inputnode, expend_matrix, [('bvec', 'in_bvec')])])
wf.connect([(expend_matrix, rot_bvec, [('out_matrix_list', 'in_matrix')])])
wf.connect([(inputnode, antsRegistrationSyNQuick, [('T1', 'fix_image')])])
wf.connect([(flirt_b0_2_T1, antsRegistrationSyNQuick, [('out_file', 'moving_image')])])
wf.connect([(inputnode, c3d_flirt2ants, [('T1', 'reference_file')])])
wf.connect([(pick_ref, c3d_flirt2ants, [('out', 'source_file')])])
wf.connect([(flirt_b0_2_T1, c3d_flirt2ants, [('out_matrix_file', 'transform_file')])])
wf.connect([(c3d_flirt2ants, change_transform, [('itk_transform', 'input_affine_file')])])
wf.connect([(antsRegistrationSyNQuick, merge_transform, [('warp', 'in1')])])
wf.connect([(antsRegistrationSyNQuick, merge_transform, [('affine_matrix', 'in2')])])
wf.connect([(change_transform, merge_transform, [('updated_affine_file', 'in3')])])
wf.connect([(inputnode, apply_transform, [('T1', 'fix_image')])])
wf.connect([(split, apply_transform, [('out_files', 'moving_image')])])
wf.connect([(merge_transform, apply_transform, [('out', 'ants_warp_affine')])])
wf.connect([(apply_transform, jacobian, [('out_warp_field', 'deformationField')])])
wf.connect([(apply_transform, jacmult, [('out_warped', 'operand_files')])])
wf.connect([(jacobian, jacmult, [('outputImage', 'in_file')])])
wf.connect([(jacmult, thres, [('out_file', 'in_file')])])
wf.connect([(thres, merge, [('out_file', 'in_files')])])
wf.connect([(merge, outputnode, [('merged_file', 'DWIs_epicorrected')])])
wf.connect([(flirt_b0_2_T1, outputnode, [('out_matrix_file', 'DWI_2_T1_Coregistration_matrix')])])
wf.connect([(antsRegistrationSyNQuick, outputnode, [('warp', 'epi_correction_deformation_field'),
('affine_matrix', 'epi_correction_affine_transform'),
('image_warped', 'epi_correction_image_warped')])])
wf.connect([(merge_transform, outputnode, [('out', 'warp_epi')])])
wf.connect([(rot_bvec, outputnode, [('out_file', 'out_bvec')])])
return wf
def create_reg_workflow(name='registration'):
"""Create a FEAT preprocessing workflow together with freesurfer
Parameters
----------
::
name : name of workflow (default: 'registration')
Inputs::
inputspec.source_files : files (filename or list of filenames to register)
inputspec.mean_image : reference image to use
inputspec.anatomical_image : anatomical image to coregister to
inputspec.target_image : registration target
Outputs::
outputspec.func2anat_transform : FLIRT transform
outputspec.anat2target_transform : FLIRT+FNIRT transform
outputspec.transformed_files : transformed files in target space
outputspec.transformed_mean : mean image in target space
Example
-------
"""
register = pe.Workflow(name=name)
inputnode = pe.Node(interface=util.IdentityInterface(fields=[
'source_files', 'mean_image', 'anatomical_image', 'target_image',
'target_image_brain', 'config_file'
]),
name='inputspec')
outputnode = pe.Node(interface=util.IdentityInterface(fields=[
'func2anat_transform',
'anat2target_transform',
'transformed_files',
'transformed_mean',
]),
name='outputspec')
"""
Estimate the tissue classes from the anatomical image. But use spm's segment
as FSL appears to be breaking.
"""
stripper = pe.Node(fsl.BET(), name='stripper')
register.connect(inputnode, 'anatomical_image', stripper, 'in_file')
fast = pe.Node(fsl.FAST(), name='fast')
register.connect(stripper, 'out_file', fast, 'in_files')
"""
Binarize the segmentation
"""
binarize = pe.Node(fsl.ImageMaths(op_string='-nan -thr 0.5 -bin'),
name='binarize')
pickindex = lambda x, i: x[i]
register.connect(fast, ('partial_volume_files', pickindex, 2), binarize,
'in_file')
"""
Calculate rigid transform from mean image to anatomical image
"""
mean2anat = pe.Node(fsl.FLIRT(), name='mean2anat')
mean2anat.inputs.dof = 6
register.connect(inputnode, 'mean_image', mean2anat, 'in_file')
register.connect(stripper, 'out_file', mean2anat, 'reference')
"""
Now use bbr cost function to improve the transform
"""
mean2anatbbr = pe.Node(fsl.FLIRT(), name='mean2anatbbr')
mean2anatbbr.inputs.dof = 6
mean2anatbbr.inputs.cost = 'bbr'
mean2anatbbr.inputs.schedule = os.path.join(os.getenv('FSLDIR'),
'etc/flirtsch/bbr.sch')
register.connect(inputnode, 'mean_image', mean2anatbbr, 'in_file')
register.connect(binarize, 'out_file', mean2anatbbr, 'wm_seg')
register.connect(inputnode, 'anatomical_image', mean2anatbbr, 'reference')
register.connect(mean2anat, 'out_matrix_file', mean2anatbbr,
'in_matrix_file')
"""
Calculate affine transform from anatomical to target
"""
anat2target_affine = pe.Node(fsl.FLIRT(), name='anat2target_linear')
anat2target_affine.inputs.searchr_x = [-180, 180]
anat2target_affine.inputs.searchr_y = [-180, 180]
anat2target_affine.inputs.searchr_z = [-180, 180]
register.connect(stripper, 'out_file', anat2target_affine, 'in_file')
register.connect(inputnode, 'target_image_brain', anat2target_affine,
'reference')
"""
Calculate nonlinear transform from anatomical to target
"""
anat2target_nonlinear = pe.Node(fsl.FNIRT(), name='anat2target_nonlinear')
anat2target_nonlinear.inputs.fieldcoeff_file = True
register.connect(anat2target_affine, 'out_matrix_file',
anat2target_nonlinear, 'affine_file')
register.connect(inputnode, 'anatomical_image', anat2target_nonlinear,
'in_file')
register.connect(inputnode, 'config_file', anat2target_nonlinear,
'config_file')
register.connect(inputnode, 'target_image', anat2target_nonlinear,
'ref_file')
"""
Transform the mean image. First to anatomical and then to target
"""
warpmean = pe.Node(fsl.ApplyWarp(interp='spline'), name='warpmean')
register.connect(inputnode, 'mean_image', warpmean, 'in_file')
register.connect(mean2anatbbr, 'out_matrix_file', warpmean, 'premat')
register.connect(inputnode, 'target_image', warpmean, 'ref_file')
register.connect(anat2target_nonlinear, 'fieldcoeff_file', warpmean,
'field_file')
"""
Transform the remaining images. First to anatomical and then to target
"""
warpall = pe.MapNode(fsl.ApplyWarp(interp='spline'),
iterfield=['in_file'],
nested=True,
name='warpall')
register.connect(inputnode, 'source_files', warpall, 'in_file')
register.connect(mean2anatbbr, 'out_matrix_file', warpall, 'premat')
register.connect(inputnode, 'target_image', warpall, 'ref_file')
register.connect(anat2target_nonlinear, 'fieldcoeff_file', warpall,
'field_file')
"""
Assign all the output files
"""
register.connect(warpmean, 'out_file', outputnode, 'transformed_mean')
register.connect(warpall, 'out_file', outputnode, 'transformed_files')
register.connect(mean2anatbbr, 'out_matrix_file', outputnode,
'func2anat_transform')
register.connect(anat2target_nonlinear, 'fieldcoeff_file', outputnode,
'anat2target_transform')
return register
def extract_dl_t1w(caps_directory,
tsv,
working_directory=None,
extract_method='image',
patch_size=50,
stride_size=50,
slice_direction=0,
slice_mode='single'):
""" This is a preprocessing pipeline to convert the MRIs in nii.gz format
into tensor versions (using pytorch format). It also prepares the
slice-level and patch-level data from the entire MRI and save them on disk.
This enables the training process:
- For slice-level CNN, all slices were extracted from the entire
MRI from three different axis. The first and last 15 slice were
discarded due to the lack of information.
- For patch-level CNN, the 3D patch (with specific patch size)
were extracted by a 3D window.
Parameters
----------
caps_directory: str
CAPS directory where stores the output of preprocessing.
tsv: str
TVS file with the subject list (participant_id and session_id).
extract_method:
Select which extract method will be applied for the outputs:
- 'image' to convert to PyTorch tensor the complete 3D image,
- 'patch' to extract 3D volumetric patches and
- 'slice' to extract 2D slices from the image
patch_size: int
Size for extracted 3D patches (only 'patch' method).
stride_size: int
Sliding size window of when extracting the patches (only 'patch' method).
slice_direction: int
Which direction the slices will be extracted (only 'slice' method):
- 0: Sagittal plane
- 1: Coronal plane
- 2: Axial plane
slice_mode: str
Mode how slices are stored (only 'slice' method):
- single: saves the slice in a single channel,
- rgb: saves the slice in three identical channels (red, green, blue)
working_directory: str
Folder containing a temporary space to save intermediate results.
e
Returns
-------
wf: class nipype.pipeline.engine.workflows.Workflow
A class du type nypipe workflow to control, setup, and execute a process
as a nypipe pipeline.
"""
import nipype.interfaces.io as nio
import nipype.interfaces.utility as nutil
import nipype.pipeline.engine as npe
from nipype.interfaces.io import DataSink
from nipype import config
import tempfile
from clinica.utils.inputs import check_caps_folder
from clinica.utils.filemanip import get_subject_id
from clinica.utils.participant import get_subject_session_list
from clinica.utils.exceptions import ClinicaBIDSError, ClinicaException
from clinica.utils.inputs import clinica_file_reader
from clinica.utils.nipype import fix_join
from .T1_preparedl_utils import (extract_slices, extract_patches,
save_as_pt, container_from_filename,
get_data_datasink)
T1W_LINEAR = {
'pattern': '*space-MNI152NLin2009cSym_res-1x1x1_T1w.nii.gz',
'description': 'T1W Image registered using T1_Linear'
}
T1W_LINEAR_CROPPED = {
'pattern':
'*space-MNI152NLin2009cSym_desc-Crop_res-1x1x1_T1w.nii.gz',
'description':
'T1W Image registered using T1_Linear and cropped'
'(matrix size 169×208×179, 1 mm isotropic voxels)'
}
if working_directory is None:
working_directory = tempfile.mkdtemp()
check_caps_folder(caps_directory)
is_bids_dir = False
use_session_tsv = False
sessions, subjects = get_subject_session_list(caps_directory, tsv,
is_bids_dir, use_session_tsv,
working_directory)
# Use hash instead of parameters for iterables folder names
# Otherwise path will be too long and generate OSError
cfg = dict(execution={'parameterize_dirs': False})
config.update_config(cfg)
# Inputs from t1_linear folder
# ========================
# T1w file:
try:
t1w_files = clinica_file_reader(subjects, sessions, caps_directory,
T1W_LINEAR_CROPPED)
except ClinicaException as e:
err = 'Clinica faced error(s) while trying to read files in your CAPS directory.\n' + str(
e)
raise ClinicaBIDSError(err)
def get_input_fields():
""""Specify the list of possible inputs of this pipelines.
Returns:
A list of (string) input fields name.
"""
return ['t1w']
# Read node
# ----------------------
read_node = npe.Node(
name="ReadingFiles",
iterables=[
('t1w', t1w_files),
],
synchronize=True,
interface=nutil.IdentityInterface(fields=get_input_fields()))
# Get subject ID node
# ----------------------
image_id_node = npe.Node(interface=nutil.Function(
input_names=['bids_or_caps_file'],
output_names=['image_id'],
function=get_subject_id),
name='ImageID')
# The processing nodes
# Node to save MRI in nii.gz format into pytorch .pt format
# ----------------------
save_as_pt = npe.MapNode(name='save_as_pt',
iterfield=['input_img'],
interface=nutil.Function(
function=save_as_pt,
input_names=['input_img'],
output_names=['output_file']))
# Extract slices node (options: 3 directions, mode)
# ----------------------
extract_slices = npe.MapNode(
name='extract_slices',
iterfield=['input_tensor'],
interface=nutil.Function(
function=extract_slices,
input_names=['input_tensor', 'slice_direction', 'slice_mode'],
output_names=['output_file_rgb', 'output_file_original']))
extract_slices.inputs.slice_direction = slice_direction
extract_slices.inputs.slice_mode = slice_mode
# Extract patches node (options: patch size and stride size)
# ----------------------
extract_patches = npe.MapNode(
name='extract_patches',
iterfield=['input_tensor'],
interface=nutil.Function(
function=extract_patches,
input_names=['input_tensor', 'patch_size', 'stride_size'],
output_names=['output_patch']))
extract_patches.inputs.patch_size = patch_size
extract_patches.inputs.stride_size = stride_size
# Output node
# ----------------------
outputnode = npe.Node(nutil.IdentityInterface(fields=['preprocessed_T1']),
name='outputnode')
# Find container path from t1w filename
# ----------------------
container_path = npe.Node(nutil.Function(
input_names=['bids_or_caps_filename'],
output_names=['container'],
function=container_from_filename),
name='ContainerPath')
# Write node
# ----------------------
write_node = npe.Node(name="WriteCaps", interface=DataSink())
write_node.inputs.base_directory = caps_directory
write_node.inputs.parameterization = False
subfolder = 'image_based'
wf = npe.Workflow(name='dl_prepare_data', base_dir=working_directory)
# Connections
# ----------------------
wf.connect([
(read_node, image_id_node, [('t1w', 'bids_or_caps_file')]),
(read_node, container_path, [('t1w', 'bids_or_caps_filename')]),
(read_node, save_as_pt, [('t1w', 'input_img')]),
(image_id_node, write_node, [('image_id', '@image_id')]),
# Connect to DataSink
])
if extract_method == 'slice':
subfolder = 'slice_based'
wf.connect([(save_as_pt, extract_slices, [('output_file',
'input_tensor')]),
(extract_slices, write_node, [('output_file_rgb',
'@slices_rgb_T1')]),
(extract_slices, write_node, [('output_file_original',
'@slices_original_T1')])])
elif extract_method == 'patch':
subfolder = 'patch_based'
wf.connect([
(save_as_pt, extract_patches, [('output_file', 'input_tensor')]),
(extract_patches, write_node, [('output_patch', '@patches_T1')])
])
else:
wf.connect([(save_as_pt, write_node, [('output_file',
'@output_pt_file')])])
wf.connect([(container_path, write_node,
[(('container', fix_join, 'deeplearning_prepare_data',
subfolder, 't1_linear'), 'container')])])
return wf
def create_susan_smooth(name="susan_smooth", separate_masks=True):
"""Create a SUSAN smoothing workflow
Parameters
----------
::
name : name of workflow (default: susan_smooth)
separate_masks : separate masks for each run
Inputs::
inputnode.in_files : functional runs (filename or list of filenames)
inputnode.fwhm : fwhm for smoothing with SUSAN
inputnode.mask_file : mask used for estimating SUSAN thresholds (but not for smoothing)
Outputs::
outputnode.smoothed_files : functional runs (filename or list of filenames)
Example
-------
>>> smooth = create_susan_smooth()
>>> smooth.inputs.inputnode.in_files = 'f3.nii'
>>> smooth.inputs.inputnode.fwhm = 5
>>> smooth.inputs.inputnode.mask_file = 'mask.nii'
>>> smooth.run() # doctest: +SKIP
"""
susan_smooth = pe.Workflow(name=name)
"""
Set up a node to define all inputs required for the preprocessing workflow
"""
inputnode = pe.Node(interface=util.IdentityInterface(
fields=['in_files', 'fwhm', 'mask_file']),
name='inputnode')
"""
Smooth each run using SUSAN with the brightness threshold set to 75%
of the median value for each run and a mask consituting the mean
functional
"""
smooth = pe.MapNode(interface=fsl.SUSAN(),
iterfield=['in_file', 'brightness_threshold', 'usans'],
name='smooth')
"""
Determine the median value of the functional runs using the mask
"""
if separate_masks:
median = pe.MapNode(interface=fsl.ImageStats(op_string='-k %s -p 50'),
iterfield=['in_file', 'mask_file'],
name='median')
else:
median = pe.MapNode(interface=fsl.ImageStats(op_string='-k %s -p 50'),
iterfield=['in_file'],
name='median')
susan_smooth.connect(inputnode, 'in_files', median, 'in_file')
susan_smooth.connect(inputnode, 'mask_file', median, 'mask_file')
"""
Mask the motion corrected functional runs with the dilated mask
"""
if separate_masks:
mask = pe.MapNode(interface=fsl.ImageMaths(suffix='_mask',
op_string='-mas'),
iterfield=['in_file', 'in_file2'],
name='mask')
else:
mask = pe.MapNode(interface=fsl.ImageMaths(suffix='_mask',
op_string='-mas'),
iterfield=['in_file'],
name='mask')
susan_smooth.connect(inputnode, 'in_files', mask, 'in_file')
susan_smooth.connect(inputnode, 'mask_file', mask, 'in_file2')
"""
Determine the mean image from each functional run
"""
meanfunc = pe.MapNode(interface=fsl.ImageMaths(op_string='-Tmean',
suffix='_mean'),
iterfield=['in_file'],
name='meanfunc2')
susan_smooth.connect(mask, 'out_file', meanfunc, 'in_file')
"""
Merge the median values with the mean functional images into a coupled list
"""
merge = pe.Node(interface=util.Merge(2, axis='hstack'), name='merge')
susan_smooth.connect(meanfunc, 'out_file', merge, 'in1')
susan_smooth.connect(median, 'out_stat', merge, 'in2')
"""
Define a function to get the brightness threshold for SUSAN
"""
susan_smooth.connect(inputnode, 'fwhm', smooth, 'fwhm')
susan_smooth.connect(inputnode, 'in_files', smooth, 'in_file')
susan_smooth.connect(median, ('out_stat', getbtthresh), smooth,
'brightness_threshold')
susan_smooth.connect(merge, ('out', getusans), smooth, 'usans')
outputnode = pe.Node(
interface=util.IdentityInterface(fields=['smoothed_files']),
name='outputnode')
susan_smooth.connect(smooth, 'smoothed_file', outputnode, 'smoothed_files')
return susan_smooth
def create_tessellation_flow(name='tessellate', out_format='stl'):
"""Tessellates the input subject's aseg.mgz volume and returns
the surfaces for each region in stereolithic (.stl) format
Example
-------
>>> from nipype.workflows.smri.freesurfer import create_tessellation_flow
>>> tessflow = create_tessellation_flow()
>>> tessflow.inputs.inputspec.subject_id = 'subj1'
>>> tessflow.inputs.inputspec.subjects_dir = '.'
>>> tessflow.inputs.inputspec.lookup_file = 'FreeSurferColorLUT.txt' # doctest: +SKIP
>>> tessflow.run() # doctest: +SKIP
Inputs::
inputspec.subject_id : freesurfer subject id
inputspec.subjects_dir : freesurfer subjects directory
inputspec.lookup_file : lookup file from freesurfer directory
Outputs::
outputspec.meshes : output region meshes in (by default) stereolithographic (.stl) format
"""
"""
Initialize the workflow
"""
tessflow = pe.Workflow(name=name)
"""
Define the inputs to the workflow.
"""
inputnode = pe.Node(niu.IdentityInterface(
fields=['subject_id', 'subjects_dir', 'lookup_file']),
name='inputspec')
"""
Define all the nodes of the workflow:
fssource: used to retrieve aseg.mgz
mri_convert : converts aseg.mgz to aseg.nii
tessellate : tessellates regions in aseg.mgz
surfconvert : converts regions to stereolithographic (.stl) format
smoother: smooths the tessellated regions
"""
fssource = pe.Node(nio.FreeSurferSource(), name='fssource')
volconvert = pe.Node(fs.MRIConvert(out_type='nii'), name='volconvert')
tessellate = pe.MapNode(fs.MRIMarchingCubes(),
iterfield=['label_value', 'out_file'],
name='tessellate')
surfconvert = pe.MapNode(fs.MRIsConvert(out_datatype='stl'),
iterfield=['in_file'],
name='surfconvert')
smoother = pe.MapNode(mf.MeshFix(),
iterfield=['in_file1'],
name='smoother')
if out_format == 'gii':
stl_to_gifti = pe.MapNode(fs.MRIsConvert(out_datatype=out_format),
iterfield=['in_file'],
name='stl_to_gifti')
smoother.inputs.save_as_stl = True
smoother.inputs.laplacian_smoothing_steps = 1
region_list_from_volume_interface = Function(
input_names=["in_file"],
output_names=["region_list"],
function=region_list_from_volume)
id_list_from_lookup_table_interface = Function(
input_names=["lookup_file", "region_list"],
output_names=["id_list"],
function=id_list_from_lookup_table)
region_list_from_volume_node = pe.Node(
interface=region_list_from_volume_interface,
name='region_list_from_volume_node')
id_list_from_lookup_table_node = pe.Node(
interface=id_list_from_lookup_table_interface,
name='id_list_from_lookup_table_node')
"""
Connect the nodes
"""
tessflow.connect([
(inputnode, fssource, [('subject_id', 'subject_id'),
('subjects_dir', 'subjects_dir')]),
(fssource, volconvert, [('aseg', 'in_file')]),
(volconvert, region_list_from_volume_node, [('out_file', 'in_file')]),
(region_list_from_volume_node, tessellate, [('region_list',
'label_value')]),
(region_list_from_volume_node, id_list_from_lookup_table_node,
[('region_list', 'region_list')]),
(inputnode, id_list_from_lookup_table_node, [('lookup_file',
'lookup_file')]),
(id_list_from_lookup_table_node, tessellate, [('id_list', 'out_file')
]),
(fssource, tessellate, [('aseg', 'in_file')]),
(tessellate, surfconvert, [('surface', 'in_file')]),
(surfconvert, smoother, [('converted', 'in_file1')]),
])
"""
Setup an outputnode that defines relevant inputs of the workflow.
"""
outputnode = pe.Node(niu.IdentityInterface(fields=["meshes"]),
name="outputspec")
if out_format == 'gii':
tessflow.connect([
(smoother, stl_to_gifti, [("mesh_file", "in_file")]),
])
tessflow.connect([
(stl_to_gifti, outputnode, [("converted", "meshes")]),
])
else:
tessflow.connect([
(smoother, outputnode, [("mesh_file", "meshes")]),
])
return tessflow
def create_parallelfeat_preproc(name='featpreproc', highpass=True):
"""Preprocess each run with FSL independently of the others
Parameters
----------
::
name : name of workflow (default: featpreproc)
highpass : boolean (default: True)
Inputs::
inputspec.func : functional runs (filename or list of filenames)
inputspec.fwhm : fwhm for smoothing with SUSAN
inputspec.highpass : HWHM in TRs (if created with highpass=True)
Outputs::
outputspec.reference : volume to which runs are realigned
outputspec.motion_parameters : motion correction parameters
outputspec.realigned_files : motion corrected files
outputspec.motion_plots : plots of motion correction parameters
outputspec.mask : mask file used to mask the brain
outputspec.smoothed_files : smoothed functional data
outputspec.highpassed_files : highpassed functional data (if highpass=True)
outputspec.mean : mean file
Example
-------
>>> preproc = create_parallelfeat_preproc()
>>> preproc.inputs.inputspec.func = ['f3.nii', 'f5.nii']
>>> preproc.inputs.inputspec.fwhm = 5
>>> preproc.inputs.inputspec.highpass = 128./(2*2.5)
>>> preproc.base_dir = '/tmp'
>>> preproc.run() # doctest: +SKIP
>>> preproc = create_parallelfeat_preproc(highpass=False)
>>> preproc.inputs.inputspec.func = 'f3.nii'
>>> preproc.inputs.inputspec.fwhm = 5
>>> preproc.base_dir = '/tmp'
>>> preproc.run() # doctest: +SKIP
"""
featpreproc = pe.Workflow(name=name)
"""
Set up a node to define all inputs required for the preprocessing workflow
"""
if highpass:
inputnode = pe.Node(interface=util.IdentityInterface(
fields=['func', 'fwhm', 'highpass']),
name='inputspec')
outputnode = pe.Node(interface=util.IdentityInterface(fields=[
'reference', 'motion_parameters', 'realigned_files',
'motion_plots', 'mask', 'smoothed_files', 'highpassed_files',
'mean'
]),
name='outputspec')
else:
inputnode = pe.Node(
interface=util.IdentityInterface(fields=['func', 'fwhm']),
name='inputspec')
outputnode = pe.Node(interface=util.IdentityInterface(fields=[
'reference', 'motion_parameters', 'realigned_files',
'motion_plots', 'mask', 'smoothed_files', 'mean'
]),
name='outputspec')
"""
Set up a node to define outputs for the preprocessing workflow
"""
"""
Convert functional images to float representation. Since there can
be more than one functional run we use a MapNode to convert each
run.
"""
img2float = pe.MapNode(interface=fsl.ImageMaths(out_data_type='float',
op_string='',
suffix='_dtype'),
iterfield=['in_file'],
name='img2float')
featpreproc.connect(inputnode, 'func', img2float, 'in_file')
"""
Extract the first volume of the first run as the reference
"""
extract_ref = pe.MapNode(interface=fsl.ExtractROI(t_size=1),
iterfield=['in_file', 't_min'],
name='extractref')
featpreproc.connect(img2float, 'out_file', extract_ref, 'in_file')
featpreproc.connect(img2float, ('out_file', pickmiddle), extract_ref,
't_min')
featpreproc.connect(extract_ref, 'roi_file', outputnode, 'reference')
"""
Realign the functional runs to the reference (1st volume of first run)
"""
motion_correct = pe.MapNode(interface=fsl.MCFLIRT(save_mats=True,
save_plots=True),
name='realign',
iterfield=['in_file', 'ref_file'])
featpreproc.connect(img2float, 'out_file', motion_correct, 'in_file')
featpreproc.connect(extract_ref, 'roi_file', motion_correct, 'ref_file')
featpreproc.connect(motion_correct, 'par_file', outputnode,
'motion_parameters')
featpreproc.connect(motion_correct, 'out_file', outputnode,
'realigned_files')
"""
Plot the estimated motion parameters
"""
plot_motion = pe.MapNode(interface=fsl.PlotMotionParams(in_source='fsl'),
name='plot_motion',
iterfield=['in_file'])
plot_motion.iterables = ('plot_type', ['rotations', 'translations'])
featpreproc.connect(motion_correct, 'par_file', plot_motion, 'in_file')
featpreproc.connect(plot_motion, 'out_file', outputnode, 'motion_plots')
"""
Extract the mean volume of the first functional run
"""
meanfunc = pe.MapNode(interface=fsl.ImageMaths(op_string='-Tmean',
suffix='_mean'),
iterfield=['in_file'],
name='meanfunc')
featpreproc.connect(motion_correct, 'out_file', meanfunc, 'in_file')
"""
Strip the skull from the mean functional to generate a mask
"""
meanfuncmask = pe.MapNode(interface=fsl.BET(mask=True,
no_output=True,
frac=0.3),
iterfield=['in_file'],
name='meanfuncmask')
featpreproc.connect(meanfunc, 'out_file', meanfuncmask, 'in_file')
"""
Mask the functional runs with the extracted mask
"""
maskfunc = pe.MapNode(interface=fsl.ImageMaths(suffix='_bet',
op_string='-mas'),
iterfield=['in_file', 'in_file2'],
name='maskfunc')
featpreproc.connect(motion_correct, 'out_file', maskfunc, 'in_file')
featpreproc.connect(meanfuncmask, 'mask_file', maskfunc, 'in_file2')
"""
Determine the 2nd and 98th percentile intensities of each functional run
"""
getthresh = pe.MapNode(interface=fsl.ImageStats(op_string='-p 2 -p 98'),
iterfield=['in_file'],
name='getthreshold')
featpreproc.connect(maskfunc, 'out_file', getthresh, 'in_file')
"""
Threshold the first run of the functional data at 10% of the 98th percentile
"""
threshold = pe.MapNode(interface=fsl.ImageMaths(out_data_type='char',
suffix='_thresh'),
iterfield=['in_file', 'op_string'],
name='threshold')
featpreproc.connect(maskfunc, 'out_file', threshold, 'in_file')
"""
Define a function to get 10% of the intensity
"""
featpreproc.connect(getthresh, ('out_stat', getthreshop), threshold,
'op_string')
"""
Determine the median value of the functional runs using the mask
"""
medianval = pe.MapNode(interface=fsl.ImageStats(op_string='-k %s -p 50'),
iterfield=['in_file', 'mask_file'],
name='medianval')
featpreproc.connect(motion_correct, 'out_file', medianval, 'in_file')
featpreproc.connect(threshold, 'out_file', medianval, 'mask_file')
"""
Dilate the mask
"""
dilatemask = pe.MapNode(interface=fsl.ImageMaths(suffix='_dil',
op_string='-dilF'),
iterfield=['in_file'],
name='dilatemask')
featpreproc.connect(threshold, 'out_file', dilatemask, 'in_file')
featpreproc.connect(dilatemask, 'out_file', outputnode, 'mask')
"""
Mask the motion corrected functional runs with the dilated mask
"""
maskfunc2 = pe.MapNode(interface=fsl.ImageMaths(suffix='_mask',
op_string='-mas'),
iterfield=['in_file', 'in_file2'],
name='maskfunc2')
featpreproc.connect(motion_correct, 'out_file', maskfunc2, 'in_file')
featpreproc.connect(dilatemask, 'out_file', maskfunc2, 'in_file2')
"""
Smooth each run using SUSAN with the brightness threshold set to 75%
of the median value for each run and a mask consituting the mean
functional
"""
smooth = create_susan_smooth()
featpreproc.connect(inputnode, 'fwhm', smooth, 'inputnode.fwhm')
featpreproc.connect(maskfunc2, 'out_file', smooth, 'inputnode.in_files')
featpreproc.connect(dilatemask, 'out_file', smooth, 'inputnode.mask_file')
"""
Mask the smoothed data with the dilated mask
"""
maskfunc3 = pe.MapNode(interface=fsl.ImageMaths(suffix='_mask',
op_string='-mas'),
iterfield=['in_file', 'in_file2'],
name='maskfunc3')
featpreproc.connect(smooth, 'outputnode.smoothed_files', maskfunc3,
'in_file')
featpreproc.connect(dilatemask, 'out_file', maskfunc3, 'in_file2')
concatnode = pe.Node(interface=util.Merge(2), name='concat')
featpreproc.connect(maskfunc2, ('out_file', tolist), concatnode, 'in1')
featpreproc.connect(maskfunc3, ('out_file', tolist), concatnode, 'in2')
"""
The following nodes select smooth or unsmoothed data depending on the
fwhm. This is because SUSAN defaults to smoothing the data with about the
voxel size of the input data if the fwhm parameter is less than 1/3 of the
voxel size.
"""
selectnode = pe.Node(interface=util.Select(), name='select')
featpreproc.connect(concatnode, 'out', selectnode, 'inlist')
featpreproc.connect(inputnode, ('fwhm', chooseindex), selectnode, 'index')
featpreproc.connect(selectnode, 'out', outputnode, 'smoothed_files')
"""
Scale the median value of the run is set to 10000
"""
meanscale = pe.MapNode(interface=fsl.ImageMaths(suffix='_gms'),
iterfield=['in_file', 'op_string'],
name='meanscale')
featpreproc.connect(selectnode, 'out', meanscale, 'in_file')
"""
Define a function to get the scaling factor for intensity normalization
"""
featpreproc.connect(medianval, ('out_stat', getmeanscale), meanscale,
'op_string')
"""
Perform temporal highpass filtering on the data
"""
if highpass:
highpass = pe.MapNode(interface=fsl.ImageMaths(suffix='_tempfilt'),
iterfield=['in_file'],
name='highpass')
featpreproc.connect(inputnode, ('highpass', highpass_operand),
highpass, 'op_string')
featpreproc.connect(meanscale, 'out_file', highpass, 'in_file')
featpreproc.connect(highpass, 'out_file', outputnode,
'highpassed_files')
"""
Generate a mean functional image from the first run
"""
meanfunc3 = pe.MapNode(interface=fsl.ImageMaths(op_string='-Tmean',
suffix='_mean'),
iterfield=['in_file'],
name='meanfunc3')
if highpass:
featpreproc.connect(highpass, 'out_file', meanfunc3, 'in_file')
else:
featpreproc.connect(meanscale, 'out_file', meanfunc3, 'in_file')
featpreproc.connect(meanfunc3, 'out_file', outputnode, 'mean')
return featpreproc
preproc = create_featreg_preproc(whichvol='first')
modelfit = create_modelfit_workflow()
fixed_fx = create_fixed_effects_flow()
"""
Add artifact detection and model specification nodes between the preprocessing
and modelfitting workflows.
"""
art = pe.MapNode(
interface=ra.ArtifactDetect(use_differences=[True, False],
use_norm=True,
norm_threshold=1,
zintensity_threshold=3,
parameter_source='FSL',
mask_type='file'),
iterfield=['realigned_files', 'realignment_parameters', 'mask_file'],
name="art")
modelspec = pe.Node(interface=model.SpecifyModel(), name="modelspec")
level1_workflow.connect([
(preproc, art, [('outputspec.motion_parameters', 'realignment_parameters'),
('outputspec.realigned_files', 'realigned_files'),
('outputspec.mask', 'mask_file')]),
(preproc, modelspec, [('outputspec.highpassed_files', 'functional_runs'),
('outputspec.motion_parameters',
'realignment_parameters')]),
(art, modelspec, [('outlier_files', 'outlier_files')]),
def create_fsl_fs_preproc(name='preproc', highpass=True, whichvol='middle'):
"""Create a FEAT preprocessing workflow together with freesurfer
Parameters
----------
::
name : name of workflow (default: preproc)
highpass : boolean (default: True)
whichvol : which volume of the first run to register to ('first', 'middle', 'mean')
Inputs::
inputspec.func : functional runs (filename or list of filenames)
inputspec.fwhm : fwhm for smoothing with SUSAN
inputspec.highpass : HWHM in TRs (if created with highpass=True)
inputspec.subject_id : freesurfer subject id
inputspec.subjects_dir : freesurfer subjects dir
Outputs::
outputspec.reference : volume to which runs are realigned
outputspec.motion_parameters : motion correction parameters
outputspec.realigned_files : motion corrected files
outputspec.motion_plots : plots of motion correction parameters
outputspec.mask_file : mask file used to mask the brain
outputspec.smoothed_files : smoothed functional data
outputspec.highpassed_files : highpassed functional data (if highpass=True)
outputspec.reg_file : bbregister registration files
outputspec.reg_cost : bbregister registration cost files
Example
-------
>>> preproc = create_fsl_fs_preproc(whichvol='first')
>>> preproc.inputs.inputspec.highpass = 128./(2*2.5)
>>> preproc.inputs.inputspec.func = ['f3.nii', 'f5.nii']
>>> preproc.inputs.inputspec.subjects_dir = '.'
>>> preproc.inputs.inputspec.subject_id = 's1'
>>> preproc.inputs.inputspec.fwhm = 6
>>> preproc.run() # doctest: +SKIP
"""
featpreproc = pe.Workflow(name=name)
"""
Set up a node to define all inputs required for the preprocessing workflow
"""
if highpass:
inputnode = pe.Node(interface=util.IdentityInterface(
fields=['func', 'fwhm', 'subject_id', 'subjects_dir', 'highpass']),
name='inputspec')
outputnode = pe.Node(interface=util.IdentityInterface(fields=[
'reference', 'motion_parameters', 'realigned_files',
'motion_plots', 'mask_file', 'smoothed_files', 'highpassed_files',
'reg_file', 'reg_cost'
]),
name='outputspec')
else:
inputnode = pe.Node(interface=util.IdentityInterface(
fields=['func', 'fwhm', 'subject_id', 'subjects_dir']),
name='inputspec')
outputnode = pe.Node(interface=util.IdentityInterface(fields=[
'reference', 'motion_parameters', 'realigned_files',
'motion_plots', 'mask_file', 'smoothed_files', 'reg_file',
'reg_cost'
]),
name='outputspec')
"""
Set up a node to define outputs for the preprocessing workflow
"""
"""
Convert functional images to float representation. Since there can
be more than one functional run we use a MapNode to convert each
run.
"""
img2float = pe.MapNode(interface=fsl.ImageMaths(out_data_type='float',
op_string='',
suffix='_dtype'),
iterfield=['in_file'],
name='img2float')
featpreproc.connect(inputnode, 'func', img2float, 'in_file')
"""
Extract the first volume of the first run as the reference
"""
if whichvol != 'mean':
extract_ref = pe.Node(interface=fsl.ExtractROI(t_size=1),
iterfield=['in_file'],
name='extractref')
featpreproc.connect(img2float, ('out_file', pickfirst), extract_ref,
'in_file')
featpreproc.connect(img2float, ('out_file', pickvol, 0, whichvol),
extract_ref, 't_min')
featpreproc.connect(extract_ref, 'roi_file', outputnode, 'reference')
"""
Realign the functional runs to the reference (1st volume of first run)
"""
motion_correct = pe.MapNode(interface=fsl.MCFLIRT(save_mats=True,
save_plots=True,
interpolation='sinc'),
name='realign',
iterfield=['in_file'])
featpreproc.connect(img2float, 'out_file', motion_correct, 'in_file')
if whichvol != 'mean':
featpreproc.connect(extract_ref, 'roi_file', motion_correct,
'ref_file')
else:
motion_correct.inputs.mean_vol = True
featpreproc.connect(motion_correct, 'mean_img', outputnode,
'reference')
featpreproc.connect(motion_correct, 'par_file', outputnode,
'motion_parameters')
featpreproc.connect(motion_correct, 'out_file', outputnode,
'realigned_files')
"""
Plot the estimated motion parameters
"""
plot_motion = pe.MapNode(interface=fsl.PlotMotionParams(in_source='fsl'),
name='plot_motion',
iterfield=['in_file'])
plot_motion.iterables = ('plot_type', ['rotations', 'translations'])
featpreproc.connect(motion_correct, 'par_file', plot_motion, 'in_file')
featpreproc.connect(plot_motion, 'out_file', outputnode, 'motion_plots')
"""Get the mask from subject for each run
"""
maskflow = create_getmask_flow()
featpreproc.connect([(inputnode, maskflow,
[('subject_id', 'inputspec.subject_id'),
('subjects_dir', 'inputspec.subjects_dir')])])
maskflow.inputs.inputspec.contrast_type = 't2'
if whichvol != 'mean':
featpreproc.connect(extract_ref, 'roi_file', maskflow,
'inputspec.source_file')
else:
featpreproc.connect(motion_correct, ('mean_img', pickfirst), maskflow,
'inputspec.source_file')
"""
Mask the functional runs with the extracted mask
"""
maskfunc = pe.MapNode(interface=fsl.ImageMaths(suffix='_bet',
op_string='-mas'),
iterfield=['in_file'],
name='maskfunc')
featpreproc.connect(motion_correct, 'out_file', maskfunc, 'in_file')
featpreproc.connect(maskflow, ('outputspec.mask_file', pickfirst),
maskfunc, 'in_file2')
"""
Smooth each run using SUSAN with the brightness threshold set to 75%
of the median value for each run and a mask consituting the mean
functional
"""
smooth = create_susan_smooth(separate_masks=False)
featpreproc.connect(inputnode, 'fwhm', smooth, 'inputnode.fwhm')
featpreproc.connect(maskfunc, 'out_file', smooth, 'inputnode.in_files')
featpreproc.connect(maskflow, ('outputspec.mask_file', pickfirst), smooth,
'inputnode.mask_file')
"""
Mask the smoothed data with the dilated mask
"""
maskfunc3 = pe.MapNode(interface=fsl.ImageMaths(suffix='_mask',
op_string='-mas'),
iterfield=['in_file'],
name='maskfunc3')
featpreproc.connect(smooth, 'outputnode.smoothed_files', maskfunc3,
'in_file')
featpreproc.connect(maskflow, ('outputspec.mask_file', pickfirst),
maskfunc3, 'in_file2')
concatnode = pe.Node(interface=util.Merge(2), name='concat')
featpreproc.connect(maskfunc, ('out_file', tolist), concatnode, 'in1')
featpreproc.connect(maskfunc3, ('out_file', tolist), concatnode, 'in2')
"""
The following nodes select smooth or unsmoothed data depending on the
fwhm. This is because SUSAN defaults to smoothing the data with about the
voxel size of the input data if the fwhm parameter is less than 1/3 of the
voxel size.
"""
selectnode = pe.Node(interface=util.Select(), name='select')
featpreproc.connect(concatnode, 'out', selectnode, 'inlist')
featpreproc.connect(inputnode, ('fwhm', chooseindex), selectnode, 'index')
featpreproc.connect(selectnode, 'out', outputnode, 'smoothed_files')
"""
Scale the median value of the run is set to 10000
"""
meanscale = pe.MapNode(interface=fsl.ImageMaths(suffix='_gms'),
iterfield=['in_file', 'op_string'],
name='meanscale')
featpreproc.connect(selectnode, 'out', meanscale, 'in_file')
"""
Determine the median value of the functional runs using the mask
"""
medianval = pe.MapNode(interface=fsl.ImageStats(op_string='-k %s -p 50'),
iterfield=['in_file'],
name='medianval')
featpreproc.connect(motion_correct, 'out_file', medianval, 'in_file')
featpreproc.connect(maskflow, ('outputspec.mask_file', pickfirst),
medianval, 'mask_file')
"""
Define a function to get the scaling factor for intensity normalization
"""
featpreproc.connect(medianval, ('out_stat', getmeanscale), meanscale,
'op_string')
"""
Perform temporal highpass filtering on the data
"""
if highpass:
highpass = pe.MapNode(interface=fsl.ImageMaths(suffix='_tempfilt'),
iterfield=['in_file'],
name='highpass')
featpreproc.connect(inputnode, ('highpass', highpass_operand),
highpass, 'op_string')
featpreproc.connect(meanscale, 'out_file', highpass, 'in_file')
featpreproc.connect(highpass, 'out_file', outputnode,
'highpassed_files')
featpreproc.connect(maskflow, ('outputspec.mask_file', pickfirst),
outputnode, 'mask_file')
featpreproc.connect(maskflow, 'outputspec.reg_file', outputnode,
'reg_file')
featpreproc.connect(maskflow, 'outputspec.reg_cost', outputnode,
'reg_cost')
return featpreproc
def CreateCorrectionWorkflow(WFname):
###### UTILITY FUNCTIONS #######
#\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\
# remove the skull from the T2 volume
def ExtractBRAINFromHead(RawScan, BrainLabels):
import os
import SimpleITK as sitk
# Remove skull from the head scan
assert os.path.exists(RawScan), "File not found: %s" % RawScan
assert os.path.exists(BrainLabels), "File not found: %s" % BrainLabels
headImage = sitk.ReadImage(RawScan)
labelsMap = sitk.ReadImage(BrainLabels)
label_mask = labelsMap>0
brainImage = sitk.Cast(headImage,sitk.sitkInt16) * sitk.Cast(label_mask,sitk.sitkInt16)
outputVolume = os.path.realpath('T2Stripped.nrrd')
sitk.WriteImage(brainImage, outputVolume)
return outputVolume
def MakeResamplerInFileList(inputT2, inputLabelMap):
imagesList = [inputT2, inputLabelMap]
return imagesList
# This function helps to pick desirable output from the output list
def pickFromList(inlist,item):
return inlist[item]
# Create registration mask for ANTs from resampled label map image
def CreateAntsRegistrationMask(brainMask):
import os
import SimpleITK as sitk
assert os.path.exists(brainMask), "File not found: %s" % brainMask
labelsMap = sitk.ReadImage(brainMask)
label_mask = labelsMap>0
# dilate the label mask
dilateFilter = sitk.BinaryDilateImageFilter()
dilateFilter.SetKernelRadius(12)
dilated_mask = dilateFilter.Execute( label_mask )
regMask = dilated_mask
registrationMask = os.path.realpath('registrationMask.nrrd')
sitk.WriteImage(regMask, registrationMask)
return registrationMask
# Save direction cosine for the input volume
def SaveDirectionCosineToMatrix(inputVolume):
import os
import SimpleITK as sitk
assert os.path.exists(inputVolume), "File not found: %s" % inputVolume
t2 = sitk.ReadImage(inputVolume)
directionCosine = t2.GetDirection()
return directionCosine
def MakeForceDCFilesList(inputB0, inputT2, inputLabelMap):
import os
assert os.path.exists(inputB0), "File not found: %s" % inputB0
assert os.path.exists(inputT2), "File not found: %s" % inputT2
assert os.path.exists(inputLabelMap), "File not found: %s" % inputLabelMap
imagesList = [inputB0, inputT2, inputLabelMap]
return imagesList
# Force DC to ID
def ForceDCtoID(inputVolume):
import os
import SimpleITK as sitk
inImage = sitk.ReadImage(inputVolume)
inImage.SetDirection((1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0))
outputVolume = os.path.realpath('IDDC_'+ os.path.basename(inputVolume))
sitk.WriteImage(inImage, outputVolume)
return outputVolume
def RestoreDCFromSavedMatrix(inputVolume, inputDirectionCosine):
import os
import SimpleITK as sitk
inImage = sitk.ReadImage(inputVolume)
inImage.SetDirection(inputDirectionCosine)
outputVolume = os.path.realpath('CorrectedDWI.nrrd')
sitk.WriteImage(inImage, outputVolume)
return outputVolume
def GetRigidTransformInverse(inputTransform):
import os
import SimpleITK as sitk
inputTx = sitk.ReadTransform(inputTransform)
versorRigidTx = sitk.VersorRigid3DTransform()
versorRigidTx.SetFixedParameters(inputTx.GetFixedParameters())
versorRigidTx.SetParameters(inputTx.GetParameters())
invTx = versorRigidTx.GetInverse()
inverseTransform = os.path.realpath('Inverse_'+ os.path.basename(inputTransform))
sitk.WriteTransform(invTx, inverseTransform)
return inverseTransform
#################################
#\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/\/
CorrectionWF = pe.Workflow(name=WFname)
inputsSpec = pe.Node(interface=IdentityInterface(fields=['T2Volume', 'DWIVolume','LabelMapVolume']),
name='inputsSpec')
outputsSpec = pe.Node(interface=IdentityInterface(fields=['CorrectedDWI','CorrectedDWI_in_T2Space','DWIBrainMask']),
name='outputsSpec')
# Step0: remove the skull from the T2 volume
ExtractBRAINFromHeadNode = pe.Node(interface=Function(function = ExtractBRAINFromHead,
input_names=['RawScan','BrainLabels'],
output_names=['outputVolume']),
name="ExtractBRAINFromHead")
CorrectionWF.connect(inputsSpec, 'T2Volume', ExtractBRAINFromHeadNode, 'RawScan')
CorrectionWF.connect(inputsSpec, 'LabelMapVolume', ExtractBRAINFromHeadNode, 'BrainLabels')
# Step1: extract B0 from DWI volume
EXTRACT_B0 = pe.Node(interface=extractNrrdVectorIndex(),name="EXTRACT_B0")
EXTRACT_B0.inputs.vectorIndex = 0
EXTRACT_B0.inputs.outputVolume = 'B0_Image.nrrd'
CorrectionWF.connect(inputsSpec,'DWIVolume',EXTRACT_B0,'inputVolume')
# Step2: Register T2 to B0 space using BRAINSFit
BFit_T2toB0 = pe.Node(interface=BRAINSFit(), name="BFit_T2toB0")
BFit_T2toB0.inputs.costMetric = "MMI"
BFit_T2toB0.inputs.numberOfSamples = 100000
BFit_T2toB0.inputs.numberOfIterations = [1500]
BFit_T2toB0.inputs.numberOfHistogramBins = 50
BFit_T2toB0.inputs.maximumStepLength = 0.2
BFit_T2toB0.inputs.minimumStepLength = [0.00005]
BFit_T2toB0.inputs.useRigid = True
BFit_T2toB0.inputs.useAffine = True
BFit_T2toB0.inputs.maskInferiorCutOffFromCenter = 65
BFit_T2toB0.inputs.maskProcessingMode = "ROIAUTO"
BFit_T2toB0.inputs.ROIAutoDilateSize = 13
BFit_T2toB0.inputs.backgroundFillValue = 0.0
BFit_T2toB0.inputs.initializeTransformMode = 'useCenterOfHeadAlign'
BFit_T2toB0.inputs.strippedOutputTransform = "T2ToB0_RigidTransform.h5"
BFit_T2toB0.inputs.writeOutputTransformInFloat = True
CorrectionWF.connect(EXTRACT_B0, 'outputVolume', BFit_T2toB0, 'fixedVolume')
CorrectionWF.connect(ExtractBRAINFromHeadNode, 'outputVolume', BFit_T2toB0, 'movingVolume')
# Step3: Use T_rigid to "resample" T2 and label map images to B0 image space
MakeResamplerInFilesListNode = pe.Node(Function(function=MakeResamplerInFileList,
input_names=['inputT2','inputLabelMap'],
output_names=['imagesList']),
name="MakeResamplerInFilesListNode")
CorrectionWF.connect([(ExtractBRAINFromHeadNode,MakeResamplerInFilesListNode,[('outputVolume','inputT2')]),
(inputsSpec,MakeResamplerInFilesListNode,[('LabelMapVolume','inputLabelMap')])])
ResampleToB0Space = pe.MapNode(interface=BRAINSResample(), name="ResampleToB0Space",
iterfield=['inputVolume', 'pixelType', 'outputVolume'])
ResampleToB0Space.inputs.interpolationMode = 'Linear'
ResampleToB0Space.inputs.outputVolume = ['T2toB0.nrrd','BRAINMaskToB0.nrrd']
ResampleToB0Space.inputs.pixelType = ['ushort','binary']
CorrectionWF.connect(BFit_T2toB0,'strippedOutputTransform',ResampleToB0Space,'warpTransform')
CorrectionWF.connect(EXTRACT_B0,'outputVolume',ResampleToB0Space,'referenceVolume')
CorrectionWF.connect(MakeResamplerInFilesListNode,'imagesList',ResampleToB0Space,'inputVolume')
# Step4: Create registration mask from resampled label map image
CreateRegistrationMask = pe.Node(interface=Function(function = CreateAntsRegistrationMask,
input_names=['brainMask'],
output_names=['registrationMask']),
name="CreateAntsRegistrationMask")
CorrectionWF.connect(ResampleToB0Space, ('outputVolume', pickFromList, 1),
CreateRegistrationMask, 'brainMask')
# Step5: Save direction cosine for the resampled T2 image
SaveDirectionCosineToMatrixNode = pe.Node(interface=Function(function = SaveDirectionCosineToMatrix,
input_names=['inputVolume'],
output_names=['directionCosine']),
name="SaveDirectionCosineToMatrix")
CorrectionWF.connect(ResampleToB0Space, ('outputVolume', pickFromList, 0),
SaveDirectionCosineToMatrixNode, 'inputVolume')
# Step6: Force DC to ID
MakeForceDCFilesListNode = pe.Node(Function(function=MakeForceDCFilesList,
input_names=['inputB0','inputT2','inputLabelMap'],
output_names=['imagesList']),
name="MakeForceDCFilesListNode")
CorrectionWF.connect([(EXTRACT_B0,MakeForceDCFilesListNode,[('outputVolume','inputB0')]),
(ResampleToB0Space,MakeForceDCFilesListNode,[(('outputVolume', pickFromList, 0),'inputT2')]),
(CreateRegistrationMask,MakeForceDCFilesListNode,[('registrationMask','inputLabelMap')])])
ForceDCtoIDNode = pe.MapNode(interface=Function(function = ForceDCtoID,
input_names=['inputVolume'],
output_names=['outputVolume']),
name="ForceDCtoID",
iterfield=['inputVolume'])
CorrectionWF.connect(MakeForceDCFilesListNode, 'imagesList', ForceDCtoIDNode, 'inputVolume')
# Step7: Run antsRegistration in one direction
antsReg_B0ToTransformedT2 = pe.Node(interface=ants.Registration(), name="antsReg_B0ToTransformedT2")
antsReg_B0ToTransformedT2.inputs.interpolation = 'Linear'
antsReg_B0ToTransformedT2.inputs.dimension = 3
antsReg_B0ToTransformedT2.inputs.transforms = ["SyN"]
antsReg_B0ToTransformedT2.inputs.transform_parameters = [(0.25, 3.0, 0.0)]
antsReg_B0ToTransformedT2.inputs.metric = ['MI']
antsReg_B0ToTransformedT2.inputs.sampling_strategy = [None]
antsReg_B0ToTransformedT2.inputs.sampling_percentage = [1.0]
antsReg_B0ToTransformedT2.inputs.metric_weight = [1.0]
antsReg_B0ToTransformedT2.inputs.radius_or_number_of_bins = [32]
antsReg_B0ToTransformedT2.inputs.number_of_iterations = [[70, 50, 40]]
antsReg_B0ToTransformedT2.inputs.convergence_threshold = [1e-6]
antsReg_B0ToTransformedT2.inputs.convergence_window_size = [10]
antsReg_B0ToTransformedT2.inputs.use_histogram_matching = [True]
antsReg_B0ToTransformedT2.inputs.shrink_factors = [[3, 2, 1]]
antsReg_B0ToTransformedT2.inputs.smoothing_sigmas = [[2, 1, 0]]
antsReg_B0ToTransformedT2.inputs.sigma_units = ["vox"]
antsReg_B0ToTransformedT2.inputs.use_estimate_learning_rate_once = [False]
antsReg_B0ToTransformedT2.inputs.write_composite_transform = True
antsReg_B0ToTransformedT2.inputs.collapse_output_transforms = False
antsReg_B0ToTransformedT2.inputs.initialize_transforms_per_stage = False
antsReg_B0ToTransformedT2.inputs.output_transform_prefix = 'Tsyn'
antsReg_B0ToTransformedT2.inputs.winsorize_lower_quantile = 0.01
antsReg_B0ToTransformedT2.inputs.winsorize_upper_quantile = 0.99
antsReg_B0ToTransformedT2.inputs.float = True
antsReg_B0ToTransformedT2.inputs.num_threads = -1
antsReg_B0ToTransformedT2.inputs.args = '--restrict-deformation 0x1x0'
CorrectionWF.connect(ForceDCtoIDNode, ('outputVolume', pickFromList, 1), antsReg_B0ToTransformedT2, 'fixed_image')
CorrectionWF.connect(ForceDCtoIDNode, ('outputVolume', pickFromList, 2), antsReg_B0ToTransformedT2, 'fixed_image_mask')
CorrectionWF.connect(ForceDCtoIDNode, ('outputVolume', pickFromList, 0), antsReg_B0ToTransformedT2, 'moving_image')
# Step8: Now, all necessary transforms are acquired. It's a time to
# transform input DWI image into T2 image space
# {DWI} --> ForceDCtoID --> gtractResampleDWIInPlace(using SyN transfrom)
# --> Restore DirectionCosine From Saved Matrix --> gtractResampleDWIInPlace(inverse of T_rigid from BFit)
# --> {CorrectedDW_in_T2Space}
DWI_ForceDCtoIDNode = pe.Node(interface=Function(function = ForceDCtoID,
input_names=['inputVolume'],
output_names=['outputVolume']),
name='DWI_ForceDCtoIDNode')
CorrectionWF.connect(inputsSpec,'DWIVolume',DWI_ForceDCtoIDNode,'inputVolume')
gtractResampleDWI_SyN = pe.Node(interface=gtractResampleDWIInPlace(),
name="gtractResampleDWI_SyN")
CorrectionWF.connect(DWI_ForceDCtoIDNode,'outputVolume',
gtractResampleDWI_SyN,'inputVolume')
CorrectionWF.connect(antsReg_B0ToTransformedT2,'composite_transform',
gtractResampleDWI_SyN,'warpDWITransform')
CorrectionWF.connect(ForceDCtoIDNode,('outputVolume', pickFromList, 1),
gtractResampleDWI_SyN,'referenceVolume') # fixed image of antsRegistration
gtractResampleDWI_SyN.inputs.outputVolume = 'IDDC_correctedDWI.nrrd'
RestoreDCFromSavedMatrixNode = pe.Node(interface=Function(function = RestoreDCFromSavedMatrix,
input_names=['inputVolume','inputDirectionCosine'],
output_names=['outputVolume']),
name='RestoreDCFromSavedMatrix')
CorrectionWF.connect(gtractResampleDWI_SyN,'outputVolume',RestoreDCFromSavedMatrixNode,'inputVolume')
CorrectionWF.connect(SaveDirectionCosineToMatrixNode,'directionCosine',RestoreDCFromSavedMatrixNode,'inputDirectionCosine')
CorrectionWF.connect(RestoreDCFromSavedMatrixNode,'outputVolume', outputsSpec, 'CorrectedDWI')
GetRigidTransformInverseNode = pe.Node(interface=Function(function = GetRigidTransformInverse,
input_names=['inputTransform'],
output_names=['inverseTransform']),
name='GetRigidTransformInverse')
CorrectionWF.connect(BFit_T2toB0,'strippedOutputTransform',GetRigidTransformInverseNode,'inputTransform')
gtractResampleDWIInPlace_Trigid = pe.Node(interface=gtractResampleDWIInPlace(),
name="gtractResampleDWIInPlace_Trigid")
CorrectionWF.connect(RestoreDCFromSavedMatrixNode,'outputVolume',
gtractResampleDWIInPlace_Trigid,'inputVolume')
CorrectionWF.connect(GetRigidTransformInverseNode,'inverseTransform',
gtractResampleDWIInPlace_Trigid,'inputTransform') #Inverse of rigid transform from BFit
gtractResampleDWIInPlace_Trigid.inputs.outputVolume = 'CorrectedDWI_in_T2Space_estimate.nrrd'
gtractResampleDWIInPlace_Trigid.inputs.outputResampledB0 = 'CorrectedDWI_in_T2Space_estimate_B0.nrrd'
# Setp9: An extra registration step to tune the alignment between the CorrecetedDWI_in_T2Space image and T2 image.
BFit_TuneRegistration = pe.Node(interface=BRAINSFit(), name="BFit_TuneRegistration")
BFit_TuneRegistration.inputs.costMetric = "MMI"
BFit_TuneRegistration.inputs.numberOfSamples = 100000
BFit_TuneRegistration.inputs.numberOfIterations = [1500]
BFit_TuneRegistration.inputs.numberOfHistogramBins = 50
BFit_TuneRegistration.inputs.maximumStepLength = 0.2
BFit_TuneRegistration.inputs.minimumStepLength = [0.00005]
BFit_TuneRegistration.inputs.useRigid = True
BFit_TuneRegistration.inputs.useAffine = True
BFit_TuneRegistration.inputs.maskInferiorCutOffFromCenter = 65
BFit_TuneRegistration.inputs.maskProcessingMode = "ROIAUTO"
BFit_TuneRegistration.inputs.ROIAutoDilateSize = 13
BFit_TuneRegistration.inputs.backgroundFillValue = 0.0
BFit_TuneRegistration.inputs.initializeTransformMode = 'useCenterOfHeadAlign'
BFit_TuneRegistration.inputs.strippedOutputTransform = "CorrectedB0inT2Space_to_T2_RigidTransform.h5"
BFit_TuneRegistration.inputs.writeOutputTransformInFloat = True
CorrectionWF.connect(ExtractBRAINFromHeadNode, 'outputVolume', BFit_TuneRegistration, 'fixedVolume') #T2 brain volume
CorrectionWF.connect(gtractResampleDWIInPlace_Trigid, 'outputResampledB0', BFit_TuneRegistration, 'movingVolume') # CorrectedB0_in_T2Space
gtractResampleDWIInPlace_TuneRigidTx = pe.Node(interface=gtractResampleDWIInPlace(),
name="gtractResampleDWIInPlace_TuneRigidTx")
CorrectionWF.connect(gtractResampleDWIInPlace_Trigid,'outputVolume',gtractResampleDWIInPlace_TuneRigidTx,'inputVolume')
CorrectionWF.connect(BFit_TuneRegistration,'strippedOutputTransform',gtractResampleDWIInPlace_TuneRigidTx,'inputTransform')
gtractResampleDWIInPlace_TuneRigidTx.inputs.outputVolume = 'CorrectedDWI_in_T2Space.nrrd'
gtractResampleDWIInPlace_TuneRigidTx.inputs.outputResampledB0 = 'CorrectedDWI_in_T2Space_B0.nrrd'
# Finally we pass the outputs of the gtractResampleDWIInPlace_TuneRigidTx to the outputsSpec
CorrectionWF.connect(gtractResampleDWIInPlace_TuneRigidTx, 'outputVolume', outputsSpec, 'CorrectedDWI_in_T2Space')
# Step10: Create brain mask from the input labelmap
DWIBRAINMASK = pe.Node(interface=BRAINSResample(), name='DWIBRAINMASK')
DWIBRAINMASK.inputs.interpolationMode = 'Linear'
DWIBRAINMASK.inputs.outputVolume = 'BrainMaskForDWI.nrrd'
DWIBRAINMASK.inputs.pixelType = 'binary'
CorrectionWF.connect(gtractResampleDWIInPlace_TuneRigidTx,'outputResampledB0',DWIBRAINMASK,'referenceVolume')
CorrectionWF.connect(inputsSpec,'LabelMapVolume',DWIBRAINMASK,'inputVolume')
CorrectionWF.connect(DWIBRAINMASK, 'outputVolume', outputsSpec, 'DWIBrainMask')
return CorrectionWF
def b0_flirt_pipeline(num_b0s, name='b0_coregistration'):
"""
Rigid registration of the B0 dataset onto the first volume. Rigid
registration is achieved using FLIRT and the normalized
correlation.
Args:
num_b0s (int): Number of the B0 volumes in the dataset.
name (str): Name of the workflow.
Inputnode:
in_file(str): B0 dataset.
Outputnode
out_b0_reg(str): The set of B0 volumes registered to the first volume.
Returns:
The workflow
"""
import nipype.pipeline.engine as pe
from nipype.interfaces import fsl
import nipype.interfaces.utility as niu
from clinica.utils.dwi import merge_volumes_tdim
inputnode = pe.Node(niu.IdentityInterface(fields=['in_file']),
name='inputnode')
fslroi_ref = pe.Node(fsl.ExtractROI(args='0 1'), name='b0_reference')
tsize = num_b0s - 1
fslroi_moving = pe.Node(fsl.ExtractROI(args='1 '+str(tsize)),
name='b0_moving')
split_moving = pe.Node(fsl.Split(dimension='t'), name='split_b0_moving')
bet_ref = pe.Node(fsl.BET(frac=0.3, mask=True, robust=True),
name='bet_ref')
dilate = pe.Node(
fsl.maths.MathsCommand(
nan2zeros=True,
args='-kernel sphere 5 -dilM'),
name='mask_dilate')
flirt = pe.MapNode(fsl.FLIRT(
interp='spline', dof=6, bins=50, save_log=True,
cost='corratio', cost_func='corratio', padding_size=10,
searchr_x=[-4, 4], searchr_y=[-4, 4], searchr_z=[-4, 4],
fine_search=1, coarse_search=10),
name='b0_co_registration', iterfield=['in_file'])
merge = pe.Node(fsl.Merge(dimension='t'), name='merge_registered_b0s')
thres = pe.MapNode(fsl.Threshold(thresh=0.0), iterfield=['in_file'],
name='remove_negative')
insert_ref = pe.Node(niu.Function(input_names=['in_file1', 'in_file2'],
output_names=['out_file'],
function=merge_volumes_tdim),
name='concat_ref_moving')
outputnode = pe.Node(niu.IdentityInterface(
fields=['out_file', 'out_xfms']),
name='outputnode')
wf = pe.Workflow(name=name)
wf.connect([
(inputnode, fslroi_ref, [('in_file', 'in_file')]),
(inputnode, fslroi_moving, [('in_file', 'in_file')]),
(fslroi_moving, split_moving, [('roi_file', 'in_file')]),
(fslroi_ref, bet_ref, [('roi_file', 'in_file')]),
(bet_ref, dilate, [('mask_file', 'in_file')]),
(dilate, flirt, [('out_file', 'ref_weight'),
('out_file', 'in_weight')]),
(fslroi_ref, flirt, [('roi_file', 'reference')]),
(split_moving, flirt, [('out_files', 'in_file')]),
(flirt, thres, [('out_file', 'in_file')]),
(thres, merge, [('out_file', 'in_files')]),
(merge, insert_ref, [('merged_file', 'in_file2')]),
(fslroi_ref, insert_ref, [('roi_file', 'in_file1')]),
(insert_ref, outputnode, [('out_file', 'out_file')]),
(flirt, outputnode, [('out_matrix_file', 'out_xfms')])
])
return wf
def create_tbss_3_postreg(name='tbss_3_postreg', estimate_skeleton=True):
"""Post-registration processing: derive mean_FA and mean_FA_skeleton from
mean of all subjects in study. Target is assumed to be FMRIB58_FA_1mm.
A pipeline that does the same as 'tbss_3_postreg -S' script from FSL
Setting 'estimate_skeleton to False will use precomputed FMRIB58_FA-skeleton_1mm
skeleton (same as 'tbss_3_postreg -T').
Example
-------
>>> from nipype.workflows.dmri.fsl import tbss
>>> tbss3 = tbss.create_tbss_3_postreg()
>>> tbss3.inputs.inputnode.fa_list = ['s1_wrapped_FA.nii', 's2_wrapped_FA.nii', 's3_wrapped_FA.nii']
Inputs::
inputnode.field_list
inputnode.fa_list
Outputs::
outputnode.groupmask
outputnode.skeleton_file
outputnode.meanfa_file
outputnode.mergefa_file
"""
# Create the inputnode
inputnode = pe.Node(interface=util.IdentityInterface(fields=['field_list',
'fa_list']),
name='inputnode')
# Apply the warpfield to the masked FA image
applywarp = pe.MapNode(interface=fsl.ApplyWarp(),
iterfield=['in_file', 'field_file'],
name="applywarp")
if fsl.no_fsl():
warn('NO FSL found')
else:
applywarp.inputs.ref_file = fsl.Info.standard_image("FMRIB58_FA_1mm.nii.gz")
# Merge the FA files into a 4D file
mergefa = pe.Node(fsl.Merge(dimension="t"),
name="mergefa")
# Get a group mask
groupmask = pe.Node(fsl.ImageMaths(op_string="-max 0 -Tmin -bin",
out_data_type="char",
suffix="_mask"),
name="groupmask")
maskgroup = pe.Node(fsl.ImageMaths(op_string="-mas",
suffix="_masked"),
name="maskgroup")
tbss3 = pe.Workflow(name=name)
tbss3.connect([
(inputnode, applywarp, [("fa_list", "in_file"),
("field_list", "field_file")]),
(applywarp, mergefa, [("out_file", "in_files")]),
(mergefa, groupmask, [("merged_file", "in_file")]),
(mergefa, maskgroup, [("merged_file", "in_file")]),
(groupmask, maskgroup, [("out_file", "in_file2")]),
])
# Create outputnode
outputnode = pe.Node(interface=util.IdentityInterface(fields=['groupmask',
'skeleton_file',
'meanfa_file',
'mergefa_file']),
name='outputnode')
if estimate_skeleton:
# Take the mean over the fourth dimension
meanfa = pe.Node(fsl.ImageMaths(op_string="-Tmean",
suffix="_mean"),
name="meanfa")
# Use the mean FA volume to generate a tract skeleton
makeskeleton = pe.Node(fsl.TractSkeleton(skeleton_file=True),
name="makeskeleton")
tbss3.connect([
(maskgroup, meanfa, [("out_file", "in_file")]),
(meanfa, makeskeleton, [("out_file", "in_file")]),
(groupmask, outputnode, [('out_file', 'groupmask')]),
(makeskeleton, outputnode, [('skeleton_file', 'skeleton_file')]),
(meanfa, outputnode, [('out_file', 'meanfa_file')]),
(maskgroup, outputnode, [('out_file', 'mergefa_file')])
])
else:
#$FSLDIR/bin/fslmaths $FSLDIR/data/standard/FMRIB58_FA_1mm -mas mean_FA_mask mean_FA
maskstd = pe.Node(fsl.ImageMaths(op_string="-mas",
suffix="_masked"),
name="maskstd")
maskstd.inputs.in_file = fsl.Info.standard_image("FMRIB58_FA_1mm.nii.gz")
#$FSLDIR/bin/fslmaths mean_FA -bin mean_FA_mask
binmaskstd = pe.Node(fsl.ImageMaths(op_string="-bin"),
name="binmaskstd")
#$FSLDIR/bin/fslmaths all_FA -mas mean_FA_mask all_FA
maskgroup2 = pe.Node(fsl.ImageMaths(op_string="-mas",
suffix="_masked"),
name="maskgroup2")
tbss3.connect([
(groupmask, maskstd, [("out_file", "in_file2")]),
(maskstd, binmaskstd, [("out_file", "in_file")]),
(maskgroup, maskgroup2, [("out_file", "in_file")]),
(binmaskstd, maskgroup2, [("out_file", "in_file2")])
])
outputnode.inputs.skeleton_file = fsl.Info.standard_image("FMRIB58_FA-skeleton_1mm.nii.gz")
tbss3.connect([
(binmaskstd, outputnode, [('out_file', 'groupmask')]),
(maskstd, outputnode, [('out_file', 'meanfa_file')]),
(maskgroup2, outputnode, [('out_file', 'mergefa_file')])
])
return tbss3
def dwi_flirt(name='DWICoregistration', excl_nodiff=False, flirt_param={}):
"""
Generates a workflow for linear registration of dwi volumes using flirt.
Inputnode
---------
reference : FILE
Mandatory input. Reference data set.
in_file : FILE
Mandatory input. Moving data set.
ref_mask : FILE
Mandatory input. Binary mask of the reference volume.
in_xfms : FILE
Mandatory input. Intialisation matrices for flirt.
in_bval : FILE
Mandatory input. B values file.
"""
import nipype.interfaces.ants as ants
import nipype.interfaces.fsl as fsl
import nipype.interfaces.utility as niu
import nipype.pipeline.engine as pe
from nipype.workflows.dmri.fsl.utils import _checkinitxfm
from nipype.workflows.dmri.fsl.utils import enhance
inputnode = pe.Node(
niu.IdentityInterface(
fields=['reference',
'in_file',
'ref_mask',
'in_xfms',
'in_bval']),
name='inputnode')
initmat = pe.Node(
niu.Function(
input_names=['in_bval',
'in_xfms',
'excl_nodiff'],
output_names=['init_xfms'],
function=_checkinitxfm),
name='InitXforms')
initmat.inputs.excl_nodiff = excl_nodiff
dilate = pe.Node(
fsl.maths.MathsCommand(
nan2zeros=True,
args='-kernel sphere 5 -dilM'),
name='MskDilate')
split = pe.Node(fsl.Split(dimension='t'), name='SplitDWIs')
n4 = pe.Node(ants.N4BiasFieldCorrection(dimension=3), name='Bias')
flirt = pe.MapNode(fsl.FLIRT(**flirt_param), name='CoRegistration',
iterfield=['in_file', 'in_matrix_file'])
thres = pe.MapNode(fsl.Threshold(thresh=0.0), iterfield=['in_file'],
name='RemoveNegative')
merge = pe.Node(fsl.Merge(dimension='t'), name='MergeDWIs')
outputnode = pe.Node(
niu.IdentityInterface(
fields=['out_file',
'out_xfms',
'out_ref']),
name='outputnode')
enhb0 = pe.Node(niu.Function(
input_names=['in_file', 'in_mask', 'clip_limit'],
output_names=['out_file'], function=enhance), name='B0Equalize')
enhb0.inputs.clip_limit = 0.015
enhdw = pe.MapNode(niu.Function(
input_names=['in_file', 'in_mask'], output_names=['out_file'],
function=enhance), name='DWEqualize', iterfield=['in_file'])
# enhb0.inputs.clip_limit = clip_limit
wf = pe.Workflow(name=name)
wf.connect([
(inputnode, split, [('in_file', 'in_file')]),
(inputnode, dilate, [('ref_mask', 'in_file')]),
(inputnode, n4, [('reference', 'input_image'),
('ref_mask', 'mask_image')]),
# (inputnode, flirt, [('ref_mask', 'reference')]),
(n4, enhb0, [('output_image', 'in_file')]),
(enhb0, flirt, [('out_file', 'reference')]),
(inputnode, initmat, [('in_xfms', 'in_xfms'),
('in_bval', 'in_bval')]),
(split, enhdw, [('out_files', 'in_file')]),
(dilate, enhdw, [('out_file', 'in_mask')]),
(dilate, flirt, [('out_file', 'ref_weight'),
('out_file', 'in_weight')]),
(enhdw, flirt, [('out_file', 'in_file')]),
(initmat, flirt, [('init_xfms', 'in_matrix_file')]),
(flirt, thres, [('out_file', 'in_file')]),
(thres, merge, [('out_file', 'in_files')]),
(merge, outputnode, [('merged_file', 'out_file')]),
(enhb0, outputnode, [('out_file', 'out_ref')]),
(flirt, outputnode, [('out_matrix_file', 'out_xfms')])
])
return wf
def create_tbss_non_FA(name='tbss_non_FA'):
"""
A pipeline that implement tbss_non_FA in FSL
Example
-------
>>> from nipype.workflows.dmri.fsl import tbss
>>> tbss_MD = tbss.create_tbss_non_FA()
>>> tbss_MD.inputs.inputnode.file_list = []
>>> tbss_MD.inputs.inputnode.field_list = []
>>> tbss_MD.inputs.inputnode.skeleton_thresh = 0.2
>>> tbss_MD.inputs.inputnode.groupmask = './xxx'
>>> tbss_MD.inputs.inputnode.meanfa_file = './xxx'
>>> tbss_MD.inputs.inputnode.distance_map = []
>>> tbss_MD.inputs.inputnode.all_FA_file = './xxx'
Inputs::
inputnode.file_list
inputnode.field_list
inputnode.skeleton_thresh
inputnode.groupmask
inputnode.meanfa_file
inputnode.distance_map
inputnode.all_FA_file
Outputs::
outputnode.projected_nonFA_file
"""
# Define the inputnode
inputnode = pe.Node(interface=util.IdentityInterface(fields=['file_list',
'field_list',
'skeleton_thresh',
'groupmask',
'meanfa_file',
'distance_map',
'all_FA_file']),
name='inputnode')
# Apply the warpfield to the non FA image
applywarp = pe.MapNode(interface=fsl.ApplyWarp(),
iterfield=['in_file', 'field_file'],
name="applywarp")
if fsl.no_fsl():
warn('NO FSL found')
else:
applywarp.inputs.ref_file = fsl.Info.standard_image("FMRIB58_FA_1mm.nii.gz")
# Merge the non FA files into a 4D file
merge = pe.Node(fsl.Merge(dimension="t"), name="merge")
#merged_file="all_FA.nii.gz"
maskgroup = pe.Node(fsl.ImageMaths(op_string="-mas",
suffix="_masked"),
name="maskgroup")
projectfa = pe.Node(fsl.TractSkeleton(project_data=True,
#projected_data = 'test.nii.gz',
use_cingulum_mask=True
),
name="projectfa")
tbss_non_FA = pe.Workflow(name=name)
tbss_non_FA.connect([
(inputnode, applywarp, [('file_list', 'in_file'),
('field_list', 'field_file'),
]),
(applywarp, merge, [("out_file", "in_files")]),
(merge, maskgroup, [("merged_file", "in_file")]),
(inputnode, maskgroup, [('groupmask', 'in_file2')]),
(maskgroup, projectfa, [('out_file', 'alt_data_file')]),
(inputnode, projectfa, [('skeleton_thresh', 'threshold'),
("meanfa_file", "in_file"),
("distance_map", "distance_map"),
("all_FA_file", 'data_file')
]),
])
# Define the outputnode
outputnode = pe.Node(interface=util.IdentityInterface(
fields=['projected_nonFA_file']),
name='outputnode')
tbss_non_FA.connect([
(projectfa, outputnode, [('projected_data', 'projected_nonFA_file'),
]),
])
return tbss_non_FA
def ecc_pipeline(name='eddy_correct'):
"""
ECC stands for Eddy currents correction.
Creates a pipelines that corrects for artifacts induced by Eddy currents in
dMRI sequences.
It takes a series of diffusion weighted images and linearly co-registers
them to one reference image (the average of all b0s in the dataset).
DWIs are also modulated by the determinant of the Jacobian as indicated by
[Jones10]_ and [Rohde04]_.
A list of rigid transformation matrices can be provided, sourcing from a
:func:`.hmc_pipeline` workflow, to initialize registrations in a *motion
free* framework.
A list of affine transformation matrices is available as output, so that
transforms can be chained (discussion
`here <https://github.com/nipy/nipype/pull/530#issuecomment-14505042>`_).
.. admonition:: References
.. [Jones10] Jones DK, `The signal intensity must be modulated by the
determinant of the Jacobian when correcting for eddy currents in
diffusion MRI
<http://cds.ismrm.org/protected/10MProceedings/files/1644_129.pdf>`_,
Proc. ISMRM 18th Annual Meeting, (2010).
.. [Rohde04] Rohde et al., `Comprehensive Approach for Correction of
Motion and Distortion in Diffusion-Weighted MRI
<http://stbb.nichd.nih.gov/pdf/com_app_cor_mri04.pdf>`_, MRM
51:103-114 (2004).
Example
-------
from nipype.workflows.dmri.fsl.artifacts import ecc_pipeline
ecc = ecc_pipeline()
ecc.inputs.inputnode.in_file = 'diffusion.nii'
ecc.inputs.inputnode.in_bval = 'diffusion.bval'
ecc.inputs.inputnode.in_mask = 'mask.nii'
ecc.run() # doctest: +SKIP
Inputs::
inputnode.in_file - input dwi file
inputnode.in_mask - weights mask of reference image (a file with data \
range sin [0.0, 1.0], indicating the weight of each voxel when computing the \
metric.
inputnode.in_bval - b-values table
inputnode.in_xfms - list of matrices to initialize registration (from \
head-motion correction)
Outputs::
outputnode.out_file - corrected dwi file
outputnode.out_xfms - list of transformation matrices
"""
import nipype.interfaces.utility as niu
import nipype.pipeline.engine as pe
import nipype.interfaces.fsl as fsl
from nipype.workflows.data import get_flirt_schedule
from nipype.workflows.dmri.fsl.utils import extract_bval
from nipype.workflows.dmri.fsl.utils import recompose_xfm
from nipype.workflows.dmri.fsl.utils import recompose_dwi
from nipype.workflows.dmri.fsl.artifacts import _xfm_jacobian
from clinica.workflows.dwi_preprocessing import dwi_flirt
from clinica.utils.dwi import merge_volumes_tdim
params = dict(dof=12, no_search=True, interp='spline', bgvalue=0,
schedule=get_flirt_schedule('ecc'))
inputnode = pe.Node(niu.IdentityInterface(
fields=['in_file', 'in_bval', 'in_mask', 'in_xfms']), name='inputnode')
getb0 = pe.Node(fsl.ExtractROI(t_min=0, t_size=1), name='get_b0')
pick_dws = pe.Node(niu.Function(
input_names=['in_dwi', 'in_bval', 'b'], output_names=['out_file'],
function=extract_bval), name='extract_dwi')
pick_dws.inputs.b = 'diff'
flirt = dwi_flirt(flirt_param=params, excl_nodiff=True)
mult = pe.MapNode(fsl.BinaryMaths(operation='mul'), name='ModulateDWIs',
iterfield=['in_file', 'operand_value'])
thres = pe.MapNode(fsl.Threshold(thresh=0.0), iterfield=['in_file'],
name='RemoveNegative')
split = pe.Node(fsl.Split(dimension='t'), name='SplitDWIs')
get_mat = pe.Node(niu.Function(
input_names=['in_bval', 'in_xfms'], output_names=['out_files'],
function=recompose_xfm), name='GatherMatrices')
merge = pe.Node(niu.Function(
input_names=['in_dwi', 'in_bval', 'in_corrected'],
output_names=['out_file'], function=recompose_dwi), name='MergeDWIs')
merged_volumes = pe.Node(niu.Function(
input_names=['in_file1', 'in_file2'],
output_names=['out_file'],
function=merge_volumes_tdim), name='merge_enhanced_ref_dwis')
outputnode = pe.Node(niu.IdentityInterface(
fields=['out_file', 'out_xfms']), name='outputnode')
wf = pe.Workflow(name=name)
wf.connect([
(inputnode, getb0, [('in_file', 'in_file')]),
(inputnode, pick_dws, [('in_file', 'in_dwi'),
('in_bval', 'in_bval')]),
(flirt, merged_volumes, [('outputnode.out_ref', 'in_file1'),
('outputnode.out_file', 'in_file2')]),
(merged_volumes, merge, [('out_file', 'in_dwi')]),
(inputnode, merge, [('in_bval', 'in_bval')]),
(inputnode, flirt, [('in_mask', 'inputnode.ref_mask'),
('in_xfms', 'inputnode.in_xfms'),
('in_bval', 'inputnode.in_bval')]),
(inputnode, get_mat, [('in_bval', 'in_bval')]),
(getb0, flirt, [('roi_file', 'inputnode.reference')]),
(pick_dws, flirt, [('out_file', 'inputnode.in_file')]),
(flirt, get_mat, [('outputnode.out_xfms', 'in_xfms')]),
(flirt, mult, [(('outputnode.out_xfms', _xfm_jacobian),
'operand_value')]),
(flirt, split, [('outputnode.out_file', 'in_file')]),
(split, mult, [('out_files', 'in_file')]),
(mult, thres, [('out_file', 'in_file')]),
(thres, merge, [('out_file', 'in_corrected')]),
(get_mat, outputnode, [('out_files', 'out_xfms')]),
(merge, outputnode, [('out_file', 'out_file')])
])
return wf
def init_func_derivatives_wf(
bids_root,
cifti_output,
freesurfer,
metadata,
output_dir,
spaces,
name='func_derivatives_wf',
):
"""
Set up a battery of datasinks to store derivatives in the right location.
Parameters
----------
bids_root : :obj:`str`
Original BIDS dataset path.
cifti_output : :obj:`bool`
Whether the ``--cifti-output`` flag was set.
freesurfer : :obj:`bool`
Whether FreeSurfer anatomical processing was run.
metadata : :obj:`dict`
Metadata dictionary associated to the BOLD run.
output_dir : :obj:`str`
Where derivatives should be written out to.
spaces : :py:class:`~niworkflows.utils.spaces.SpatialReferences`
A container for storing, organizing, and parsing spatial normalizations. Composed of
:py:class:`~niworkflows.utils.spaces.Reference` objects representing spatial references.
Each ``Reference`` contains a space, which is a string of either TemplateFlow template IDs
(e.g., ``MNI152Lin``, ``MNI152NLin6Asym``, ``MNIPediatricAsym``), nonstandard references
(e.g., ``T1w`` or ``anat``, ``sbref``, ``run``, etc.), or a custom template located in
the TemplateFlow root directory. Each ``Reference`` may also contain a spec, which is a
dictionary with template specifications (e.g., a specification of ``{'resolution': 2}``
would lead to resampling on a 2mm resolution of the space).
use_aroma : :obj:`bool`
Whether ``--use-aroma`` flag was set.
name : :obj:`str`
This workflow's identifier (default: ``func_derivatives_wf``).
"""
from ...niworkflows.engine.workflows import LiterateWorkflow as Workflow
from ...niworkflows.interfaces.utility import KeySelect
from ...smriprep.workflows.outputs import _bids_relative
nonstd_spaces = set(spaces.get_nonstandard())
workflow = Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'bold_aparc_std', 'bold_aparc_t1', 'bold_aseg_std', 'bold_aseg_t1',
'bold_cifti', 'bold_mask_std', 'bold_mask_t1', 'bold_std',
'bold_std_ref', 'bold_t1', 'bold_t1_ref', 'bold_native',
'bold_native_ref', 'bold_mask_native', 'cifti_variant',
'cifti_metadata', 'cifti_density', 'confounds', 'confounds_metadata',
'source_file', 'surf_files', 'surf_refs', 'template',
'spatial_reference', 'cbf', 'meancbf', 'score', 'avgscore', 'scrub',
'basil', 'pv', 'cbf_t1', 'meancbf_t1', 'att_t1', 'score_t1',
'avgscore_t1', 'scrub_t1', 'basil_t1', 'pv_t1', 'cbf_std',
'meancbf_std', 'score_std', 'avgscore_std', 'scrub_std', 'basil_std',
'pv_std', 'qc_file', 'cbf_hvoxf', 'score_hvoxf', 'scrub_hvoxf',
'basil_hvoxf', 'pvc_hvoxf', 'cbf_sc207', 'score_sc207', 'scrub_sc207',
'basil_sc207', 'pvc_sc207', 'cbf_sc217', 'score_sc217', 'scrub_sc217',
'basil_sc217', 'pvc_sc217', 'cbf_sc407', 'score_sc407', 'scrub_sc407',
'basil_sc407', 'pvc_sc407', 'cbf_sc417', 'score_sc417', 'scrub_sc417',
'basil_sc417', 'pvc_sc417'
]),
name='inputnode')
raw_sources = pe.Node(niu.Function(function=_bids_relative),
name='raw_sources')
raw_sources.inputs.bids_root = bids_root
ds_confounds = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='confounds',
suffix='regressors'),
name="ds_confounds",
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, raw_sources, [('source_file', 'in_files')]),
(inputnode, ds_confounds, [('source_file', 'source_file'),
('confounds', 'in_file'),
('confounds_metadata', 'meta_dict')]),
])
qcfile = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='quality_control',
suffix='cbf',
compress=False),
name='qcfile',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, qcfile, [('source_file', 'source_file'),
('qc_file', 'in_file')]),
])
cbf_hvoxf = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='HavardOxford',
suffix='mean_cbf',
compress=False),
name='cbf_hvoxf',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
score_hvoxf = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='HavardOxford',
suffix='mean_score',
compress=False),
name='score_hvoxf',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
scrub_hvoxf = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='HavardOxford',
suffix='mean_scrub',
compress=False),
name='scrub_hvoxf',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
basil_hvoxf = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='HavardOxford',
suffix='mean_basil',
compress=False),
name='basil_hvoxf',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
pvc_hvoxf = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='HavardOxford',
suffix='mean_pvc',
compress=False),
name='pvc_hvoxf',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, cbf_hvoxf, [('source_file', 'source_file'),
('cbf_hvoxf', 'in_file')]),
(inputnode, score_hvoxf, [('source_file', 'source_file'),
('score_hvoxf', 'in_file')]),
(inputnode, scrub_hvoxf, [('source_file', 'source_file'),
('scrub_hvoxf', 'in_file')]),
(inputnode, basil_hvoxf, [('source_file', 'source_file'),
('basil_hvoxf', 'in_file')]),
(inputnode, pvc_hvoxf, [('source_file', 'source_file'),
('pvc_hvoxf', 'in_file')]),
])
cbf_sc207 = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='schaefer200x7',
suffix='mean_cbf',
compress=False),
name='cbf_sc207',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
score_sc207 = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='schaefer200x7',
suffix='mean_score',
compress=False),
name='score_sc207',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
scrub_sc207 = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='schaefer200x7',
suffix='mean_scrub',
compress=False),
name='scrub_sc207',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
basil_sc207 = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='schaefer200x7',
suffix='mean_basil',
compress=False),
name='basil_sc207',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
pvc_sc207 = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='schaefer200x7',
suffix='mean_pvc',
compress=False),
name='pvc_sc207',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, cbf_sc207, [('source_file', 'source_file'),
('cbf_sc207', 'in_file')]),
(inputnode, score_sc207, [('source_file', 'source_file'),
('score_sc207', 'in_file')]),
(inputnode, scrub_sc207, [('source_file', 'source_file'),
('scrub_sc207', 'in_file')]),
(inputnode, basil_sc207, [('source_file', 'source_file'),
('basil_sc207', 'in_file')]),
(inputnode, pvc_sc207, [('source_file', 'source_file'),
('pvc_sc207', 'in_file')]),
])
cbf_sc217 = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='schaefer200x17',
suffix='mean_cbf',
compress=False),
name='cbf_sc217',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
score_sc217 = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='schaefer200x17',
suffix='mean_score',
compress=False),
name='score_sc217',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
scrub_sc217 = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='schaefer200x17',
suffix='mean_scrub',
compress=False),
name='scrub_sc217',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
basil_sc217 = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='schaefer200x17',
suffix='mean_basil',
compress=False),
name='basil_sc217',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
pvc_sc217 = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='schaefer200x17',
suffix='mean_pvc',
compress=False),
name='pvc_sc217',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, cbf_sc217, [('source_file', 'source_file'),
('cbf_sc217', 'in_file')]),
(inputnode, score_sc217, [('source_file', 'source_file'),
('score_sc217', 'in_file')]),
(inputnode, scrub_sc217, [('source_file', 'source_file'),
('scrub_sc217', 'in_file')]),
(inputnode, basil_sc217, [('source_file', 'source_file'),
('basil_sc217', 'in_file')]),
(inputnode, pvc_sc217, [('source_file', 'source_file'),
('pvc_sc217', 'in_file')]),
])
cbf_sc407 = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='schaefer400x7',
suffix='mean_cbf',
compress=False),
name='cbf_sc407',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
score_sc407 = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='schaefer400x7',
suffix='mean_score',
compress=False),
name='score_sc407',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
scrub_sc407 = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='schaefer400x7',
suffix='mean_scrub',
compress=False),
name='scrub_sc407',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
basil_sc407 = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='schaefer400x7',
suffix='mean_basil',
compress=False),
name='basil_sc407',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
pvc_sc407 = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='schaefer400x7',
suffix='mean_pvc',
compress=False),
name='pvc_sc407',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, cbf_sc407, [('source_file', 'source_file'),
('cbf_sc407', 'in_file')]),
(inputnode, score_sc407, [('source_file', 'source_file'),
('score_sc407', 'in_file')]),
(inputnode, scrub_sc407, [('source_file', 'source_file'),
('scrub_sc407', 'in_file')]),
(inputnode, basil_sc407, [('source_file', 'source_file'),
('basil_sc407', 'in_file')]),
(inputnode, pvc_sc407, [('source_file', 'source_file'),
('pvc_sc407', 'in_file')]),
])
cbf_sc417 = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='schaefer400x17',
suffix='mean_cbf',
compress=False),
name='cbf_sc417',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
score_sc417 = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='schaefer400x17',
suffix='mean_score',
compress=False),
name='score_sc417',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
scrub_sc417 = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='schaefer400x17',
suffix='mean_scrub',
compress=False),
name='scrub_sc417',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
basil_sc417 = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='schaefer400x17',
suffix='mean_basil',
compress=False),
name='basil_sc417',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
pvc_sc417 = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='schaefer400x17',
suffix='mean_pvc',
compress=False),
name='pvc_sc417',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, cbf_sc417, [('source_file', 'source_file'),
('cbf_sc417', 'in_file')]),
(inputnode, score_sc417, [('source_file', 'source_file'),
('score_sc417', 'in_file')]),
(inputnode, scrub_sc417, [('source_file', 'source_file'),
('scrub_sc417', 'in_file')]),
(inputnode, basil_sc417, [('source_file', 'source_file'),
('basil_sc417', 'in_file')]),
(inputnode, pvc_sc417, [('source_file', 'source_file'),
('pvc_sc417', 'in_file')]),
])
if nonstd_spaces.intersection(('func', 'run', 'bold', 'boldref', 'sbref')):
ds_bold_native = pe.Node(DerivativesDataSink(
base_directory=output_dir,
desc='preproc',
compress=True,
SkullStripped=False,
RepetitionTime=metadata.get('RepetitionTime'),
TaskName=metadata.get('TaskName')),
name='ds_bold_native',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_bold_native_ref = pe.Node(DerivativesDataSink(
base_directory=output_dir,
suffix='aslref',
compress=True,
dismiss_entities=("echo", )),
name='ds_bold_native_ref',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_bold_mask_native = pe.Node(DerivativesDataSink(
base_directory=output_dir,
desc='brain',
suffix='mask',
compress=True,
dismiss_entities=("echo", )),
name='ds_bold_mask_native',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
cbfnative = pe.Node(DerivativesDataSink(base_directory=output_dir,
suffix='cbf',
compress=True),
name='cbfnative',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
meancbfnative = pe.Node(DerivativesDataSink(base_directory=output_dir,
suffix='mean_cbf',
compress=True),
name='meancbfnative',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
scorenative = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='score',
suffix='cbf',
compress=True),
name='scorenative',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
meanscorenative = pe.Node(DerivativesDataSink(
base_directory=output_dir,
desc='score',
suffix='mean_cbf',
compress=True),
name='meanscorenative',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
scrubnative = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='scrub',
suffix='cbf',
compress=True),
name='scrubnative',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
basilnative = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='basil',
suffix='cbf',
compress=True),
name='basilnative',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
pvnative = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='pvc',
suffix='cbf',
compress=True),
name='pvcnative',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, ds_bold_native, [('source_file', 'source_file'),
('bold_native', 'in_file')]),
(inputnode, ds_bold_native_ref, [('source_file', 'source_file'),
('bold_native_ref', 'in_file')]),
(inputnode, ds_bold_mask_native, [('source_file', 'source_file'),
('bold_mask_native', 'in_file')
]),
(inputnode, cbfnative, [('source_file', 'source_file'),
('cbf', 'in_file')]),
(inputnode, meancbfnative, [('source_file', 'source_file'),
('meancbf', 'in_file')]),
(inputnode, scorenative, [('source_file', 'source_file'),
('score', 'in_file')]),
(inputnode, meanscorenative, [('source_file', 'source_file'),
('avgscore', 'in_file')]),
(inputnode, scrubnative, [('source_file', 'source_file'),
('scrub', 'in_file')]),
(inputnode, basilnative, [('source_file', 'source_file'),
('basil', 'in_file')]),
(inputnode, pvnative, [('source_file', 'source_file'),
('pv', 'in_file')]),
(raw_sources, ds_bold_mask_native, [('out', 'RawSources')]),
])
# Resample to T1w space
if nonstd_spaces.intersection(('T1w', 'anat')):
ds_bold_t1 = pe.Node(DerivativesDataSink(
base_directory=output_dir,
space='T1w',
desc='preproc',
compress=True,
SkullStripped=False,
RepetitionTime=metadata.get('RepetitionTime'),
TaskName=metadata.get('TaskName'),
dismiss_entities=("echo", )),
name='ds_bold_t1',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_bold_t1_ref = pe.Node(DerivativesDataSink(
base_directory=output_dir,
space='T1w',
suffix='aslref',
compress=True,
dismiss_entities=("echo", )),
name='ds_bold_t1_ref',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_bold_mask_t1 = pe.Node(DerivativesDataSink(
base_directory=output_dir,
space='T1w',
desc='brain',
suffix='mask',
compress=True,
dismiss_entities=("echo", )),
name='ds_bold_mask_t1',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
cbfnativet1 = pe.Node(DerivativesDataSink(base_directory=output_dir,
suffix='cbf',
space='T1w',
compress=True),
name='cbfnativet1',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
meancbfnativet1 = pe.Node(DerivativesDataSink(
base_directory=output_dir,
suffix='mean_cbf',
space='T1w',
compress=True),
name='meancbfnativet1',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
scorenativet1 = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='score',
suffix='cbf',
space='T1w',
compress=True),
name='scorenativet1',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
meanscorenativet1 = pe.Node(DerivativesDataSink(
base_directory=output_dir,
suffix='mean_cbf',
desc='score',
space='T1w',
compress=True),
name='meanscorenativet1',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
scrubnativet1 = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='scrub',
suffix='cbf',
space='T1w',
compress=True),
name='scrubnativet1',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
basilnativet1 = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='basil',
suffix='cbf',
space='T1w',
compress=True),
name='basilnativet1',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
pvnativet1 = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='pvc',
suffix='cbf',
space='T1w',
compress=True),
name='pvcnativet1',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, ds_bold_t1, [('source_file', 'source_file'),
('bold_t1', 'in_file')]),
(inputnode, ds_bold_t1_ref, [('source_file', 'source_file'),
('bold_t1_ref', 'in_file')]),
(inputnode, ds_bold_mask_t1, [('source_file', 'source_file'),
('bold_mask_t1', 'in_file')]),
(inputnode, cbfnativet1, [('source_file', 'source_file'),
('cbf_t1', 'in_file')]),
(inputnode, meancbfnativet1, [('source_file', 'source_file'),
('meancbf_t1', 'in_file')]),
(inputnode, scorenativet1, [('source_file', 'source_file'),
('score_t1', 'in_file')]),
(inputnode, meanscorenativet1, [('source_file', 'source_file'),
('avgscore_t1', 'in_file')]),
(inputnode, scrubnativet1, [('source_file', 'source_file'),
('scrub_t1', 'in_file')]),
(inputnode, basilnativet1, [('source_file', 'source_file'),
('basil_t1', 'in_file')]),
(inputnode, pvnativet1, [('source_file', 'source_file'),
('pv_t1', 'in_file')]),
(raw_sources, ds_bold_mask_t1, [('out', 'RawSources')]),
])
if freesurfer:
ds_bold_aseg_t1 = pe.Node(DerivativesDataSink(
base_directory=output_dir,
space='T1w',
desc='aseg',
suffix='dseg',
compress=True,
dismiss_entities=("echo", )),
name='ds_bold_aseg_t1',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_bold_aparc_t1 = pe.Node(DerivativesDataSink(
base_directory=output_dir,
space='T1w',
desc='aparcaseg',
suffix='dseg',
compress=True,
dismiss_entities=("echo", )),
name='ds_bold_aparc_t1',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, ds_bold_aseg_t1, [('source_file', 'source_file'),
('bold_aseg_t1', 'in_file')]),
(inputnode, ds_bold_aparc_t1, [('source_file', 'source_file'),
('bold_aparc_t1', 'in_file')]),
])
if getattr(spaces, '_cached') is None:
return workflow
# Store resamplings in standard spaces when listed in --output-spaces
if spaces.cached.references:
from ...niworkflows.interfaces.space import SpaceDataSource
spacesource = pe.Node(SpaceDataSource(),
name='spacesource',
run_without_submitting=True)
spacesource.iterables = ('in_tuple', [
(s.fullname, s.spec) for s in spaces.cached.get_standard(dim=(3, ))
])
select_std = pe.Node(KeySelect(fields=[
'template', 'bold_std', 'bold_std_ref', 'bold_mask_std', 'cbf_std',
'meancbf_std', 'score_std', 'avgscore_std', 'scrub_std',
'basil_std', 'pv_std'
]),
name='select_std',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_bold_std = pe.Node(DerivativesDataSink(
base_directory=output_dir,
desc='preproc',
compress=True,
SkullStripped=False,
RepetitionTime=metadata.get('RepetitionTime'),
TaskName=metadata.get('TaskName'),
dismiss_entities=("echo", )),
name='ds_bold_std',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_bold_std_ref = pe.Node(DerivativesDataSink(
base_directory=output_dir,
suffix='aslref',
compress=True,
dismiss_entities=("echo", )),
name='ds_bold_std_ref',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_bold_mask_std = pe.Node(DerivativesDataSink(
base_directory=output_dir,
desc='brain',
suffix='mask',
compress=True,
dismiss_entities=("echo", )),
name='ds_bold_mask_std',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
cbfstd = pe.Node(DerivativesDataSink(base_directory=output_dir,
suffix='cbf',
compress=True),
name='cbfstd',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
meancbfstd = pe.Node(DerivativesDataSink(base_directory=output_dir,
suffix='mean_cbf',
compress=True),
name='meancbfstd',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
scorestd = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='score',
suffix='cbf',
compress=True),
name='scorestd',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
meanscorestd = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='score',
suffix='mean_cbf',
compress=True),
name='meanscorestd',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
scrubstd = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='scrub',
suffix='cbf',
compress=True),
name='scrubstd',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
basilstd = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='basil',
suffix='cbf',
compress=True),
name='basilstd',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
pvstd = pe.Node(DerivativesDataSink(base_directory=output_dir,
desc='pvc',
suffix='cbf',
compress=True),
name='pvcstd',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, ds_bold_std, [('source_file', 'source_file')]),
(inputnode, ds_bold_std_ref, [('source_file', 'source_file')]),
(inputnode, ds_bold_mask_std, [('source_file', 'source_file')]),
(inputnode, cbfstd, [('source_file', 'source_file')]),
(inputnode, meancbfstd, [('source_file', 'source_file')]),
(inputnode, scorestd, [('source_file', 'source_file')]),
(inputnode, meanscorestd, [('source_file', 'source_file')]),
(inputnode, scrubstd, [('source_file', 'source_file')]),
(inputnode, basilstd, [('source_file', 'source_file')]),
(inputnode, pvstd, [('source_file', 'source_file')]),
(inputnode, select_std, [('bold_std', 'bold_std'),
('bold_std_ref', 'bold_std_ref'),
('bold_mask_std', 'bold_mask_std'),
('cbf_std', 'cbf_std'),
('meancbf_std', 'meancbf_std'),
('score_std', 'score_std'),
('avgscore_std', 'avgscore_std'),
('scrub_std', 'scrub_std'),
('basil_std', 'basil_std'),
('pv_std', 'pv_std'),
('template', 'template'),
('spatial_reference', 'keys')]),
(spacesource, select_std, [('uid', 'key')]),
(select_std, ds_bold_std, [('bold_std', 'in_file')]),
(spacesource, ds_bold_std, [('space', 'space'),
('cohort', 'cohort'),
('resolution', 'resolution'),
('density', 'density')]),
(select_std, ds_bold_std_ref, [('bold_std_ref', 'in_file')]),
(spacesource, ds_bold_std_ref, [('space', 'space'),
('cohort', 'cohort'),
('resolution', 'resolution'),
('density', 'density')]),
(select_std, ds_bold_mask_std, [('bold_mask_std', 'in_file')]),
(spacesource, ds_bold_mask_std, [('space', 'space'),
('cohort', 'cohort'),
('resolution', 'resolution'),
('density', 'density')]),
(select_std, cbfstd, [('cbf_std', 'in_file')]),
(spacesource, cbfstd, [('space', 'space'), ('cohort', 'cohort'),
('resolution', 'resolution'),
('density', 'density')]),
(select_std, meancbfstd, [('meancbf_std', 'in_file')]),
(spacesource, meancbfstd, [('space', 'space'),
('cohort', 'cohort'),
('resolution', 'resolution'),
('density', 'density')]),
(select_std, scorestd, [('score_std', 'in_file')]),
(spacesource, scorestd, [('space', 'space'), ('cohort', 'cohort'),
('resolution', 'resolution'),
('density', 'density')]),
(select_std, meanscorestd, [('avgscore_std', 'in_file')]),
(spacesource, meanscorestd, [('space', 'space'),
('cohort', 'cohort'),
('resolution', 'resolution'),
('density', 'density')]),
(select_std, scrubstd, [('scrub_std', 'in_file')]),
(spacesource, scrubstd, [('space', 'space'), ('cohort', 'cohort'),
('resolution', 'resolution'),
('density', 'density')]),
(select_std, basilstd, [('basil_std', 'in_file')]),
(spacesource, basilstd, [('space', 'space'), ('cohort', 'cohort'),
('resolution', 'resolution'),
('density', 'density')]),
(select_std, pvstd, [('pv_std', 'in_file')]),
(spacesource, pvstd, [('space', 'space'), ('cohort', 'cohort'),
('resolution', 'resolution'),
('density', 'density')]),
(raw_sources, ds_bold_mask_std, [('out', 'RawSources')]),
])
if freesurfer:
select_fs_std = pe.Node(KeySelect(
fields=['bold_aseg_std', 'bold_aparc_std', 'template']),
name='select_fs_std',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_bold_aseg_std = pe.Node(DerivativesDataSink(
base_directory=output_dir,
desc='aseg',
suffix='dseg',
compress=True,
dismiss_entities=("echo", )),
name='ds_bold_aseg_std',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
ds_bold_aparc_std = pe.Node(DerivativesDataSink(
base_directory=output_dir,
desc='aparcaseg',
suffix='dseg',
compress=True,
dismiss_entities=("echo", )),
name='ds_bold_aparc_std',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(spacesource, select_fs_std, [('uid', 'key')]),
(inputnode, select_fs_std, [('bold_aseg_std', 'bold_aseg_std'),
('bold_aparc_std',
'bold_aparc_std'),
('template', 'template'),
('spatial_reference', 'keys')]),
(select_fs_std, ds_bold_aseg_std, [('bold_aseg_std', 'in_file')
]),
(spacesource, ds_bold_aseg_std, [('space', 'space'),
('cohort', 'cohort'),
('resolution', 'resolution'),
('density', 'density')]),
(select_fs_std, ds_bold_aparc_std, [('bold_aparc_std',
'in_file')]),
(spacesource, ds_bold_aparc_std, [('space', 'space'),
('cohort', 'cohort'),
('resolution', 'resolution'),
('density', 'density')]),
(inputnode, ds_bold_aseg_std, [('source_file', 'source_file')
]),
(inputnode, ds_bold_aparc_std, [('source_file', 'source_file')
])
])
fs_outputs = spaces.cached.get_fs_spaces()
if freesurfer and fs_outputs:
from ...niworkflows.interfaces.surf import Path2BIDS
select_fs_surf = pe.Node(KeySelect(fields=['surfaces', 'surf_kwargs']),
name='select_fs_surf',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
select_fs_surf.iterables = [('key', fs_outputs)]
select_fs_surf.inputs.surf_kwargs = [{'space': s} for s in fs_outputs]
name_surfs = pe.MapNode(Path2BIDS(pattern=r'(?P<hemi>[lr])h.\w+'),
iterfield='in_file',
name='name_surfs',
run_without_submitting=True)
ds_bold_surfs = pe.MapNode(DerivativesDataSink(
base_directory=output_dir,
extension="func.gii",
dismiss_entities=("echo", )),
iterfield=['in_file', 'hemi'],
name='ds_bold_surfs',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, select_fs_surf, [('surf_files', 'surfaces'),
('surf_refs', 'keys')]),
(select_fs_surf, name_surfs, [('surfaces', 'in_file')]),
(inputnode, ds_bold_surfs, [('source_file', 'source_file')]),
(select_fs_surf, ds_bold_surfs, [('surfaces', 'in_file'),
('key', 'space')]),
(name_surfs, ds_bold_surfs, [('hemi', 'hemi')]),
])
# CIFTI output
if cifti_output:
ds_bold_cifti = pe.Node(DerivativesDataSink(
base_directory=output_dir,
suffix='bold',
compress=False,
dismiss_entities=("echo", )),
name='ds_bold_cifti',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([(inputnode, ds_bold_cifti,
[(('bold_cifti', _unlist), 'in_file'),
('source_file', 'source_file'),
(('cifti_metadata', _get_surface), 'space'),
('cifti_density', 'density'),
(('cifti_metadata', _read_json), 'meta_dict')])])
return workflow
def init_autorecon_resume_wf(omp_nthreads, name='autorecon_resume_wf'):
r"""
Resume recon-all execution, assuming the `-autorecon1` stage has been completed.
In order to utilize resources efficiently, this is broken down into seven
sub-stages; after the first stage, the second and third stages may be run
simultaneously, and the fifth and sixth stages may be run simultaneously,
if resources permit; the fourth stage must be run prior to the fifth and
sixth, and the seventh must be run after::
$ recon-all -sd <output dir>/freesurfer -subjid sub-<subject_label> \
-autorecon2-volonly
$ recon-all -sd <output dir>/freesurfer -subjid sub-<subject_label> \
-autorecon-hemi lh -T2pial \
-noparcstats -noparcstats2 -noparcstats3 -nohyporelabel -nobalabels
$ recon-all -sd <output dir>/freesurfer -subjid sub-<subject_label> \
-autorecon-hemi rh -T2pial \
-noparcstats -noparcstats2 -noparcstats3 -nohyporelabel -nobalabels
$ recon-all -sd <output dir>/freesurfer -subjid sub-<subject_label> \
-cortribbon
$ recon-all -sd <output dir>/freesurfer -subjid sub-<subject_label> \
-autorecon-hemi lh -nohyporelabel
$ recon-all -sd <output dir>/freesurfer -subjid sub-<subject_label> \
-autorecon-hemi rh -nohyporelabel
$ recon-all -sd <output dir>/freesurfer -subjid sub-<subject_label> \
-autorecon3
The parcellation statistics steps are excluded from the second and third
stages, because they require calculation of the cortical ribbon volume
(the fourth stage).
Hypointensity relabeling is excluded from hemisphere-specific steps to avoid
race conditions, as it is a volumetric operation.
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from smriprep.workflows.surfaces import init_autorecon_resume_wf
wf = init_autorecon_resume_wf(omp_nthreads=1)
Inputs
------
subjects_dir
FreeSurfer SUBJECTS_DIR
subject_id
FreeSurfer subject ID
use_T2
Refine pial surface using T2w image
use_FLAIR
Refine pial surface using FLAIR image
Outputs
-------
subjects_dir
FreeSurfer SUBJECTS_DIR
subject_id
FreeSurfer subject ID
"""
workflow = Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(
fields=['subjects_dir', 'subject_id', 'use_T2', 'use_FLAIR']),
name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(fields=['subjects_dir', 'subject_id']),
name='outputnode')
autorecon2_vol = pe.Node(ReconAll(directive='autorecon2-volonly',
openmp=omp_nthreads),
n_procs=omp_nthreads,
mem_gb=5,
name='autorecon2_vol')
autorecon2_vol.interface._always_run = True
autorecon_surfs = pe.MapNode(ReconAll(directive='autorecon-hemi',
flags=[
'-noparcstats', '-noparcstats2',
'-noparcstats3',
'-nohyporelabel', '-nobalabels'
],
openmp=omp_nthreads),
iterfield='hemi',
n_procs=omp_nthreads,
mem_gb=5,
name='autorecon_surfs')
autorecon_surfs.inputs.hemi = ['lh', 'rh']
autorecon_surfs.interface._always_run = True
# -cortribbon is a prerequisite for -parcstats, -parcstats2, -parcstats3
cortribbon = pe.Node(ReconAll(directive=Undefined, steps=['cortribbon']),
name='cortribbon')
cortribbon.interface._always_run = True
# -parcstats* can be run per-hemisphere
# -hyporelabel is volumetric, even though it's part of -autorecon-hemi
parcstats = pe.MapNode(ReconAll(directive='autorecon-hemi',
flags=['-nohyporelabel'],
openmp=omp_nthreads),
iterfield='hemi',
n_procs=omp_nthreads,
mem_gb=5,
name='parcstats')
parcstats.inputs.hemi = ['lh', 'rh']
parcstats.interface._always_run = True
# Runs: -hyporelabel -aparc2aseg -apas2aseg -segstats -wmparc
# All volumetric, so don't
autorecon3 = pe.Node(ReconAll(directive='autorecon3', openmp=omp_nthreads),
n_procs=omp_nthreads,
mem_gb=5,
name='autorecon3')
autorecon3.interface._always_run = True
def _dedup(in_list):
vals = set(in_list)
if len(vals) > 1:
raise ValueError(
"Non-identical values can't be deduplicated:\n{!r}".format(
in_list))
return vals.pop()
workflow.connect([
(inputnode, autorecon_surfs, [('use_T2', 'use_T2'),
('use_FLAIR', 'use_FLAIR')]),
(inputnode, autorecon2_vol, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(autorecon2_vol, autorecon_surfs, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(autorecon_surfs, cortribbon,
[(('subjects_dir', _dedup), 'subjects_dir'),
(('subject_id', _dedup), 'subject_id')]),
(cortribbon, parcstats, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(parcstats, autorecon3, [(('subjects_dir', _dedup), 'subjects_dir'),
(('subject_id', _dedup), 'subject_id')]),
(autorecon3, outputnode, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
])
return workflow
