def fmri_bmsk_workflow(name='fMRIBrainMask', use_bet=False):
"""Comute brain mask of an fmri dataset"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['in_file']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['out_file']),
name='outputnode')
if not use_bet:
afni_msk = pe.Node(afp.Automask(outputtype='NIFTI_GZ'),
name='afni_msk')
# Connect brain mask extraction
workflow.connect([(inputnode, afni_msk, [('in_file', 'in_file')]),
(afni_msk, outputnode, [('out_file', 'out_file')])])
else:
from nipype.interfaces.fsl import BET, ErodeImage
bet_msk = pe.Node(BET(mask=True, functional=True), name='bet_msk')
erode = pe.Node(ErodeImage(kernel_shape='box', kernel_size=1.0),
name='erode')
# Connect brain mask extraction
workflow.connect([(inputnode, bet_msk, [('in_file', 'in_file')]),
(bet_msk, erode, [('mask_file', 'in_file')]),
(erode, outputnode, [('out_file', 'out_file')])])
return workflow
def create_bo_func_preproc(slice_timing_correction = False, wf_name = 'bo_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
----------
slice_timing_correction : boolean
Slice timing Correction option
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
inputspec.start_idx : string
Starting volume/slice of the functional image (optional)
inputspec.stop_idx : string
Last volume/slice of the functional image (optional)
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.drop_tr : string (nifti file)
Path to Output image with the initial few slices dropped
outputspec.slice_time_corrected : string (nifti file)
Path to Slice time corrected image
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:
- Get the start and the end volume index of the functional run. If not defined by the user, return the first and last volume.
get_idx(in_files, stop_idx, start_idx)
- Dropping the initial TRs. For details see `3dcalc <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dcalc.html>`_::
3dcalc -a rest.nii.gz[4..299]
-expr 'a'
-prefix rest_3dc.nii.gz
- Slice timing correction. For details see `3dshift <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dTshift.html>`_::
3dTshift -TR 2.1s
-slice 18
-tpattern alt+z
-prefix rest_3dc_shift.nii.gz rest_3dc.nii.gz
- 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.
- Unwarp the motion corrected using a regularized B0 field map that is in RPI space, one of the outputs from the calib_preproc using FSL FUGUE
fugue --nocheck=on
-i rest_3dc_RPI_3dv.nii.gz
--loadfmap=cal_reg_bo_RPI
--unwarpdir=x
--dwell=1
-u rest_3dc_RPI_3dv_unwarped.nii.gz
- 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_unwarped_automask.nii.gz
rest_3dc_RPI_3dv_unwarped.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_unwarped.nii.gz
-b rest_3dc_RPI_3dv_unwarped_automask.nii.gz
-expr 'a*b'
-prefix rest_3dc_RPI_3dv_unwarped_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_unwarped_3dc.nii.gz
-ing 10000 rest_3dc_RPI_3dv_unwarped_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_unwarped_3dc_3dT.nii.gz rest_3dc_RPI_3dv_unwarped_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_unwarped_3dc_maths.nii.gz
-Tmin -bin rest_3dc_RPI_3dv_unwarped_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
--------
>>> from func_preproc import *
>>> preproc = create_func_preproc(slice_timing_correction=True)
>>> preproc.inputs.inputspec.func='sub1/func/rest.nii.gz'
>>> preproc.inputs.scan_params.TR = '2.0'
>>> preproc.inputs.scan_params.ref_slice = 19
>>> preproc.inputs.scan_params.acquisition = 'alt+z2'
>>> preproc.run() #doctest: +SKIP
>>> from func_preproc import *
>>> preproc = create_func_preproc(slice_timing_correction=False)
>>> preproc.inputs.inputspec.func='sub1/func/rest.nii.gz'
>>> preproc.inputs.inputspec.start_idx = 4
>>> preproc.inputs.inputspec.stop_idx = 250
>>> preproc.run() #doctest: +SKIP
"""
preproc = pe.Workflow(name=wf_name)
inputNode = pe.Node(util.IdentityInterface(fields=['rest',
'calib_reg_bo_RPI',
'start_idx',
'stop_idx']),
name='inputspec')
scan_params = pe.Node(util.IdentityInterface(fields=['tr',
'acquisition',
'ref_slice']),
name = 'scan_params')
outputNode = pe.Node(util.IdentityInterface(fields=['drop_tr',
'refit',
'reorient',
'reorient_mean',
'motion_correct',
'motion_correct_ref',
'movement_parameters',
'max_displacement',
'bo_unwarped',
'mask',
'mask_preunwarp',
'skullstrip',
'example_func',
'preprocessed',
'preprocessed_mask',
'slice_time_corrected']),
name='outputspec')
func_get_idx = pe.Node(util.Function(input_names=['in_files',
'stop_idx',
'start_idx'],
output_names=['stopidx',
'startidx'],
function=get_idx), name='func_get_idx')
preproc.connect(inputNode, 'rest',
func_get_idx, 'in_files')
preproc.connect(inputNode, 'start_idx',
func_get_idx, 'start_idx')
preproc.connect(inputNode, 'stop_idx',
func_get_idx, 'stop_idx')
func_drop_trs = pe.Node(interface=preprocess.Calc(),
name='func_drop_trs')
func_drop_trs.inputs.expr = 'a'
func_drop_trs.inputs.outputtype = 'NIFTI_GZ'
preproc.connect(inputNode, 'rest',
func_drop_trs, 'in_file_a')
preproc.connect(func_get_idx, 'startidx',
func_drop_trs, 'start_idx')
preproc.connect(func_get_idx, 'stopidx',
func_drop_trs, 'stop_idx')
preproc.connect(func_drop_trs, 'out_file',
outputNode, 'drop_tr')
func_slice_timing_correction = pe.Node(interface=preprocess.TShift(),
name = 'func_slice_timing_correction')
func_slice_timing_correction.inputs.outputtype = 'NIFTI_GZ'
func_deoblique = pe.Node(interface=preprocess.Refit(),
name='func_deoblique')
func_deoblique.inputs.deoblique = True
if slice_timing_correction:
preproc.connect(func_drop_trs, 'out_file',
func_slice_timing_correction,'in_file')
preproc.connect(scan_params, 'tr',
func_slice_timing_correction, 'tr')
preproc.connect(scan_params, 'acquisition',
func_slice_timing_correction, 'tpattern')
preproc.connect(scan_params, 'ref_slice',
func_slice_timing_correction, 'tslice')
preproc.connect(func_slice_timing_correction, 'out_file',
func_deoblique, 'in_file')
preproc.connect(func_slice_timing_correction, 'out_file',
outputNode, 'slice_time_corrected')
else:
preproc.connect(func_drop_trs, 'out_file',
func_deoblique, 'in_file')
preproc.connect(func_deoblique, 'out_file',
outputNode, 'refit')
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')
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')
func_get_brain_mask = pe.Node(interface=preprocess.Automask(),
name='func_get_brain_mask')
# func_get_brain_mask.inputs.dilate = 1
func_get_brain_mask.inputs.outputtype = 'NIFTI_GZ'
#-------------------------
func_bo_unwarp = pe.Node(interface=fsl.FUGUE(),name='bo_unwarp')
#func_bo_unwarp.inputs.in_file='lfo_mc'
func_bo_unwarp.inputs.args='--nocheck=on'
#func_bo_unwarp.inputs.fmap_in_file='calib'
func_bo_unwarp.inputs.dwell_time=1.0
func_bo_unwarp.inputs.unwarp_direction='x'
preproc.connect(inputNode, 'calib_reg_bo_RPI',
func_bo_unwarp, 'fmap_in_file')
preproc.connect(func_motion_correct_A, 'out_file',
func_bo_unwarp, 'in_file')
preproc.connect(func_bo_unwarp, 'unwarped_file',
outputNode, 'bo_unwarped')
preproc.connect(func_bo_unwarp, 'unwarped_file',
func_get_brain_mask, 'in_file')
#--------------------------
preproc.connect(func_get_brain_mask, 'out_file',
outputNode, 'mask')
#------------ ALSO give the example_func_preunwarp
func_get_brain_mask_A = func_get_brain_mask.clone('func_get_brain_mask_A')
preproc.connect(func_motion_correct_A, 'out_file',
func_get_brain_mask_A, 'in_file')
preproc.connect(func_get_brain_mask_A, 'out_file',
outputNode, 'mask_preunwarp')
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_bo_unwarp, 'unwarped_file',
func_edge_detect, 'in_file_a')
preproc.connect(func_get_brain_mask, 'out_file',
func_edge_detect, 'in_file_b')
preproc.connect(func_edge_detect, 'out_file',
outputNode, 'skullstrip')
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 functional_brain_mask_workflow(workflow, resource_pool, config):
# resource pool should have:
# func_motion_correct
import os
import sys
import nipype.interfaces.io as nio
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as util
import nipype.interfaces.fsl as fsl
from nipype.interfaces.afni import preprocess
#check_input_resources(resource_pool, "func_motion_correct")
if "use_bet" not in config.keys():
config["use_bet"] = False
if "func_motion_correct" not in resource_pool.keys():
from functional_preproc import func_motion_correct_workflow
workflow, resource_pool = \
func_motion_correct_workflow(workflow, resource_pool, config)
if config["use_bet"] == False:
func_get_brain_mask = pe.Node(interface=preprocess.Automask(),
name='func_get_brain_mask')
func_get_brain_mask.inputs.outputtype = 'NIFTI_GZ'
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
#if isinstance(tuple, resource_pool["func_motion_correct"]):
if len(resource_pool["func_motion_correct"]) == 2:
node, out_file = resource_pool["func_motion_correct"]
workflow.connect(node, out_file, func_get_brain_mask, 'in_file')
else:
func_get_brain_mask.inputs.in_file = \
resource_pool["func_motion_correct"]
if config["use_bet"] == False:
resource_pool["functional_brain_mask"] = (func_get_brain_mask, \
'out_file')
else:
workflow.connect(func_get_brain_mask, 'mask_file', erode_one_voxel,
'in_file')
resource_pool["functional_brain_mask"] = (erode_one_voxel, 'out_file')
return workflow, resource_pool
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 skullstrip_functional(skullstrip_tool='afni',
anatomical_mask_dilation=False,
wf_name='skullstrip_functional'):
skullstrip_tool = skullstrip_tool.lower()
if skullstrip_tool != 'afni' and skullstrip_tool != 'fsl' and skullstrip_tool != 'fsl_afni' and skullstrip_tool != 'anatomical_refined':
raise Exception(
"\n\n[!] Error: The 'tool' parameter of the "
"'skullstrip_functional' workflow must be either "
"'afni' or 'fsl' or 'fsl_afni' or 'anatomical_refined'.\n\nTool input: "
"{0}\n\n".format(skullstrip_tool))
wf = pe.Workflow(name=wf_name)
input_node = pe.Node(util.IdentityInterface(
fields=['func', 'anatomical_brain_mask', 'anat_skull']),
name='inputspec')
output_node = pe.Node(
util.IdentityInterface(fields=['func_brain', 'func_brain_mask']),
name='outputspec')
if skullstrip_tool == 'afni':
func_get_brain_mask = pe.Node(interface=preprocess.Automask(),
name='func_get_brain_mask_AFNI')
func_get_brain_mask.inputs.outputtype = 'NIFTI_GZ'
wf.connect(input_node, 'func', func_get_brain_mask, 'in_file')
wf.connect(func_get_brain_mask, 'out_file', output_node,
'func_brain_mask')
elif skullstrip_tool == 'fsl':
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
wf.connect(input_node, 'func', func_get_brain_mask, 'in_file')
wf.connect(func_get_brain_mask, 'mask_file', erode_one_voxel,
'in_file')
wf.connect(erode_one_voxel, 'out_file', output_node, 'func_brain_mask')
elif skullstrip_tool == 'fsl_afni':
skullstrip_first_pass = pe.Node(fsl.BET(frac=0.2,
mask=True,
functional=True),
name='skullstrip_first_pass')
bet_dilate = pe.Node(fsl.DilateImage(operation='max',
kernel_shape='sphere',
kernel_size=6.0,
internal_datatype='char'),
name='skullstrip_first_dilate')
bet_mask = pe.Node(fsl.ApplyMask(), name='skullstrip_first_mask')
unifize = pe.Node(afni_utils.Unifize(
t2=True,
outputtype='NIFTI_GZ',
args='-clfrac 0.2 -rbt 18.3 65.0 90.0',
out_file="uni.nii.gz"),
name='unifize')
skullstrip_second_pass = pe.Node(preprocess.Automask(
dilate=1, outputtype='NIFTI_GZ'),
name='skullstrip_second_pass')
combine_masks = pe.Node(fsl.BinaryMaths(operation='mul'),
name='combine_masks')
wf.connect([
(input_node, skullstrip_first_pass, [('func', 'in_file')]),
(skullstrip_first_pass, bet_dilate, [('mask_file', 'in_file')]),
(bet_dilate, bet_mask, [('out_file', 'mask_file')]),
(skullstrip_first_pass, bet_mask, [('out_file', 'in_file')]),
(bet_mask, unifize, [('out_file', 'in_file')]),
(unifize, skullstrip_second_pass, [('out_file', 'in_file')]),
(skullstrip_first_pass, combine_masks, [('mask_file', 'in_file')]),
(skullstrip_second_pass, combine_masks, [('out_file',
'operand_file')]),
(combine_masks, output_node, [('out_file', 'func_brain_mask')])
])
# Refine functional mask by registering anatomical mask to functional space
elif skullstrip_tool == 'anatomical_refined':
# Get functional mean to use later as reference, when transform anatomical mask to functional space
func_skull_mean = pe.Node(interface=afni_utils.TStat(),
name='func_skull_mean')
func_skull_mean.inputs.options = '-mean'
func_skull_mean.inputs.outputtype = 'NIFTI_GZ'
wf.connect(input_node, 'func', func_skull_mean, 'in_file')
# Register func to anat
linear_reg_func_to_anat = pe.Node(interface=fsl.FLIRT(),
name='linear_reg_func_to_anat')
linear_reg_func_to_anat.inputs.cost = 'mutualinfo'
linear_reg_func_to_anat.inputs.dof = 6
wf.connect(func_skull_mean, 'out_file', linear_reg_func_to_anat,
'in_file')
wf.connect(input_node, 'anat_skull', linear_reg_func_to_anat,
'reference')
# Inverse func to anat affine
inv_func_to_anat_affine = pe.Node(interface=fsl.ConvertXFM(),
name='inv_func_to_anat_affine')
inv_func_to_anat_affine.inputs.invert_xfm = True
wf.connect(linear_reg_func_to_anat, 'out_matrix_file',
inv_func_to_anat_affine, 'in_file')
# Transform anatomical mask to functional space
linear_trans_mask_anat_to_func = pe.Node(
interface=fsl.FLIRT(), name='linear_trans_mask_anat_to_func')
linear_trans_mask_anat_to_func.inputs.apply_xfm = True
linear_trans_mask_anat_to_func.inputs.cost = 'mutualinfo'
linear_trans_mask_anat_to_func.inputs.dof = 6
linear_trans_mask_anat_to_func.inputs.interp = 'nearestneighbour'
# Dialate anatomical mask, if 'anatomical_mask_dilation : True' in config file
if anatomical_mask_dilation:
anat_mask_dilate = pe.Node(interface=afni.MaskTool(),
name='anat_mask_dilate')
anat_mask_dilate.inputs.dilate_inputs = '1'
anat_mask_dilate.inputs.outputtype = 'NIFTI_GZ'
wf.connect(input_node, 'anatomical_brain_mask', anat_mask_dilate,
'in_file')
wf.connect(anat_mask_dilate, 'out_file',
linear_trans_mask_anat_to_func, 'in_file')
else:
wf.connect(input_node, 'anatomical_brain_mask',
linear_trans_mask_anat_to_func, 'in_file')
wf.connect(func_skull_mean, 'out_file', linear_trans_mask_anat_to_func,
'reference')
wf.connect(inv_func_to_anat_affine, 'out_file',
linear_trans_mask_anat_to_func, 'in_matrix_file')
wf.connect(linear_trans_mask_anat_to_func, 'out_file', output_node,
'func_brain_mask')
func_edge_detect = pe.Node(interface=afni_utils.Calc(),
name='func_extract_brain')
func_edge_detect.inputs.expr = 'a*b'
func_edge_detect.inputs.outputtype = 'NIFTI_GZ'
wf.connect(input_node, 'func', func_edge_detect, 'in_file_a')
if skullstrip_tool == 'afni':
wf.connect(func_get_brain_mask, 'out_file', func_edge_detect,
'in_file_b')
elif skullstrip_tool == 'fsl':
wf.connect(erode_one_voxel, 'out_file', func_edge_detect, 'in_file_b')
elif skullstrip_tool == 'fsl_afni':
wf.connect(combine_masks, 'out_file', func_edge_detect, 'in_file_b')
elif skullstrip_tool == 'anatomical_refined':
wf.connect(linear_trans_mask_anat_to_func, 'out_file',
func_edge_detect, 'in_file_b')
wf.connect(func_edge_detect, 'out_file', output_node, 'func_brain')
return wf
def skullstrip_functional(tool='afni', wf_name='skullstrip_functional'):
tool = tool.lower()
if tool != 'afni' and tool != 'fsl' and tool != 'fsl_afni':
raise Exception("\n\n[!] Error: The 'tool' parameter of the "
"'skullstrip_functional' workflow must be either "
"'afni' or 'fsl'.\n\nTool input: "
"{0}\n\n".format(tool))
wf = pe.Workflow(name=wf_name)
input_node = pe.Node(util.IdentityInterface(fields=['func']),
name='inputspec')
output_node = pe.Node(
util.IdentityInterface(fields=['func_brain', 'func_brain_mask']),
name='outputspec')
if tool == 'afni':
func_get_brain_mask = pe.Node(interface=preprocess.Automask(),
name='func_get_brain_mask_AFNI')
func_get_brain_mask.inputs.outputtype = 'NIFTI_GZ'
wf.connect(input_node, 'func', func_get_brain_mask, 'in_file')
wf.connect(func_get_brain_mask, 'out_file', output_node,
'func_brain_mask')
elif tool == 'fsl':
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
wf.connect(input_node, 'func', func_get_brain_mask, 'in_file')
wf.connect(func_get_brain_mask, 'mask_file', erode_one_voxel,
'in_file')
wf.connect(erode_one_voxel, 'out_file', output_node, 'func_brain_mask')
elif tool == 'fsl_afni':
skullstrip_first_pass = pe.Node(fsl.BET(frac=0.2,
mask=True,
functional=True),
name='skullstrip_first_pass')
bet_dilate = pe.Node(fsl.DilateImage(operation='max',
kernel_shape='sphere',
kernel_size=6.0,
internal_datatype='char'),
name='skullstrip_first_dilate')
bet_mask = pe.Node(fsl.ApplyMask(), name='skullstrip_first_mask')
unifize = pe.Node(afni_utils.Unifize(
t2=True,
outputtype='NIFTI_GZ',
args='-clfrac 0.2 -rbt 18.3 65.0 90.0',
out_file="uni.nii.gz"),
name='unifize')
skullstrip_second_pass = pe.Node(preprocess.Automask(
dilate=1, outputtype='NIFTI_GZ'),
name='skullstrip_second_pass')
combine_masks = pe.Node(fsl.BinaryMaths(operation='mul'),
name='combine_masks')
wf.connect([
(input_node, skullstrip_first_pass, [('func', 'in_file')]),
(skullstrip_first_pass, bet_dilate, [('mask_file', 'in_file')]),
(bet_dilate, bet_mask, [('out_file', 'mask_file')]),
(skullstrip_first_pass, bet_mask, [('out_file', 'in_file')]),
(bet_mask, unifize, [('out_file', 'in_file')]),
(unifize, skullstrip_second_pass, [('out_file', 'in_file')]),
(skullstrip_first_pass, combine_masks, [('mask_file', 'in_file')]),
(skullstrip_second_pass, combine_masks, [('out_file',
'operand_file')]),
(combine_masks, output_node, [('out_file', 'func_brain_mask')])
])
func_edge_detect = pe.Node(interface=afni_utils.Calc(),
name='func_extract_brain')
func_edge_detect.inputs.expr = 'a*b'
func_edge_detect.inputs.outputtype = 'NIFTI_GZ'
wf.connect(input_node, 'func', func_edge_detect, 'in_file_a')
if tool == 'afni':
wf.connect(func_get_brain_mask, 'out_file', func_edge_detect,
'in_file_b')
elif tool == 'fsl':
wf.connect(erode_one_voxel, 'out_file', func_edge_detect, 'in_file_b')
elif tool == 'fsl_afni':
wf.connect(combine_masks, 'out_file', func_edge_detect, 'in_file_b')
wf.connect(func_edge_detect, 'out_file', output_node, 'func_brain')
return wf
def functional_brain_mask_workflow(workflow, resource_pool, config, name="_"):
"""Build and run a Nipype workflow to generate a functional brain mask
using AFNI's 3dAutomask.
- If any resources/outputs required by this workflow are not in the
resource pool, this workflow will call pre-requisite workflow builder
functions to further populate the pipeline with workflows which will
calculate/generate these necessary pre-requisites.
Expected Resources in Resource Pool
- func_reorient: The deobliqued, reoriented functional timeseries.
New Resources Added to Resource Pool
- functional_brain_mask: The binary brain mask of the functional time
series.
Workflow Steps
1. AFNI's 3dAutomask to generate the mask.
:type workflow: Nipype workflow object
:param workflow: A Nipype workflow object which can already contain other
connected nodes; this function will insert the following
workflow into this one provided.
:type resource_pool: dict
:param resource_pool: A dictionary defining input files and pointers to
Nipype node outputs / workflow connections; the keys
are the resource names.
:type config: dict
:param config: A dictionary defining the configuration settings for the
workflow, such as directory paths or toggled options.
:type name: str
:param name: (default: "_") A string to append to the end of each node
name.
:rtype: Nipype workflow object
:return: The Nipype workflow originally provided, but with this function's
sub-workflow connected into it.
:rtype: dict
:return: The resource pool originally provided, but updated (if
applicable) with the newest outputs and connections.
"""
import copy
import nipype.pipeline.engine as pe
from nipype.interfaces.afni import preprocess
if "func_reorient" not in resource_pool.keys():
from functional_preproc import func_preproc_workflow
old_rp = copy.copy(resource_pool)
workflow, resource_pool = \
func_preproc_workflow(workflow, resource_pool, config, name)
if resource_pool == old_rp:
return workflow, resource_pool
func_get_brain_mask = pe.Node(interface=preprocess.Automask(),
name='func_get_brain_mask%s' % name)
func_get_brain_mask.inputs.outputtype = 'NIFTI_GZ'
if len(resource_pool["func_reorient"]) == 2:
node, out_file = resource_pool["func_reorient"]
workflow.connect(node, out_file, func_get_brain_mask, 'in_file')
else:
func_get_brain_mask.inputs.in_file = \
resource_pool["func_reorient"]
resource_pool["functional_brain_mask"] = (func_get_brain_mask, 'out_file')
return workflow, resource_pool
