def create_moco_pipeline(name='motion_correction'):
# initiate workflow
moco = Workflow(name='motion_correction')
# set fsl output
fsl.FSLCommand.set_default_output_type('NIFTI_GZ')
# inputnode
inputnode = Node(util.IdentityInterface(fields=['epi']), name='inputnode')
# outputnode
outputnode = Node(util.IdentityInterface(fields=[
'epi_moco', 'par_moco', 'mat_moco', 'rms_moco', 'epi_mean', 'rotplot',
'transplot', 'dispplots', 'tsnr_file'
]),
name='outputnode')
# mcflirt motion correction to 1st volume
mcflirt = Node(fsl.MCFLIRT(save_mats=True,
save_plots=True,
save_rms=True,
ref_vol=1,
out_file='rest_realigned.nii.gz'),
name='mcflirt')
# plot motion parameters
rotplotter = Node(fsl.PlotMotionParams(in_source='fsl',
plot_type='rotations',
out_file='rotation_plot.png'),
name='rotplotter')
transplotter = Node(fsl.PlotMotionParams(in_source='fsl',
plot_type='translations',
out_file='translation_plot.png'),
name='transplotter')
dispplotter = MapNode(interface=fsl.PlotMotionParams(
in_source='fsl',
plot_type='displacement',
),
name='dispplotter',
iterfield=['in_file'])
dispplotter.iterables = ('plot_type', ['displacement'])
# calculate tmean
tmean = Node(fsl.maths.MeanImage(dimension='T',
out_file='rest_realigned_mean.nii.gz'),
name='tmean')
# calculate tsnr
tsnr = Node(confounds.TSNR(), name='tsnr')
# create connections
moco.connect([(inputnode, mcflirt, [('epi', 'in_file')]),
(mcflirt, tmean, [('out_file', 'in_file')]),
(mcflirt, rotplotter, [('par_file', 'in_file')]),
(mcflirt, transplotter, [('par_file', 'in_file')]),
(mcflirt, dispplotter, [('rms_files', 'in_file')]),
(tmean, outputnode, [('out_file', 'epi_mean')]),
(mcflirt, outputnode, [('out_file', 'epi_moco'),
('par_file', 'par_moco'),
('mat_file', 'mat_moco'),
('rms_files', 'rms_moco')]),
(rotplotter, outputnode, [('out_file', 'rotplot')]),
(transplotter, outputnode, [('out_file', 'transplot')]),
(dispplotter, outputnode, [('out_file', 'dispplots')]),
(mcflirt, tsnr, [('out_file', 'in_file')]),
(tsnr, outputnode, [('tsnr_file', 'tsnr_file')])])
return moco
def create_moco_pipeline(working_dir, ds_dir, name='motion_correction'):
"""
Workflow for motion correction to 1st volume
based on https://github.com/NeuroanatomyAndConnectivity/pipelines/blob/master/src/lsd_lemon/func_preproc/moco.py
"""
# initiate workflow
moco_wf = Workflow(name=name)
moco_wf.base_dir = os.path.join(working_dir, 'LeiCA_resting',
'rsfMRI_preprocessing')
# set fsl output
fsl.FSLCommand.set_default_output_type('NIFTI_GZ')
# I/O NODES
inputnode = Node(util.IdentityInterface(fields=['epi', 'vols_to_drop']),
name='inputnode')
outputnode = Node(util.IdentityInterface(fields=[
'epi_moco', 'par_moco', 'mat_moco', 'rms_moco',
'initial_mean_epi_moco', 'rotplot', 'transplot', 'dispplots',
'tsnr_file', 'epi_mask'
]),
name='outputnode')
ds = Node(nio.DataSink(base_directory=ds_dir), name='ds')
ds.inputs.substitutions = [('_TR_id_', 'TR_')]
# REMOVE FIRST VOLUMES
drop_vols = Node(util.Function(input_names=['in_file', 't_min'],
output_names=['out_file'],
function=strip_rois_func),
name='remove_vol')
moco_wf.connect(inputnode, 'epi', drop_vols, 'in_file')
moco_wf.connect(inputnode, 'vols_to_drop', drop_vols, 't_min')
# MCFILRT MOCO TO 1st VOLUME
mcflirt = Node(fsl.MCFLIRT(save_mats=True,
save_plots=True,
save_rms=True,
ref_vol=0,
out_file='rest_realigned.nii.gz'),
name='mcflirt')
moco_wf.connect(drop_vols, 'out_file', mcflirt, 'in_file')
moco_wf.connect([(mcflirt, ds, [('par_file', 'realign.par.@par'),
('mat_file', 'realign.MAT.@mat'),
('rms_files', 'realign.plots.@rms')])])
moco_wf.connect([(mcflirt, outputnode, [('out_file', 'epi_moco'),
('par_file', 'par_moco'),
('mat_file', 'mat_moco'),
('rms_files', 'rms_moco')])])
# CREATE MEAN EPI (INTENSITY NORMALIZED)
initial_mean_epi_moco = Node(fsl.maths.MeanImage(
dimension='T', out_file='initial_mean_epi_moco.nii.gz'),
name='initial_mean_epi_moco')
moco_wf.connect(mcflirt, 'out_file', initial_mean_epi_moco, 'in_file')
moco_wf.connect(initial_mean_epi_moco, 'out_file', outputnode,
'initial_mean_epi_moco')
moco_wf.connect(initial_mean_epi_moco, 'out_file', ds,
'QC.initial_mean_epi_moco')
# PLOT MOTION PARAMETERS
rotplotter = Node(fsl.PlotMotionParams(in_source='fsl',
plot_type='rotations',
out_file='rotation_plot.png'),
name='rotplotter')
moco_wf.connect(mcflirt, 'par_file', rotplotter, 'in_file')
moco_wf.connect(rotplotter, 'out_file', ds, 'realign.plots.@rotplot')
transplotter = Node(fsl.PlotMotionParams(in_source='fsl',
plot_type='translations',
out_file='translation_plot.png'),
name='transplotter')
moco_wf.connect(mcflirt, 'par_file', transplotter, 'in_file')
moco_wf.connect(transplotter, 'out_file', ds, 'realign.plots.@transplot')
dispplotter = MapNode(interface=fsl.PlotMotionParams(
in_source='fsl', plot_type='displacement'),
name='dispplotter',
iterfield=['in_file'])
dispplotter.iterables = ('plot_type', ['displacement'])
moco_wf.connect(mcflirt, 'rms_files', dispplotter, 'in_file')
moco_wf.connect(dispplotter, 'out_file', ds, 'realign.plots.@dispplots')
moco_wf.write_graph(dotfilename=moco_wf.name,
graph2use='flat',
format='pdf')
return moco_wf
def create_moco_pipeline(name='motion_correction'):
# initiate workflow
moco = Workflow(name='motion_correction')
# set fsl output
fsl.FSLCommand.set_default_output_type('NIFTI_GZ')
# inputnode
inputnode = Node(util.IdentityInterface(fields=['epi']),
name='inputnode')
# outputnode
outputnode = Node(util.IdentityInterface(fields=['epi_moco',
'par_moco',
'mat_moco',
'rms_moco',
'epi_mean',
'rotplot',
'transplot',
'dispplots',
'tsnr_file']),
name='outputnode')
# mcflirt motion correction to 1st volume
mcflirt = Node(fsl.MCFLIRT(save_mats=True,
save_plots=True,
save_rms=True,
#ref_vol=1,
mean_vol = True,
out_file='rest_realigned.nii.gz'
),
name='mcflirt')
# plot motion parameters
rotplotter = Node(fsl.PlotMotionParams(in_source='fsl',
plot_type='rotations',
out_file='rotation_plot.png'),
name='rotplotter')
transplotter = Node(fsl.PlotMotionParams(in_source='fsl',
plot_type='translations',
out_file='translation_plot.png'),
name='transplotter')
dispplotter = MapNode(interface=fsl.PlotMotionParams(in_source='fsl',
plot_type='displacement',
),
name='dispplotter',
iterfield=['in_file'])
dispplotter.iterables = ('plot_type', ['displacement'])
# calculate tmean
tmean = Node(fsl.maths.MeanImage(dimension='T',
out_file='rest_realigned_mean.nii.gz'),
name='tmean')
# calculate tsnr
tsnr = Node(misc.TSNR(),
name='tsnr')
# create connections
moco.connect([(inputnode, mcflirt, [('epi', 'in_file')]),
(mcflirt, tmean, [('out_file', 'in_file')]),
(mcflirt, rotplotter, [('par_file', 'in_file')]),
(mcflirt, transplotter, [('par_file', 'in_file')]),
(mcflirt, dispplotter, [('rms_files', 'in_file')]),
(tmean, outputnode, [('out_file', 'epi_mean')]),
(mcflirt, outputnode, [('out_file', 'epi_moco'),
('par_file', 'par_moco'),
('mat_file', 'mat_moco'),
('rms_files', 'rms_moco')]),
(rotplotter, outputnode, [('out_file', 'rotplot')]),
(transplotter, outputnode, [('out_file', 'transplot')]),
(dispplotter, outputnode, [('out_file', 'dispplots')]),
(mcflirt, tsnr, [('out_file', 'in_file')]),
(tsnr, outputnode, [('tsnr_file', 'tsnr_file')])
])
return moco
def create_moco_pipeline(working_dir, ds_dir, name='motion_correction'):
"""
Workflow for motion correction to 1st volume
based on https://github.com/NeuroanatomyAndConnectivity/pipelines/blob/master/src/lsd_lemon/func_preproc/moco.py
"""
# initiate workflow
moco_wf = Workflow(name=name)
moco_wf.base_dir = os.path.join(working_dir,'LeiCA_resting', 'rsfMRI_preprocessing')
# set fsl output
fsl.FSLCommand.set_default_output_type('NIFTI_GZ')
# I/O NODES
inputnode = Node(util.IdentityInterface(fields=['epi',
'vols_to_drop']),
name='inputnode')
outputnode = Node(util.IdentityInterface(fields=['epi_moco',
'par_moco',
'mat_moco',
'rms_moco',
'initial_mean_epi_moco',
'rotplot',
'transplot',
'dispplots',
'tsnr_file',
'epi_mask']),
name='outputnode')
ds = Node(nio.DataSink(base_directory=ds_dir), name='ds')
ds.inputs.substitutions = [('_TR_id_', 'TR_')]
# REMOVE FIRST VOLUMES
drop_vols = Node(util.Function(input_names=['in_file','t_min'],
output_names=['out_file'],
function=strip_rois_func),
name='remove_vol')
moco_wf.connect(inputnode, 'epi', drop_vols, 'in_file')
moco_wf.connect(inputnode, 'vols_to_drop', drop_vols, 't_min')
# MCFILRT MOCO TO 1st VOLUME
mcflirt = Node(fsl.MCFLIRT(save_mats=True,
save_plots=True,
save_rms=True,
ref_vol=0,
out_file='rest_realigned.nii.gz'
),
name='mcflirt')
moco_wf.connect(drop_vols, 'out_file', mcflirt, 'in_file')
moco_wf.connect([(mcflirt, ds, [('par_file', 'realign.par.@par'),
('mat_file', 'realign.MAT.@mat'),
('rms_files', 'realign.plots.@rms')])])
moco_wf.connect([(mcflirt, outputnode, [('out_file', 'epi_moco'),
('par_file', 'par_moco'),
('mat_file', 'mat_moco'),
('rms_files', 'rms_moco')])])
# CREATE MEAN EPI (INTENSITY NORMALIZED)
initial_mean_epi_moco = Node(fsl.maths.MeanImage(dimension='T',
out_file='initial_mean_epi_moco.nii.gz'),
name='initial_mean_epi_moco')
moco_wf.connect(mcflirt, 'out_file', initial_mean_epi_moco, 'in_file')
moco_wf.connect(initial_mean_epi_moco, 'out_file', outputnode, 'initial_mean_epi_moco')
moco_wf.connect(initial_mean_epi_moco, 'out_file', ds, 'QC.initial_mean_epi_moco')
# PLOT MOTION PARAMETERS
rotplotter = Node(fsl.PlotMotionParams(in_source='fsl',
plot_type='rotations',
out_file='rotation_plot.png'),
name='rotplotter')
moco_wf.connect(mcflirt, 'par_file', rotplotter, 'in_file')
moco_wf.connect(rotplotter, 'out_file', ds, 'realign.plots.@rotplot')
transplotter = Node(fsl.PlotMotionParams(in_source='fsl',
plot_type='translations',
out_file='translation_plot.png'),
name='transplotter')
moco_wf.connect(mcflirt, 'par_file', transplotter, 'in_file')
moco_wf.connect(transplotter, 'out_file', ds, 'realign.plots.@transplot')
dispplotter = MapNode(interface=fsl.PlotMotionParams(in_source='fsl',
plot_type='displacement'),
name='dispplotter',
iterfield=['in_file'])
dispplotter.iterables = ('plot_type', ['displacement'])
moco_wf.connect(mcflirt, 'rms_files', dispplotter, 'in_file')
moco_wf.connect(dispplotter, 'out_file', ds, 'realign.plots.@dispplots')
moco_wf.write_graph(dotfilename=moco_wf.name, graph2use='flat', format='pdf')
return moco_wf
