def create_func_preproc(use_bet=False, wf_name='func_preproc'):
"""
The main purpose of this workflow is to process functional data. Raw rest file is deobliqued and reoriented
into RPI. Then take the mean intensity values over all time points for each voxel and use this image
to calculate motion parameters. The image is then skullstripped, normalized and a processed mask is
obtained to use it further in Image analysis.
Parameters
----------
wf_name : string
Workflow name
Returns
-------
func_preproc : workflow object
Functional Preprocessing workflow object
Notes
-----
`Source <https://github.com/FCP-INDI/C-PAC/blob/master/CPAC/func_preproc/func_preproc.py>`_
Workflow Inputs::
inputspec.rest : func/rest file or a list of func/rest nifti file
User input functional(T2) Image, in any of the 8 orientations
scan_params.tr : string
Subject TR
scan_params.acquistion : string
Acquisition pattern (interleaved/sequential, ascending/descending)
scan_params.ref_slice : integer
Reference slice for slice timing correction
Workflow Outputs::
outputspec.refit : string (nifti file)
Path to deobliqued anatomical data
outputspec.reorient : string (nifti file)
Path to RPI oriented anatomical data
outputspec.motion_correct_ref : string (nifti file)
Path to Mean intensity Motion corrected image
(base reference image for the second motion correction run)
outputspec.motion_correct : string (nifti file)
Path to motion corrected output file
outputspec.max_displacement : string (Mat file)
Path to maximum displacement (in mm) for brain voxels in each volume
outputspec.movement_parameters : string (Mat file)
Path to 1D file containing six movement/motion parameters(3 Translation, 3 Rotations)
in different columns (roll pitch yaw dS dL dP)
outputspec.skullstrip : string (nifti file)
Path to skull stripped Motion Corrected Image
outputspec.mask : string (nifti file)
Path to brain-only mask
outputspec.example_func : string (nifti file)
Mean, Skull Stripped, Motion Corrected output T2 Image path
(Image with mean intensity values across voxels)
outputpsec.preprocessed : string (nifti file)
output skull stripped, motion corrected T2 image
with normalized intensity values
outputspec.preprocessed_mask : string (nifti file)
Mask obtained from normalized preprocessed image
Order of commands:
- Deobliqing the scans. For details see `3drefit <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3drefit.html>`_::
3drefit -deoblique rest_3dc.nii.gz
- Re-orienting the Image into Right-to-Left Posterior-to-Anterior Inferior-to-Superior (RPI) orientation. For details see `3dresample <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dresample.html>`_::
3dresample -orient RPI
-prefix rest_3dc_RPI.nii.gz
-inset rest_3dc.nii.gz
- Calculate voxel wise statistics. Get the RPI Image with mean intensity values over all timepoints for each voxel. For details see `3dTstat <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dTstat.html>`_::
3dTstat -mean
-prefix rest_3dc_RPI_3dT.nii.gz
rest_3dc_RPI.nii.gz
- Motion Correction. For details see `3dvolreg <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dvolreg.html>`_::
3dvolreg -Fourier
-twopass
-base rest_3dc_RPI_3dT.nii.gz/
-zpad 4
-maxdisp1D rest_3dc_RPI_3dvmd1D.1D
-1Dfile rest_3dc_RPI_3dv1D.1D
-prefix rest_3dc_RPI_3dv.nii.gz
rest_3dc_RPI.nii.gz
The base image or the reference image is the mean intensity RPI image obtained in the above the step.For each volume
in RPI-oriented T2 image, the command, aligns the image with the base mean image and calculates the motion, displacement
and movement parameters. It also outputs the aligned 4D volume and movement and displacement parameters for each volume.
- Calculate voxel wise statistics. Get the motion corrected output Image from the above step, with mean intensity values over all timepoints for each voxel.
For details see `3dTstat <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dTstat.html>`_::
3dTstat -mean
-prefix rest_3dc_RPI_3dv_3dT.nii.gz
rest_3dc_RPI_3dv.nii.gz
- Motion Correction and get motion, movement and displacement parameters. For details see `3dvolreg <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dvolreg.html>`_::
3dvolreg -Fourier
-twopass
-base rest_3dc_RPI_3dv_3dT.nii.gz
-zpad 4
-maxdisp1D rest_3dc_RPI_3dvmd1D.1D
-1Dfile rest_3dc_RPI_3dv1D.1D
-prefix rest_3dc_RPI_3dv.nii.gz
rest_3dc_RPI.nii.gz
The base image or the reference image is the mean intensity motion corrected image obtained from the above the step (first 3dvolreg run).
For each volume in RPI-oriented T2 image, the command, aligns the image with the base mean image and calculates the motion, displacement
and movement parameters. It also outputs the aligned 4D volume and movement and displacement parameters for each volume.
- Create a brain-only mask. For details see `3dautomask <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dAutomask.html>`_::
3dAutomask
-prefix rest_3dc_RPI_3dv_automask.nii.gz
rest_3dc_RPI_3dv.nii.gz
- Edge Detect(remove skull) and get the brain only. For details see `3dcalc <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dcalc.html>`_::
3dcalc -a rest_3dc_RPI_3dv.nii.gz
-b rest_3dc_RPI_3dv_automask.nii.gz
-expr 'a*b'
-prefix rest_3dc_RPI_3dv_3dc.nii.gz
- Normalizing the image intensity values. For details see `fslmaths <http://www.fmrib.ox.ac.uk/fsl/avwutils/index.html>`_::
fslmaths rest_3dc_RPI_3dv_3dc.nii.gz
-ing 10000 rest_3dc_RPI_3dv_3dc_maths.nii.gz
-odt float
Normalized intensity = (TrueValue*10000)/global4Dmean
- Calculate mean of skull stripped image. For details see `3dTstat <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dTstat.html>`_::
3dTstat -mean -prefix rest_3dc_RPI_3dv_3dc_3dT.nii.gz rest_3dc_RPI_3dv_3dc.nii.gz
- Create Mask (Generate mask from Normalized data). For details see `fslmaths <http://www.fmrib.ox.ac.uk/fsl/avwutils/index.html>`_::
fslmaths rest_3dc_RPI_3dv_3dc_maths.nii.gz
-Tmin -bin rest_3dc_RPI_3dv_3dc_maths_maths.nii.gz
-odt char
High Level Workflow Graph:
.. image:: ../images/func_preproc.dot.png
:width: 1000
Detailed Workflow Graph:
.. image:: ../images/func_preproc_detailed.dot.png
:width: 1000
Examples
--------
>>> import func_preproc
>>> preproc = create_func_preproc(bet=True)
>>> preproc.inputs.inputspec.func='sub1/func/rest.nii.gz'
>>> preproc.run() #doctest: +SKIP
>>> import func_preproc
>>> preproc = create_func_preproc(bet=False)
>>> preproc.inputs.inputspec.func='sub1/func/rest.nii.gz'
>>> preproc.run() #doctest: +SKIP
"""
preproc = pe.Workflow(name=wf_name)
inputNode = pe.Node(util.IdentityInterface(fields=['func']),
name='inputspec')
outputNode = pe.Node(
util.IdentityInterface(fields=[
'refit',
'reorient',
'reorient_mean',
'motion_correct',
'motion_correct_ref',
'movement_parameters',
'max_displacement',
# 'xform_matrix',
'mask',
'skullstrip',
'example_func',
'preprocessed',
'preprocessed_mask',
'slice_time_corrected',
'oned_matrix_save'
]),
name='outputspec')
try:
from nipype.interfaces.afni import utils as afni_utils
func_deoblique = pe.Node(interface=afni_utils.Refit(),
name='func_deoblique')
except ImportError:
func_deoblique = pe.Node(interface=preprocess.Refit(),
name='func_deoblique')
func_deoblique.inputs.deoblique = True
preproc.connect(inputNode, 'func', func_deoblique, 'in_file')
try:
func_reorient = pe.Node(interface=afni_utils.Resample(),
name='func_reorient')
except UnboundLocalError:
func_reorient = pe.Node(interface=preprocess.Resample(),
name='func_reorient')
func_reorient.inputs.orientation = 'RPI'
func_reorient.inputs.outputtype = 'NIFTI_GZ'
preproc.connect(func_deoblique, 'out_file', func_reorient, 'in_file')
preproc.connect(func_reorient, 'out_file', outputNode, 'reorient')
try:
func_get_mean_RPI = pe.Node(interface=afni_utils.TStat(),
name='func_get_mean_RPI')
except UnboundLocalError:
func_get_mean_RPI = pe.Node(interface=preprocess.TStat(),
name='func_get_mean_RPI')
func_get_mean_RPI.inputs.options = '-mean'
func_get_mean_RPI.inputs.outputtype = 'NIFTI_GZ'
preproc.connect(func_reorient, 'out_file', func_get_mean_RPI, 'in_file')
# calculate motion parameters
func_motion_correct = pe.Node(interface=preprocess.Volreg(),
name='func_motion_correct')
func_motion_correct.inputs.args = '-Fourier -twopass'
func_motion_correct.inputs.zpad = 4
func_motion_correct.inputs.outputtype = 'NIFTI_GZ'
preproc.connect(func_reorient, 'out_file', func_motion_correct, 'in_file')
preproc.connect(func_get_mean_RPI, 'out_file', func_motion_correct,
'basefile')
func_get_mean_motion = func_get_mean_RPI.clone('func_get_mean_motion')
preproc.connect(func_motion_correct, 'out_file', func_get_mean_motion,
'in_file')
preproc.connect(func_get_mean_motion, 'out_file', outputNode,
'motion_correct_ref')
func_motion_correct_A = func_motion_correct.clone('func_motion_correct_A')
func_motion_correct_A.inputs.md1d_file = 'max_displacement.1D'
preproc.connect(func_reorient, 'out_file', func_motion_correct_A,
'in_file')
preproc.connect(func_get_mean_motion, 'out_file', func_motion_correct_A,
'basefile')
preproc.connect(func_motion_correct_A, 'out_file', outputNode,
'motion_correct')
preproc.connect(func_motion_correct_A, 'md1d_file', outputNode,
'max_displacement')
preproc.connect(func_motion_correct_A, 'oned_file', outputNode,
'movement_parameters')
preproc.connect(func_motion_correct_A, 'oned_matrix_save', outputNode,
'oned_matrix_save')
if not use_bet:
func_get_brain_mask = pe.Node(interface=preprocess.Automask(),
name='func_get_brain_mask')
func_get_brain_mask.inputs.outputtype = 'NIFTI_GZ'
preproc.connect(func_motion_correct_A, 'out_file', func_get_brain_mask,
'in_file')
preproc.connect(func_get_brain_mask, 'out_file', outputNode, 'mask')
else:
func_get_brain_mask = pe.Node(interface=fsl.BET(),
name='func_get_brain_mask_BET')
func_get_brain_mask.inputs.mask = True
func_get_brain_mask.inputs.functional = True
erode_one_voxel = pe.Node(interface=fsl.ErodeImage(),
name='erode_one_voxel')
erode_one_voxel.inputs.kernel_shape = 'box'
erode_one_voxel.inputs.kernel_size = 1.0
preproc.connect(func_motion_correct_A, 'out_file', func_get_brain_mask,
'in_file')
preproc.connect(func_get_brain_mask, 'mask_file', erode_one_voxel,
'in_file')
preproc.connect(erode_one_voxel, 'out_file', outputNode, 'mask')
try:
func_edge_detect = pe.Node(interface=afni_utils.Calc(),
name='func_edge_detect')
except UnboundLocalError:
func_edge_detect = pe.Node(interface=preprocess.Calc(),
name='func_edge_detect')
func_edge_detect.inputs.expr = 'a*b'
func_edge_detect.inputs.outputtype = 'NIFTI_GZ'
preproc.connect(func_motion_correct_A, 'out_file', func_edge_detect,
'in_file_a')
if not use_bet:
preproc.connect(func_get_brain_mask, 'out_file', func_edge_detect,
'in_file_b')
else:
preproc.connect(erode_one_voxel, 'out_file', func_edge_detect,
'in_file_b')
preproc.connect(func_edge_detect, 'out_file', outputNode, 'skullstrip')
try:
func_mean_skullstrip = pe.Node(interface=afni_utils.TStat(),
name='func_mean_skullstrip')
except UnboundLocalError:
func_mean_skullstrip = pe.Node(interface=preprocess.TStat(),
name='func_mean_skullstrip')
func_mean_skullstrip.inputs.options = '-mean'
func_mean_skullstrip.inputs.outputtype = 'NIFTI_GZ'
preproc.connect(func_edge_detect, 'out_file', func_mean_skullstrip,
'in_file')
preproc.connect(func_mean_skullstrip, 'out_file', outputNode,
'example_func')
func_normalize = pe.Node(interface=fsl.ImageMaths(), name='func_normalize')
func_normalize.inputs.op_string = '-ing 10000'
func_normalize.inputs.out_data_type = 'float'
preproc.connect(func_edge_detect, 'out_file', func_normalize, 'in_file')
preproc.connect(func_normalize, 'out_file', outputNode, 'preprocessed')
func_mask_normalize = pe.Node(interface=fsl.ImageMaths(),
name='func_mask_normalize')
func_mask_normalize.inputs.op_string = '-Tmin -bin'
func_mask_normalize.inputs.out_data_type = 'char'
preproc.connect(func_normalize, 'out_file', func_mask_normalize, 'in_file')
preproc.connect(func_mask_normalize, 'out_file', outputNode,
'preprocessed_mask')
return preproc
def create_asl_preproc(c, strat, wf_name='asl_preproc'):
# resource_pool = strat?
# print('resource pool asl preproc: ', str(strat.get_resource_pool()))
# allocate a workflow object
asl_workflow = pe.Workflow(name=wf_name)
asl_workflow.base_dir = c.workingDirectory
# configure the workflow's input spec
inputNode = pe.Node(util.IdentityInterface(fields=[
'asl_file', 'anatomical_skull', 'anatomical_brain', 'seg_wm_pve'
]),
name='inputspec')
# configure the workflow's output spec
outputNode = pe.Node(util.IdentityInterface(
fields=['meanasl', 'perfusion_image', 'diffdata', 'diffdata_mean']),
name='outputspec')
# get segmentation output dir and file stub
# create nodes for de-obliquing and reorienting
try:
from nipype.interfaces.afni import utils as afni_utils
func_deoblique = pe.Node(interface=afni_utils.Refit(),
name='func_deoblique')
except ImportError:
func_deoblique = pe.Node(interface=preprocess.Refit(),
name='func_deoblique')
func_deoblique.inputs.deoblique = True
asl_workflow.connect(inputNode, 'asl_file', func_deoblique, 'in_file')
try:
func_reorient = pe.Node(interface=afni_utils.Resample(),
name='func_reorient')
except UnboundLocalError:
func_reorient = pe.Node(interface=preprocess.Resample(),
name='func_reorient')
func_reorient.inputs.orientation = 'RPI'
func_reorient.inputs.outputtype = 'NIFTI_GZ'
# connect deoblique to reorient
asl_workflow.connect(func_deoblique, 'out_file', func_reorient, 'in_file')
# create node for splitting control and label pairs (unused currently)
split_pairs_imports = ['import os', 'import subprocess']
split_ASL_pairs = pe.Node(interface=util.Function(
input_names=['asl_file'],
output_names=['control_image', 'label_image'],
function=split_pairs,
imports=split_pairs_imports),
name='split_pairs')
# create node for calculating subtracted images
diffdata_imports = ['import os', 'import subprocess']
run_diffdata = pe.Node(interface=util.Function(
input_names=['asl_file'],
output_names=['diffdata_image', 'diffdata_mean'],
function=diffdata,
imports=diffdata_imports),
name='diffdata')
asl_workflow.connect(func_reorient, 'out_file', run_diffdata, 'asl_file')
asl_workflow.connect(run_diffdata, 'diffdata_image', outputNode,
'diffdata')
asl_workflow.connect(run_diffdata, 'diffdata_mean', outputNode,
'diffdata_mean')
# create node for oxford_asl (perfusion image)
asl_imports = ['import os', 'import subprocess']
run_oxford_asl = pe.Node(interface=util.Function(
input_names=[
'asl_file', 'anatomical_skull', 'anatomical_brain', 'seg'
],
output_names=['perfusion_image', 'asl2anat_linear_xfm', 'asl2anat'],
function=oxford_asl,
imports=asl_imports),
name='run_oxford_asl')
# wire inputs from resource pool to ASL preprocessing FSL script
# connect output of reorient to run_oxford_asl
asl_workflow.connect(func_reorient, 'out_file', run_oxford_asl, 'asl_file')
asl_workflow.connect(inputNode, 'seg_wm_pve', run_oxford_asl, 'seg')
# pass the anatomical to the workflow
asl_workflow.connect(inputNode, 'anatomical_skull', run_oxford_asl,
'anatomical_skull')
# pass the anatomical to the workflow
asl_workflow.connect(inputNode, 'anatomical_brain', run_oxford_asl,
'anatomical_brain')
# connect oxford_asl outputs to outputNode
asl_workflow.connect(run_oxford_asl, 'asl2anat_linear_xfm', outputNode,
'asl2anat_linear_xfm')
asl_workflow.connect(run_oxford_asl, 'asl2anat', outputNode, 'asl2anat')
asl_workflow.connect(run_oxford_asl, 'perfusion_image', outputNode,
'perfusion_image')
strat.update_resource_pool({
'mean_asl_in_anat': (run_oxford_asl, 'anat_asl'),
'asl_to_anat_linear_xfm': (run_oxford_asl, 'asl2anat_linear_xfm')
})
# Take mean of the asl data for registration
try:
get_mean_asl = pe.Node(interface=afni_utils.TStat(),
name='get_mean_asl')
except UnboundLocalError:
get_mean_asl = pe.Node(interface=preprocess.TStat(),
name='get_mean_asl')
get_mean_asl.inputs.options = '-mean'
get_mean_asl.inputs.outputtype = 'NIFTI_GZ'
asl_workflow.connect(func_reorient, 'out_file', get_mean_asl, 'in_file')
asl_workflow.connect(get_mean_asl, 'out_file', outputNode, 'meanasl')
return asl_workflow
def create_anat_preproc(already_skullstripped=False):
"""
The main purpose of this workflow is to process T1 scans. Raw mprage file is deobliqued, reoriented
into RPI and skullstripped. Also, a whole brain only mask is generated from the skull stripped image
for later use in registration.
Returns
-------
anat_preproc : workflow
Anatomical Preprocessing Workflow
Notes
-----
`Source <https://github.com/FCP-INDI/C-PAC/blob/master/CPAC/anat_preproc/anat_preproc.py>`_
Workflow Inputs::
inputspec.anat : mprage file or a list of mprage nifti file
User input anatomical(T1) Image, in any of the 8 orientations
Workflow Outputs::
outputspec.refit : nifti file
Deobliqued anatomical data
outputspec.reorient : nifti file
RPI oriented anatomical data
outputspec.skullstrip : nifti file
Skull Stripped RPI oriented mprage file with normalized intensities.
outputspec.brain : nifti file
Skull Stripped RPI Brain Image with original intensity values and not normalized or scaled.
Order of commands:
- Deobliqing the scans. For details see `3drefit <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3drefit.html>`_::
3drefit -deoblique mprage.nii.gz
- Re-orienting the Image into Right-to-Left Posterior-to-Anterior Inferior-to-Superior (RPI) orientation. For details see `3dresample <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dresample.html>`_::
3dresample -orient RPI -prefix mprage_RPI.nii.gz -inset mprage.nii.gz
- SkullStripping the image. For details see `3dSkullStrip <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dSkullStrip.html>`_::
3dSkullStrip -input mprage_RPI.nii.gz -o_ply mprage_RPI_3dT.nii.gz
- The skull stripping step modifies the intensity values. To get back the original intensity values, we do an element wise product of RPI data with step function of skull Stripped data. For details see `3dcalc <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dcalc.html>`_::
3dcalc -a mprage_RPI.nii.gz -b mprage_RPI_3dT.nii.gz -expr 'a*step(b)' -prefix mprage_RPI_3dc.nii.gz
High Level Workflow Graph:
.. image:: ../images/anatpreproc_graph.dot.png
:width: 500
Detailed Workflow Graph:
.. image:: ../images/anatpreproc_graph_detailed.dot.png
:width: 500
Examples
--------
>>> import anat
>>> preproc = create_anat_preproc()
>>> preproc.inputs.inputspec.anat='sub1/anat/mprage.nii.gz'
>>> preproc.run() #doctest: +SKIP
"""
preproc = pe.Workflow(name='anat_preproc')
inputNode = pe.Node(util.IdentityInterface(fields=['anat']),
name='inputspec')
outputNode = pe.Node(util.IdentityInterface(
fields=['refit', 'reorient', 'skullstrip', 'brain']),
name='outputspec')
try:
from nipype.interfaces.afni import utils as afni_utils
anat_deoblique = pe.Node(interface=afni_utils.Refit(),
name='anat_deoblique')
except ImportError:
anat_deoblique = pe.Node(interface=preprocess.Refit(),
name='anat_deoblique')
anat_deoblique.inputs.deoblique = True
try:
anat_reorient = pe.Node(interface=afni_utils.Resample(),
name='anat_reorient')
except UnboundLocalError:
anat_reorient = pe.Node(interface=preprocess.Resample(),
name='anat_reorient')
anat_reorient.inputs.orientation = 'RPI'
anat_reorient.inputs.outputtype = 'NIFTI_GZ'
if not already_skullstripped:
anat_skullstrip = pe.Node(interface=preprocess.SkullStrip(),
name='anat_skullstrip')
anat_skullstrip.inputs.outputtype = 'NIFTI_GZ'
try:
anat_skullstrip_orig_vol = pe.Node(interface=afni_utils.Calc(),
name='anat_skullstrip_orig_vol')
except UnboundLocalError:
anat_skullstrip_orig_vol = pe.Node(interface=preprocess.Calc(),
name='anat_skullstrip_orig_vol')
anat_skullstrip_orig_vol.inputs.expr = 'a*step(b)'
anat_skullstrip_orig_vol.inputs.outputtype = 'NIFTI_GZ'
preproc.connect(inputNode, 'anat', anat_deoblique, 'in_file')
preproc.connect(anat_deoblique, 'out_file', anat_reorient, 'in_file')
if not already_skullstripped:
preproc.connect(anat_reorient, 'out_file', anat_skullstrip, 'in_file')
preproc.connect(anat_skullstrip, 'out_file', anat_skullstrip_orig_vol,
'in_file_b')
else:
preproc.connect(anat_reorient, 'out_file', anat_skullstrip_orig_vol,
'in_file_b')
preproc.connect(anat_reorient, 'out_file', anat_skullstrip_orig_vol,
'in_file_a')
preproc.connect(anat_deoblique, 'out_file', outputNode, 'refit')
preproc.connect(anat_reorient, 'out_file', outputNode, 'reorient')
if not already_skullstripped:
preproc.connect(anat_skullstrip, 'out_file', outputNode, 'skullstrip')
preproc.connect(anat_skullstrip_orig_vol, 'out_file', outputNode, 'brain')
return preproc
def create_func_preproc(skullstrip_tool,
n4_correction,
anatomical_mask_dilation=False,
wf_name='func_preproc'):
"""
The main purpose of this workflow is to process functional data. Raw rest file is deobliqued and reoriented
into RPI. Then take the mean intensity values over all time points for each voxel and use this image
to calculate motion parameters. The image is then skullstripped, normalized and a processed mask is
obtained to use it further in Image analysis.
Parameters
----------
wf_name : string
Workflow name
Returns
-------
func_preproc : workflow object
Functional Preprocessing workflow object
Notes
-----
`Source <https://github.com/FCP-INDI/C-PAC/blob/master/CPAC/func_preproc/func_preproc.py>`_
Workflow Inputs::
inputspec.func : func nifti file
User input functional(T2) Image, in any of the 8 orientations
inputspec.twopass : boolean
Perform two-pass on volume registration
Workflow Outputs::
outputspec.refit : string (nifti file)
Path to deobliqued anatomical data
outputspec.reorient : string (nifti file)
Path to RPI oriented anatomical data
outputspec.motion_correct_ref : string (nifti file)
Path to Mean intensity Motion corrected image
(base reference image for the second motion correction run)
outputspec.motion_correct : string (nifti file)
Path to motion corrected output file
outputspec.max_displacement : string (Mat file)
Path to maximum displacement (in mm) for brain voxels in each volume
outputspec.movement_parameters : string (Mat file)
Path to 1D file containing six movement/motion parameters(3 Translation, 3 Rotations)
in different columns (roll pitch yaw dS dL dP)
outputspec.skullstrip : string (nifti file)
Path to skull stripped Motion Corrected Image
outputspec.mask : string (nifti file)
Path to brain-only mask
outputspec.func_mean : string (nifti file)
Mean, Skull Stripped, Motion Corrected output T2 Image path
(Image with mean intensity values across voxels)
outputpsec.preprocessed : string (nifti file)
output skull stripped, motion corrected T2 image
with normalized intensity values
outputspec.preprocessed_mask : string (nifti file)
Mask obtained from normalized preprocessed image
Order of commands:
- Deobliqing the scans. For details see `3drefit <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3drefit.html>`_::
3drefit -deoblique rest_3dc.nii.gz
- Re-orienting the Image into Right-to-Left Posterior-to-Anterior Inferior-to-Superior (RPI) orientation. For details see `3dresample <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dresample.html>`_::
3dresample -orient RPI
-prefix rest_3dc_RPI.nii.gz
-inset rest_3dc.nii.gz
- Calculate voxel wise statistics. Get the RPI Image with mean intensity values over all timepoints for each voxel. For details see `3dTstat <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dTstat.html>`_::
3dTstat -mean
-prefix rest_3dc_RPI_3dT.nii.gz
rest_3dc_RPI.nii.gz
- Motion Correction. For details see `3dvolreg <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dvolreg.html>`_::
3dvolreg -Fourier
-twopass
-base rest_3dc_RPI_3dT.nii.gz/
-zpad 4
-maxdisp1D rest_3dc_RPI_3dvmd1D.1D
-1Dfile rest_3dc_RPI_3dv1D.1D
-prefix rest_3dc_RPI_3dv.nii.gz
rest_3dc_RPI.nii.gz
The base image or the reference image is the mean intensity RPI image obtained in the above the step.For each volume
in RPI-oriented T2 image, the command, aligns the image with the base mean image and calculates the motion, displacement
and movement parameters. It also outputs the aligned 4D volume and movement and displacement parameters for each volume.
- Calculate voxel wise statistics. Get the motion corrected output Image from the above step, with mean intensity values over all timepoints for each voxel.
For details see `3dTstat <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dTstat.html>`_::
3dTstat -mean
-prefix rest_3dc_RPI_3dv_3dT.nii.gz
rest_3dc_RPI_3dv.nii.gz
- Motion Correction and get motion, movement and displacement parameters. For details see `3dvolreg <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dvolreg.html>`_::
3dvolreg -Fourier
-twopass
-base rest_3dc_RPI_3dv_3dT.nii.gz
-zpad 4
-maxdisp1D rest_3dc_RPI_3dvmd1D.1D
-1Dfile rest_3dc_RPI_3dv1D.1D
-prefix rest_3dc_RPI_3dv.nii.gz
rest_3dc_RPI.nii.gz
The base image or the reference image is the mean intensity motion corrected image obtained from the above the step (first 3dvolreg run).
For each volume in RPI-oriented T2 image, the command, aligns the image with the base mean image and calculates the motion, displacement
and movement parameters. It also outputs the aligned 4D volume and movement and displacement parameters for each volume.
- Create a brain-only mask. For details see `3dautomask <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dAutomask.html>`_::
3dAutomask
-prefix rest_3dc_RPI_3dv_automask.nii.gz
rest_3dc_RPI_3dv.nii.gz
- Edge Detect(remove skull) and get the brain only. For details see `3dcalc <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dcalc.html>`_::
3dcalc -a rest_3dc_RPI_3dv.nii.gz
-b rest_3dc_RPI_3dv_automask.nii.gz
-expr 'a*b'
-prefix rest_3dc_RPI_3dv_3dc.nii.gz
- Normalizing the image intensity values. For details see `fslmaths <http://www.fmrib.ox.ac.uk/fsl/avwutils/index.html>`_::
fslmaths rest_3dc_RPI_3dv_3dc.nii.gz
-ing 10000 rest_3dc_RPI_3dv_3dc_maths.nii.gz
-odt float
Normalized intensity = (TrueValue*10000)/global4Dmean
- Calculate mean of skull stripped image. For details see `3dTstat <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dTstat.html>`_::
3dTstat -mean -prefix rest_3dc_RPI_3dv_3dc_3dT.nii.gz rest_3dc_RPI_3dv_3dc.nii.gz
- Create Mask (Generate mask from Normalized data). For details see `fslmaths <http://www.fmrib.ox.ac.uk/fsl/avwutils/index.html>`_::
fslmaths rest_3dc_RPI_3dv_3dc_maths.nii.gz
-Tmin -bin rest_3dc_RPI_3dv_3dc_maths_maths.nii.gz
-odt char
.. exec::
from CPAC.func_preproc import create_func_preproc
wf = create_func_preproc()
wf.write_graph(
graph2use='orig',
dotfilename='./images/generated/func_preproc.dot'
)
High Level Workflow Graph:
.. image:: ../images/generated/func_preproc.png
:width: 1000
Detailed Workflow Graph:
.. image:: ../images/generated/func_preproc_detailed.png
:width: 1000
Examples
--------
>>> import func_preproc
>>> preproc = create_func_preproc(bet=True)
>>> preproc.inputs.inputspec.func='sub1/func/rest.nii.gz'
>>> preproc.run() #doctest: +SKIP
>>> import func_preproc
>>> preproc = create_func_preproc(bet=False)
>>> preproc.inputs.inputspec.func='sub1/func/rest.nii.gz'
>>> preproc.run() #doctest: +SKIP
"""
preproc = pe.Workflow(name=wf_name)
input_node = pe.Node(util.IdentityInterface(
fields=['func', 'twopass', 'anatomical_brain_mask', 'anat_skull']),
name='inputspec')
output_node = pe.Node(util.IdentityInterface(fields=[
'refit', 'reorient', 'reorient_mean', 'motion_correct',
'motion_correct_ref', 'movement_parameters', 'max_displacement',
'mask', 'skullstrip', 'func_mean', 'preprocessed', 'preprocessed_mask',
'slice_time_corrected', 'transform_matrices'
]),
name='outputspec')
func_deoblique = pe.Node(interface=afni_utils.Refit(),
name='func_deoblique')
func_deoblique.inputs.deoblique = True
preproc.connect(input_node, 'func', func_deoblique, 'in_file')
func_reorient = pe.Node(interface=afni_utils.Resample(),
name='func_reorient')
func_reorient.inputs.orientation = 'RPI'
func_reorient.inputs.outputtype = 'NIFTI_GZ'
preproc.connect(func_deoblique, 'out_file', func_reorient, 'in_file')
preproc.connect(func_reorient, 'out_file', output_node, 'reorient')
func_get_mean_RPI = pe.Node(interface=afni_utils.TStat(),
name='func_get_mean_RPI')
func_get_mean_RPI.inputs.options = '-mean'
func_get_mean_RPI.inputs.outputtype = 'NIFTI_GZ'
preproc.connect(func_reorient, 'out_file', func_get_mean_RPI, 'in_file')
# calculate motion parameters
func_motion_correct = pe.Node(interface=preprocess.Volreg(),
name='func_motion_correct_3dvolreg')
func_motion_correct.inputs.zpad = 4
func_motion_correct.inputs.outputtype = 'NIFTI_GZ'
preproc.connect([
(input_node, func_motion_correct,
[(('twopass', collect_arguments, '-twopass', '-Fourier'), 'args')]),
])
preproc.connect(func_get_mean_RPI, 'out_file', func_motion_correct,
'basefile')
preproc.connect(func_reorient, 'out_file', func_motion_correct, 'in_file')
func_get_mean_motion = func_get_mean_RPI.clone('func_get_mean_motion')
preproc.connect(func_motion_correct, 'out_file', func_get_mean_motion,
'in_file')
preproc.connect(func_get_mean_motion, 'out_file', output_node,
'motion_correct_ref')
func_motion_correct_A = func_motion_correct.clone('func_motion_correct_A')
func_motion_correct_A.inputs.md1d_file = 'max_displacement.1D'
preproc.connect([
(input_node, func_motion_correct_A,
[(('twopass', collect_arguments, '-twopass', '-Fourier'), 'args')]),
])
preproc.connect(func_reorient, 'out_file', func_motion_correct_A,
'in_file')
preproc.connect(func_get_mean_motion, 'out_file', func_motion_correct_A,
'basefile')
preproc.connect(func_motion_correct_A, 'out_file', output_node,
'motion_correct')
preproc.connect(func_motion_correct_A, 'md1d_file', output_node,
'max_displacement')
preproc.connect(func_motion_correct_A, 'oned_file', output_node,
'movement_parameters')
preproc.connect(func_motion_correct_A, 'oned_matrix_save', output_node,
'transform_matrices')
skullstrip_func = skullstrip_functional(skullstrip_tool,
anatomical_mask_dilation,
"{0}_skullstrip".format(wf_name))
preproc.connect(func_motion_correct_A, 'out_file', skullstrip_func,
'inputspec.func')
preproc.connect(input_node, 'anatomical_brain_mask', skullstrip_func,
'inputspec.anatomical_brain_mask')
preproc.connect(input_node, 'anat_skull', skullstrip_func,
'inputspec.anat_skull')
preproc.connect(skullstrip_func, 'outputspec.func_brain', output_node,
'skullstrip')
preproc.connect(skullstrip_func, 'outputspec.func_brain_mask', output_node,
'mask')
func_mean = pe.Node(interface=afni_utils.TStat(), name='func_mean')
func_mean.inputs.options = '-mean'
func_mean.inputs.outputtype = 'NIFTI_GZ'
preproc.connect(skullstrip_func, 'outputspec.func_brain', func_mean,
'in_file')
if n4_correction:
func_mean_n4_corrected = pe.Node(interface=ants.N4BiasFieldCorrection(
dimension=3, copy_header=True, bspline_fitting_distance=200),
shrink_factor=2,
name='func_mean_n4_corrected')
func_mean_n4_corrected.inputs.args = '-r True'
# func_mean_n4_corrected.inputs.rescale_intensities = True
preproc.connect(func_mean, 'out_file', func_mean_n4_corrected,
'input_image')
preproc.connect(func_mean_n4_corrected, 'output_image', output_node,
'func_mean')
else:
preproc.connect(func_mean, 'out_file', output_node, 'func_mean')
func_normalize = pe.Node(interface=fsl.ImageMaths(), name='func_normalize')
func_normalize.inputs.op_string = '-ing 10000'
func_normalize.inputs.out_data_type = 'float'
preproc.connect(skullstrip_func, 'outputspec.func_brain', func_normalize,
'in_file')
preproc.connect(func_normalize, 'out_file', output_node, 'preprocessed')
func_mask_normalize = pe.Node(interface=fsl.ImageMaths(),
name='func_mask_normalize')
func_mask_normalize.inputs.op_string = '-Tmin -bin'
func_mask_normalize.inputs.out_data_type = 'char'
preproc.connect(func_normalize, 'out_file', func_mask_normalize, 'in_file')
preproc.connect(func_mask_normalize, 'out_file', output_node,
'preprocessed_mask')
return preproc