def statistics_pipeline(self, **name_maps):
pipeline = self.new_pipeline(name='statistics',
name_maps=name_maps,
desc="Calculate statistics")
merge_visits = pipeline.add(
'merge_visits',
Merge(numinputs=1),
inputs={'in1': ('selected_metric', text_format)},
joinsource=self.VISIT_ID,
joinfield=['in1'])
merge_subjects = pipeline.add('merge_subjects',
Merge(numinputs=1, ravel_inputs=True),
inputs={'in1': (merge_visits, 'out')},
joinsource=self.SUBJECT_ID,
joinfield=['in1'])
concat = pipeline.add('concat',
ConcatFloats(),
inputs={'in_files': (merge_subjects, 'out')})
pipeline.add('extract_metrics',
ExtractMetrics(),
inputs={'in_list': (concat, 'out_list')},
outputs={
'average': ('avg', float),
'std_dev': ('std', float)
})
return pipeline
def warp_segments(name="warp_segments"):
import nipype.pipeline.engine as pe
from nipype.interfaces.utility import IdentityInterface
wf = pe.Workflow(name=name)
seg = fs_segment()
inputspec = pe.Node(IdentityInterface(fields=[
'subject_id', 'subjects_dir', 'warp_file', 'ants_affine',
'warped_brain'
]),
name="inputspec")
from nipype.interfaces.ants import ApplyTransforms
ap = pe.MapNode(ApplyTransforms(interpolation='NearestNeighbor'),
name="apply_transforms",
iterfield=["input_image"])
wf.connect(inputspec, "subject_id", seg, "inputspec.subject_id")
wf.connect(inputspec, "subjects_dir", seg, "inputspec.subjects_dir")
from nipype.interfaces.utility import Merge
merge = pe.Node(Merge(3), name="merge")
wf.connect(seg, "outputspec.wm", merge, 'in1')
wf.connect(seg, "outputspec.gm", merge, "in2")
wf.connect(seg, "outputspec.csf", merge, "in3")
wf.connect(merge, "out", ap, "input_image")
wf.connect(inputspec, "warped_brain", ap, "reference_image")
merge1 = pe.Node(Merge(2), name="get_transformations")
wf.connect(inputspec, "warp_file", merge1, "in1")
wf.connect(inputspec, "ants_affine", merge1, "in2")
wf.connect(merge1, "out", ap, "transforms")
outputspec = pe.Node(IdentityInterface(fields=["out_files"]),
name='outputspec')
wf.connect(ap, "output_image", outputspec, "out_files")
return wf
def pipeline4(self, **name_maps):
pipeline = self.new_pipeline(
'pipeline4',
desc="",
citations=[],
name_maps=name_maps)
merge1 = pipeline.add(
'merge1',
Merge(
numinputs=1,
ravel_inputs=True),
inputs={
'in1': ('derived_field1', int)},
joinsource=self.SUBJECT_ID,
joinfield='in1')
merge2 = pipeline.add(
'merge2',
Merge(
numinputs=1,
ravel_inputs=True),
inputs={
'in1': (merge1, 'out')},
joinsource=self.VISIT_ID,
joinfield='in1')
pipeline.add(
'math',
TestMath(
op='add',
as_file=False),
inputs={
'x': (merge2, 'out')},
outputs={
'derived_field4': ('z', int)})
return pipeline
def pipeline2(self):
pipeline = self.pipeline(
name='pipeline2',
inputs=[
FilesetSpec('ones', text_format),
FilesetSpec('twos', text_format)
],
outputs=[FieldSpec('threes', float),
FieldSpec('fours', float)],
desc=("A pipeline that tests loading of requirements in "
"map nodes"),
references=[],
)
# Convert from DICOM to NIfTI.gz format on input
merge = pipeline.create_node(Merge(2), "merge")
maths = pipeline.create_map_node(TestMathWithReq(),
"maths",
iterfield='x',
requirements=[(notinstalled1_req,
notinstalled2_req,
first_req),
second_req])
split = pipeline.create_node(Split(), 'split')
split.inputs.splits = [1, 1]
split.inputs.squeeze = True
maths.inputs.op = 'add'
maths.inputs.y = 2
pipeline.connect_input('ones', merge, 'in1')
pipeline.connect_input('twos', merge, 'in2')
pipeline.connect(merge, 'out', maths, 'x')
pipeline.connect(maths, 'z', split, 'inlist')
pipeline.connect_output('threes', split, 'out1')
pipeline.connect_output('fours', split, 'out2')
return pipeline
def pipeline2(self, **name_maps):
pipeline = self.new_pipeline(
name='pipeline2',
desc=("A pipeline that tests loading of requirements in "
"map nodes"),
name_maps=name_maps)
# Convert from DICOM to NIfTI.gz format on input
merge = pipeline.add("merge", Merge(2))
maths = pipeline.add(
"maths",
TestMathWithReq(),
iterfield='x',
requirements=[first_req.v('0.15.9'),
second_req.v('1.0.2')])
split = pipeline.add('split', Split())
split.inputs.splits = [1, 1]
split.inputs.squeeze = True
maths.inputs.op = 'add'
maths.inputs.y = 2
pipeline.connect_input('ones', merge, 'in1', text_format)
pipeline.connect_input('twos', merge, 'in2', text_format)
pipeline.connect(merge, 'out', maths, 'x')
pipeline.connect(maths, 'z', split, 'inlist')
pipeline.connect_output('threes', split, 'out1', text_format)
pipeline.connect_output('fours', split, 'out2', text_format)
return pipeline
def create(self):
sampleT1s = Node(interface=Select(), name='sampleT1s')
sampleT2s = Node(interface=Select(), name='sampleT2s')
sampleLabels = Node(interface=Select(), name='sampleLabels')
testT1s = Node(interface=Select(), name='testT1s')
testT2s = Node(interface=Select(), name='testT2s')
testLabels = Node(interface=Select(), name='testLabels')
intensityImages = Node(interface=Merge(2), name='intensityImages')
jointFusion = Node(interface=JointFusion(), name='jointFusion')
jointFusion.inputs.dimension = 3
jointFusion.inputs.modalities = 1 #TODO: verify 2 for T1/T2
jointFusion.inputs.method = 'Joint[0.1, 2]'
jointFusion.inputs.output_label_image = 'fusion_neuro2012_20.nii.gz'
outputs = Node(
interface=IdentityInterface(fields=['output_label_image']),
run_without_submitting=True,
name='outputspec')
self.connect([ # Don't worry about T2s now per Regina
# (sampleT1s, intensityImages, [('out', 'in1')]),
# (sampleT2s, intensityImages, [('out', 'in2')]),
# (intensityImages, jointFusion, [('out', 'warped_intensity_images')]),
(sampleT1s, jointFusion, [('out', 'warped_intensity_images')]),
#END: per Regina
(sampleLabels, jointFusion, [('out', 'warped_label_images')]),
(jointFusion, outputs, [('output_label_image',
'output_label_image')]),
])
def gather_outputs_pipeline(self, **name_maps):
pipeline = self.new_pipeline(
name='gather_motion_detection_outputs',
desc=("Pipeline to gather together all the outputs from "
"the motion detection pipeline."),
citations=[fsl_cite],
name_maps=name_maps)
merge_inputs = pipeline.add(
'merge_inputs',
Merge(5),
inputs={
'in1': ('mean_displacement_plot', png_format),
'in2': ('motion_par', text_format),
'in3': ('correction_factors', text_format),
'in4': ('severe_motion_detection_report', text_format),
'in5': ('timestamps', directory_format)
})
pipeline.add(
'copy2dir',
CopyToDir(),
inputs={'in_files': (merge_inputs, 'out')},
outputs={'motion_detection_output': ('out_dir', directory_format)})
return pipeline
def make_workflow(name=''):
w = Workflow(f'prf_to_csv_' + name)
n_in = Node(IdentityInterface(fields=[
'atlas',
'func_to_struct',
'struct_to_freesurfer',
'run_1',
'run_2',
'subject',
'session',
]),
name='input')
n_out = Node(IdentityInterface(fields=[
'csv_file',
]), name='output')
n_prf = Node(PRF(), 'prf')
n_prf.inputs.threshold = 10
n_mean = Node(interface=TStat(), name='mean')
n_mean.inputs.args = '-mean'
n_mean.inputs.outputtype = 'NIFTI_GZ'
n_merge = Node(Merge(len(REGIONS)), 'merge')
n_csv = {}
w_roi = {}
for i, r in enumerate(REGIONS):
w_roi[r] = make_workflow_roi(r)
n_csv[r] = Node(PRF2CSV(), 'prf2csv_' + r)
n_csv[r].inputs.threshold = 0.5
n_csv[r].inputs.region = r
w.connect(n_in, 'atlas', w_roi[r], 'input.atlas')
w.connect(n_in, 'func_to_struct', w_roi[r], 'input.func_to_struct')
w.connect(n_in, 'struct_to_freesurfer', w_roi[r],
'input.struct_to_freesurfer')
w.connect(n_mean, 'out_file', w_roi[r], 'input.ref')
w.connect(n_in, 'subject', n_csv[r], 'subject')
w.connect(n_in, 'session', n_csv[r], 'session')
for k in ['r2', 'phi', 'rho', 'sigma', 'hrf']:
w.connect(n_prf, k + '_file', n_csv[r], k + '_file')
w.connect(w_roi[r], 'output.mask_file', n_csv[r], 'mask_file')
w.connect(n_csv[r], 'csv_file', n_merge, 'in' + str(i + 1))
w.connect(n_in, 'run_1', n_prf, 'nii1_file')
w.connect(n_in, 'run_2', n_prf, 'nii2_file')
w.connect(n_in, 'subject', n_prf, 'subject')
w.connect(n_in, 'session', n_prf, 'session')
w.connect(n_in, 'run_1', n_mean, 'in_file')
w.connect(n_merge, 'out', n_out, 'csv_file')
return w
def make_workflow_roi(region):
"""
Benson_ROI_Names = {'V1', 'V2', 'V3', 'hV4', 'VO1', 'VO2', 'LO1', 'LO2', 'TO1', 'TO2', 'V3B', 'V3A'};
Wang_ROI_Names = [
'V1v', 'V1d', 'V2v', 'V2d', 'V3v', 'V3d', 'hV4', 'VO1', 'VO2', 'PHC1', 'PHC2',
'TO2', 'TO1', 'LO2', 'LO1', 'V3B', 'V3A', 'IPS0', 'IPS1', 'IPS2', 'IPS3', 'IPS4' ,
'IPS5', 'SPL1', 'FEF'];
"""
w = Workflow(f'roi_{region}')
n_in = Node(IdentityInterface(fields=[
'atlas',
'func_to_struct',
'struct_to_freesurfer',
'ref',
]),
name='input')
n_out = Node(IdentityInterface(fields=[
'mask_file',
]), name='output')
n_m = Node(Merge(2), 'merge')
n_v = Node(MathsCommand(), region)
n_v.inputs.out_file = 'roi.nii.gz'
n_v.inputs.nan2zeros = True
if region == 'V1':
n_v.inputs.args = '-uthr 1 -bin'
elif region == 'V2':
n_v.inputs.args = '-thr 2 -uthr 3 -bin'
elif region == 'V3':
n_v.inputs.args = '-thr 4 -uthr 5 -bin'
else:
raise ValueError(f'Unknown region {region}. It should be V1, V2, V3')
at = Node(ApplyTransforms(), 'applytransform')
at.inputs.dimension = 3
at.inputs.output_image = 'roi_func.nii.gz'
at.inputs.interpolation = 'Linear'
at.inputs.default_value = 0
at.inputs.invert_transform_flags = [True, True]
w.connect(n_in, 'atlas', n_v, 'in_file')
w.connect(n_v, 'out_file', at, 'input_image')
w.connect(n_in, 'ref', at, 'reference_image')
w.connect(n_in, 'struct_to_freesurfer', n_m, 'in1')
w.connect(n_in, 'func_to_struct', n_m, 'in2')
w.connect(n_m, 'out', at, 'transforms')
w.connect(at, 'output_image', n_out, 'mask_file')
return w
def brain_extraction_pipeline(self, **name_maps):
"""
Generates a whole brain mask using MRtrix's 'dwi2mask' command
Parameters
----------
mask_tool: Str
Can be either 'bet' or 'dwi2mask' depending on which mask tool you
want to use
"""
if self.branch('bet_method', 'mrtrix'):
pipeline = self.new_pipeline(
'brain_extraction',
desc="Generate brain mask from b0 images",
citations=[mrtrix_cite],
name_maps=name_maps)
if self.provided('coreg_ref'):
series = 'series_coreg'
else:
series = 'series_preproc'
# Create mask node
masker = pipeline.add(
'dwi2mask',
BrainMask(
out_file='brain_mask.nii.gz'),
inputs={
'in_file': (series, nifti_gz_format),
'grad_fsl': self.fsl_grads(pipeline, coregistered=False)},
outputs={
'brain_mask': ('out_file', nifti_gz_format)},
requirements=[mrtrix_req.v('3.0rc3')])
merge = pipeline.add(
'merge_operands',
Merge(2),
inputs={
'in1': ('mag_preproc', nifti_gz_format),
'in2': (masker, 'out_file')})
pipeline.add(
'apply_mask',
MRCalc(
operation='multiply'),
inputs={
'operands': (merge, 'out')},
outputs={
'brain': ('out_file', nifti_gz_format)},
requirements=[mrtrix_req.v('3.0rc3')])
else:
pipeline = super().brain_extraction_pipeline(**name_maps)
return pipeline
def __init__(self, in1=0, **options):
from nipype.interfaces.utility import Merge
n = 1
for ef in options:
if 'in' in ef:
n += 1
mi = Merge(n)
mi.inputs.in1 = in1
for ef in options:
setattr(mi.inputs, ef, options[ef])
self.res = mi.run()
def pipeline1(self, **name_maps):
pipeline = self.new_pipeline(
'pipeline1',
desc="",
citations=[],
name_maps=name_maps)
math1 = pipeline.add(
'math1',
TestMath(
op='add'),
inputs={
'x': ('acquired_fileset1', text_format),
'y': ('acquired_fileset2', text_format)},
requirements=[
a_req.v('1.0.1'),
b_req.v(2)])
math2 = pipeline.add(
'math2',
TestMath(
op='add'),
inputs={
'y': ('acquired_field1', float),
'x': (math1, 'z')},
requirements=[
c_req.v(0.1)])
# Set up different requirements based on switch
math3_reqs = [a_req.v(1)]
if self.branch('extra_req'):
math3_reqs.append(d_req.v('0.8.6'))
math3 = pipeline.add(
'math3',
TestMath(
op='mul',
y=self.parameter('multiplier')),
inputs={
'x': (math2, 'z')},
requirements=[
b_req.v('2.7.0', '3.0')])
pipeline.add(
'merge1',
Merge(3),
inputs={
'in1': (math1, 'z'),
'in2': (math2, 'z'),
'in3': (math3, 'z')},
outputs={
'derived_field1': ('out', float)},
requirements=math3_reqs)
return pipeline
def create_report_pipeline(pipeline_name="report"): #, contrasts):
inputnode = pe.Node(interface=util.IdentityInterface(fields=[
'struct', "raw_stat_images", "thresholded_stat_images",
"ggmm_thresholded_stat_images", "mask", "plot_realign", 'contrasts',
'task_name'
]),
name="inputnode")
raw_stat_visualise = create_visualise_masked_overlay(
pipeline_name=pipeline_name, name="raw_stat")
thresholded_stat_visualise = create_visualise_thresholded_overlay(
pipeline_name=pipeline_name, name="thresholded_stat")
psmerge_raw = pe.Node(interface=neuroutils.PsMerge(), name="psmerge_raw")
psmerge_raw.inputs.out_file = "merged.pdf"
psmerge_th = psmerge_raw.clone(name="psmerge_th")
psmerge_ggmm_th = psmerge_raw.clone(name="psmerge_ggmm_th")
psmerge_all = psmerge_raw.clone(name="psmerge_all")
mergeinputs = pe.Node(interface=Merge(4), name="mergeinputs")
report = pe.Workflow(name="report")
report.connect([
(inputnode,
raw_stat_visualise, [("struct", "inputnode.background"),
("raw_stat_images", "inputnode.overlays"),
("mask", "inputnode.mask"),
('contrasts', 'inputnode.contrasts'),
('task_name', 'inputnode.task_name')]),
(inputnode, thresholded_stat_visualise,
[("struct", "inputnode.background"),
("thresholded_stat_images", "inputnode.overlays"),
("ggmm_thresholded_stat_images", "inputnode.ggmm_overlays"),
('contrasts', 'inputnode.contrasts'),
('task_name', 'inputnode.task_name')]),
(raw_stat_visualise, psmerge_raw, [("plot.plot", "in_files")]),
(thresholded_stat_visualise, psmerge_th, [("plot.plot", "in_files")]),
(thresholded_stat_visualise, psmerge_ggmm_th, [("plot_ggmm.plot",
"in_files")]),
(inputnode, mergeinputs, [("plot_realign", "in1")]),
(psmerge_raw, mergeinputs, [("merged_file", "in2")]),
(psmerge_th, mergeinputs, [("merged_file", "in3")]),
(psmerge_ggmm_th, mergeinputs, [("merged_file", "in4")]),
(mergeinputs, psmerge_all, [("out", "in_files")]),
])
return report
def pipeline5(self, **name_maps):
pipeline = self.new_pipeline('pipeline5',
desc="",
citations=[],
name_maps=name_maps)
merge = pipeline.add('merge',
Merge(numinputs=3),
inputs={
'in1': ('derived_field2', int),
'in2': ('derived_field3', int),
'in3': ('derived_field4', int)
})
pipeline.add('math',
TestMath(op='add', as_file=False),
inputs={'x': (merge, 'out')},
outputs={'derived_field5': ('z', float)})
return pipeline
def pipeline_factory(self, incr, input, output): # @ReservedAssignment
pipeline = self.create_pipeline(
name=output,
inputs=[DatasetSpec(input, mrtrix_format)],
outputs=[DatasetSpec(output, mrtrix_format)],
desc=("A dummy pipeline used to test 'partial-complete' method"),
version=1,
citations=[])
# Nodes
operands = pipeline.create_node(Merge(2), name='merge')
mult = pipeline.create_node(MRCalc(),
name="convert1",
requirements=[mrtrix3_req])
operands.inputs.in2 = incr
mult.inputs.operation = 'add'
# Connect inputs
pipeline.connect_input(input, operands, 'in1')
# Connect inter-nodes
pipeline.connect(operands, 'out', mult, 'operands')
# Connect outputs
pipeline.connect_output(output, mult, 'out_file')
return pipeline
def pipeline(self):
pipeline = self.create_pipeline(
name='pipeline',
inputs=[DatasetSpec('ones', nifti_gz_format)],
outputs=[DatasetSpec('twos', nifti_gz_format)],
desc=("A pipeline that tests loading of requirements"),
version=1,
citations=[],
)
# Convert from DICOM to NIfTI.gz format on input
input_merge = pipeline.create_node(Merge(2), "input_merge")
maths = pipeline.create_node(MRMath(),
"maths",
requirements=[(dummy1_req, dummy2_req,
mrtrix3_req),
dcm2niix1_req])
pipeline.connect_input('ones', input_merge, 'in1')
pipeline.connect_input('ones', input_merge, 'in2')
pipeline.connect(input_merge, 'out', maths, 'in_files')
maths.inputs.operation = 'sum'
pipeline.connect_output('twos', maths, 'out_file')
pipeline.assert_connected()
return pipeline
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 = 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', 'segmentation_files', 'anat2target',
'aparc'
]),
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(), 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')
"""
Estimate the tissue classes from the anatomical image. But use spm's segment
as FSL appears to be breaking.
"""
stripper = Node(fsl.BET(), name='stripper')
register.connect(convert, 'out_file', stripper, 'in_file')
fast = Node(fsl.FAST(), name='fast')
register.connect(stripper, 'out_file', fast, 'in_files')
"""
Binarize the segmentation
"""
binarize = MapNode(fsl.ImageMaths(op_string='-nan -thr 0.9 -ero -bin'),
iterfield=['in_file'],
name='binarize')
register.connect(fast, 'partial_volume_files', binarize, 'in_file')
"""
Apply inverse transform to take segmentations to functional space
"""
applyxfm = MapNode(freesurfer.ApplyVolTransform(inverse=True,
interp='nearest'),
iterfield=['target_file'],
name='inverse_transform')
register.connect(inputnode, 'subjects_dir', applyxfm, 'subjects_dir')
register.connect(bbregister, 'out_reg_file', applyxfm, 'reg_file')
register.connect(binarize, 'out_file', applyxfm, 'target_file')
register.connect(inputnode, 'mean_image', applyxfm, 'source_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': '-l nodes=1:ppn=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
"""
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 = 3
warpmean.inputs.interpolation = 'Linear'
warpmean.inputs.invert_transform_flags = [False, False]
warpmean.inputs.terminal_output = 'file'
warpmean.inputs.args = '--float'
warpmean.inputs.num_threads = 4
register.connect(inputnode, 'target_image', warpmean, 'reference_image')
register.connect(inputnode, 'mean_image', warpmean, 'input_image')
register.connect(merge, 'out', warpmean, 'transforms')
"""
Assign all the output files
"""
register.connect(reg, 'warped_image', outputnode, 'anat2target')
register.connect(warpmean, 'output_image', outputnode, 'transformed_mean')
register.connect(applyxfm, 'transformed_file', outputnode,
'segmentation_files')
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(reg, 'composite_transform', outputnode,
'anat2target_transform')
register.connect(merge, 'out', outputnode, 'transforms')
return register
def builder(subject_id,
subId,
project_dir,
data_dir,
output_dir,
output_final_dir,
output_interm_dir,
layout,
anat=None,
funcs=None,
fmaps=None,
task_name='',
session=None,
apply_trim=False,
apply_dist_corr=False,
apply_smooth=False,
apply_filter=False,
mni_template='2mm',
apply_n4=True,
ants_threads=8,
readable_crash_files=False,
write_logs=True):
"""
Core function that returns a workflow. See wfmaker for more details.
Args:
subject_id: name of subject folder for final outputted sub-folder name
subId: abbreviate name of subject for intermediate outputted sub-folder name
project_dir: full path to root of project
data_dir: full path to raw data files
output_dir: upper level output dir (others will be nested within this)
output_final_dir: final preprocessed sub-dir name
output_interm_dir: intermediate preprcess sub-dir name
layout: BIDS layout instance
"""
##################
### PATH SETUP ###
##################
if session is not None:
session = int(session)
if session < 10:
session = '0' + str(session)
else:
session = str(session)
# Set MNI template
MNItemplate = os.path.join(get_resource_path(),
'MNI152_T1_' + mni_template + '_brain.nii.gz')
MNImask = os.path.join(get_resource_path(),
'MNI152_T1_' + mni_template + '_brain_mask.nii.gz')
MNItemplatehasskull = os.path.join(get_resource_path(),
'MNI152_T1_' + mni_template + '.nii.gz')
# Set ANTs files
bet_ants_template = os.path.join(get_resource_path(),
'OASIS_template.nii.gz')
bet_ants_prob_mask = os.path.join(
get_resource_path(), 'OASIS_BrainCerebellumProbabilityMask.nii.gz')
bet_ants_registration_mask = os.path.join(
get_resource_path(), 'OASIS_BrainCerebellumRegistrationMask.nii.gz')
#################################
### NIPYPE IMPORTS AND CONFIG ###
#################################
# Update nipype global config because workflow.config[] = ..., doesn't seem to work
# Can't store nipype config/rc file in container anyway so set them globaly before importing and setting up workflow as suggested here: http://nipype.readthedocs.io/en/latest/users/config_file.html#config-file
# Create subject's intermediate directory before configuring nipype and the workflow because that's where we'll save log files in addition to intermediate files
if not os.path.exists(os.path.join(output_interm_dir, subId, 'logs')):
os.makedirs(os.path.join(output_interm_dir, subId, 'logs'))
log_dir = os.path.join(output_interm_dir, subId, 'logs')
from nipype import config
if readable_crash_files:
cfg = dict(execution={'crashfile_format': 'txt'})
config.update_config(cfg)
config.update_config({
'logging': {
'log_directory': log_dir,
'log_to_file': write_logs
},
'execution': {
'crashdump_dir': log_dir
}
})
from nipype import logging
logging.update_logging(config)
# Now import everything else
from nipype.interfaces.io import DataSink
from nipype.interfaces.utility import Merge, IdentityInterface
from nipype.pipeline.engine import Node, Workflow
from nipype.interfaces.nipy.preprocess import ComputeMask
from nipype.algorithms.rapidart import ArtifactDetect
from nipype.interfaces.ants.segmentation import BrainExtraction, N4BiasFieldCorrection
from nipype.interfaces.ants import Registration, ApplyTransforms
from nipype.interfaces.fsl import MCFLIRT, TOPUP, ApplyTOPUP
from nipype.interfaces.fsl.maths import MeanImage
from nipype.interfaces.fsl import Merge as MERGE
from nipype.interfaces.fsl.utils import Smooth
from nipype.interfaces.nipy.preprocess import Trim
from .interfaces import Plot_Coregistration_Montage, Plot_Quality_Control, Plot_Realignment_Parameters, Create_Covariates, Down_Sample_Precision, Create_Encoding_File, Filter_In_Mask
##################
### INPUT NODE ###
##################
# Turn functional file list into interable Node
func_scans = Node(IdentityInterface(fields=['scan']), name='func_scans')
func_scans.iterables = ('scan', funcs)
# Get TR for use in filtering below; we're assuming all BOLD runs have the same TR
tr_length = layout.get_metadata(funcs[0])['RepetitionTime']
#####################################
## TRIM ##
#####################################
if apply_trim:
trim = Node(Trim(), name='trim')
trim.inputs.begin_index = apply_trim
#####################################
## DISTORTION CORRECTION ##
#####################################
if apply_dist_corr:
# Get fmap file locations
fmaps = [
f.filename for f in layout.get(
subject=subId, modality='fmap', extensions='.nii.gz')
]
if not fmaps:
raise IOError(
"Distortion Correction requested but field map scans not found..."
)
# Get fmap metadata
totalReadoutTimes, measurements, fmap_pes = [], [], []
for i, fmap in enumerate(fmaps):
# Grab total readout time for each fmap
totalReadoutTimes.append(
layout.get_metadata(fmap)['TotalReadoutTime'])
# Grab measurements (for some reason pyBIDS doesn't grab dcm_meta... fields from side-car json file and json.load, doesn't either; so instead just read the header using nibabel to determine number of scans)
measurements.append(nib.load(fmap).header['dim'][4])
# Get phase encoding direction
fmap_pe = layout.get_metadata(fmap)["PhaseEncodingDirection"]
fmap_pes.append(fmap_pe)
encoding_file_writer = Node(interface=Create_Encoding_File(),
name='create_encoding')
encoding_file_writer.inputs.totalReadoutTimes = totalReadoutTimes
encoding_file_writer.inputs.fmaps = fmaps
encoding_file_writer.inputs.fmap_pes = fmap_pes
encoding_file_writer.inputs.measurements = measurements
encoding_file_writer.inputs.file_name = 'encoding_file.txt'
merge_to_file_list = Node(interface=Merge(2),
infields=['in1', 'in2'],
name='merge_to_file_list')
merge_to_file_list.inputs.in1 = fmaps[0]
merge_to_file_list.inputs.in1 = fmaps[1]
# Merge AP and PA distortion correction scans
merger = Node(interface=MERGE(dimension='t'), name='merger')
merger.inputs.output_type = 'NIFTI_GZ'
merger.inputs.in_files = fmaps
merger.inputs.merged_file = 'merged_epi.nii.gz'
# Create distortion correction map
topup = Node(interface=TOPUP(), name='topup')
topup.inputs.output_type = 'NIFTI_GZ'
# Apply distortion correction to other scans
apply_topup = Node(interface=ApplyTOPUP(), name='apply_topup')
apply_topup.inputs.output_type = 'NIFTI_GZ'
apply_topup.inputs.method = 'jac'
apply_topup.inputs.interp = 'spline'
###################################
### REALIGN ###
###################################
realign_fsl = Node(MCFLIRT(), name="realign")
realign_fsl.inputs.cost = 'mutualinfo'
realign_fsl.inputs.mean_vol = True
realign_fsl.inputs.output_type = 'NIFTI_GZ'
realign_fsl.inputs.save_mats = True
realign_fsl.inputs.save_rms = True
realign_fsl.inputs.save_plots = True
###################################
### MEAN EPIs ###
###################################
# For coregistration after realignment
mean_epi = Node(MeanImage(), name='mean_epi')
mean_epi.inputs.dimension = 'T'
# For after normalization is done to plot checks
mean_norm_epi = Node(MeanImage(), name='mean_norm_epi')
mean_norm_epi.inputs.dimension = 'T'
###################################
### MASK, ART, COV CREATION ###
###################################
compute_mask = Node(ComputeMask(), name='compute_mask')
compute_mask.inputs.m = .05
art = Node(ArtifactDetect(), name='art')
art.inputs.use_differences = [True, False]
art.inputs.use_norm = True
art.inputs.norm_threshold = 1
art.inputs.zintensity_threshold = 3
art.inputs.mask_type = 'file'
art.inputs.parameter_source = 'FSL'
make_cov = Node(Create_Covariates(), name='make_cov')
################################
### N4 BIAS FIELD CORRECTION ###
################################
if apply_n4:
n4_correction = Node(N4BiasFieldCorrection(), name='n4_correction')
n4_correction.inputs.copy_header = True
n4_correction.inputs.save_bias = False
n4_correction.inputs.num_threads = ants_threads
n4_correction.inputs.input_image = anat
###################################
### BRAIN EXTRACTION ###
###################################
brain_extraction_ants = Node(BrainExtraction(), name='brain_extraction')
brain_extraction_ants.inputs.dimension = 3
brain_extraction_ants.inputs.use_floatingpoint_precision = 1
brain_extraction_ants.inputs.num_threads = ants_threads
brain_extraction_ants.inputs.brain_probability_mask = bet_ants_prob_mask
brain_extraction_ants.inputs.keep_temporary_files = 1
brain_extraction_ants.inputs.brain_template = bet_ants_template
brain_extraction_ants.inputs.extraction_registration_mask = bet_ants_registration_mask
brain_extraction_ants.inputs.out_prefix = 'bet'
###################################
### COREGISTRATION ###
###################################
coregistration = Node(Registration(), name='coregistration')
coregistration.inputs.float = False
coregistration.inputs.output_transform_prefix = "meanEpi2highres"
coregistration.inputs.transforms = ['Rigid']
coregistration.inputs.transform_parameters = [(0.1, ), (0.1, )]
coregistration.inputs.number_of_iterations = [[1000, 500, 250, 100]]
coregistration.inputs.dimension = 3
coregistration.inputs.num_threads = ants_threads
coregistration.inputs.write_composite_transform = True
coregistration.inputs.collapse_output_transforms = True
coregistration.inputs.metric = ['MI']
coregistration.inputs.metric_weight = [1]
coregistration.inputs.radius_or_number_of_bins = [32]
coregistration.inputs.sampling_strategy = ['Regular']
coregistration.inputs.sampling_percentage = [0.25]
coregistration.inputs.convergence_threshold = [1e-08]
coregistration.inputs.convergence_window_size = [10]
coregistration.inputs.smoothing_sigmas = [[3, 2, 1, 0]]
coregistration.inputs.sigma_units = ['mm']
coregistration.inputs.shrink_factors = [[4, 3, 2, 1]]
coregistration.inputs.use_estimate_learning_rate_once = [True]
coregistration.inputs.use_histogram_matching = [False]
coregistration.inputs.initial_moving_transform_com = True
coregistration.inputs.output_warped_image = True
coregistration.inputs.winsorize_lower_quantile = 0.01
coregistration.inputs.winsorize_upper_quantile = 0.99
###################################
### NORMALIZATION ###
###################################
# Settings Explanations
# Only a few key settings are worth adjusting and most others relate to how ANTs optimizer starts or iterates and won't make a ton of difference
# Brian Avants referred to these settings as the last "best tested" when he was aligning fMRI data: https://github.com/ANTsX/ANTsRCore/blob/master/R/antsRegistration.R#L275
# Things that matter the most:
# smoothing_sigmas:
# how much gaussian smoothing to apply when performing registration, probably want the upper limit of this to match the resolution that the data is collected at e.g. 3mm
# Old settings [[3,2,1,0]]*3
# shrink_factors
# The coarseness with which to do registration
# Old settings [[8,4,2,1]] * 3
# >= 8 may result is some problems causing big chunks of cortex with little fine grain spatial structure to be moved to other parts of cortex
# Other settings
# transform_parameters:
# how much regularization to do for fitting that transformation
# for syn this pertains to both the gradient regularization term, and the flow, and elastic terms. Leave the syn settings alone as they seem to be the most well tested across published data sets
# radius_or_number_of_bins
# This is the bin size for MI metrics and 32 is probably adequate for most use cases. Increasing this might increase precision (e.g. to 64) but takes exponentially longer
# use_histogram_matching
# Use image intensity distribution to guide registration
# Leave it on for within modality registration (e.g. T1 -> MNI), but off for between modality registration (e.g. EPI -> T1)
# convergence_threshold
# threshold for optimizer
# convergence_window_size
# how many samples should optimizer average to compute threshold?
# sampling_strategy
# what strategy should ANTs use to initialize the transform. Regular here refers to approximately random sampling around the center of the image mass
normalization = Node(Registration(), name='normalization')
normalization.inputs.float = False
normalization.inputs.collapse_output_transforms = True
normalization.inputs.convergence_threshold = [1e-06]
normalization.inputs.convergence_window_size = [10]
normalization.inputs.dimension = 3
normalization.inputs.fixed_image = MNItemplate
normalization.inputs.initial_moving_transform_com = True
normalization.inputs.metric = ['MI', 'MI', 'CC']
normalization.inputs.metric_weight = [1.0] * 3
normalization.inputs.number_of_iterations = [[1000, 500, 250, 100],
[1000, 500, 250, 100],
[100, 70, 50, 20]]
normalization.inputs.num_threads = ants_threads
normalization.inputs.output_transform_prefix = 'anat2template'
normalization.inputs.output_inverse_warped_image = True
normalization.inputs.output_warped_image = True
normalization.inputs.radius_or_number_of_bins = [32, 32, 4]
normalization.inputs.sampling_percentage = [0.25, 0.25, 1]
normalization.inputs.sampling_strategy = ['Regular', 'Regular', 'None']
normalization.inputs.shrink_factors = [[8, 4, 2, 1]] * 3
normalization.inputs.sigma_units = ['vox'] * 3
normalization.inputs.smoothing_sigmas = [[3, 2, 1, 0]] * 3
normalization.inputs.transforms = ['Rigid', 'Affine', 'SyN']
normalization.inputs.transform_parameters = [(0.1, ), (0.1, ),
(0.1, 3.0, 0.0)]
normalization.inputs.use_histogram_matching = True
normalization.inputs.winsorize_lower_quantile = 0.005
normalization.inputs.winsorize_upper_quantile = 0.995
normalization.inputs.write_composite_transform = True
# NEW SETTINGS (need to be adjusted; specifically shink_factors and smoothing_sigmas need to be the same length)
# normalization = Node(Registration(), name='normalization')
# normalization.inputs.float = False
# normalization.inputs.collapse_output_transforms = True
# normalization.inputs.convergence_threshold = [1e-06, 1e-06, 1e-07]
# normalization.inputs.convergence_window_size = [10]
# normalization.inputs.dimension = 3
# normalization.inputs.fixed_image = MNItemplate
# normalization.inputs.initial_moving_transform_com = True
# normalization.inputs.metric = ['MI', 'MI', 'CC']
# normalization.inputs.metric_weight = [1.0]*3
# normalization.inputs.number_of_iterations = [[1000, 500, 250, 100],
# [1000, 500, 250, 100],
# [100, 70, 50, 20]]
# normalization.inputs.num_threads = ants_threads
# normalization.inputs.output_transform_prefix = 'anat2template'
# normalization.inputs.output_inverse_warped_image = True
# normalization.inputs.output_warped_image = True
# normalization.inputs.radius_or_number_of_bins = [32, 32, 4]
# normalization.inputs.sampling_percentage = [0.25, 0.25, 1]
# normalization.inputs.sampling_strategy = ['Regular',
# 'Regular',
# 'None']
# normalization.inputs.shrink_factors = [[4, 3, 2, 1]]*3
# normalization.inputs.sigma_units = ['vox']*3
# normalization.inputs.smoothing_sigmas = [[2, 1], [2, 1], [3, 2, 1, 0]]
# normalization.inputs.transforms = ['Rigid', 'Affine', 'SyN']
# normalization.inputs.transform_parameters = [(0.1,),
# (0.1,),
# (0.1, 3.0, 0.0)]
# normalization.inputs.use_histogram_matching = True
# normalization.inputs.winsorize_lower_quantile = 0.005
# normalization.inputs.winsorize_upper_quantile = 0.995
# normalization.inputs.write_composite_transform = True
###################################
### APPLY TRANSFORMS AND SMOOTH ###
###################################
merge_transforms = Node(Merge(2),
iterfield=['in2'],
name='merge_transforms')
# Used for epi -> mni, via (coreg + norm)
apply_transforms = Node(ApplyTransforms(),
iterfield=['input_image'],
name='apply_transforms')
apply_transforms.inputs.input_image_type = 3
apply_transforms.inputs.float = False
apply_transforms.inputs.num_threads = 12
apply_transforms.inputs.environ = {}
apply_transforms.inputs.interpolation = 'BSpline'
apply_transforms.inputs.invert_transform_flags = [False, False]
apply_transforms.inputs.reference_image = MNItemplate
# Used for t1 segmented -> mni, via (norm)
apply_transform_seg = Node(ApplyTransforms(), name='apply_transform_seg')
apply_transform_seg.inputs.input_image_type = 3
apply_transform_seg.inputs.float = False
apply_transform_seg.inputs.num_threads = 12
apply_transform_seg.inputs.environ = {}
apply_transform_seg.inputs.interpolation = 'MultiLabel'
apply_transform_seg.inputs.invert_transform_flags = [False]
apply_transform_seg.inputs.reference_image = MNItemplate
###################################
### PLOTS ###
###################################
plot_realign = Node(Plot_Realignment_Parameters(), name="plot_realign")
plot_qa = Node(Plot_Quality_Control(), name="plot_qa")
plot_normalization_check = Node(Plot_Coregistration_Montage(),
name="plot_normalization_check")
plot_normalization_check.inputs.canonical_img = MNItemplatehasskull
############################################
### FILTER, SMOOTH, DOWNSAMPLE PRECISION ###
############################################
# Use cosanlab_preproc for down sampling
down_samp = Node(Down_Sample_Precision(), name="down_samp")
# Use FSL for smoothing
if apply_smooth:
smooth = Node(Smooth(), name='smooth')
if isinstance(apply_smooth, list):
smooth.iterables = ("fwhm", apply_smooth)
elif isinstance(apply_smooth, int) or isinstance(apply_smooth, float):
smooth.inputs.fwhm = apply_smooth
else:
raise ValueError("apply_smooth must be a list or int/float")
# Use cosanlab_preproc for low-pass filtering
if apply_filter:
lp_filter = Node(Filter_In_Mask(), name='lp_filter')
lp_filter.inputs.mask = MNImask
lp_filter.inputs.sampling_rate = tr_length
lp_filter.inputs.high_pass_cutoff = 0
if isinstance(apply_filter, list):
lp_filter.iterables = ("low_pass_cutoff", apply_filter)
elif isinstance(apply_filter, int) or isinstance(apply_filter, float):
lp_filter.inputs.low_pass_cutoff = apply_filter
else:
raise ValueError("apply_filter must be a list or int/float")
###################
### OUTPUT NODE ###
###################
# Collect all final outputs in the output dir and get rid of file name additions
datasink = Node(DataSink(), name='datasink')
if session:
datasink.inputs.base_directory = os.path.join(output_final_dir,
subject_id)
datasink.inputs.container = 'ses-' + session
else:
datasink.inputs.base_directory = output_final_dir
datasink.inputs.container = subject_id
# Remove substitutions
data_dir_parts = data_dir.split('/')[1:]
if session:
prefix = ['_scan_'] + data_dir_parts + [subject_id] + [
'ses-' + session
] + ['func']
else:
prefix = ['_scan_'] + data_dir_parts + [subject_id] + ['func']
func_scan_names = [os.path.split(elem)[-1] for elem in funcs]
to_replace = []
for elem in func_scan_names:
bold_name = elem.split(subject_id + '_')[-1]
bold_name = bold_name.split('.nii.gz')[0]
to_replace.append(('..'.join(prefix + [elem]), bold_name))
datasink.inputs.substitutions = to_replace
#####################
### INIT WORKFLOW ###
#####################
# If we have sessions provide the full path to the subject's intermediate directory
# and only rely on workflow init to create the session container *within* that directory
# Otherwise just point to the intermediate directory and let the workflow init create the subject container within the intermediate directory
if session:
workflow = Workflow(name='ses_' + session)
workflow.base_dir = os.path.join(output_interm_dir, subId)
else:
workflow = Workflow(name=subId)
workflow.base_dir = output_interm_dir
############################
######### PART (1a) #########
# func -> discorr -> trim -> realign
# OR
# func -> trim -> realign
# OR
# func -> discorr -> realign
# OR
# func -> realign
############################
if apply_dist_corr:
workflow.connect([(encoding_file_writer, topup, [('encoding_file',
'encoding_file')]),
(encoding_file_writer, apply_topup,
[('encoding_file', 'encoding_file')]),
(merger, topup, [('merged_file', 'in_file')]),
(func_scans, apply_topup, [('scan', 'in_files')]),
(topup, apply_topup,
[('out_fieldcoef', 'in_topup_fieldcoef'),
('out_movpar', 'in_topup_movpar')])])
if apply_trim:
# Dist Corr + Trim
workflow.connect([(apply_topup, trim, [('out_corrected', 'in_file')
]),
(trim, realign_fsl, [('out_file', 'in_file')])])
else:
# Dist Corr + No Trim
workflow.connect([(apply_topup, realign_fsl, [('out_corrected',
'in_file')])])
else:
if apply_trim:
# No Dist Corr + Trim
workflow.connect([(func_scans, trim, [('scan', 'in_file')]),
(trim, realign_fsl, [('out_file', 'in_file')])])
else:
# No Dist Corr + No Trim
workflow.connect([
(func_scans, realign_fsl, [('scan', 'in_file')]),
])
############################
######### PART (1n) #########
# anat -> N4 -> bet
# OR
# anat -> bet
############################
if apply_n4:
workflow.connect([(n4_correction, brain_extraction_ants,
[('output_image', 'anatomical_image')])])
else:
brain_extraction_ants.inputs.anatomical_image = anat
##########################################
############### PART (2) #################
# realign -> coreg -> mni (via t1)
# t1 -> mni
# covariate creation
# plot creation
###########################################
workflow.connect([
(realign_fsl, plot_realign, [('par_file', 'realignment_parameters')]),
(realign_fsl, plot_qa, [('out_file', 'dat_img')]),
(realign_fsl, art, [('out_file', 'realigned_files'),
('par_file', 'realignment_parameters')]),
(realign_fsl, mean_epi, [('out_file', 'in_file')]),
(realign_fsl, make_cov, [('par_file', 'realignment_parameters')]),
(mean_epi, compute_mask, [('out_file', 'mean_volume')]),
(compute_mask, art, [('brain_mask', 'mask_file')]),
(art, make_cov, [('outlier_files', 'spike_id')]),
(art, plot_realign, [('outlier_files', 'outliers')]),
(plot_qa, make_cov, [('fd_outliers', 'fd_outliers')]),
(brain_extraction_ants, coregistration, [('BrainExtractionBrain',
'fixed_image')]),
(mean_epi, coregistration, [('out_file', 'moving_image')]),
(brain_extraction_ants, normalization, [('BrainExtractionBrain',
'moving_image')]),
(coregistration, merge_transforms, [('composite_transform', 'in2')]),
(normalization, merge_transforms, [('composite_transform', 'in1')]),
(merge_transforms, apply_transforms, [('out', 'transforms')]),
(realign_fsl, apply_transforms, [('out_file', 'input_image')]),
(apply_transforms, mean_norm_epi, [('output_image', 'in_file')]),
(normalization, apply_transform_seg, [('composite_transform',
'transforms')]),
(brain_extraction_ants, apply_transform_seg,
[('BrainExtractionSegmentation', 'input_image')]),
(mean_norm_epi, plot_normalization_check, [('out_file', 'wra_img')])
])
##################################################
################### PART (3) #####################
# epi (in mni) -> filter -> smooth -> down sample
# OR
# epi (in mni) -> filter -> down sample
# OR
# epi (in mni) -> smooth -> down sample
# OR
# epi (in mni) -> down sample
###################################################
if apply_filter:
workflow.connect([(apply_transforms, lp_filter, [('output_image',
'in_file')])])
if apply_smooth:
# Filtering + Smoothing
workflow.connect([(lp_filter, smooth, [('out_file', 'in_file')]),
(smooth, down_samp, [('smoothed_file', 'in_file')
])])
else:
# Filtering + No Smoothing
workflow.connect([(lp_filter, down_samp, [('out_file', 'in_file')])
])
else:
if apply_smooth:
# No Filtering + Smoothing
workflow.connect([
(apply_transforms, smooth, [('output_image', 'in_file')]),
(smooth, down_samp, [('smoothed_file', 'in_file')])
])
else:
# No Filtering + No Smoothing
workflow.connect([(apply_transforms, down_samp, [('output_image',
'in_file')])])
##########################################
############### PART (4) #################
# down sample -> save
# plots -> save
# covs -> save
# t1 (in mni) -> save
# t1 segmented masks (in mni) -> save
# realignment parms -> save
##########################################
workflow.connect([
(down_samp, datasink, [('out_file', 'functional.@down_samp')]),
(plot_realign, datasink, [('plot', 'functional.@plot_realign')]),
(plot_qa, datasink, [('plot', 'functional.@plot_qa')]),
(plot_normalization_check, datasink,
[('plot', 'functional.@plot_normalization')]),
(make_cov, datasink, [('covariates', 'functional.@covariates')]),
(normalization, datasink, [('warped_image', 'structural.@normanat')]),
(apply_transform_seg, datasink, [('output_image',
'structural.@normanatseg')]),
(realign_fsl, datasink, [('par_file', 'functional.@motionparams')])
])
if not os.path.exists(os.path.join(output_dir, 'pipeline.png')):
workflow.write_graph(dotfilename=os.path.join(output_dir, 'pipeline'),
format='png')
print(f"Creating workflow for subject: {subject_id}")
if ants_threads != 8:
print(
f"ANTs will utilize the user-requested {ants_threads} threads for parallel processing."
)
return workflow
def create_pipeline_flow(self, cmp_deriv_subject_directory,
nipype_deriv_subject_directory):
# subject_directory = self.subject_directory
# datasource.inputs.subject = self.subject
if self.parcellation_scheme == 'Lausanne2008':
bids_atlas_label = 'L2008'
elif self.parcellation_scheme == 'Lausanne2018':
bids_atlas_label = 'L2018'
elif self.parcellation_scheme == 'NativeFreesurfer':
bids_atlas_label = 'Desikan'
# Data sinker for output
sinker = pe.Node(nio.DataSink(), name="bold_sinker")
sinker.inputs.base_directory = os.path.join(
cmp_deriv_subject_directory)
if self.parcellation_scheme == 'NativeFreesurfer':
sinker.inputs.substitutions = [
(
'eroded_brain_registered.nii.gz', self.subject + '_space-meanBOLD_desc-eroded_label-brain_dseg.nii.gz'),
('eroded_csf_registered.nii.gz', self.subject +
'_space-meanBOLD_desc-eroded_label-CSF_dseg.nii.gz'),
('wm_mask_registered.nii.gz', self.subject +
'_space-meanBOLD_label-WM_dseg.nii.gz'),
('eroded_wm_registered.nii.gz', self.subject +
'_space-meanBOLD_desc-eroded_label-WM_dseg.nii.gz'),
('fMRI_despike_st_mcf.nii.gz_mean_reg.nii.gz',
self.subject + '_meanBOLD.nii.gz'),
('fMRI_despike_st_mcf.nii.gz.par', self.subject + '_motion.par'),
('FD.npy', self.subject + '_desc-scrubbing_FD.npy'),
('DVARS.npy', self.subject + '_desc-scrubbing_DVARS.npy'),
('fMRI_bandpass.nii.gz', self.subject +
'_desc-bandpass_task-rest_bold.nii.gz'),
(self.subject + '_label-' + bids_atlas_label + '_atlas_flirt.nii.gz',
self.subject + '_space-meanBOLD_label-' + bids_atlas_label + '_atlas.nii.gz'),
# (self.subject+'_T1w_parc_freesurferaparc_flirt.nii.gz',self.subject+'_space-meanBOLD_label-Desikan_atlas.nii.gz'),
('connectome_freesurferaparc', self.subject + \
'_label-Desikan_conndata-network_connectivity'),
('averageTimeseries_freesurferaparc',
self.subject + '_atlas-Desikan_timeseries'),
]
else:
sinker.inputs.substitutions = [
('eroded_brain_registered.nii.gz', self.subject +
'_space-meanBOLD_desc-eroded_label-brain_dseg.nii.gz'),
('wm_mask_registered.nii.gz',
self.subject + '_space-meanBOLD_label-WM_dseg.nii.gz'),
('eroded_csf_registered.nii.gz', self.subject +
'_space-meanBOLD_desc-eroded_label-CSF_dseg.nii.gz'),
('eroded_wm_registered.nii.gz', self.subject +
'_space-meanBOLD_desc-eroded_label-WM_dseg.nii.gz'),
('fMRI_despike_st_mcf.nii.gz_mean_reg.nii.gz',
self.subject + '_meanBOLD.nii.gz'),
('fMRI_despike_st_mcf.nii.gz.par',
self.subject + '_motion.tsv'),
('FD.npy', self.subject + '_desc-scrubbing_FD.npy'),
('DVARS.npy', self.subject + '_desc-scrubbing_DVARS.npy'),
('fMRI_bandpass.nii.gz',
self.subject + '_desc-bandpass_task-rest_bold.nii.gz'),
(self.subject + '_label-' + bids_atlas_label +
'_atlas_flirt.nii.gz',
self.subject + '_space-meanBOLD_label-' + bids_atlas_label +
'_atlas.nii.gz'),
(self.subject + '_label-' + bids_atlas_label +
'_desc-scale1_atlas_flirt.nii.gz',
self.subject + '_space-meanBOLD_label-' + bids_atlas_label +
'_desc-scale1_atlas.nii.gz'),
(self.subject + '_label-' + bids_atlas_label +
'_desc-scale1_atlas_flirt.nii.gz',
self.subject + '_space-meanBOLD_label-' + bids_atlas_label +
'_desc-scale2_atlas.nii.gz'),
(self.subject + '_label-' + bids_atlas_label +
'_desc-scale1_atlas_flirt.nii.gz',
self.subject + '_space-meanBOLD_label-' + bids_atlas_label +
'_desc-scale3_atlas.nii.gz'),
(self.subject + '_label-' + bids_atlas_label +
'_desc-scale1_atlas_flirt.nii.gz',
self.subject + '_space-meanBOLD_label-' + bids_atlas_label +
'_desc-scale4_atlas.nii.gz'),
(self.subject + '_label-' + bids_atlas_label +
'_desc-scale1_atlas_flirt.nii.gz',
self.subject + '_space-meanBOLD_label-' + bids_atlas_label +
'_desc-scale5_atlas.nii.gz'),
('connectome_freesurferaparc', self.subject +
'_label-Desikan_conndata-network_connectivity'),
('connectome_scale1',
self.subject + '_label-' + bids_atlas_label +
'_desc-scale1_conndata-network_connectivity'),
('connectome_scale2',
self.subject + '_label-' + bids_atlas_label +
'_desc-scale2_conndata-network_connectivity'),
('connectome_scale3',
self.subject + '_label-' + bids_atlas_label +
'_desc-scale3_conndata-network_connectivity'),
('connectome_scale4',
self.subject + '_label-' + bids_atlas_label +
'_desc-scale4_conndata-network_connectivity'),
('connectome_scale5',
self.subject + '_label-' + bids_atlas_label +
'_desc-scale5_conndata-network_connectivity'),
('averageTimeseries_scale1', self.subject + '_atlas-' +
bids_atlas_label + '_desc-scale1_timeseries'),
('averageTimeseries_scale2', self.subject + '_atlas-' +
bids_atlas_label + '_desc-scale2_timeseries'),
('averageTimeseries_scale3', self.subject + '_atlas-' +
bids_atlas_label + '_desc-scale3_timeseries'),
('averageTimeseries_scale4', self.subject + '_atlas-' +
bids_atlas_label + '_desc-scale4_timeseries'),
('averageTimeseries_scale5', self.subject + '_atlas-' +
bids_atlas_label + '_desc-scale5_timeseries'),
]
# Data import
datasource = pe.Node(interface=nio.DataGrabber(outfields=[
'fMRI', 'T1', 'T2', 'aseg', 'brain', 'brain_mask', 'wm_mask_file',
'wm_eroded', 'brain_eroded', 'csf_eroded', 'roi_volume_s1',
'roi_volume_s2', 'roi_volume_s3', 'roi_volume_s4', 'roi_volume_s5',
'roi_graphml_s1', 'roi_graphml_s2', 'roi_graphml_s3',
'roi_graphml_s4', 'roi_graphml_s5'
]),
name='datasource')
datasource.inputs.base_directory = cmp_deriv_subject_directory
datasource.inputs.template = '*'
datasource.inputs.raise_on_empty = False
# datasource.inputs.field_template = dict(fMRI='fMRI.nii.gz',T1='T1.nii.gz',T2='T2.nii.gz')
if self.parcellation_scheme == 'NativeFreesurfer':
datasource.inputs.field_template = dict(
fMRI='func/' + self.subject +
'_task-rest_desc-cmp_bold.nii.gz',
T1='anat/' + self.subject + '_desc-head_T1w.nii.gz',
T2='anat/' + self.subject + '_T2w.nii.gz',
aseg='anat/' + self.subject + '_desc-aseg_desg.nii.gz',
brain='anat/' + self.subject + '_desc-brain_T1w.nii.gz',
brain_mask='anat/' + self.subject + '_desc-brain_mask.nii.gz',
wm_mask_file='anat/' + self.subject + '_label-WM_dseg.nii.gz',
wm_eroded='anat/' + self.subject +
'_label-WM_desc-eroded_dseg.nii.gz',
brain_eroded='anat/' + self.subject +
'_label-brain_desc-eroded_dseg.nii.gz',
csf_eroded='anat/' + self.subject +
'_label-CSF_desc-eroded_dseg.nii.gz',
roi_volume_s1='anat/' + self.subject +
'_label-Desikan_atlas.nii.gz',
roi_volume_s2='anat/irrelevant.nii.gz',
roi_volume_s3='anat/irrelevant.nii.gz',
roi_volume_s4='anat/irrelevant.nii.gz',
roi_volume_s5='anat/irrelevant.nii.gz',
roi_graphml_s1='anat/' + self.subject +
'_label-Desikan_atlas.graphml',
roi_graphml_s2='anat/irrelevant.graphml',
roi_graphml_s3='anat/irrelevant.graphml',
roi_graphml_s4='anat/irrelevant.graphml',
roi_graphml_s5='anat/irrelevant.graphml')
else:
datasource.inputs.field_template = dict(
fMRI='func/' + self.subject +
'_task-rest_desc-cmp_bold.nii.gz',
T1='anat/' + self.subject + '_desc-head_T1w.nii.gz',
T2='anat/' + self.subject + '_T2w.nii.gz',
aseg='anat/' + self.subject + '_desc-aseg_desg.nii.gz',
brain='anat/' + self.subject + '_desc-brain_T1w.nii.gz',
brain_mask='anat/' + self.subject + '_desc-brain_mask.nii.gz',
wm_mask_file='anat/' + self.subject + '_label-WM_dseg.nii.gz',
wm_eroded='anat/' + self.subject +
'_label-WM_desc-eroded_dseg.nii.gz',
brain_eroded='anat/' + self.subject +
'_label-brain_desc-eroded_dseg.nii.gz',
csf_eroded='anat/' + self.subject +
'_label-CSF_desc-eroded_dseg.nii.gz',
roi_volume_s1='anat/' + self.subject + '_label-' +
bids_atlas_label + '_desc-scale1_atlas.nii.gz',
roi_volume_s2='anat/' + self.subject + '_label-' +
bids_atlas_label + '_desc-scale2_atlas.nii.gz',
roi_volume_s3='anat/' + self.subject + '_label-' +
bids_atlas_label + '_desc-scale3_atlas.nii.gz',
roi_volume_s4='anat/' + self.subject + '_label-' +
bids_atlas_label + '_desc-scale4_atlas.nii.gz',
roi_volume_s5='anat/' + self.subject + '_label-' +
bids_atlas_label + '_desc-scale5_atlas.nii.gz',
roi_graphml_s1='anat/' + self.subject + '_label-' +
bids_atlas_label + '_desc-scale1_atlas.graphml',
roi_graphml_s2='anat/' + self.subject + '_label-' +
bids_atlas_label + '_desc-scale2_atlas.graphml',
roi_graphml_s3='anat/' + self.subject + '_label-' +
bids_atlas_label + '_desc-scale3_atlas.graphml',
roi_graphml_s4='anat/' + self.subject + '_label-' +
bids_atlas_label + '_desc-scale4_atlas.graphml',
roi_graphml_s5='anat/' + self.subject + '_label-' +
bids_atlas_label + '_desc-scale5_atlas.graphml')
datasource.inputs.sort_filelist = False
# Clear previous outputs
self.clear_stages_outputs()
# Create fMRI flow
fMRI_flow = pe.Workflow(
name='fMRI_pipeline',
base_dir=os.path.abspath(nipype_deriv_subject_directory))
fMRI_inputnode = pe.Node(interface=util.IdentityInterface(fields=[
"fMRI", "T1", "T2", "subjects_dir", "subject_id", "wm_mask_file",
"roi_volumes", "roi_graphMLs", "wm_eroded", "brain_eroded",
"csf_eroded"
]),
name="inputnode")
fMRI_inputnode.inputs.parcellation_scheme = self.parcellation_scheme
fMRI_inputnode.inputs.atlas_info = self.atlas_info
fMRI_inputnode.subjects_dir = self.subjects_dir
# fMRI_inputnode.subject_id = self.subject_id
fMRI_inputnode.subject_id = os.path.basename(self.subject_id)
# print('fMRI_inputnode.subjects_dir : {}'.format(fMRI_inputnode.subjects_dir))
# print('fMRI_inputnode.subject_id : {}'.format(fMRI_inputnode.subject_id))
fMRI_outputnode = pe.Node(
interface=util.IdentityInterface(fields=["connectivity_matrices"]),
name="outputnode")
fMRI_flow.add_nodes([fMRI_inputnode, fMRI_outputnode])
merge_roi_volumes = pe.Node(interface=Merge(5),
name='merge_roi_volumes')
merge_roi_graphmls = pe.Node(interface=Merge(5),
name='merge_roi_graphmls')
def remove_non_existing_scales(roi_volumes):
out_roi_volumes = []
for vol in roi_volumes:
if vol is not None:
out_roi_volumes.append(vol)
return out_roi_volumes
fMRI_flow.connect([(datasource,
merge_roi_volumes, [("roi_volume_s1", "in1"),
("roi_volume_s2", "in2"),
("roi_volume_s3", "in3"),
("roi_volume_s4", "in4"),
("roi_volume_s5", "in5")])])
fMRI_flow.connect([(datasource, merge_roi_graphmls,
[("roi_graphml_s1", "in1"),
("roi_graphml_s2", "in2"),
("roi_graphml_s3", "in3"),
("roi_graphml_s4", "in4"),
("roi_graphml_s5", "in5")])])
fMRI_flow.connect([
(datasource, fMRI_inputnode, [("fMRI", "fMRI"), ("T1", "T1"),
("T2", "T2"), ("aseg", "aseg"),
("wm_mask_file", "wm_mask_file"),
("brain_eroded", "brain_eroded"),
("wm_eroded", "wm_eroded"),
("csf_eroded", "csf_eroded")]),
# ,( "roi_volumes","roi_volumes")])
(merge_roi_volumes, fMRI_inputnode,
[(("out", remove_non_existing_scales), "roi_volumes")]),
(merge_roi_graphmls, fMRI_inputnode,
[(("out", remove_non_existing_scales), "roi_graphMLs")]),
])
if self.stages['Preprocessing'].enabled:
preproc_flow = self.create_stage_flow("Preprocessing")
fMRI_flow.connect([
(fMRI_inputnode, preproc_flow, [("fMRI",
"inputnode.functional")]),
(preproc_flow, sinker, [("outputnode.mean_vol",
"func.@mean_vol")]),
])
if self.stages['Registration'].enabled:
reg_flow = self.create_stage_flow("Registration")
fMRI_flow.connect([
(fMRI_inputnode, reg_flow, [('T1', 'inputnode.T1')]),
(fMRI_inputnode, reg_flow, [('T2', 'inputnode.T2')]),
(preproc_flow, reg_flow, [('outputnode.mean_vol',
'inputnode.target')]),
(fMRI_inputnode, reg_flow,
[('wm_mask_file', 'inputnode.wm_mask'),
('roi_volumes', 'inputnode.roi_volumes'),
('brain_eroded', 'inputnode.eroded_brain'),
('wm_eroded', 'inputnode.eroded_wm'),
('csf_eroded', 'inputnode.eroded_csf')]),
(reg_flow, sinker, [('outputnode.wm_mask_registered_crop',
'anat.@registered_wm'),
('outputnode.roi_volumes_registered_crop',
'anat.@registered_roi_volumes'),
('outputnode.eroded_wm_registered_crop',
'anat.@eroded_wm'),
('outputnode.eroded_csf_registered_crop',
'anat.@eroded_csf'),
('outputnode.eroded_brain_registered_crop',
'anat.@eroded_brain')]),
])
# if self.stages['FunctionalMRI'].config.global_nuisance:
# fMRI_flow.connect([
# (fMRI_inputnode,reg_flow,[('brain_eroded','inputnode.eroded_brain')])
# ])
# if self.stages['FunctionalMRI'].config.csf:
# fMRI_flow.connect([
# (fMRI_inputnode,reg_flow,[('csf_eroded','inputnode.eroded_csf')])
# ])
if self.stages['FunctionalMRI'].enabled:
func_flow = self.create_stage_flow("FunctionalMRI")
fMRI_flow.connect([
(preproc_flow, func_flow, [('outputnode.functional_preproc',
'inputnode.preproc_file')]),
(reg_flow, func_flow,
[('outputnode.wm_mask_registered_crop',
'inputnode.registered_wm'),
('outputnode.roi_volumes_registered_crop',
'inputnode.registered_roi_volumes'),
('outputnode.eroded_wm_registered_crop',
'inputnode.eroded_wm'),
('outputnode.eroded_csf_registered_crop',
'inputnode.eroded_csf'),
('outputnode.eroded_brain_registered_crop',
'inputnode.eroded_brain')]),
(func_flow, sinker, [('outputnode.func_file',
'func.@func_file'),
("outputnode.FD", "func.@FD"),
("outputnode.DVARS", "func.@DVARS")]),
])
if self.stages['FunctionalMRI'].config.scrubbing or self.stages[
'FunctionalMRI'].config.motion:
fMRI_flow.connect([
(preproc_flow, func_flow, [("outputnode.par_file",
"inputnode.motion_par_file")]),
(preproc_flow, sinker, [("outputnode.par_file",
"func.@motion_par_file")])
])
if self.stages['Connectome'].enabled:
self.stages['Connectome'].config.subject = self.global_conf.subject
con_flow = self.create_stage_flow("Connectome")
fMRI_flow.connect([
(fMRI_inputnode, con_flow,
[('parcellation_scheme', 'inputnode.parcellation_scheme'),
('roi_graphMLs', 'inputnode.roi_graphMLs')]),
(func_flow, con_flow,
[('outputnode.func_file', 'inputnode.func_file'),
("outputnode.FD", "inputnode.FD"),
("outputnode.DVARS", "inputnode.DVARS")]),
(reg_flow, con_flow,
[("outputnode.roi_volumes_registered_crop",
"inputnode.roi_volumes_registered")]),
(con_flow, fMRI_outputnode,
[("outputnode.connectivity_matrices", "connectivity_matrices")
]),
(con_flow, sinker, [("outputnode.connectivity_matrices",
"func.@connectivity_matrices")]),
(con_flow, sinker, [("outputnode.avg_timeseries",
"func.@avg_timeseries")])
])
# if self.parcellation_scheme == "Custom":
# fMRI_flow.connect(
# [(fMRI_inputnode, con_flow, [('atlas_info', 'inputnode.atlas_info')])])
return fMRI_flow
def petpvc_mask(wf_name="petpvc_mask"):
""" A Workflow that returns a 4D merge of 4 volumes for PETPVC: GM, WM, CSF and background.
Parameters
----------
wf_name: str
The name of the workflow.
Nipype.Inputs
-------------
pvcmask_input.tissues: list of existing files
List of tissue files in anatomical space, the 3 file
paths must be in this order: GM, WM, CSF
Nipype.Outputs
--------------
pvcmask_output.petpvc_mask: existing file
A 4D volume file with these maps in order: GM, WM, CSF, background
pvcmask_output.brain_mask: existing file
A mask that is a binarised sum of the tissues file with fslmaths.
Can be used as brain mask in anatomical space for the PET image.
Returns
-------
wf: nipype Workflow
"""
# define nodes
# specify input and output fields
in_fields = ["tissues"]
out_fields = [
"petpvc_mask",
"brain_mask",
]
# input
pvcmask_input = setup_node(IdentityInterface(fields=in_fields,
mandatory_inputs=True),
name="pvcmask_input")
tissues = setup_node(IdentityInterface(fields=["gm", "wm", "csf"],
mandatory_inputs=True),
name="tissues")
merge_list = setup_node(Merge(4), name="merge_list")
# maths for background
img_bkg = setup_node(Function(
function=math_img,
input_names=["formula", "out_file", "gm", "wm", "csf"],
output_names=["out_file"],
imports=['from neuro_pypes.interfaces.nilearn import ni2file']),
name='background')
img_bkg.inputs.out_file = "tissue_bkg.nii.gz"
img_bkg.inputs.formula = "np.maximum((-((gm + wm + csf) - 1)), 0)"
# maths for brain mask
brain_mask = setup_node(Function(
function=math_img,
input_names=["formula", "out_file", "gm", "wm", "csf"],
output_names=["out_file"],
imports=['from neuro_pypes.interfaces.nilearn import ni2file']),
name='brain_mask')
brain_mask.inputs.out_file = "tissues_brain_mask.nii.gz"
brain_mask.inputs.formula = "np.abs(gm + wm + csf) > 0"
# concat the tissues images and the background for PETPVC
merge_tissues = setup_node(Function(
function=concat_imgs,
input_names=["in_files"],
output_names=["out_file"],
imports=['from neuro_pypes.interfaces.nilearn import ni2file']),
name='merge_tissues')
merge_tissues.inputs.out_file = "petpvc_mask.nii.gz"
# output
pvcmask_output = setup_node(IdentityInterface(fields=out_fields),
name="pvcmask_output")
# Create the workflow object
wf = Workflow(name=wf_name)
# Connect the nodes
wf.connect([
# separate [GM, WM, CSF] into [GM] and [WM, CSF]
(pvcmask_input, tissues, [(("tissues", selectindex, 0), "gm"),
(("tissues", selectindex, 1), "wm"),
(("tissues", selectindex, 2), "csf")]),
(tissues, img_bkg, [("gm", "gm"), ("wm", "wm"), ("csf", "csf")]),
(tissues, brain_mask, [("gm", "gm"), ("wm", "wm"), ("csf", "csf")]),
(tissues, merge_list, [("gm", "in1"), ("wm", "in2"), ("csf", "in3")]),
# create a list of [GM, WM, CSF, BKG]
(img_bkg, merge_list, [("out_file", "in4")]),
# merge into 4D: [GM, WM, CSF, BKG]
(merge_list, merge_tissues, [("out", "in_files")]),
# output
(merge_tissues, pvcmask_output, [("out_file", "petpvc_mask")]),
(brain_mask, pvcmask_output, [("out_file", "brain_mask")]),
])
return wf
def firstlevel_wf(subject_id, sink_directory, name='wmaze_frstlvl_wf'):
frstlvl_wf = Workflow(name='frstlvl_wf')
info = dict(
task_mri_files=[['subject_id',
'wmaze']], #dictionary used in datasource
motion_noise_files=[['subject_id']])
#function node to call subjectinfo function with name, onset, duration, and amplitude info
subject_info = Node(Function(input_names=['subject_id'],
output_names=['output'],
function=subjectinfo),
name='subject_info')
subject_info.inputs.ignore_exception = False
subject_info.inputs.subject_id = subject_id
#function node to define contrasts
getcontrasts = Node(Function(input_names=['subject_id', 'info'],
output_names=['contrasts'],
function=get_contrasts),
name='getcontrasts')
getcontrasts.inputs.ignore_exception = False
getcontrasts.inputs.subject_id = subject_id
frstlvl_wf.connect(subject_info, 'output', getcontrasts, 'info')
#function node to substitute names of folders and files created during pipeline
getsubs = Node(
Function(
input_names=['cons'],
output_names=['subs'],
# Calls the function 'get_subs'
function=get_subs),
name='getsubs')
getsubs.inputs.ignore_exception = False
getsubs.inputs.subject_id = subject_id
frstlvl_wf.connect(subject_info, 'output', getsubs, 'info')
frstlvl_wf.connect(getcontrasts, 'contrasts', getsubs, 'cons')
#datasource node to get task_mri and motion-noise files
datasource = Node(DataGrabber(infields=['subject_id'],
outfields=info.keys()),
name='datasource')
datasource.inputs.template = '*'
datasource.inputs.subject_id = subject_id
datasource.inputs.base_directory = os.path.abspath(
'/home/data/madlab/data/mri/wmaze/preproc/')
datasource.inputs.field_template = dict(
task_mri_files=
'%s/func/smoothed_fullspectrum/_maskfunc2*/*%s*.nii.gz', #functional files
motion_noise_files='%s/noise/filter_regressor??.txt'
) #filter regressor noise files
datasource.inputs.template_args = info
datasource.inputs.sort_filelist = True
datasource.inputs.ignore_exception = False
datasource.inputs.raise_on_empty = True
#function node to remove last three volumes from functional data
fslroi_epi = MapNode(
ExtractROI(t_min=0,
t_size=197), #start from first volume and end on -3
iterfield=['in_file'],
name='fslroi_epi')
fslroi_epi.output_type = 'NIFTI_GZ'
fslroi_epi.terminal_output = 'stream'
frstlvl_wf.connect(datasource, 'task_mri_files', fslroi_epi, 'in_file')
#function node to modify the motion and noise files to be single regressors
motionnoise = Node(Function(input_names=['subjinfo', 'files'],
output_names=['subjinfo'],
function=motion_noise),
name='motionnoise')
motionnoise.inputs.ignore_exception = False
frstlvl_wf.connect(subject_info, 'output', motionnoise, 'subjinfo')
frstlvl_wf.connect(datasource, 'motion_noise_files', motionnoise, 'files')
#node to create model specifications compatible with spm/fsl designers (requires subjectinfo to be received in the form of a Bunch)
specify_model = Node(SpecifyModel(), name='specify_model')
specify_model.inputs.high_pass_filter_cutoff = -1.0 #high-pass filter cutoff in seconds
specify_model.inputs.ignore_exception = False
specify_model.inputs.input_units = 'secs' #input units in either 'secs' or 'scans'
specify_model.inputs.time_repetition = 2.0 #TR
frstlvl_wf.connect(
fslroi_epi, 'roi_file', specify_model,
'functional_runs') #editted data files for model -- list of 4D files
#list of event description files in 3 column format corresponding to onsets, durations, and amplitudes
frstlvl_wf.connect(motionnoise, 'subjinfo', specify_model, 'subject_info')
#node for basic interface class generating identity mappings
modelfit_inputspec = Node(IdentityInterface(fields=[
'session_info', 'interscan_interval', 'contrasts', 'film_threshold',
'functional_data', 'bases', 'model_serial_correlations'
],
mandatory_inputs=True),
name='modelfit_inputspec')
modelfit_inputspec.inputs.bases = {'dgamma': {'derivs': False}}
modelfit_inputspec.inputs.film_threshold = 0.0
modelfit_inputspec.inputs.interscan_interval = 2.0
modelfit_inputspec.inputs.model_serial_correlations = True
frstlvl_wf.connect(fslroi_epi, 'roi_file', modelfit_inputspec,
'functional_data')
frstlvl_wf.connect(getcontrasts, 'contrasts', modelfit_inputspec,
'contrasts')
frstlvl_wf.connect(specify_model, 'session_info', modelfit_inputspec,
'session_info')
#node for first level SPM design matrix to demonstrate contrasts and motion/noise regressors
level1_design = MapNode(Level1Design(),
iterfield=['contrasts', 'session_info'],
name='level1_design')
level1_design.inputs.ignore_exception = False
frstlvl_wf.connect(modelfit_inputspec, 'interscan_interval', level1_design,
'interscan_interval')
frstlvl_wf.connect(modelfit_inputspec, 'session_info', level1_design,
'session_info')
frstlvl_wf.connect(modelfit_inputspec, 'contrasts', level1_design,
'contrasts')
frstlvl_wf.connect(modelfit_inputspec, 'bases', level1_design, 'bases')
frstlvl_wf.connect(modelfit_inputspec, 'model_serial_correlations',
level1_design, 'model_serial_correlations')
#MapNode to generate a design.mat file for each run
generate_model = MapNode(FEATModel(),
iterfield=['fsf_file', 'ev_files'],
name='generate_model')
generate_model.inputs.environ = {'FSLOUTPUTTYPE': 'NIFTI_GZ'}
generate_model.inputs.ignore_exception = False
generate_model.inputs.output_type = 'NIFTI_GZ'
generate_model.inputs.terminal_output = 'stream'
frstlvl_wf.connect(level1_design, 'fsf_files', generate_model, 'fsf_file')
frstlvl_wf.connect(level1_design, 'ev_files', generate_model, 'ev_files')
#MapNode to estimate the model using FILMGLS -- fits the design matrix to the voxel timeseries
estimate_model = MapNode(FILMGLS(),
iterfield=['design_file', 'in_file', 'tcon_file'],
name='estimate_model')
estimate_model.inputs.environ = {'FSLOUTPUTTYPE': 'NIFTI_GZ'}
estimate_model.inputs.ignore_exception = False
estimate_model.inputs.mask_size = 5 #Susan-smooth mask size
estimate_model.inputs.output_type = 'NIFTI_GZ'
estimate_model.inputs.results_dir = 'results'
estimate_model.inputs.smooth_autocorr = True #smooth auto-correlation estimates
estimate_model.inputs.terminal_output = 'stream'
frstlvl_wf.connect(modelfit_inputspec, 'film_threshold', estimate_model,
'threshold')
frstlvl_wf.connect(modelfit_inputspec, 'functional_data', estimate_model,
'in_file')
frstlvl_wf.connect(
generate_model, 'design_file', estimate_model,
'design_file') #mat file containing ascii matrix for design
frstlvl_wf.connect(generate_model, 'con_file', estimate_model,
'tcon_file') #contrast file containing contrast vectors
#merge node to merge the contrasts - necessary for fsl 5.0.7 and greater
merge_contrasts = MapNode(Merge(2),
iterfield=['in1'],
name='merge_contrasts')
frstlvl_wf.connect(estimate_model, 'zstats', merge_contrasts, 'in1')
#MapNode to transform the z2pval
z2pval = MapNode(ImageMaths(), iterfield=['in_file'], name='z2pval')
z2pval.inputs.environ = {'FSLOUTPUTTYPE': 'NIFTI_GZ'}
z2pval.inputs.ignore_exception = False
z2pval.inputs.op_string = '-ztop' #defines the operation used
z2pval.inputs.output_type = 'NIFTI_GZ'
z2pval.inputs.suffix = '_pval'
z2pval.inputs.terminal_output = 'stream'
frstlvl_wf.connect(merge_contrasts, ('out', pop_lambda), z2pval, 'in_file')
#outputspec node using IdentityInterface() to receive information from estimate_model, merge_contrasts, z2pval, generate_model, and estimate_model
modelfit_outputspec = Node(IdentityInterface(fields=[
'copes', 'varcopes', 'dof_file', 'pfiles', 'parameter_estimates',
'zstats', 'design_image', 'design_file', 'design_cov', 'sigmasquareds'
],
mandatory_inputs=True),
name='modelfit_outputspec')
frstlvl_wf.connect(estimate_model, 'copes', modelfit_outputspec,
'copes') #lvl1 cope files
frstlvl_wf.connect(estimate_model, 'varcopes', modelfit_outputspec,
'varcopes') #lvl1 varcope files
frstlvl_wf.connect(merge_contrasts, 'out', modelfit_outputspec,
'zstats') #zstats across runs
frstlvl_wf.connect(z2pval, 'out_file', modelfit_outputspec, 'pfiles')
frstlvl_wf.connect(
generate_model, 'design_image', modelfit_outputspec,
'design_image') #graphical representation of design matrix
frstlvl_wf.connect(
generate_model, 'design_file', modelfit_outputspec,
'design_file') #mat file containing ascii matrix for design
frstlvl_wf.connect(
generate_model, 'design_cov', modelfit_outputspec,
'design_cov') #graphical representation of design covariance
frstlvl_wf.connect(estimate_model, 'param_estimates', modelfit_outputspec,
'parameter_estimates'
) #parameter estimates for columns of design matrix
frstlvl_wf.connect(estimate_model, 'dof_file', modelfit_outputspec,
'dof_file') #degrees of freedom
frstlvl_wf.connect(estimate_model, 'sigmasquareds', modelfit_outputspec,
'sigmasquareds') #summary of residuals
#datasink node to save output from multiple points in the pipeline
sinkd = MapNode(DataSink(),
iterfield=[
'substitutions', 'modelfit.contrasts.@copes',
'modelfit.contrasts.@varcopes', 'modelfit.estimates',
'modelfit.contrasts.@zstats'
],
name='sinkd')
sinkd.inputs.base_directory = sink_directory
sinkd.inputs.container = subject_id
frstlvl_wf.connect(getsubs, 'subs', sinkd, 'substitutions')
frstlvl_wf.connect(modelfit_outputspec, 'parameter_estimates', sinkd,
'modelfit.estimates')
frstlvl_wf.connect(modelfit_outputspec, 'sigmasquareds', sinkd,
'modelfit.estimates.@sigsq')
frstlvl_wf.connect(modelfit_outputspec, 'dof_file', sinkd, 'modelfit.dofs')
frstlvl_wf.connect(modelfit_outputspec, 'copes', sinkd,
'modelfit.contrasts.@copes')
frstlvl_wf.connect(modelfit_outputspec, 'varcopes', sinkd,
'modelfit.contrasts.@varcopes')
frstlvl_wf.connect(modelfit_outputspec, 'zstats', sinkd,
'modelfit.contrasts.@zstats')
frstlvl_wf.connect(modelfit_outputspec, 'design_image', sinkd,
'modelfit.design')
frstlvl_wf.connect(modelfit_outputspec, 'design_cov', sinkd,
'modelfit.design.@cov')
frstlvl_wf.connect(modelfit_outputspec, 'design_file', sinkd,
'modelfit.design.@matrix')
frstlvl_wf.connect(modelfit_outputspec, 'pfiles', sinkd,
'modelfit.contrasts.@pstats')
return frstlvl_wf
def segmentation(projectid, subjectid, sessionid, master_config, onlyT1=True, pipeline_name=''):
import os.path
import nipype.pipeline.engine as pe
import nipype.interfaces.io as nio
from nipype.interfaces import ants
from nipype.interfaces.utility import IdentityInterface, Function, Merge
# Set universal pipeline options
from nipype import config
config.update_config(master_config)
from PipeLineFunctionHelpers import ClipT1ImageWithBrainMask
from .WorkupT1T2BRAINSCut import CreateBRAINSCutWorkflow
from utilities.distributed import modify_qsub_args
from nipype.interfaces.semtools import BRAINSSnapShotWriter
# CLUSTER_QUEUE=master_config['queue']
CLUSTER_QUEUE_LONG = master_config['long_q']
baw200 = pe.Workflow(name=pipeline_name)
# HACK: print for debugging
for key, itme in list(master_config.items()):
print(("-" * 30))
print((key, ":", itme))
print(("-" * 30))
# END HACK
inputsSpec = pe.Node(interface=IdentityInterface(fields=['t1_average',
't2_average',
'template_t1',
'hncma_atlas',
'LMIatlasToSubject_tx',
'inputLabels',
'inputHeadLabels',
'posteriorImages',
'UpdatedPosteriorsList',
'atlasToSubjectRegistrationState',
'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',
'trainModelFile_txtD0060NT0060_gz',
]),
run_without_submitting=True, name='inputspec')
# outputsSpec = pe.Node(interface=IdentityInterface(fields=[...]),
# run_without_submitting=True, name='outputspec')
currentClipT1ImageWithBrainMaskName = 'ClipT1ImageWithBrainMask_' + str(subjectid) + "_" + str(sessionid)
ClipT1ImageWithBrainMaskNode = pe.Node(interface=Function(function=ClipT1ImageWithBrainMask,
input_names=['t1_image', 'brain_labels',
'clipped_file_name'],
output_names=['clipped_file']),
name=currentClipT1ImageWithBrainMaskName)
ClipT1ImageWithBrainMaskNode.inputs.clipped_file_name = 'clipped_from_BABC_labels_t1.nii.gz'
baw200.connect([(inputsSpec, ClipT1ImageWithBrainMaskNode, [('t1_average', 't1_image'),
('inputLabels', 'brain_labels')])])
currentA2SantsRegistrationPostABCSyN = 'A2SantsRegistrationPostABCSyN_' + str(subjectid) + "_" + str(sessionid)
## TODO: It would be great to update the BRAINSABC atlasToSubjectTransform at this point, but
## That requires more testing, and fixes to ANTS to properly collapse transforms.
## For now we are simply creating a dummy node to pass through
A2SantsRegistrationPostABCSyN = pe.Node(interface=ants.Registration(), name=currentA2SantsRegistrationPostABCSyN)
many_cpu_ANTsSyN_options_dictionary = {'qsub_args': modify_qsub_args(CLUSTER_QUEUE_LONG, 8, 8, 16),
'overwrite': True}
A2SantsRegistrationPostABCSyN.plugin_args = many_cpu_ANTsSyN_options_dictionary
CommonANTsRegistrationSettings(
antsRegistrationNode=A2SantsRegistrationPostABCSyN,
registrationTypeDescription="A2SantsRegistrationPostABCSyN",
output_transform_prefix='AtlasToSubjectPostBABC_SyN',
output_warped_image='atlas2subjectPostBABC.nii.gz',
output_inverse_warped_image='subject2atlasPostBABC.nii.gz',
save_state='SavedInternalSyNStatePostBABC.h5',
invert_initial_moving_transform=False,
initial_moving_transform=None)
## TODO: Try multi-modal registration here
baw200.connect([(inputsSpec, A2SantsRegistrationPostABCSyN, [('atlasToSubjectRegistrationState', 'restore_state'),
('t1_average', 'fixed_image'),
('template_t1', 'moving_image')])
])
myLocalSegWF = CreateBRAINSCutWorkflow(projectid,
subjectid,
sessionid,
master_config['queue'],
master_config['long_q'],
"Segmentation",
onlyT1)
MergeStage2AverageImagesName = "99_mergeAvergeStage2Images_" + str(sessionid)
MergeStage2AverageImages = pe.Node(interface=Merge(2), run_without_submitting=True,
name=MergeStage2AverageImagesName)
baw200.connect([(inputsSpec, myLocalSegWF, [('t1_average', 'inputspec.T1Volume'),
('template_t1', 'inputspec.template_t1'),
('posteriorImages', "inputspec.posteriorDictionary"),
('inputLabels', 'inputspec.RegistrationROI'), ]),
(inputsSpec, MergeStage2AverageImages, [('t1_average', 'in1')]),
(A2SantsRegistrationPostABCSyN, myLocalSegWF, [('composite_transform',
'inputspec.atlasToSubjectTransform')])
])
baw200.connect([(inputsSpec, myLocalSegWF,
[
('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'),
('trainModelFile_txtD0060NT0060_gz', 'inputspec.trainModelFile_txtD0060NT0060_gz')
]
)]
)
if not onlyT1:
baw200.connect([(inputsSpec, myLocalSegWF, [('t2_average', 'inputspec.T2Volume')]),
(inputsSpec, MergeStage2AverageImages, [('t2_average', 'in2')])])
file_count = 15 # Count of files to merge into MergeSessionSubjectToAtlas
else:
file_count = 14 # Count of files to merge into MergeSessionSubjectToAtlas
## NOTE: Element 0 of AccumulatePriorsList is the accumulated GM tissue
# baw200.connect([(AccumulateLikeTissuePosteriorsNode, myLocalSegWF,
# [(('AccumulatePriorsList', getListIndex, 0), "inputspec.TotalGM")]),
# ])
### Now define where the final organized outputs should go.
DataSink = pe.Node(nio.DataSink(), name="CleanedDenoisedSegmentation_DS_" + str(subjectid) + "_" + str(sessionid))
DataSink.overwrite = master_config['ds_overwrite']
DataSink.inputs.base_directory = master_config['resultdir']
# DataSink.inputs.regexp_substitutions = GenerateOutputPattern(projectid, subjectid, sessionid,'BRAINSCut')
# DataSink.inputs.regexp_substitutions = GenerateBRAINSCutImagesOutputPattern(projectid, subjectid, sessionid)
DataSink.inputs.substitutions = [
('Segmentations', os.path.join(projectid, subjectid, sessionid, 'CleanedDenoisedRFSegmentations')),
('subjectANNLabel_', ''),
('ANNContinuousPrediction', ''),
('subject.nii.gz', '.nii.gz'),
('_seg.nii.gz', '_seg.nii.gz'),
('.nii.gz', '_seg.nii.gz'),
('_seg_seg', '_seg')]
baw200.connect([(myLocalSegWF, DataSink, [('outputspec.outputBinaryLeftCaudate', 'Segmentations.@LeftCaudate'),
('outputspec.outputBinaryRightCaudate', 'Segmentations.@RightCaudate'),
('outputspec.outputBinaryLeftHippocampus',
'Segmentations.@LeftHippocampus'),
('outputspec.outputBinaryRightHippocampus',
'Segmentations.@RightHippocampus'),
('outputspec.outputBinaryLeftPutamen', 'Segmentations.@LeftPutamen'),
('outputspec.outputBinaryRightPutamen', 'Segmentations.@RightPutamen'),
('outputspec.outputBinaryLeftThalamus', 'Segmentations.@LeftThalamus'),
('outputspec.outputBinaryRightThalamus', 'Segmentations.@RightThalamus'),
('outputspec.outputBinaryLeftAccumben', 'Segmentations.@LeftAccumben'),
('outputspec.outputBinaryRightAccumben', 'Segmentations.@RightAccumben'),
('outputspec.outputBinaryLeftGlobus', 'Segmentations.@LeftGlobus'),
('outputspec.outputBinaryRightGlobus', 'Segmentations.@RightGlobus'),
('outputspec.outputLabelImageName', 'Segmentations.@LabelImageName'),
('outputspec.outputCSVFileName', 'Segmentations.@CSVFileName')]),
# (myLocalSegWF, DataSink, [('outputspec.cleaned_labels', 'Segmentations.@cleaned_labels')])
])
MergeStage2BinaryVolumesName = "99_MergeStage2BinaryVolumes_" + str(sessionid)
MergeStage2BinaryVolumes = pe.Node(interface=Merge(12), run_without_submitting=True,
name=MergeStage2BinaryVolumesName)
baw200.connect([(myLocalSegWF, MergeStage2BinaryVolumes, [('outputspec.outputBinaryLeftAccumben', 'in1'),
('outputspec.outputBinaryLeftCaudate', 'in2'),
('outputspec.outputBinaryLeftPutamen', 'in3'),
('outputspec.outputBinaryLeftGlobus', 'in4'),
('outputspec.outputBinaryLeftThalamus', 'in5'),
('outputspec.outputBinaryLeftHippocampus', 'in6'),
('outputspec.outputBinaryRightAccumben', 'in7'),
('outputspec.outputBinaryRightCaudate', 'in8'),
('outputspec.outputBinaryRightPutamen', 'in9'),
('outputspec.outputBinaryRightGlobus', 'in10'),
('outputspec.outputBinaryRightThalamus', 'in11'),
('outputspec.outputBinaryRightHippocampus', 'in12')])
])
## SnapShotWriter for Segmented result checking:
SnapShotWriterNodeName = "SnapShotWriter_" + str(sessionid)
SnapShotWriter = pe.Node(interface=BRAINSSnapShotWriter(), name=SnapShotWriterNodeName)
SnapShotWriter.inputs.outputFilename = 'snapShot' + str(sessionid) + '.png' # output specification
SnapShotWriter.inputs.inputPlaneDirection = [2, 1, 1, 1, 1, 0, 0]
SnapShotWriter.inputs.inputSliceToExtractInPhysicalPoint = [-3, -7, -3, 5, 7, 22, -22]
baw200.connect([(MergeStage2AverageImages, SnapShotWriter, [('out', 'inputVolumes')]),
(MergeStage2BinaryVolumes, SnapShotWriter, [('out', 'inputBinaryVolumes')]),
(SnapShotWriter, DataSink, [('outputFilename', 'Segmentations.@outputSnapShot')])
])
# currentAntsLabelWarpToSubject = 'AntsLabelWarpToSubject' + str(subjectid) + "_" + str(sessionid)
# AntsLabelWarpToSubject = pe.Node(interface=ants.ApplyTransforms(), name=currentAntsLabelWarpToSubject)
#
# AntsLabelWarpToSubject.inputs.num_threads = -1
# AntsLabelWarpToSubject.inputs.dimension = 3
# AntsLabelWarpToSubject.inputs.output_image = 'warped_hncma_atlas_seg.nii.gz'
# AntsLabelWarpToSubject.inputs.interpolation = "MultiLabel"
#
# baw200.connect([(A2SantsRegistrationPostABCSyN, AntsLabelWarpToSubject, [('composite_transform', 'transforms')]),
# (inputsSpec, AntsLabelWarpToSubject, [('t1_average', 'reference_image'),
# ('hncma_atlas', 'input_image')])
# ])
# #####
# ### Now define where the final organized outputs should go.
# AntsLabelWarpedToSubject_DSName = "AntsLabelWarpedToSubject_DS_" + str(sessionid)
# AntsLabelWarpedToSubject_DS = pe.Node(nio.DataSink(), name=AntsLabelWarpedToSubject_DSName)
# AntsLabelWarpedToSubject_DS.overwrite = master_config['ds_overwrite']
# AntsLabelWarpedToSubject_DS.inputs.base_directory = master_config['resultdir']
# AntsLabelWarpedToSubject_DS.inputs.substitutions = [('AntsLabelWarpedToSubject', os.path.join(projectid, subjectid, sessionid, 'AntsLabelWarpedToSubject'))]
#
# baw200.connect([(AntsLabelWarpToSubject, AntsLabelWarpedToSubject_DS, [('output_image', 'AntsLabelWarpedToSubject')])])
MergeSessionSubjectToAtlasName = "99_MergeSessionSubjectToAtlas_" + str(sessionid)
MergeSessionSubjectToAtlas = pe.Node(interface=Merge(file_count), run_without_submitting=True,
name=MergeSessionSubjectToAtlasName)
baw200.connect([(myLocalSegWF, MergeSessionSubjectToAtlas, [('outputspec.outputBinaryLeftAccumben', 'in1'),
('outputspec.outputBinaryLeftCaudate', 'in2'),
('outputspec.outputBinaryLeftPutamen', 'in3'),
('outputspec.outputBinaryLeftGlobus', 'in4'),
('outputspec.outputBinaryLeftThalamus', 'in5'),
('outputspec.outputBinaryLeftHippocampus', 'in6'),
('outputspec.outputBinaryRightAccumben', 'in7'),
('outputspec.outputBinaryRightCaudate', 'in8'),
('outputspec.outputBinaryRightPutamen', 'in9'),
('outputspec.outputBinaryRightGlobus', 'in10'),
('outputspec.outputBinaryRightThalamus', 'in11'),
('outputspec.outputBinaryRightHippocampus', 'in12')]),
# (FixWMPartitioningNode, MergeSessionSubjectToAtlas, [('UpdatedPosteriorsList', 'in13')]),
(inputsSpec, MergeSessionSubjectToAtlas, [('UpdatedPosteriorsList', 'in13')]),
(inputsSpec, MergeSessionSubjectToAtlas, [('t1_average', 'in14')])
])
if not onlyT1:
assert file_count == 15
baw200.connect([(inputsSpec, MergeSessionSubjectToAtlas, [('t2_average', 'in15')])])
LinearSubjectToAtlasANTsApplyTransformsName = 'LinearSubjectToAtlasANTsApplyTransforms_' + str(sessionid)
LinearSubjectToAtlasANTsApplyTransforms = pe.MapNode(interface=ants.ApplyTransforms(), iterfield=['input_image'],
name=LinearSubjectToAtlasANTsApplyTransformsName)
LinearSubjectToAtlasANTsApplyTransforms.inputs.num_threads = -1
LinearSubjectToAtlasANTsApplyTransforms.inputs.interpolation = 'Linear'
baw200.connect(
[(A2SantsRegistrationPostABCSyN, LinearSubjectToAtlasANTsApplyTransforms, [('inverse_composite_transform',
'transforms')]),
(inputsSpec, LinearSubjectToAtlasANTsApplyTransforms, [('template_t1', 'reference_image')]),
(MergeSessionSubjectToAtlas, LinearSubjectToAtlasANTsApplyTransforms, [('out', 'input_image')])
])
MergeMultiLabelSessionSubjectToAtlasName = "99_MergeMultiLabelSessionSubjectToAtlas_" + str(sessionid)
MergeMultiLabelSessionSubjectToAtlas = pe.Node(interface=Merge(2), run_without_submitting=True,
name=MergeMultiLabelSessionSubjectToAtlasName)
baw200.connect([(inputsSpec, MergeMultiLabelSessionSubjectToAtlas, [('inputLabels', 'in1'),
('inputHeadLabels', 'in2')])
])
### This is taking this sessions RF label map back into NAC atlas space.
# {
MultiLabelSubjectToAtlasANTsApplyTransformsName = 'MultiLabelSubjectToAtlasANTsApplyTransforms_' + str(
sessionid) + '_map'
MultiLabelSubjectToAtlasANTsApplyTransforms = pe.MapNode(interface=ants.ApplyTransforms(),
iterfield=['input_image'],
name=MultiLabelSubjectToAtlasANTsApplyTransformsName)
MultiLabelSubjectToAtlasANTsApplyTransforms.inputs.num_threads = -1
MultiLabelSubjectToAtlasANTsApplyTransforms.inputs.interpolation = 'MultiLabel'
baw200.connect([(A2SantsRegistrationPostABCSyN, MultiLabelSubjectToAtlasANTsApplyTransforms,
[('inverse_composite_transform', 'transforms')]),
(inputsSpec, MultiLabelSubjectToAtlasANTsApplyTransforms, [('template_t1', 'reference_image')]),
(MergeMultiLabelSessionSubjectToAtlas, MultiLabelSubjectToAtlasANTsApplyTransforms,
[('out', 'input_image')])
])
# }
### Now we must take the sessions to THIS SUBJECTS personalized atlas.
# {
# }
### Now define where the final organized outputs should go.
Subj2Atlas_DSName = "SubjectToAtlas_DS_" + str(sessionid)
Subj2Atlas_DS = pe.Node(nio.DataSink(), name=Subj2Atlas_DSName)
Subj2Atlas_DS.overwrite = master_config['ds_overwrite']
Subj2Atlas_DS.inputs.base_directory = master_config['resultdir']
Subj2Atlas_DS.inputs.regexp_substitutions = [(r'_LinearSubjectToAtlasANTsApplyTransforms_[^/]*',
r'' + sessionid + '/')]
baw200.connect([(LinearSubjectToAtlasANTsApplyTransforms, Subj2Atlas_DS,
[('output_image', 'SubjectToAtlasWarped.@linear_output_images')])])
Subj2AtlasTransforms_DSName = "SubjectToAtlasTransforms_DS_" + str(sessionid)
Subj2AtlasTransforms_DS = pe.Node(nio.DataSink(), name=Subj2AtlasTransforms_DSName)
Subj2AtlasTransforms_DS.overwrite = master_config['ds_overwrite']
Subj2AtlasTransforms_DS.inputs.base_directory = master_config['resultdir']
Subj2AtlasTransforms_DS.inputs.regexp_substitutions = [(r'SubjectToAtlasWarped',
r'SubjectToAtlasWarped/' + sessionid + '/')]
baw200.connect([(A2SantsRegistrationPostABCSyN, Subj2AtlasTransforms_DS,
[('composite_transform', 'SubjectToAtlasWarped.@composite_transform'),
('inverse_composite_transform', 'SubjectToAtlasWarped.@inverse_composite_transform')])])
# baw200.connect([(MultiLabelSubjectToAtlasANTsApplyTransforms, Subj2Atlas_DS, [('output_image', 'SubjectToAtlasWarped.@multilabel_output_images')])])
if master_config['plugin_name'].startswith(
'SGE'): # for some nodes, the qsub call needs to be modified on the cluster
A2SantsRegistrationPostABCSyN.plugin_args = {'template': master_config['plugin_args']['template'],
'overwrite': True,
'qsub_args': modify_qsub_args(master_config['queue'], 8, 8, 24)}
SnapShotWriter.plugin_args = {'template': master_config['plugin_args']['template'], 'overwrite': True,
'qsub_args': modify_qsub_args(master_config['queue'], 1, 1, 1)}
LinearSubjectToAtlasANTsApplyTransforms.plugin_args = {'template': master_config['plugin_args']['template'],
'overwrite': True,
'qsub_args': modify_qsub_args(master_config['queue'], 1,
1, 1)}
MultiLabelSubjectToAtlasANTsApplyTransforms.plugin_args = {'template': master_config['plugin_args']['template'],
'overwrite': True,
'qsub_args': modify_qsub_args(master_config['queue'],
1, 1, 1)}
return baw200
def CreateBRAINSCutWorkflow(projectid, subjectid, sessionid, CLUSTER_QUEUE,
CLUSTER_QUEUE_LONG, WFName, t1Only):
cutWF = pe.Workflow(
name=GenerateWFName(projectid, subjectid, sessionid, WFName))
inputsSpec = pe.Node(interface=IdentityInterface(fields=[
'T1Volume', 'T2Volume', 'posteriorDictionary', 'RegistrationROI',
'atlasToSubjectTransform', 'template_t1', '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',
'trainModelFile_txtD0060NT0060_gz'
]),
name='inputspec')
#Denoised T1 input for BRAINSCut
denosingTimeStep = 0.0625
denosingConductance = 0.4
denosingIteration = 5
DenoisedT1 = pe.Node(interface=GradientAnisotropicDiffusionImageFilter(),
name="DenoisedT1")
DenoisedT1.inputs.timeStep = denosingTimeStep
DenoisedT1.inputs.conductance = denosingConductance
DenoisedT1.inputs.numberOfIterations = denosingIteration
DenoisedT1.inputs.outputVolume = "DenoisedT1.nii.gz"
cutWF.connect(inputsSpec, 'T1Volume', DenoisedT1, 'inputVolume')
#Gradient Anistropic Diffusion T1 images for BRAINSCut
GADT1 = pe.Node(interface=GradientAnisotropicDiffusionImageFilter(),
name="GADT1")
GADT1.inputs.timeStep = 0.025
GADT1.inputs.conductance = 1
GADT1.inputs.numberOfIterations = 5
GADT1.inputs.outputVolume = "GADT1.nii.gz"
cutWF.connect(inputsSpec, 'T1Volume', GADT1, 'inputVolume')
if not t1Only:
#Denoised T1 input for BRAINSCut
DenoisedT2 = pe.Node(
interface=GradientAnisotropicDiffusionImageFilter(),
name="DenoisedT2")
DenoisedT2.inputs.timeStep = denosingTimeStep
DenoisedT2.inputs.conductance = denosingConductance
DenoisedT2.inputs.numberOfIterations = denosingIteration
DenoisedT2.inputs.outputVolume = "DenoisedT2.nii.gz"
cutWF.connect(inputsSpec, 'T2Volume', DenoisedT2, 'inputVolume')
#Gradient Anistropic Diffusion T1 images for BRAINSCut
GADT2 = pe.Node(interface=GradientAnisotropicDiffusionImageFilter(),
name="GADT2")
GADT2.inputs.timeStep = 0.025
GADT2.inputs.conductance = 1
GADT2.inputs.numberOfIterations = 5
GADT2.inputs.outputVolume = "GADT2.nii.gz"
cutWF.connect(inputsSpec, 'T2Volume', GADT2, 'inputVolume')
#Sum the gradient images for BRAINSCut
SGI = pe.Node(interface=GenerateSummedGradientImage(), name="SGI")
SGI.inputs.outputFileName = "SummedGradImage.nii.gz"
cutWF.connect(GADT1, 'outputVolume', SGI, 'inputVolume1')
cutWF.connect(GADT2, 'outputVolume', SGI, 'inputVolume2')
#BRAINSCut
RF12BC = pe.Node(interface=RF12BRAINSCutWrapper(),
name="IQR_NORM_SEP_RF12_BRAINSCut")
# HACK
# import os
# RF12BC.inputs.environ = dict(os.environ)
# many_cpu_RF12BC_options_dictionary = {'qsub_args': modify_qsub_args(CLUSTER_QUEUE,4,2,2), 'overwrite': True}
# RF12BC.plugin_args = many_cpu_RF12BC_options_dictionary
# END HACK
RF12BC.inputs.trainingVectorFilename = "trainingVectorFilename.txt"
RF12BC.inputs.xmlFilename = "BRAINSCutSegmentationDefinition.xml"
RF12BC.inputs.vectorNormalization = "IQR"
RF12BC.inputs.outputBinaryLeftCaudate = 'subjectANNLabel_l_caudate.nii.gz'
RF12BC.inputs.outputBinaryRightCaudate = 'subjectANNLabel_r_caudate.nii.gz'
RF12BC.inputs.outputBinaryLeftHippocampus = 'subjectANNLabel_l_hippocampus.nii.gz'
RF12BC.inputs.outputBinaryRightHippocampus = 'subjectANNLabel_r_hippocampus.nii.gz'
RF12BC.inputs.outputBinaryLeftPutamen = 'subjectANNLabel_l_putamen.nii.gz'
RF12BC.inputs.outputBinaryRightPutamen = 'subjectANNLabel_r_putamen.nii.gz'
RF12BC.inputs.outputBinaryLeftThalamus = 'subjectANNLabel_l_thalamus.nii.gz'
RF12BC.inputs.outputBinaryRightThalamus = 'subjectANNLabel_r_thalamus.nii.gz'
RF12BC.inputs.outputBinaryLeftAccumben = 'subjectANNLabel_l_accumben.nii.gz'
RF12BC.inputs.outputBinaryRightAccumben = 'subjectANNLabel_r_accumben.nii.gz'
RF12BC.inputs.outputBinaryLeftGlobus = 'subjectANNLabel_l_globus.nii.gz'
RF12BC.inputs.outputBinaryRightGlobus = 'subjectANNLabel_r_globus.nii.gz'
cutWF.connect(DenoisedT1, 'outputVolume', RF12BC, 'inputSubjectT1Filename')
from PipeLineFunctionHelpers import MakeInclusionMaskForGMStructures
makeCandidateRegionNode = pe.Node(
interface=Function(['posteriorDictionary', 'candidateRegionFileName'],
['outputCandidateRegionFileName'],
function=MakeInclusionMaskForGMStructures),
name="MakeCandidateRegion")
makeCandidateRegionNode.inputs.candidateRegionFileName = "RF12_CandidateRegionMask.nii.gz"
cutWF.connect(inputsSpec, 'posteriorDictionary', makeCandidateRegionNode,
'posteriorDictionary')
cutWF.connect(makeCandidateRegionNode, 'outputCandidateRegionFileName',
RF12BC, 'candidateRegion')
cutWF.connect([(
inputsSpec,
RF12BC,
[
('template_t1', 'inputTemplateT1'),
# ('template_brain', 'inputTemplateRegistrationROIFilename'),
('rho', 'inputTemplateRhoFilename'),
('phi', 'inputTemplatePhiFilename'),
('theta', 'inputTemplateThetaFilename'),
('l_caudate_ProbabilityMap', 'probabilityMapsLeftCaudate'),
('r_caudate_ProbabilityMap', 'probabilityMapsRightCaudate'),
('l_hippocampus_ProbabilityMap', 'probabilityMapsLeftHippocampus'),
('r_hippocampus_ProbabilityMap',
'probabilityMapsRightHippocampus'),
('l_putamen_ProbabilityMap', 'probabilityMapsLeftPutamen'),
('r_putamen_ProbabilityMap', 'probabilityMapsRightPutamen'),
('l_thalamus_ProbabilityMap', 'probabilityMapsLeftThalamus'),
('r_thalamus_ProbabilityMap', 'probabilityMapsRightThalamus'),
('l_accumben_ProbabilityMap', 'probabilityMapsLeftAccumben'),
('r_accumben_ProbabilityMap', 'probabilityMapsRightAccumben'),
('l_globus_ProbabilityMap', 'probabilityMapsLeftGlobus'),
('r_globus_ProbabilityMap', 'probabilityMapsRightGlobus'),
])])
# TODO:
if not t1Only:
cutWF.connect(DenoisedT2, 'outputVolume', RF12BC,
'inputSubjectT2Filename')
# cutWF.connect(inputsSpec,'TotalGM',RF12BC,'inputSubjectTotalGMFilename')
# cutWF.connect(inputsSpec,'RegistrationROI',RF12BC,'inputSubjectRegistrationROIFilename')
# Error cutWF.connect(SGI,'outputVolume',RF12BC,'inputSubjectGadSGFilename')
cutWF.connect(SGI, 'outputFileName', RF12BC,
'inputSubjectGadSGFilename')
cutWF.connect(inputsSpec, 'trainModelFile_txtD0060NT0060_gz', RF12BC,
'modelFilename')
else:
### TODO: Replace with proper atlas file name in the future!!! This is a HACK
### to avoid changing the hash keys of the input files from the atlas.
def ChangeModelPathDirectory(multiModalFileName):
return multiModalFileName.replace('modelFiles', 'T1OnlyModels')
cutWF.connect([(inputsSpec, RF12BC,
[(('trainModelFile_txtD0060NT0060_gz',
ChangeModelPathDirectory), 'modelFilename')])])
## Need to index from next line cutWF.connect(inputsSpec,'atlasToSubjectTransform',RF12BC,'deformationFromTemplateToSubject')
cutWF.connect([
(inputsSpec, RF12BC, [(('atlasToSubjectTransform', getListIndex, 0),
'deformationFromTemplateToSubject')]),
])
mergeAllLabels = pe.Node(interface=Merge(12), name="labelMergeNode")
# NOTE: Ordering is important
cutWF.connect(RF12BC, 'outputBinaryLeftCaudate', mergeAllLabels, 'in1')
cutWF.connect(RF12BC, 'outputBinaryRightCaudate', mergeAllLabels, 'in2')
cutWF.connect(RF12BC, 'outputBinaryLeftPutamen', mergeAllLabels, 'in3')
cutWF.connect(RF12BC, 'outputBinaryRightPutamen', mergeAllLabels, 'in4')
cutWF.connect(RF12BC, 'outputBinaryLeftHippocampus', mergeAllLabels, 'in5')
cutWF.connect(RF12BC, 'outputBinaryRightHippocampus', mergeAllLabels,
'in6')
cutWF.connect(RF12BC, 'outputBinaryLeftThalamus', mergeAllLabels, 'in7')
cutWF.connect(RF12BC, 'outputBinaryRightThalamus', mergeAllLabels,
'in8') # HACK: CHECK ORDERING
cutWF.connect(RF12BC, 'outputBinaryLeftAccumben', mergeAllLabels, 'in9')
cutWF.connect(RF12BC, 'outputBinaryRightAccumben', mergeAllLabels, 'in10')
cutWF.connect(RF12BC, 'outputBinaryLeftGlobus', mergeAllLabels, 'in11')
cutWF.connect(RF12BC, 'outputBinaryRightGlobus', mergeAllLabels, 'in12')
computeOneLabelMap = pe.Node(interface=Function([
'listOfImages', 'LabelImageName', 'CSVFileName', 'posteriorDictionary',
'projectid', 'subjectid', 'sessionid'
], [
'outputLabelImageName', 'outputCSVFileName', 'CleanedLeftCaudate',
'CleanedRightCaudate', 'CleanedLeftHippocampus',
'CleanedRightHippocampus', 'CleanedLeftPutamen', 'CleanedRightPutamen',
'CleanedLeftThalamus', 'CleanedRightThalamus', 'CleanedLeftAccumben',
'CleanedRightAccumben', 'CleanedLeftGlobus', 'CleanedRightGlobus'
],
function=CreateLabelMap),
name="ComputeOneLabelMap")
computeOneLabelMap.inputs.projectid = projectid
computeOneLabelMap.inputs.subjectid = subjectid
computeOneLabelMap.inputs.sessionid = sessionid
computeOneLabelMap.inputs.LabelImageName = "allLabels.nii.gz"
computeOneLabelMap.inputs.CSVFileName = "allLabels_seg.csv"
cutWF.connect(inputsSpec, 'posteriorDictionary', computeOneLabelMap,
'posteriorDictionary')
cutWF.connect(mergeAllLabels, 'out', computeOneLabelMap, 'listOfImages')
outputsSpec = pe.Node(interface=IdentityInterface(fields=[
'outputBinaryLeftCaudate', 'outputBinaryRightCaudate',
'outputBinaryLeftHippocampus', 'outputBinaryRightHippocampus',
'outputBinaryLeftPutamen', 'outputBinaryRightPutamen',
'outputBinaryLeftThalamus', 'outputBinaryRightThalamus',
'outputBinaryLeftAccumben', 'outputBinaryRightAccumben',
'outputBinaryLeftGlobus', 'outputBinaryRightGlobus',
'outputLabelImageName', 'outputCSVFileName', 'xmlFilename'
]),
name='outputspec')
cutWF.connect(computeOneLabelMap, 'outputLabelImageName', outputsSpec,
'outputLabelImageName')
cutWF.connect(computeOneLabelMap, 'outputCSVFileName', outputsSpec,
'outputCSVFileName')
cutWF.connect(computeOneLabelMap, 'CleanedLeftCaudate', outputsSpec,
'outputBinaryLeftCaudate')
cutWF.connect(computeOneLabelMap, 'CleanedRightCaudate', outputsSpec,
'outputBinaryRightCaudate')
cutWF.connect(computeOneLabelMap, 'CleanedLeftHippocampus', outputsSpec,
'outputBinaryLeftHippocampus')
cutWF.connect(computeOneLabelMap, 'CleanedRightHippocampus', outputsSpec,
'outputBinaryRightHippocampus')
cutWF.connect(computeOneLabelMap, 'CleanedLeftPutamen', outputsSpec,
'outputBinaryLeftPutamen')
cutWF.connect(computeOneLabelMap, 'CleanedRightPutamen', outputsSpec,
'outputBinaryRightPutamen')
cutWF.connect(computeOneLabelMap, 'CleanedLeftThalamus', outputsSpec,
'outputBinaryLeftThalamus')
cutWF.connect(computeOneLabelMap, 'CleanedRightThalamus', outputsSpec,
'outputBinaryRightThalamus')
cutWF.connect(computeOneLabelMap, 'CleanedLeftAccumben', outputsSpec,
'outputBinaryLeftAccumben')
cutWF.connect(computeOneLabelMap, 'CleanedRightAccumben', outputsSpec,
'outputBinaryRightAccumben')
cutWF.connect(computeOneLabelMap, 'CleanedLeftGlobus', outputsSpec,
'outputBinaryLeftGlobus')
cutWF.connect(computeOneLabelMap, 'CleanedRightGlobus', outputsSpec,
'outputBinaryRightGlobus')
cutWF.connect(RF12BC, 'xmlFilename', outputsSpec, 'xmlFilename')
return cutWF
def CreateANTSRegistrationWorkflow(WFname,
CLUSTER_QUEUE,
CLUSTER_QUEUE_LONG,
NumberOfThreads=-1):
ANTSWF = pe.Workflow(name=WFname)
inputsSpec = pe.Node(interface=IdentityInterface(fields=[
'fixedVolumesList', 'movingVolumesList', 'initial_moving_transform',
'fixedBinaryVolume', 'movingBinaryVolume', 'warpFixedVolumesList'
]),
name='inputspec')
print("""Run ANTS Registration""")
BFitAtlasToSubject = pe.Node(interface=BRAINSFit(), name="bfA2S")
BF_cpu_sge_options_dictionary = {
'qsub_args': modify_qsub_args(CLUSTER_QUEUE, 4, 4, 24),
'overwrite': True
}
BFitAtlasToSubject.plugin_args = BF_cpu_sge_options_dictionary
BFitAtlasToSubject.inputs.costMetric = "MMI"
BFitAtlasToSubject.inputs.numberOfSamples = 1000000
BFitAtlasToSubject.inputs.numberOfIterations = [1500]
BFitAtlasToSubject.inputs.numberOfHistogramBins = 50
BFitAtlasToSubject.inputs.maximumStepLength = 0.2
BFitAtlasToSubject.inputs.minimumStepLength = [0.000005]
BFitAtlasToSubject.inputs.useAffine = True # Using initial transform from BRAINSABC
BFitAtlasToSubject.inputs.maskInferiorCutOffFromCenter = 65
BFitAtlasToSubject.inputs.outputVolume = "Trial_Initializer_Output.nii.gz"
# Bug in BRAINSFit PREDICTIMG-1379 BFitAtlasToSubject.inputs.outputFixedVolumeROI="FixedROI.nii.gz"
# Bug in BRAINSFit PREDICTIMG-1379 BFitAtlasToSubject.inputs.outputMovingVolumeROI="MovingROI.nii.gz"
BFitAtlasToSubject.inputs.outputTransform = "Trial_Initializer_Output.h5"
BFitAtlasToSubject.inputs.maskProcessingMode = "ROIAUTO"
BFitAtlasToSubject.inputs.ROIAutoDilateSize = 4
# BFitAtlasToSubject.inputs.maskProcessingMode="ROI"
# ANTSWF.connect(inputsSpec,'fixedBinaryVolume',BFitAtlasToSubject,'fixedBinaryVolume')
# ANTSWF.connect(inputsSpec,'movingBinaryVolume',BFitAtlasToSubject,'movingBinaryVolume')
ANTSWF.connect(inputsSpec, 'fixedVolumesList', BFitAtlasToSubject,
'fixedVolume')
ANTSWF.connect(inputsSpec, 'movingVolumesList', BFitAtlasToSubject,
'movingVolume')
ANTSWF.connect(inputsSpec, 'initial_moving_transform', BFitAtlasToSubject,
'initialTransform')
ComputeAtlasToSubjectTransform = pe.Node(interface=antsRegistration(),
name="antsA2S")
many_cpu_sge_options_dictionary = {
'qsub_args': modify_qsub_args(CLUSTER_QUEUE, 8, 8, 24),
'overwrite': True
}
ComputeAtlasToSubjectTransform.plugin_args = many_cpu_sge_options_dictionary
ComputeAtlasToSubjectTransform.inputs.dimension = 3
ComputeAtlasToSubjectTransform.inputs.metric = 'CC' # This is a family of interfaces, CC,MeanSquares,Demons,GC,MI,Mattes
ComputeAtlasToSubjectTransform.inputs.transform = 'SyN[0.25,3.0,0.0]'
ComputeAtlasToSubjectTransform.inputs.number_of_iterations = [250, 100, 20]
ComputeAtlasToSubjectTransform.inputs.convergence_threshold = 1e-7
ComputeAtlasToSubjectTransform.inputs.smoothing_sigmas = [0, 0, 0]
ComputeAtlasToSubjectTransform.inputs.sigma_units = ["vox"]
ComputeAtlasToSubjectTransform.inputs.shrink_factors = [3, 2, 1]
ComputeAtlasToSubjectTransform.inputs.use_estimate_learning_rate_once = True
ComputeAtlasToSubjectTransform.inputs.use_histogram_matching = True
ComputeAtlasToSubjectTransform.inputs.invert_initial_moving_transform = False
ComputeAtlasToSubjectTransform.inputs.output_transform_prefix = 'antsRegPrefix_'
ComputeAtlasToSubjectTransform.inputs.output_warped_image = 'moving_to_fixed.nii.gz'
ComputeAtlasToSubjectTransform.inputs.output_inverse_warped_image = 'fixed_to_moving.nii.gz'
# ComputeAtlasToSubjectTransform.inputs.num_threads=-1
# if os.environ.has_key('NSLOTS'):
# ComputeAtlasToSubjectTransform.inputs.num_threads=int(os.environ.has_key('NSLOTS'))
# else:
# ComputeAtlasToSubjectTransform.inputs.num_threads=NumberOfThreads
# ComputeAtlasToSubjectTransform.inputs.fixedMask=SUBJ_A_small_T2_mask.nii.gz
# ComputeAtlasToSubjectTransform.inputs.movingMask=SUBJ_B_small_T2_mask.nii.gz
ANTSWF.connect(inputsSpec, 'fixedVolumesList',
ComputeAtlasToSubjectTransform, "fixed_image")
ANTSWF.connect(inputsSpec, 'movingVolumesList',
ComputeAtlasToSubjectTransform, "moving_image")
ANTSWF.connect(BFitAtlasToSubject, 'outputTransform',
ComputeAtlasToSubjectTransform, 'initial_moving_transform')
if 1 == 1:
mergeAffineWarp = pe.Node(interface=Merge(2), name="Merge_AffineWarp")
ANTSWF.connect(ComputeAtlasToSubjectTransform, 'warp_transform',
mergeAffineWarp, 'in1')
ANTSWF.connect(BFitAtlasToSubject, 'outputTransform', mergeAffineWarp,
'in2')
from nipype.interfaces.ants import WarpImageMultiTransform
debugWarpTest = pe.Node(interface=WarpImageMultiTransform(),
name="dbgWarpTest")
# Not allowed as an input debugWarpTest.inputs.output_image = 'debugWarpedMovingToFixed.nii.gz'
ANTSWF.connect(inputsSpec, 'fixedVolumesList', debugWarpTest,
'reference_image')
ANTSWF.connect(inputsSpec, 'movingVolumesList', debugWarpTest,
'moving_image')
ANTSWF.connect(mergeAffineWarp, 'out', debugWarpTest,
'transformation_series')
#############
outputsSpec = pe.Node(interface=IdentityInterface(fields=[
'warped_image', 'inverse_warped_image', 'warp_transform',
'inverse_warp_transform', 'affine_transform'
]),
name='outputspec')
ANTSWF.connect(ComputeAtlasToSubjectTransform, 'warped_image', outputsSpec,
'warped_image')
ANTSWF.connect(ComputeAtlasToSubjectTransform, 'inverse_warped_image',
outputsSpec, 'inverse_warped_image')
ANTSWF.connect(ComputeAtlasToSubjectTransform, 'warp_transform',
outputsSpec, 'warp_transform')
ANTSWF.connect(ComputeAtlasToSubjectTransform, 'inverse_warp_transform',
outputsSpec, 'inverse_warp_transform')
ANTSWF.connect(BFitAtlasToSubject, 'outputTransform', outputsSpec,
'affine_transform')
return ANTSWF
def create_workflow(files,
target_file,
subject_id,
TR,
slice_times,
norm_threshold=1,
num_components=5,
vol_fwhm=None,
surf_fwhm=None,
lowpass_freq=-1,
highpass_freq=-1,
subjects_dir=None,
sink_directory=os.getcwd(),
target_subject=['fsaverage3', 'fsaverage4'],
name='resting'):
wf = Workflow(name=name)
# Rename files in case they are named identically
name_unique = MapNode(Rename(format_string='rest_%(run)02d'),
iterfield=['in_file', 'run'],
name='rename')
name_unique.inputs.keep_ext = True
name_unique.inputs.run = list(range(1, len(files) + 1))
name_unique.inputs.in_file = files
realign = Node(interface=spm.Realign(), name="realign")
realign.inputs.jobtype = 'estwrite'
num_slices = len(slice_times)
slice_timing = Node(interface=spm.SliceTiming(), name="slice_timing")
slice_timing.inputs.num_slices = num_slices
slice_timing.inputs.time_repetition = TR
slice_timing.inputs.time_acquisition = TR - TR / float(num_slices)
slice_timing.inputs.slice_order = (np.argsort(slice_times) + 1).tolist()
slice_timing.inputs.ref_slice = int(num_slices / 2)
# Comute TSNR on realigned data regressing polynomials upto order 2
tsnr = MapNode(TSNR(regress_poly=2), iterfield=['in_file'], name='tsnr')
wf.connect(slice_timing, 'timecorrected_files', tsnr, 'in_file')
# Compute the median image across runs
calc_median = Node(Function(input_names=['in_files'],
output_names=['median_file'],
function=median,
imports=imports),
name='median')
wf.connect(tsnr, 'detrended_file', calc_median, 'in_files')
"""Segment and Register
"""
registration = create_reg_workflow(name='registration')
wf.connect(calc_median, 'median_file', registration,
'inputspec.mean_image')
registration.inputs.inputspec.subject_id = subject_id
registration.inputs.inputspec.subjects_dir = subjects_dir
registration.inputs.inputspec.target_image = target_file
"""Use :class:`nipype.algorithms.rapidart` to determine which of the
images in the functional series are outliers based on deviations in
intensity or movement.
"""
art = Node(interface=ArtifactDetect(), name="art")
art.inputs.use_differences = [True, True]
art.inputs.use_norm = True
art.inputs.norm_threshold = norm_threshold
art.inputs.zintensity_threshold = 9
art.inputs.mask_type = 'spm_global'
art.inputs.parameter_source = 'SPM'
"""Here we are connecting all the nodes together. Notice that we add the merge node only if you choose
to use 4D. Also `get_vox_dims` function is passed along the input volume of normalise to set the optimal
voxel sizes.
"""
wf.connect([
(name_unique, realign, [('out_file', 'in_files')]),
(realign, slice_timing, [('realigned_files', 'in_files')]),
(slice_timing, art, [('timecorrected_files', 'realigned_files')]),
(realign, art, [('realignment_parameters', 'realignment_parameters')]),
])
def selectindex(files, idx):
import numpy as np
from nipype.utils.filemanip import filename_to_list, list_to_filename
return list_to_filename(
np.array(filename_to_list(files))[idx].tolist())
mask = Node(fsl.BET(), name='getmask')
mask.inputs.mask = True
wf.connect(calc_median, 'median_file', mask, 'in_file')
# get segmentation in normalized functional space
def merge_files(in1, in2):
out_files = filename_to_list(in1)
out_files.extend(filename_to_list(in2))
return out_files
# filter some noise
# Compute motion regressors
motreg = Node(Function(
input_names=['motion_params', 'order', 'derivatives'],
output_names=['out_files'],
function=motion_regressors,
imports=imports),
name='getmotionregress')
wf.connect(realign, 'realignment_parameters', motreg, 'motion_params')
# Create a filter to remove motion and art confounds
createfilter1 = Node(Function(
input_names=['motion_params', 'comp_norm', 'outliers', 'detrend_poly'],
output_names=['out_files'],
function=build_filter1,
imports=imports),
name='makemotionbasedfilter')
createfilter1.inputs.detrend_poly = 2
wf.connect(motreg, 'out_files', createfilter1, 'motion_params')
wf.connect(art, 'norm_files', createfilter1, 'comp_norm')
wf.connect(art, 'outlier_files', createfilter1, 'outliers')
filter1 = MapNode(fsl.GLM(out_f_name='F_mcart.nii',
out_pf_name='pF_mcart.nii',
demean=True),
iterfield=['in_file', 'design', 'out_res_name'],
name='filtermotion')
wf.connect(slice_timing, 'timecorrected_files', filter1, 'in_file')
wf.connect(slice_timing, ('timecorrected_files', rename, '_filtermotart'),
filter1, 'out_res_name')
wf.connect(createfilter1, 'out_files', filter1, 'design')
createfilter2 = MapNode(Function(input_names=[
'realigned_file', 'mask_file', 'num_components', 'extra_regressors'
],
output_names=['out_files'],
function=extract_noise_components,
imports=imports),
iterfield=['realigned_file', 'extra_regressors'],
name='makecompcorrfilter')
createfilter2.inputs.num_components = num_components
wf.connect(createfilter1, 'out_files', createfilter2, 'extra_regressors')
wf.connect(filter1, 'out_res', createfilter2, 'realigned_file')
wf.connect(registration,
('outputspec.segmentation_files', selectindex, [0, 2]),
createfilter2, 'mask_file')
filter2 = MapNode(fsl.GLM(out_f_name='F.nii',
out_pf_name='pF.nii',
demean=True),
iterfield=['in_file', 'design', 'out_res_name'],
name='filter_noise_nosmooth')
wf.connect(filter1, 'out_res', filter2, 'in_file')
wf.connect(filter1, ('out_res', rename, '_cleaned'), filter2,
'out_res_name')
wf.connect(createfilter2, 'out_files', filter2, 'design')
wf.connect(mask, 'mask_file', filter2, 'mask')
bandpass = Node(Function(
input_names=['files', 'lowpass_freq', 'highpass_freq', 'fs'],
output_names=['out_files'],
function=bandpass_filter,
imports=imports),
name='bandpass_unsmooth')
bandpass.inputs.fs = 1. / TR
bandpass.inputs.highpass_freq = highpass_freq
bandpass.inputs.lowpass_freq = lowpass_freq
wf.connect(filter2, 'out_res', bandpass, 'files')
"""Smooth the functional data using
:class:`nipype.interfaces.spm.Smooth`.
"""
smooth = Node(interface=spm.Smooth(), name="smooth")
smooth.inputs.fwhm = vol_fwhm
wf.connect(bandpass, 'out_files', smooth, 'in_files')
collector = Node(Merge(2), name='collect_streams')
wf.connect(smooth, 'smoothed_files', collector, 'in1')
wf.connect(bandpass, 'out_files', collector, 'in2')
"""
Transform the remaining images. First to anatomical and then to target
"""
warpall = MapNode(ants.ApplyTransforms(),
iterfield=['input_image'],
name='warpall')
warpall.inputs.input_image_type = 3
warpall.inputs.interpolation = 'Linear'
warpall.inputs.invert_transform_flags = [False, False]
warpall.inputs.terminal_output = 'file'
warpall.inputs.reference_image = target_file
warpall.inputs.args = '--float'
warpall.inputs.num_threads = 1
# transform to target
wf.connect(collector, 'out', warpall, 'input_image')
wf.connect(registration, 'outputspec.transforms', warpall, 'transforms')
mask_target = Node(fsl.ImageMaths(op_string='-bin'), name='target_mask')
wf.connect(registration, 'outputspec.anat2target', mask_target, 'in_file')
maskts = MapNode(fsl.ApplyMask(), iterfield=['in_file'], name='ts_masker')
wf.connect(warpall, 'output_image', maskts, 'in_file')
wf.connect(mask_target, 'out_file', maskts, 'mask_file')
# map to surface
# extract aparc+aseg ROIs
# extract subcortical ROIs
# extract target space ROIs
# combine subcortical and cortical rois into a single cifti file
#######
# Convert aparc to subject functional space
# Sample the average time series in aparc ROIs
sampleaparc = MapNode(
freesurfer.SegStats(default_color_table=True),
iterfield=['in_file', 'summary_file', 'avgwf_txt_file'],
name='aparc_ts')
sampleaparc.inputs.segment_id = ([8] + list(range(10, 14)) +
[17, 18, 26, 47] + list(range(49, 55)) +
[58] + list(range(1001, 1036)) +
list(range(2001, 2036)))
wf.connect(registration, 'outputspec.aparc', sampleaparc,
'segmentation_file')
wf.connect(collector, 'out', sampleaparc, 'in_file')
def get_names(files, suffix):
"""Generate appropriate names for output files
"""
from nipype.utils.filemanip import (split_filename, filename_to_list,
list_to_filename)
out_names = []
for filename in files:
_, name, _ = split_filename(filename)
out_names.append(name + suffix)
return list_to_filename(out_names)
wf.connect(collector, ('out', get_names, '_avgwf.txt'), sampleaparc,
'avgwf_txt_file')
wf.connect(collector, ('out', get_names, '_summary.stats'), sampleaparc,
'summary_file')
# Sample the time series onto the surface of the target surface. Performs
# sampling into left and right hemisphere
target = Node(IdentityInterface(fields=['target_subject']), name='target')
target.iterables = ('target_subject', filename_to_list(target_subject))
samplerlh = MapNode(freesurfer.SampleToSurface(),
iterfield=['source_file'],
name='sampler_lh')
samplerlh.inputs.sampling_method = "average"
samplerlh.inputs.sampling_range = (0.1, 0.9, 0.1)
samplerlh.inputs.sampling_units = "frac"
samplerlh.inputs.interp_method = "trilinear"
samplerlh.inputs.smooth_surf = surf_fwhm
# samplerlh.inputs.cortex_mask = True
samplerlh.inputs.out_type = 'niigz'
samplerlh.inputs.subjects_dir = subjects_dir
samplerrh = samplerlh.clone('sampler_rh')
samplerlh.inputs.hemi = 'lh'
wf.connect(collector, 'out', samplerlh, 'source_file')
wf.connect(registration, 'outputspec.out_reg_file', samplerlh, 'reg_file')
wf.connect(target, 'target_subject', samplerlh, 'target_subject')
samplerrh.set_input('hemi', 'rh')
wf.connect(collector, 'out', samplerrh, 'source_file')
wf.connect(registration, 'outputspec.out_reg_file', samplerrh, 'reg_file')
wf.connect(target, 'target_subject', samplerrh, 'target_subject')
# Combine left and right hemisphere to text file
combiner = MapNode(Function(input_names=['left', 'right'],
output_names=['out_file'],
function=combine_hemi,
imports=imports),
iterfield=['left', 'right'],
name="combiner")
wf.connect(samplerlh, 'out_file', combiner, 'left')
wf.connect(samplerrh, 'out_file', combiner, 'right')
# Sample the time series file for each subcortical roi
ts2txt = MapNode(Function(
input_names=['timeseries_file', 'label_file', 'indices'],
output_names=['out_file'],
function=extract_subrois,
imports=imports),
iterfield=['timeseries_file'],
name='getsubcortts')
ts2txt.inputs.indices = [8] + list(range(10, 14)) + [17, 18, 26, 47] +\
list(range(49, 55)) + [58]
ts2txt.inputs.label_file = \
os.path.abspath(('OASIS-TRT-20_jointfusion_DKT31_CMA_labels_in_MNI152_'
'2mm_v2.nii.gz'))
wf.connect(maskts, 'out_file', ts2txt, 'timeseries_file')
######
substitutions = [('_target_subject_', ''),
('_filtermotart_cleaned_bp_trans_masked', ''),
('_filtermotart_cleaned_bp', '')]
regex_subs = [
('_ts_masker.*/sar', '/smooth/'),
('_ts_masker.*/ar', '/unsmooth/'),
('_combiner.*/sar', '/smooth/'),
('_combiner.*/ar', '/unsmooth/'),
('_aparc_ts.*/sar', '/smooth/'),
('_aparc_ts.*/ar', '/unsmooth/'),
('_getsubcortts.*/sar', '/smooth/'),
('_getsubcortts.*/ar', '/unsmooth/'),
('series/sar', 'series/smooth/'),
('series/ar', 'series/unsmooth/'),
('_inverse_transform./', ''),
]
# Save the relevant data into an output directory
datasink = Node(interface=DataSink(), name="datasink")
datasink.inputs.base_directory = sink_directory
datasink.inputs.container = subject_id
datasink.inputs.substitutions = substitutions
datasink.inputs.regexp_substitutions = regex_subs # (r'(/_.*(\d+/))', r'/run\2')
wf.connect(realign, 'realignment_parameters', datasink,
'resting.qa.motion')
wf.connect(art, 'norm_files', datasink, 'resting.qa.art.@norm')
wf.connect(art, 'intensity_files', datasink, 'resting.qa.art.@intensity')
wf.connect(art, 'outlier_files', datasink, 'resting.qa.art.@outlier_files')
wf.connect(registration, 'outputspec.segmentation_files', datasink,
'resting.mask_files')
wf.connect(registration, 'outputspec.anat2target', datasink,
'resting.qa.ants')
wf.connect(mask, 'mask_file', datasink, 'resting.mask_files.@brainmask')
wf.connect(mask_target, 'out_file', datasink, 'resting.mask_files.target')
wf.connect(filter1, 'out_f', datasink, 'resting.qa.compmaps.@mc_F')
wf.connect(filter1, 'out_pf', datasink, 'resting.qa.compmaps.@mc_pF')
wf.connect(filter2, 'out_f', datasink, 'resting.qa.compmaps')
wf.connect(filter2, 'out_pf', datasink, 'resting.qa.compmaps.@p')
wf.connect(bandpass, 'out_files', datasink,
'resting.timeseries.@bandpassed')
wf.connect(smooth, 'smoothed_files', datasink,
'resting.timeseries.@smoothed')
wf.connect(createfilter1, 'out_files', datasink,
'resting.regress.@regressors')
wf.connect(createfilter2, 'out_files', datasink,
'resting.regress.@compcorr')
wf.connect(maskts, 'out_file', datasink, 'resting.timeseries.target')
wf.connect(sampleaparc, 'summary_file', datasink,
'resting.parcellations.aparc')
wf.connect(sampleaparc, 'avgwf_txt_file', datasink,
'resting.parcellations.aparc.@avgwf')
wf.connect(ts2txt, 'out_file', datasink,
'resting.parcellations.grayo.@subcortical')
datasink2 = Node(interface=DataSink(), name="datasink2")
datasink2.inputs.base_directory = sink_directory
datasink2.inputs.container = subject_id
datasink2.inputs.substitutions = substitutions
datasink2.inputs.regexp_substitutions = regex_subs # (r'(/_.*(\d+/))', r'/run\2')
wf.connect(combiner, 'out_file', datasink2,
'resting.parcellations.grayo.@surface')
return wf
def spm_mrpet_preprocessing(wf_name="spm_mrpet_preproc"):
""" Run the PET pre-processing workflow against the
gunzip_pet.in_file files.
It depends on the anat_preproc_workflow, so if this
has not been run, this function will run it too.
# TODO: organize the anat2pet hack/condition somehow:
If anat2pet:
- SPM12 Coregister T1 and tissues to PET
- PVC the PET image in PET space
- SPM12 Warp PET to MNI
else:
- SPM12 Coregister PET to T1
- PVC the PET image in anatomical space
- SPM12 Warp PET in anatomical space to MNI through the
`anat_to_mni_warp`.
Parameters
----------
wf_name: str
Name of the workflow.
Nipype Inputs
-------------
pet_input.in_file: traits.File
The raw NIFTI_GZ PET image file
pet_input.anat: traits.File
Path to the high-contrast anatomical image.
Reference file of the warp_field, i.e., the
anatomical image in its native space.
pet_input.anat_to_mni_warp: traits.File
The warp field from the transformation of the
anatomical image to the standard MNI space.
pet_input.atlas_anat: traits.File
The atlas file in anatomical space.
pet_input.tissues: list of traits.File
List of tissues files from the New Segment process.
At least the first 3 tissues must be present.
Nipype outputs
--------------
pet_output.pvc_out: existing file
The results of the PVC process
pet_output.brain_mask: existing file
A brain mask calculated with the tissues file.
pet_output.coreg_ref: existing file
The coregistered reference image to PET space.
pet_output.coreg_others: list of existing files
List of coregistered files from coreg_pet.apply_to_files
pet_output.pvc_warped: existing file
Results from PETPVC normalized to MNI.
The result of every internal pre-processing step
is normalized to MNI here.
pet_output.warp_field: existing files
Spatial normalization parameters .mat files
pet_output.gm_norm: existing file
The output of the grey matter intensity
normalization process.
This is the last step in the PET signal correction,
before registration.
pet_output.atlas_pet: existing file
Atlas image warped to PET space.
If the `atlas_file` option is an existing file and
`normalize_atlas` is True.
Returns
-------
wf: nipype Workflow
"""
# specify input and output fields
in_fields = ["in_file",
"anat",
"anat_to_mni_warp",
"tissues",]
out_fields = ["brain_mask",
"coreg_others",
"coreg_ref",
"pvc_warped",
"pet_warped", # 'pet_warped' is a dummy entry to keep the fields pattern.
"warp_field",
"pvc_out",
"pvc_mask",
"gm_norm",]
do_atlas, _ = check_atlas_file()
if do_atlas:
in_fields += ["atlas_anat"]
out_fields += ["atlas_pet" ]
# input
pet_input = setup_node(IdentityInterface(fields=in_fields, mandatory_inputs=True),
name="pet_input")
# workflow to perform partial volume correction
petpvc = petpvc_workflow(wf_name="petpvc")
merge_list = setup_node(Merge(4), name='merge_for_unzip')
gunzipper = pe.MapNode(Gunzip(), name="gunzip", iterfield=['in_file'])
warp_pet = setup_node(spm_normalize(), name="warp_pet")
tpm_bbox = setup_node(Function(function=get_bounding_box,
input_names=["in_file"],
output_names=["bbox"]),
name="tpm_bbox")
tpm_bbox.inputs.in_file = spm_tpm_priors_path()
# output
pet_output = setup_node(IdentityInterface(fields=out_fields), name="pet_output")
# Create the workflow object
wf = pe.Workflow(name=wf_name)
# check how to perform the registration, to decide how to build the pipeline
anat2pet = get_config_setting('registration.anat2pet', False)
if anat2pet:
wf.connect([
# inputs
(pet_input, petpvc, [("in_file", "pvc_input.in_file"),
("anat", "pvc_input.reference_file"),
("tissues", "pvc_input.tissues")]),
# gunzip some files for SPM Normalize
(petpvc, merge_list, [("pvc_output.pvc_out", "in1"),
("pvc_output.brain_mask", "in2"),
("pvc_output.gm_norm", "in3")]),
(pet_input, merge_list, [("in_file", "in4")]),
(merge_list, gunzipper, [("out", "in_file")]),
# warp the PET PVCed to MNI
(petpvc, warp_pet, [("pvc_output.coreg_ref", "image_to_align")]),
(gunzipper, warp_pet, [("out_file", "apply_to_files")]),
(tpm_bbox, warp_pet, [("bbox", "write_bounding_box")]),
# output
(petpvc, pet_output, [("pvc_output.pvc_out", "pvc_out"),
("pvc_output.brain_mask", "brain_mask"),
("pvc_output.coreg_ref", "coreg_ref"),
("pvc_output.coreg_others", "coreg_others"),
("pvc_output.gm_norm", "gm_norm")]),
# output
(warp_pet, pet_output, [("normalized_files", "pvc_warped"),
("deformation_field", "warp_field")]),
])
else: # PET 2 ANAT
collector = setup_node(Merge(2), name='merge_for_warp')
apply_warp = setup_node(spm_apply_deformations(), name="warp_pet")
wf.connect([
# inputs
(pet_input, petpvc, [("in_file", "pvc_input.in_file"),
("anat", "pvc_input.reference_file"),
("tissues", "pvc_input.tissues")]),
# gunzip some files for SPM Normalize
(petpvc, merge_list, [("pvc_output.pvc_out", "in1"),
("pvc_output.brain_mask", "in2"),
("pvc_output.gm_norm", "in3")]),
(pet_input, merge_list, [("in_file", "in4")]),
(merge_list, gunzipper, [("out", "in_file")]),
# warp the PET PVCed to MNI
(gunzipper, collector, [("out_file", "in1")]),
(petpvc, collector, [("pvc_output.coreg_ref", "in2")]),
(pet_input, apply_warp, [("anat_to_mni_warp", "deformation_file")]),
(collector, apply_warp, [("out", "apply_to_files")]),
(tpm_bbox, apply_warp, [("bbox", "write_bounding_box")]),
# output
(petpvc, pet_output, [("pvc_output.pvc_out", "pvc_out"),
("pvc_output.brain_mask", "brain_mask"),
("pvc_output.petpvc_mask", "petpvc_mask"),
("pvc_output.coreg_ref", "coreg_ref"),
("pvc_output.coreg_others", "coreg_others"),
("pvc_output.gm_norm", "gm_norm")]),
# output
(apply_warp, pet_output, [("normalized_files", "pvc_warped"),
("deformation_field", "warp_field")]),
])
if do_atlas:
coreg_atlas = setup_node(spm_coregister(cost_function="mi"), name="coreg_atlas")
# set the registration interpolation to nearest neighbour.
coreg_atlas.inputs.write_interp = 0
wf.connect([
(pet_input, coreg_atlas, [("anat", "source")]),
(petpvc, coreg_atlas, [("pvc_output.coreg_ref", "target")]),
(pet_input, coreg_atlas, [("atlas_anat", "apply_to_files")]),
(coreg_atlas, pet_output, [("coregistered_files", "atlas_pet")]),
])
return wf
def spm_mrpet_grouptemplate_preprocessing(wf_name="spm_mrpet_grouptemplate_preproc"):
""" Run the PET pre-processing workflow against the gunzip_pet.in_file files.
It depends on the anat_preproc_workflow, so if this has not been run, this function
will run it too.
This is identical to the workflow defined in `spm_mrpet_preprocessing`,
with the only difference that we now normalize all subjects agains a custom
template using the spm Old Normalize interface.
It does:
- SPM12 Coregister T1 and tissues to PET
- PVC the PET image in PET space
- SPM12 Warp PET to the given template
Parameters
----------
wf_name: str
Name of the workflow.
Nipype Inputs
-------------
pet_input.in_file: traits.File
The raw NIFTI_GZ PET image file.
pet_input.atlas_anat: traits.File
The atlas file in anatomical space.
pet_input.anat: traits.File
Path to the high-contrast anatomical image.
Reference file of the warp_field, i.e., the anatomical image in its native space.
pet_input.tissues: list of traits.File
List of tissues files from the New Segment process. At least the first
3 tissues must be present.
pet_input.pet_template: traits.File
The template file for inter-subject registration reference.
Nipype outputs
--------------
pet_output.pvc_out: existing file
The results of the PVC process.
pet_output.brain_mask: existing file
A brain mask calculated with the tissues file.
pet_output.coreg_ref: existing file
The coregistered reference image to PET space.
pet_output.coreg_others: list of existing files
List of coregistered files from coreg_pet.apply_to_files.
pet_output.pet_warped: existing file
PET image normalized to the group template.
pet_output.pvc_warped: existing file
The outputs of the PETPVC workflow normalized to the group template.
The result of every internal pre-processing step is normalized to the
group template here.
pet_output.warp_field: existing files
Spatial normalization parameters .mat files.
pet_output.gm_norm: existing file
The output of the grey matter intensity normalization process.
This is the last step in the PET signal correction, before registration.
pet_output.atlas_pet: existing file
Atlas image warped to PET space.
If the `atlas_file` option is an existing file and `normalize_atlas` is True.
Returns
-------
wf: nipype Workflow
"""
# specify input and output fields
in_fields = ["in_file",
"anat",
"tissues",
"pet_template"]
out_fields = ["brain_mask",
"coreg_others",
"coreg_ref",
"pvc_warped",
"pet_warped",
"warp_field",
"pvc_out",
"pvc_mask",
"gm_norm",]
do_atlas, _ = check_atlas_file()
if do_atlas:
in_fields += ["atlas_anat"]
out_fields += ["atlas_pet" ]
# input
pet_input = setup_node(IdentityInterface(fields=in_fields, mandatory_inputs=True),
name="pet_input")
# workflow to perform partial volume correction
petpvc = petpvc_workflow(wf_name="petpvc")
unzip_mrg = setup_node(Merge(4), name='merge_for_unzip')
gunzipper = pe.MapNode(Gunzip(), name="gunzip", iterfield=['in_file'])
# warp each subject to the group template
gunzip_template = setup_node(Gunzip(), name="gunzip_template",)
gunzip_pet = setup_node(Gunzip(), name="gunzip_pet",)
warp_mrg = setup_node(Merge(2), name='merge_for_warp')
warp2template = setup_node(spm.Normalize(jobtype="estwrite", out_prefix="wgrptemplate_"),
name="warp2template",)
get_bbox = setup_node(Function(function=get_bounding_box,
input_names=["in_file"],
output_names=["bbox"]),
name="get_bbox")
# output
pet_output = setup_node(IdentityInterface(fields=out_fields), name="pet_output")
# Create the workflow object
wf = pe.Workflow(name=wf_name)
wf.connect([
# inputs
(pet_input, petpvc, [("in_file", "pvc_input.in_file"),
("anat", "pvc_input.reference_file"),
("tissues", "pvc_input.tissues")]),
# get template bounding box to apply to results
(pet_input, get_bbox, [("pet_template", "in_file")]),
# gunzip some inputs
(pet_input, gunzip_pet, [("in_file", "in_file")]),
(pet_input, gunzip_template, [("pet_template", "in_file")]),
# gunzip some files for SPM Normalize
(petpvc, unzip_mrg, [("pvc_output.pvc_out", "in1"),
("pvc_output.brain_mask", "in2"),
("pvc_output.gm_norm", "in3")]),
(pet_input, unzip_mrg, [("in_file", "in4")]),
(unzip_mrg, gunzipper, [("out", "in_file")]),
(gunzipper, warp_mrg, [("out_file", "in1")]),
(warp_mrg, warp2template, [(("out", flatten_list), "apply_to_files")]),
# prepare the target parameters of the warp to template
(gunzip_pet, warp2template, [("out_file", "source")]),
(gunzip_template, warp2template, [("out_file", "template")]),
(get_bbox, warp2template, [("bbox", "write_bounding_box")]),
# output
(warp2template, pet_output, [("normalization_parameters", "warp_field"),
("normalized_files" , "pvc_warped"),
("normalized_source", "pet_warped"),
]),
# output
(petpvc, pet_output, [("pvc_output.pvc_out", "pvc_out"),
("pvc_output.brain_mask", "brain_mask"),
("pvc_output.coreg_ref", "coreg_ref"),
("pvc_output.coreg_others", "coreg_others"),
("pvc_output.gm_norm", "gm_norm")]),
])
if do_atlas:
coreg_atlas = setup_node(spm_coregister(cost_function="mi"), name="coreg_atlas")
# set the registration interpolation to nearest neighbour.
coreg_atlas.inputs.write_interp = 0
wf.connect([
(pet_input, coreg_atlas, [("anat", "source")]),
(petpvc, coreg_atlas, [("pvc_output.coreg_ref", "target")]),
(pet_input, coreg_atlas, [("atlas_anat", "apply_to_files")]),
(coreg_atlas, pet_output, [("coregistered_files", "atlas_pet")]),
# warp the atlas to the template space as well
(coreg_atlas, warp_mrg, [("coregistered_files", "in2")]),
])
return wf
def connectome(subject_list, base_directory, out_directory):
# ==================================================================
# Loading required packages
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as util
from nipype.interfaces.freesurfer import ApplyVolTransform
from nipype.interfaces.freesurfer import BBRegister
import nipype.interfaces.fsl as fsl
import nipype.interfaces.diffusion_toolkit as dtk
from nipype.interfaces.utility import Merge
import numpy as np
from additional_interfaces import AtlasValues
from additional_interfaces import AparcStats
from additional_interfaces import CalcMatrix
from additional_interfaces import FreeSurferValues
from additional_interfaces import Tractography
from additional_pipelines import DWIPreproc
from additional_pipelines import SubjectSpaceParcellation
from additional_pipelines import T1Preproc
from nipype import SelectFiles
import os
# ==================================================================
# Defining the nodes for the workflow
# Getting the subject ID
infosource = pe.Node(
interface=util.IdentityInterface(fields=['subject_id']),
name='infosource')
infosource.iterables = ('subject_id', subject_list)
# Getting the relevant diffusion-weighted data
templates = dict(T1='{subject_id}/anat/{subject_id}_T1w.nii.gz',
dwi='{subject_id}/dwi/{subject_id}_dwi.nii.gz',
bvec='{subject_id}/dwi/{subject_id}_dwi.bvec',
bval='{subject_id}/dwi/{subject_id}_dwi.bval')
selectfiles = pe.Node(SelectFiles(templates), name='selectfiles')
selectfiles.inputs.base_directory = os.path.abspath(base_directory)
# ==============================================================
# T1 processing
t1_preproc = pe.Node(interface=T1Preproc(), name='t1_preproc')
t1_preproc.inputs.out_directory = out_directory + '/connectome/'
t1_preproc.inputs.template_directory = template_directory
# DWI processing
dwi_preproc = pe.Node(interface=DWIPreproc(), name='dwi_preproc')
dwi_preproc.inputs.out_directory = out_directory + '/connectome/'
dwi_preproc.inputs.acqparams = acquisition_parameters
dwi_preproc.inputs.index_file = index_file
dwi_preproc.inputs.out_directory = out_directory + '/connectome/'
# Eroding the brain mask
erode_mask = pe.Node(interface=fsl.maths.ErodeImage(),
name='erode_mask')
# Reconstruction and tractography
tractography = pe.Node(interface=Tractography(), name='tractography')
tractography.iterables = ('model', ['CSA', 'CSD'])
# smoothing the tracts
smooth = pe.Node(interface=dtk.SplineFilter(step_length=0.5),
name='smooth')
# Moving to subject space
subject_parcellation = pe.Node(interface=SubjectSpaceParcellation(),
name='subject_parcellation')
subject_parcellation.inputs.source_subject = 'fsaverage'
subject_parcellation.inputs.source_annot_file = 'aparc'
subject_parcellation.inputs.out_directory = out_directory + '/connectome/'
subject_parcellation.inputs.parcellation_directory = parcellation_directory
# Co-registering T1 and dwi
bbreg = pe.Node(interface=BBRegister(), name='bbreg')
bbreg.inputs.init = 'fsl'
bbreg.inputs.contrast_type = 't2'
applyreg = pe.Node(interface=ApplyVolTransform(), name='applyreg')
applyreg.inputs.interp = 'nearest'
applyreg.inputs.inverse = True
# Merge outputs to pass on to CalcMatrix
merge = pe.Node(interface=Merge(3), name='merge')
# calcuating the connectome matrix
calc_matrix = pe.MapNode(interface=CalcMatrix(),
name='calc_matrix',
iterfield=['scalar_file'])
calc_matrix.iterables = ('threshold', np.arange(0, 100, 10))
# Getting values of diffusion measures
FA_values = pe.Node(interface=AtlasValues(), name='FA_values')
RD_values = pe.Node(interface=AtlasValues(), name='RD_values')
AD_values = pe.Node(interface=AtlasValues(), name='AD_values')
MD_values = pe.Node(interface=AtlasValues(), name='MD_values')
# Getting additional surface measures
aparcstats = pe.Node(interface=AparcStats(), name='aparcstats')
aparcstats.inputs.parcellation_name = 'aparc'
freesurfer_values = pe.Node(interface=FreeSurferValues(),
name='freesurfer_values')
freesurfer_values.inputs.parcellation_name = 'aparc'
# ==================================================================
# Setting up the workflow
connectome = pe.Workflow(name='connectome')
# Reading in files
connectome.connect(infosource, 'subject_id', selectfiles, 'subject_id')
# DWI preprocessing
connectome.connect(infosource, 'subject_id', dwi_preproc, 'subject_id')
connectome.connect(selectfiles, 'dwi', dwi_preproc, 'dwi')
connectome.connect(selectfiles, 'bval', dwi_preproc, 'bvals')
connectome.connect(selectfiles, 'bvec', dwi_preproc, 'bvecs')
# CSD model and streamline tracking
connectome.connect(dwi_preproc, 'mask', erode_mask, 'in_file')
connectome.connect(selectfiles, 'bvec', tractography, 'bvec')
connectome.connect(selectfiles, 'bval', tractography, 'bval')
connectome.connect(dwi_preproc, 'dwi', tractography, 'in_file')
connectome.connect(dwi_preproc, 'FA', tractography, 'FA')
connectome.connect(erode_mask, 'out_file', tractography, 'brain_mask')
# Smoothing the trackfile
connectome.connect(tractography, 'out_track', smooth, 'track_file')
# Preprocessing the T1-weighted file
connectome.connect(infosource, 'subject_id', t1_preproc, 'subject_id')
connectome.connect(selectfiles, 'T1', t1_preproc, 'T1')
connectome.connect(t1_preproc, 'wm', subject_parcellation, 'wm')
connectome.connect(t1_preproc, 'subjects_dir', subject_parcellation,
'subjects_dir')
connectome.connect(t1_preproc, 'subject_id', subject_parcellation,
'subject_id')
# Getting the parcellation into diffusion space
connectome.connect(t1_preproc, 'subject_id', bbreg, 'subject_id')
connectome.connect(t1_preproc, 'subjects_dir', bbreg, 'subjects_dir')
connectome.connect(dwi_preproc, 'b0', bbreg, 'source_file')
connectome.connect(dwi_preproc, 'b0', applyreg, 'source_file')
connectome.connect(bbreg, 'out_reg_file', applyreg, 'reg_file')
connectome.connect(subject_parcellation, 'renum_expanded', applyreg,
'target_file')
# Calculating the FA connectome
connectome.connect(tractography, 'out_file', calc_matrix, 'track_file')
connectome.connect(dwi_preproc, 'FA', merge, 'in1')
connectome.connect(dwi_preproc, 'RD', merge, 'in2')
connectome.connect(tractography, 'GFA', merge, 'in3')
connectome.connect(merge, 'out', calc_matrix, 'scalar_file')
connectome.connect(applyreg, 'transformed_file', calc_matrix,
'ROI_file')
# Getting values for additional measures
connectome.connect(dwi_preproc, 'FA', FA_values, 'morpho_filename')
connectome.connect(dwi_preproc, 'RD', RD_values, 'morpho_filename')
connectome.connect(dwi_preproc, 'AD', AD_values, 'morpho_filename')
connectome.connect(dwi_preproc, 'MD', MD_values, 'morpho_filename')
connectome.connect(applyreg, 'transformed_file', FA_values,
'atlas_filename')
connectome.connect(applyreg, 'transformed_file', RD_values,
'atlas_filename')
connectome.connect(applyreg, 'transformed_file', AD_values,
'atlas_filename')
connectome.connect(applyreg, 'transformed_file', MD_values,
'atlas_filename')
# Getting FreeSurfer morphological values
connectome.connect(t1_preproc, 'subject_id', aparcstats, 'subject_id')
connectome.connect(t1_preproc, 'subjects_dir', aparcstats,
'subjects_dir')
connectome.connect(aparcstats, 'lh_stats', freesurfer_values,
'lh_filename')
connectome.connect(aparcstats, 'rh_stats', freesurfer_values,
'rh_filename')
# ==================================================================
# Running the workflow
connectome.base_dir = os.path.abspath(out_directory)
connectome.write_graph()
connectome.run()
def rest_noise_filter_wf(wf_name='rest_noise_removal'):
""" Create a resting-state fMRI noise removal node.
Nipype Inputs
-------------
rest_noise_input.in_file
rest_noise_input.brain_mask
rest_noise_input.wm_mask
rest_noise_input.csf_mask
rest_noise_input.motion_params
Nipy motion parameters.
Nipype Outputs
--------------
rest_noise_output.tsnr_file
A SNR estimation volume file for QA purposes.
rest_noise_output.motion_corrected
The fMRI motion corrected image.
rest_noise_output.nuis_corrected
The resulting nuisance corrected image.
This will be the same as 'motion_corrected' if compcor
is disabled.
rest_noise_output.motion_regressors
Motion regressors file.
rest_noise_output.compcor_regressors
CompCor regressors file.
rest_noise_output.art_displacement_files
One image file containing the voxel-displacement timeseries.
rest_noise_output.art_intensity_files
One file containing the global intensity values determined
from the brainmask.
rest_noise_output.art_norm_files
One file containing the composite norm.
rest_noise_output.art_outlier_files
One file containing a list of 0-based indices corresponding
to outlier volumes.
rest_noise_output.art_plot_files
One image file containing the detected outliers.
rest_noise_output.art_statistic_files
One file containing information about the different types of
artifacts and if design info is provided then details of
stimulus correlated motion and a listing or artifacts by
event type.
Returns
-------
rm_nuisance_wf: nipype Workflow
"""
# Create the workflow object
wf = pe.Workflow(name=wf_name)
in_fields = [
"in_file",
"brain_mask",
"wm_mask",
"csf_mask",
"motion_params",
]
out_fields = [
"tsnr_file",
"motion_corrected",
"nuis_corrected",
"motion_regressors",
"compcor_regressors",
"gsr_regressors",
"art_displacement_files",
"art_intensity_files",
"art_norm_files",
"art_outlier_files",
"art_plot_files",
"art_statistic_files",
]
# input identities
rest_noise_input = setup_node(IdentityInterface(fields=in_fields,
mandatory_inputs=True),
name="rest_noise_input")
# get the settings for filters
filters = _get_params_for('rest_filter')
# Compute TSNR on realigned data regressing polynomial up to order 2
tsnr = setup_node(TSNR(regress_poly=2), name='tsnr')
# Use :class:`nipype.algorithms.rapidart` to determine which of the
# images in the functional series are outliers based on deviations in
# intensity or movement.
art = setup_node(rapidart_fmri_artifact_detection(),
name="detect_artifacts")
# Compute motion regressors
motion_regs = setup_node(Function(
input_names=[
'motion_params',
'order',
'derivatives',
],
output_names=['out_files'],
function=motion_regressors,
),
name='motion_regressors')
# Create a filter to remove motion and art confounds
motart_pars = setup_node(Function(
input_names=['motion_params', 'comp_norm', 'outliers', 'detrend_poly'],
output_names=['out_files'],
function=create_regressors),
name='motart_parameters')
motion_filter = setup_node(fsl.GLM(out_f_name='F_mcart.nii.gz',
out_pf_name='pF_mcart.nii.gz',
demean=True),
name='motion_filter')
# Noise confound regressors
compcor_pars = setup_node(Function(
input_names=[
'realigned_file', 'mask_file', 'num_components', 'extra_regressors'
],
output_names=['out_files'],
function=extract_noise_components,
),
name='compcor_pars')
#compcor_pars = setup_node(ACompCor(), name='compcor_pars')
#compcor_pars.inputs.components_file = 'noise_components.txt'
compcor_filter = setup_node(fsl.GLM(out_f_name='F.nii.gz',
out_pf_name='pF.nii.gz',
demean=True),
name='compcor_filter')
# Global signal regression
gsr_pars = setup_node(Function(
input_names=[
'realigned_file', 'mask_file', 'num_components', 'extra_regressors'
],
output_names=['out_files'],
function=extract_noise_components,
),
name='gsr_pars')
gsr_filter = setup_node(fsl.GLM(out_f_name='F_gsr.nii.gz',
out_pf_name='pF_gsr.nii.gz',
demean=True),
name='gsr_filter')
# output identities
rest_noise_output = setup_node(IdentityInterface(fields=out_fields,
mandatory_inputs=True),
name="rest_noise_output")
# Connect the nodes
wf.connect([
# tsnr
(rest_noise_input, tsnr, [("in_file", "in_file")]),
# artifact detection
(rest_noise_input, art, [
("in_file", "realigned_files"),
("motion_params", "realignment_parameters"),
("brain_mask", "mask_file"),
]),
# calculte motion regressors
(rest_noise_input, motion_regs, [("motion_params", "motion_params")]),
# create motion and confound regressors parameters file
(art, motart_pars, [
("norm_files", "comp_norm"),
("outlier_files", "outliers"),
]),
(motion_regs, motart_pars, [("out_files", "motion_params")]),
# motion filtering
(rest_noise_input, motion_filter, [
("in_file", "in_file"),
(("in_file", rename, "_filtermotart"), "out_res_name"),
]),
(motart_pars, motion_filter, [(("out_files", selectindex, [0]),
"design")]),
# output
(tsnr, rest_noise_output, [("tsnr_file", "tsnr_file")]),
(motart_pars, rest_noise_output, [("out_files", "motion_regressors")]),
(motion_filter, rest_noise_output, [("out_res", "motion_corrected")]),
(art, rest_noise_output, [
("displacement_files", "art_displacement_files"),
("intensity_files", "art_intensity_files"),
("norm_files", "art_norm_files"),
("outlier_files", "art_outlier_files"),
("plot_files", "art_plot_files"),
("statistic_files", "art_statistic_files"),
]),
])
last_filter = motion_filter
# compcor filter
if filters['compcor_csf'] or filters['compcor_wm']:
wf.connect([
# calculate compcor regressor and parameters file
(motart_pars, compcor_pars, [
(("out_files", selectindex, [0]), "extra_regressors"),
]),
(motion_filter, compcor_pars, [
("out_res", "realigned_file"),
]),
# the compcor filter
(motion_filter, compcor_filter, [
("out_res", "in_file"),
(("out_res", rename, "_cleaned"), "out_res_name"),
]),
(compcor_pars, compcor_filter, [(("out_files", selectindex, [0]),
"design")]),
#(compcor_pars, compcor_filter, [("components_file", "design")]),
(rest_noise_input, compcor_filter, [("brain_mask", "mask")]),
# output
(compcor_pars, rest_noise_output, [("out_files",
"compcor_regressors")]),
#(compcor_pars, rest_noise_output, [("components_file", "compcor_regressors")]),
])
last_filter = compcor_filter
# global signal regression
if filters['gsr']:
wf.connect([
# calculate gsr regressors parameters file
(last_filter, gsr_pars, [("out_res", "realigned_file")]),
(rest_noise_input, gsr_pars, [("brain_mask", "mask_file")]),
# the output file name
(rest_noise_input, gsr_filter, [("brain_mask", "mask")]),
(last_filter, gsr_filter, [
("out_res", "in_file"),
(("out_res", rename, "_gsr"), "out_res_name"),
]),
(gsr_pars, gsr_filter, [(("out_files", selectindex, [0]), "design")
]),
# output
(gsr_pars, rest_noise_output, [("out_files", "gsr_regressors")]),
])
last_filter = gsr_filter
# connect the final nuisance correction output node
wf.connect([
(last_filter, rest_noise_output, [("out_res", "nuis_corrected")]),
])
if filters['compcor_csf'] and filters['compcor_wm']:
mask_merge = setup_node(Merge(2), name="mask_merge")
wf.connect([
## the mask for the compcor filter
(rest_noise_input, mask_merge, [("wm_mask", "in1")]),
(rest_noise_input, mask_merge, [("csf_mask", "in2")]),
(mask_merge, compcor_pars, [("out", "mask_file")]),
])
elif filters['compcor_csf']:
wf.connect([
## the mask for the compcor filter
(rest_noise_input, compcor_pars, [("csf_mask", "mask_file")]),
])
elif filters['compcor_wm']:
wf.connect([
## the mask for the compcor filter
(rest_noise_input, compcor_pars, [("wm_mask", "mask_file")]),
])
return wf
def make_neuromet_fs_workflow(self):
# Infosource: Iterate through subject names
infosource = Node(interface=IdentityInterface(fields=['subject_id']),
name="infosource")
infosource.iterables = ('subject_id', self.subject_list)
mask_source = Node(interface=GetMaskValue(csv_file=self.mask_file),
name='get_mask')
split_sub_str = Node(Function(['subject_str'],
['subject_id', 'session_id'],
self.split_subject_ses),
name='split_sub_str')
# Datasource: Build subjects' filenames from IDs
info = dict(mask=[[
'subject_id', 'session_id', 'anat', 'subject_id', 'session_id',
'mask', 'ro_brain_bin.nii.gz'
]],
uni_bias_corr=[[
'subject_id', 'session_id', 'anat', 'subject_id',
'session_id', 'UNI', 'ro_bfcorr.nii'
]],
den_ro=[[
'subject_id', 'session_id', 'anat', 'subject_id',
'session_id', 'UNIDEN', 'ro_bfcorr.nii'
]])
datasource = Node(interface=DataGrabber(
infields=['subject_id', 'session_id', 'mask'],
outfields=['mask', 'uni_bias_corr', 'den_ro']),
name='datasource')
datasource.inputs.base_directory = self.derivatives_dir
datasource.inputs.template = 'sub-NeuroMET%s/ses-0%s/%s/sub-NeuroMET%s_ses-0%s_desc-%s_%s'
datasource.inputs.template_args = info
datasource.inputs.sort_filelist = False
sink = self.make_sink()
comb_imgs = self.make_comb_imgs()
freesurfer = self.make_freesurfer()
neuromet_fs = Workflow(name='NeuroMET', base_dir=self.temp_dir)
neuromet_fs.connect(infosource, 'subject_id', split_sub_str,
'subject_str')
neuromet_fs.connect(split_sub_str, 'subject_id', datasource,
'subject_id')
neuromet_fs.connect(split_sub_str, 'session_id', datasource,
'session_id')
neuromet_fs.connect(infosource, 'subject_id', mask_source,
'subject_id')
neuromet_fs.connect(mask_source, 'mask_value', datasource, 'mask')
neuromet_fs.connect(datasource, 'uni_bias_corr', comb_imgs,
'mask_uni_bias.in_file')
neuromet_fs.connect(datasource, 'mask', comb_imgs,
'mask_uni_bias.mask_file')
neuromet_fs.connect(datasource, 'den_ro', comb_imgs,
'uni_brain_den_surr_mas.in_file')
neuromet_fs.connect(comb_imgs, 'uni_brain_den_surr_add.out_file',
freesurfer, 'fs_recon1.T1_files')
neuromet_fs.connect(datasource, 'mask', freesurfer,
'fs_mriconv.in_file')
out_dir_source = Node(interface=IdentityInterface(
fields=['out_dir'], mandatory_inputs=True),
name='out_dir_source')
out_dir_source.inputs.out_dir = self.bids_root
make_list_str = Node(interface=Merge(2), name='make_list_of_paths')
merge_strs = Node(interface=OsPathJoin(), name='merge_sub_id_dir')
neuromet_fs.connect(comb_imgs, 'uni_brain_den_surr_add.out_file', sink,
'@img')
#neuromet_fs.connect(infosource, 'subject_id', copy_freesurfer_dir, 'sub_id')
#neuromet_fs.connect(freesurfer, 'segment_hp.subjects_dir', copy_freesurfer_dir, 'in_dir')
neuromet_fs.connect(freesurfer, 'segment_hp.subjects_dir',
make_list_str, 'in1')
neuromet_fs.connect(freesurfer, 'segment_hp.subject_id', make_list_str,
'in2')
neuromet_fs.connect(make_list_str, 'out', merge_strs, 'str_list')
neuromet_fs.connect(merge_strs, 'out_path', sink, '@recon_all')
#neuromet_fs.connect(out_dir_source, 'out_dir', copy_freesurfer_dir, 'out_dir')
#ToDo:
# 04.12.2020 QDec + Adjust volumes. It hangs if qdec is in a workflow, works a single interface
#neuromet_fs.connect(freesurfer, 'segment_hp.subject_id', qdec, 'devnull')
#neuromet_fs.connect(datasource, 'base_directory', qdec, 'basedir')
#neuromet_fs.connect(qdec, 'stats_directory', adj_vol, 'stats_directory')
#neuromet_fs.connect(qdec, 'stats_directory', sink, '@stat_dir')
#neuromet_fs.connect(adj_vol, 'adjusted_stats', sink, '@adj_stats')
return neuromet_fs