def create_pipeline(name="dsi_track", opt="", ensemble=""):
parameters = {'nos': 5000}
ensemble_dict = {'angle': 'angle_thres', 'min_length': 'min_length'}
inputnode = pe.Node(interface=util.IdentityInterface(
fields=["odf", "seed", "angle", "algorithm", "min_length"]),
name="inputnode")
if opt is not None:
opt_list = opt.split(',')
for o in opt_list:
try:
[key, value] = o.split(':')
parameters[key] = value
except ValueError:
print(o + ': irregular format, skipping')
if ensemble:
tckgen = pe.MapNode(dsi.FiberTrack(),
name='track',
iterfield=ensemble_dict[ensemble])
gunzip = pe.MapNode(interface=Gunzip(),
name="gunzip",
iterfield='in_file')
else:
tckgen = pe.Node(dsi.FiberTrack(), name='track')
gunzip = pe.Node(interface=Gunzip(), name="gunzip")
tckgen.inputs.nos = int(parameters['nos'])
tckmerge = pe.Node(interface=dtk.TrackMerge(), name="merge")
output_fields = ["tck"]
outputnode = pe.Node(
interface=util.IdentityInterface(fields=output_fields),
name="outputnode")
workflow = pe.Workflow(name=name)
workflow.base_output_dir = name
workflow.connect([(inputnode, tckgen, [("odf", "in_file"),
("angle", "angle_thres"),
("min_length", "min_length")]),
(tckgen, gunzip, [("out_file", "in_file")])])
if inputnode.inputs.seed:
workflow.connect([(inputnode, tckgen, [("seed", "seed_image")])])
if ensemble:
workflow.connect([(gunzip, tckmerge, [("out_file", "track_files")]),
(tckmerge, outputnode, [("track_file", "tck")])])
else:
workflow.connect([(gunzip, outputnode, [("out_file", "tck")])])
return workflow
def gunzipper(in_files):
from nipype.algorithms.misc import Gunzip
if isinstance(in_files,list):
outputs = []
for i in in_files:
if i.endswith('.gz'):
res = Gunzip(in_file=i).run()
outputs.append(res.outputs.out_file)
else: outputs.append(i)
else:
if in_files.endswith('.gz'):
res = Gunzip(in_file=in_files).run()
outputs = res.outputs.out_file
else: outputs = in_files
return outputs
def spm_warp_to_mni(wf_name="spm_warp_to_mni"):
""" Run Gunzip and SPM Normalize12 to the list of files input and outputs the list of warped files.
It does:
- Warp each individual input image to the standard SPM template
Parameters
----------
wf_name: str
Name of the workflow.
Nipype Inputs
-------------
warp_input.in_files: list of traits.File
The raw NIFTI_GZ image files
Nipype outputs
--------------
warp_output.warped_files: list of existing file
The warped files.
Returns
-------
wf: nipype Workflow
"""
# input
# check if spm_pet_preproc.do_petpvc is True
in_fields = ["in_files"]
out_fields = ["warped_files"]
input = setup_node(
IdentityInterface(fields=in_fields, mandatory_inputs=True),
name="warp_input",
)
gunzip = pe.MapNode(Gunzip(), name="gunzip", iterfield=['in_file'])
warp = setup_node(spm.Normalize12(jobtype='estwrite',
affine_regularization_type='mni'),
name="normalize12",
type="map",
iterfield=['image_to_align'])
# output
output = setup_node(IdentityInterface(fields=out_fields),
name="warp_output")
# Create the workflow object
wf = pe.Workflow(name=wf_name)
wf.connect([
# inputs
(input, gunzip, [("in_files", "in_file")]),
(gunzip, warp, [("out_file", "image_to_align")]),
# output
(warp, output, [("normalized_image", "warped_files")]),
])
return wf
def make_segment(self):
# Ref: http://nipype.readthedocs.io/en/0.12.1/interfaces/generated/nipype.interfaces.fsl.utils.html#reorient2std
ro = Node(interface=fsl.Reorient2Std(), name='ro')
# Ref: http://nipype.readthedocs.io/en/latest/interfaces/generated/interfaces.spm/preprocess.html#segment
seg = Node(interface=spm.NewSegment(channel_info=(0.0001, 60, (True,
True))),
name="seg")
spm_tissues_split = Node(Function(['in_list'], ['gm', 'wm', 'csf'],
self.spm_tissues),
name='spm_tissues_split')
gzip = Node(Function(['in_list'], ['out_list'], self.gzip_spm),
name='gzip')
segment = Workflow(name='Segment', base_dir=self.temp_dir)
gunzip = Node(interface=Gunzip(), name='gunzip')
# for new segment
segment.connect(ro, 'out_file', gunzip, 'in_file')
segment.connect(gunzip, 'out_file', seg, 'channel_files')
segment.connect(seg, 'native_class_images', spm_tissues_split,
'in_list')
return segment
def test_Gunzip_outputs():
output_map = dict(out_file=dict(), )
outputs = Gunzip.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_Gunzip_outputs():
output_map = dict(out_file=dict(),
)
outputs = Gunzip.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def unzip_nii(in_file):
from nipype.algorithms.misc import Gunzip
from nipype.utils.filemanip import split_filename
from traits.trait_base import _Undefined
if (in_file is None) or isinstance(in_file, _Undefined):
return None
if not isinstance(in_file, str): # type(in_file) is list:
return [unzip_nii(f) for f in in_file]
_, base, ext = split_filename(in_file)
# Not compressed
if ext[-3:].lower() != ".gz":
return in_file
# Compressed
gunzip = Gunzip(in_file=in_file)
gunzip.run()
return gunzip.aggregate_outputs().out_file
def test_Gunzip_inputs():
input_map = dict(ignore_exception=dict(nohash=True,
usedefault=True,
),
in_file=dict(mandatory=True,
),
)
inputs = Gunzip.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def test_Gunzip_inputs():
input_map = dict(
ignore_exception=dict(
nohash=True,
usedefault=True,
),
in_file=dict(mandatory=True, ),
)
inputs = Gunzip.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def test_spm(name='test_spm_3d'):
"""
A simple workflow to test SPM's installation. By default will split the 4D volume in
time-steps.
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['in_data']),
name='inputnode')
dgr = pe.Node(nio.DataGrabber(template="feeds/data/fmri.nii.gz",
outfields=['out_file'],
sort_filelist=False),
name='datasource')
stc = pe.Node(spm.SliceTiming(num_slices=21,
time_repetition=1.0,
time_acquisition=2. - 2. / 32,
slice_order=list(range(21, 0, -1)),
ref_slice=10),
name='stc')
realign_estimate = pe.Node(spm.Realign(jobtype='estimate'),
name='realign_estimate')
realign_write = pe.Node(spm.Realign(jobtype='write'), name='realign_write')
realign_estwrite = pe.Node(spm.Realign(jobtype='estwrite'),
name='realign_estwrite')
smooth = pe.Node(spm.Smooth(fwhm=[6, 6, 6]), name='smooth')
if name == 'test_spm_3d':
split = pe.Node(fsl.Split(dimension="t", output_type="NIFTI"),
name="split")
workflow.connect([(dgr, split, [(('out_file', _get_first), 'in_file')
]),
(split, stc, [("out_files", "in_files")])])
elif name == 'test_spm_4d':
gunzip = pe.Node(Gunzip(), name="gunzip")
workflow.connect([(dgr, gunzip, [(('out_file', _get_first), 'in_file')
]),
(gunzip, stc, [("out_file", "in_files")])])
else:
raise NotImplementedError(
'No implementation of the test workflow \'{}\' was found'.format(
name))
workflow.connect([
(inputnode, dgr, [('in_data', 'base_directory')]),
(stc, realign_estimate, [('timecorrected_files', 'in_files')]),
(realign_estimate, realign_write, [('modified_in_files', 'in_files')]),
(stc, realign_estwrite, [('timecorrected_files', 'in_files')]),
(realign_write, smooth, [('realigned_files', 'in_files')])
])
return workflow
def Couple_Preproc_Pipeline(base_dir=None,
output_dir=None,
subject_id=None,
spm_path=None):
""" Create a preprocessing workflow for the Couples Conflict Study using nipype
Args:
base_dir: path to data folder where raw subject folder is located
output_dir: path to where key output files should be saved
subject_id: subject_id (str)
spm_path: path to spm folder
Returns:
workflow: a nipype workflow that can be run
"""
from nipype.interfaces.dcm2nii import Dcm2nii
from nipype.interfaces.fsl import Merge, TOPUP, ApplyTOPUP
import nipype.interfaces.io as nio
import nipype.interfaces.utility as util
from nipype.interfaces.utility import Merge as Merge_List
from nipype.pipeline.engine import Node, Workflow
from nipype.interfaces.fsl.maths import UnaryMaths
from nipype.interfaces.nipy.preprocess import Trim
from nipype.algorithms.rapidart import ArtifactDetect
from nipype.interfaces import spm
from nipype.interfaces.spm import Normalize12
from nipype.algorithms.misc import Gunzip
from nipype.interfaces.nipy.preprocess import ComputeMask
import nipype.interfaces.matlab as mlab
from nltools.utils import get_resource_path, get_vox_dims, get_n_volumes
from nltools.interfaces import Plot_Coregistration_Montage, PlotRealignmentParameters, Create_Covariates
import os
import glob
########################################
## Setup Paths and Nodes
########################################
# Specify Paths
canonical_file = os.path.join(spm_path, 'canonical', 'single_subj_T1.nii')
template_file = os.path.join(spm_path, 'tpm', 'TPM.nii')
# Set the way matlab should be called
mlab.MatlabCommand.set_default_matlab_cmd("matlab -nodesktop -nosplash")
mlab.MatlabCommand.set_default_paths(spm_path)
# Get File Names for different types of scans. Parse into separate processing streams
datasource = Node(interface=nio.DataGrabber(
infields=['subject_id'], outfields=['struct', 'ap', 'pa']),
name='datasource')
datasource.inputs.base_directory = base_dir
datasource.inputs.template = '*'
datasource.inputs.field_template = {
'struct': '%s/Study*/t1w_32ch_mpr_08mm*',
'ap': '%s/Study*/distortion_corr_32ch_ap*',
'pa': '%s/Study*/distortion_corr_32ch_pa*'
}
datasource.inputs.template_args = {
'struct': [['subject_id']],
'ap': [['subject_id']],
'pa': [['subject_id']]
}
datasource.inputs.subject_id = subject_id
datasource.inputs.sort_filelist = True
# iterate over functional scans to define paths
scan_file_list = glob.glob(
os.path.join(base_dir, subject_id, 'Study*', '*'))
func_list = [s for s in scan_file_list if "romcon_ap_32ch_mb8" in s]
func_list = [s for s in func_list
if "SBRef" not in s] # Exclude sbref for now.
func_source = Node(interface=util.IdentityInterface(fields=['scan']),
name="func_source")
func_source.iterables = ('scan', func_list)
# Create Separate Converter Nodes for each different type of file. (dist corr scans need to be done before functional)
ap_dcm2nii = Node(interface=Dcm2nii(), name='ap_dcm2nii')
ap_dcm2nii.inputs.gzip_output = True
ap_dcm2nii.inputs.output_dir = '.'
ap_dcm2nii.inputs.date_in_filename = False
pa_dcm2nii = Node(interface=Dcm2nii(), name='pa_dcm2nii')
pa_dcm2nii.inputs.gzip_output = True
pa_dcm2nii.inputs.output_dir = '.'
pa_dcm2nii.inputs.date_in_filename = False
f_dcm2nii = Node(interface=Dcm2nii(), name='f_dcm2nii')
f_dcm2nii.inputs.gzip_output = True
f_dcm2nii.inputs.output_dir = '.'
f_dcm2nii.inputs.date_in_filename = False
s_dcm2nii = Node(interface=Dcm2nii(), name='s_dcm2nii')
s_dcm2nii.inputs.gzip_output = True
s_dcm2nii.inputs.output_dir = '.'
s_dcm2nii.inputs.date_in_filename = False
########################################
## Setup Nodes for distortion correction
########################################
# merge output files into list
merge_to_file_list = Node(interface=Merge_List(2),
infields=['in1', 'in2'],
name='merge_to_file_list')
# fsl merge AP + PA files (depends on direction)
merger = Node(interface=Merge(dimension='t'), name='merger')
merger.inputs.output_type = 'NIFTI_GZ'
# use topup to create distortion correction map
topup = Node(interface=TOPUP(), name='topup')
topup.inputs.encoding_file = os.path.join(get_resource_path(),
'epi_params_APPA_MB8.txt')
topup.inputs.output_type = "NIFTI_GZ"
topup.inputs.config = 'b02b0.cnf'
# apply topup to all functional images
apply_topup = Node(interface=ApplyTOPUP(), name='apply_topup')
apply_topup.inputs.in_index = [1]
apply_topup.inputs.encoding_file = os.path.join(get_resource_path(),
'epi_params_APPA_MB8.txt')
apply_topup.inputs.output_type = "NIFTI_GZ"
apply_topup.inputs.method = 'jac'
apply_topup.inputs.interp = 'spline'
# Clear out Zeros from spline interpolation using absolute value.
abs_maths = Node(interface=UnaryMaths(), name='abs_maths')
abs_maths.inputs.operation = 'abs'
########################################
## Preprocessing
########################################
# Trim - remove first 10 TRs
n_vols = 10
trim = Node(interface=Trim(), name='trim')
trim.inputs.begin_index = n_vols
#Realignment - 6 parameters - realign to first image of very first series.
realign = Node(interface=spm.Realign(), name="realign")
realign.inputs.register_to_mean = True
#Coregister - 12 parameters
coregister = Node(interface=spm.Coregister(), name="coregister")
coregister.inputs.jobtype = 'estwrite'
#Plot Realignment
plot_realign = Node(interface=PlotRealignmentParameters(),
name="plot_realign")
#Artifact Detection
art = Node(interface=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 = 'SPM'
# Gunzip - unzip the functional and structural images
gunzip_struc = Node(Gunzip(), name="gunzip_struc")
gunzip_func = Node(Gunzip(), name="gunzip_func")
# Normalize - normalizes functional and structural images to the MNI template
normalize = Node(interface=Normalize12(jobtype='estwrite',
tpm=template_file),
name="normalize")
#Plot normalization Check
plot_normalization_check = Node(interface=Plot_Coregistration_Montage(),
name="plot_normalization_check")
plot_normalization_check.inputs.canonical_img = canonical_file
#Create Mask
compute_mask = Node(interface=ComputeMask(), name="compute_mask")
#remove lower 5% of histogram of mean image
compute_mask.inputs.m = .05
#Smooth
#implicit masking (.im) = 0, dtype = 0
smooth = Node(interface=spm.Smooth(), name="smooth")
smooth.inputs.fwhm = 6
#Create Covariate matrix
make_cov = Node(interface=Create_Covariates(), name="make_cov")
# Create a datasink to clean up output files
datasink = Node(interface=nio.DataSink(), name='datasink')
datasink.inputs.base_directory = output_dir
datasink.inputs.container = subject_id
########################################
# Create Workflow
########################################
workflow = Workflow(name='Preprocessed')
workflow.base_dir = os.path.join(base_dir, subject_id)
workflow.connect([
(datasource, ap_dcm2nii, [('ap', 'source_dir')]),
(datasource, pa_dcm2nii, [('pa', 'source_dir')]),
(datasource, s_dcm2nii, [('struct', 'source_dir')]),
(func_source, f_dcm2nii, [('scan', 'source_dir')]),
(ap_dcm2nii, merge_to_file_list, [('converted_files', 'in1')]),
(pa_dcm2nii, merge_to_file_list, [('converted_files', 'in2')]),
(merge_to_file_list, merger, [('out', 'in_files')]),
(merger, topup, [('merged_file', 'in_file')]),
(topup, apply_topup, [('out_fieldcoef', 'in_topup_fieldcoef'),
('out_movpar', 'in_topup_movpar')]),
(f_dcm2nii, trim, [('converted_files', 'in_file')]),
(trim, apply_topup, [('out_file', 'in_files')]),
(apply_topup, abs_maths, [('out_corrected', 'in_file')]),
(abs_maths, gunzip_func, [('out_file', 'in_file')]),
(gunzip_func, realign, [('out_file', 'in_files')]),
(s_dcm2nii, gunzip_struc, [('converted_files', 'in_file')]),
(gunzip_struc, coregister, [('out_file', 'source')]),
(coregister, normalize, [('coregistered_source', 'image_to_align')]),
(realign, coregister, [('mean_image', 'target'),
('realigned_files', 'apply_to_files')]),
(realign, normalize, [(('mean_image', get_vox_dims),
'write_voxel_sizes')]),
(coregister, normalize, [('coregistered_files', 'apply_to_files')]),
(normalize, smooth, [('normalized_files', 'in_files')]),
(realign, compute_mask, [('mean_image', 'mean_volume')]),
(compute_mask, art, [('brain_mask', 'mask_file')]),
(realign, art, [('realignment_parameters', 'realignment_parameters'),
('realigned_files', 'realigned_files')]),
(realign, plot_realign, [('realignment_parameters',
'realignment_parameters')]),
(normalize, plot_normalization_check, [('normalized_files', 'wra_img')
]),
(realign, make_cov, [('realignment_parameters',
'realignment_parameters')]),
(art, make_cov, [('outlier_files', 'spike_id')]),
(normalize, datasink, [('normalized_files', 'structural.@normalize')]),
(coregister, datasink, [('coregistered_source', 'structural.@struct')
]),
(topup, datasink, [('out_fieldcoef', 'distortion.@fieldcoef')]),
(topup, datasink, [('out_movpar', 'distortion.@movpar')]),
(smooth, datasink, [('smoothed_files', 'functional.@smooth')]),
(plot_realign, datasink, [('plot', 'functional.@plot_realign')]),
(plot_normalization_check, datasink,
[('plot', 'functional.@plot_normalization')]),
(make_cov, datasink, [('covariates', 'functional.@covariates')])
])
return workflow
bet_fmri = Node(BET(frac=0.6, functional=True, output_type='NIFTI_GZ'),
name="bet_fmri")
# FAST - Image Segmentation
segmentation = Node(FAST(output_type='NIFTI'), name="segmentation")
# Normalize - normalizes functional and structural images to the MNI template
normalize_fmri = Node(Normalize12(jobtype='estwrite',
tpm=template,
write_voxel_sizes=[2, 2, 2],
write_bounding_box=[[-90, -126, -72],
[90, 90, 108]]),
name="normalize_fmri")
gunzip = Node(Gunzip(), name="gunzip")
normalize_t1 = Node(Normalize12(
jobtype='estwrite',
tpm=template,
write_voxel_sizes=[iso_size, iso_size, iso_size],
write_bounding_box=[[-90, -126, -72], [90, 90, 108]]),
name="normalize_t1")
normalize_masks = Node(Normalize12(
jobtype='estwrite',
tpm=template,
write_voxel_sizes=[iso_size, iso_size, iso_size],
write_bounding_box=[[-90, -126, -72], [90, 90, 108]]),
name="normalize_masks")
def fmri_cleanup_wf(wf_name="fmri_cleanup"):
""" Run the resting-state fMRI pre-processing workflow against the rest files in `data_dir`.
Tasks:
- Trim first 6 volumes of the rs-fMRI file.
- Slice Timing correction.
- Motion and nuisance correction.
- Calculate brain mask in fMRI space.
- Bandpass frequency filtering for resting-state fMRI.
- Smoothing.
- Tissue maps co-registration to fMRI space.
Parameters
----------
wf_name: str
Nipype Inputs
-------------
rest_input.in_file: traits.File
The resting-state fMRI file.
rest_input.anat: traits.File
Path to the high-contrast anatomical image.
rest_input.tissues: list of traits.File
Paths to the tissue segmentations in anatomical space.
Expected to have this order: GM, WM and CSF.
rest_input.highpass_sigma:traits.Float
Band pass timeseries filter higher bound in Hz.
rest_input.lowpass_sigma: traits.Float
Band pass timeseries filter lower bound in Hz.
Nipype Outputs
--------------
rest_output.smooth: traits.File
The isotropically smoothed time filtered nuisance corrected image.
rest_output.nuis_corrected: traits.File
The nuisance corrected fMRI file.
rest_output.motion_params: traits.File
The affine transformation file.
rest_output.time_filtered: traits.File
The bandpass time filtered fMRI file.
rest_output.epi_brain_mask: traits.File
An estimated brain mask from mean EPI volume.
rest_output.tissues_brain_mask: traits.File
A brain mask calculated from the addition of coregistered
GM, WM and CSF segmentation volumes from the anatomical
segmentation.
rest_output.tissues: list of traits.File
The tissues segmentation volume in fMRI space.
Expected to have this order: GM, WM and CSF.
rest_output.anat: traits.File
The T1w image in fMRI space.
rest_output.avg_epi: traits.File
The average EPI image in fMRI space after slice-time and motion correction.
rest_output.motion_regressors: traits.File
rest_output.compcor_regressors: traits.File
rest_output.art_displacement_files
One image file containing the voxel-displacement timeseries.
rest_output.art_intensity_files
One file containing the global intensity values determined from the brainmask.
rest_output.art_norm_files
One file containing the composite norm.
rest_output.art_outlier_files
One file containing a list of 0-based indices corresponding to outlier volumes.
rest_output.art_plot_files
One image file containing the detected outliers.
rest_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
-------
wf: nipype Workflow
"""
# Create the workflow object
wf = pe.Workflow(name=wf_name)
# specify input and output fields
in_fields = [
"in_file",
"anat",
"atlas_anat",
"coreg_target",
"tissues",
"lowpass_freq",
"highpass_freq",
]
out_fields = [
"motion_corrected",
"motion_params",
"tissues",
"anat",
"avg_epi",
"time_filtered",
"smooth",
"tsnr_file",
"epi_brain_mask",
"tissues_brain_mask",
"motion_regressors",
"compcor_regressors",
"gsr_regressors",
"nuis_corrected",
"art_displacement_files",
"art_intensity_files",
"art_norm_files",
"art_outlier_files",
"art_plot_files",
"art_statistic_files",
]
# input identities
rest_input = setup_node(IdentityInterface(fields=in_fields, mandatory_inputs=True),
name="rest_input")
# rs-fMRI preprocessing nodes
trim = setup_node(Trim(), name="trim")
stc_wf = auto_spm_slicetime()
realign = setup_node(nipy_motion_correction(), name='realign')
# average
average = setup_node(
Function(
function=mean_img,
input_names=["in_file"],
output_names=["out_file"],
imports=['from neuro_pypes.interfaces.nilearn import ni2file']
),
name='average_epi'
)
mean_gunzip = setup_node(Gunzip(), name="mean_gunzip")
# co-registration nodes
coreg = setup_node(spm_coregister(cost_function="mi"), name="coreg_fmri")
brain_sel = setup_node(Select(index=[0, 1, 2]), name="brain_sel")
# brain mask made with EPI
epi_mask = setup_node(ComputeMask(), name='epi_mask')
# brain mask made with the merge of the tissue segmentations
tissue_mask = setup_node(fsl.MultiImageMaths(), name='tissue_mask')
tissue_mask.inputs.op_string = "-add %s -add %s -abs -kernel gauss 4 -dilM -ero -kernel gauss 1 -dilM -bin"
tissue_mask.inputs.out_file = "tissue_brain_mask.nii.gz"
# select tissues
gm_select = setup_node(Select(index=[0]), name="gm_sel")
wmcsf_select = setup_node(Select(index=[1, 2]), name="wmcsf_sel")
# noise filter
noise_wf = rest_noise_filter_wf()
wm_select = setup_node(Select(index=[1]), name="wm_sel")
csf_select = setup_node(Select(index=[2]), name="csf_sel")
# bandpass filtering
bandpass = setup_node(
Function(
input_names=['files', 'lowpass_freq', 'highpass_freq', 'tr'],
output_names=['out_files'],
function=bandpass_filter
),
name='bandpass'
)
# smooth
smooth = setup_node(
Function(
function=smooth_img,
input_names=["in_file", "fwhm"],
output_names=["out_file"],
imports=['from neuro_pypes.interfaces.nilearn import ni2file']
),
name="smooth"
)
smooth.inputs.fwhm = get_config_setting('fmri_smooth.fwhm', default=8)
smooth.inputs.out_file = "smooth_std_{}.nii.gz".format(wf_name)
# output identities
rest_output = setup_node(IdentityInterface(fields=out_fields), name="rest_output")
# Connect the nodes
wf.connect([
# trim
(rest_input, trim, [("in_file", "in_file")]),
# slice time correction
(trim, stc_wf, [("out_file", "stc_input.in_file")]),
# motion correction
(stc_wf, realign, [("stc_output.timecorrected_files", "in_file")]),
# coregistration target
(realign, average, [("out_file", "in_file")]),
(average, mean_gunzip, [("out_file", "in_file")]),
(mean_gunzip, coreg, [("out_file", "target")]),
# epi brain mask
(average, epi_mask, [("out_file", "mean_volume")]),
# coregistration
(rest_input, coreg, [("anat", "source")]),
(rest_input, brain_sel, [("tissues", "inlist")]),
(brain_sel, coreg, [(("out", flatten_list), "apply_to_files")]),
# tissue brain mask
(coreg, gm_select, [("coregistered_files", "inlist")]),
(coreg, wmcsf_select, [("coregistered_files", "inlist")]),
(gm_select, tissue_mask, [(("out", flatten_list), "in_file")]),
(wmcsf_select, tissue_mask, [(("out", flatten_list), "operand_files")]),
# nuisance correction
(coreg, wm_select, [("coregistered_files", "inlist",)]),
(coreg, csf_select, [("coregistered_files", "inlist",)]),
(realign, noise_wf, [("out_file", "rest_noise_input.in_file",)]),
(tissue_mask, noise_wf, [("out_file", "rest_noise_input.brain_mask")]),
(wm_select, noise_wf, [(("out", flatten_list), "rest_noise_input.wm_mask")]),
(csf_select, noise_wf, [(("out", flatten_list), "rest_noise_input.csf_mask")]),
(realign, noise_wf, [("par_file", "rest_noise_input.motion_params",)]),
# temporal filtering
(noise_wf, bandpass, [("rest_noise_output.nuis_corrected", "files")]),
# (realign, bandpass, [("out_file", "files")]),
(stc_wf, bandpass, [("stc_output.time_repetition", "tr")]),
(rest_input, bandpass, [
("lowpass_freq", "lowpass_freq"),
("highpass_freq", "highpass_freq"),
]),
(bandpass, smooth, [("out_files", "in_file")]),
# output
(epi_mask, rest_output, [("brain_mask", "epi_brain_mask")]),
(tissue_mask, rest_output, [("out_file", "tissues_brain_mask")]),
(realign, rest_output, [
("out_file", "motion_corrected"),
("par_file", "motion_params"),
]),
(coreg, rest_output, [
("coregistered_files", "tissues"),
("coregistered_source", "anat"),
]),
(noise_wf, rest_output, [
("rest_noise_output.motion_regressors", "motion_regressors"),
("rest_noise_output.compcor_regressors", "compcor_regressors"),
("rest_noise_output.gsr_regressors", "gsr_regressors"),
("rest_noise_output.nuis_corrected", "nuis_corrected"),
("rest_noise_output.tsnr_file", "tsnr_file"),
("rest_noise_output.art_displacement_files", "art_displacement_files"),
("rest_noise_output.art_intensity_files", "art_intensity_files"),
("rest_noise_output.art_norm_files", "art_norm_files"),
("rest_noise_output.art_outlier_files", "art_outlier_files"),
("rest_noise_output.art_plot_files", "art_plot_files"),
("rest_noise_output.art_statistic_files", "art_statistic_files"),
]),
(average, rest_output, [("out_file", "avg_epi")]),
(bandpass, rest_output, [("out_files", "time_filtered")]),
(smooth, rest_output, [("out_file", "smooth")]),
])
return wf
subject_list = [
d for d in os.listdir(DATA_DIR) if os.path.isdir(os.path.join(DATA_DIR, d))
]
# Infosource - function free node to iterate over the list of subject names (and/or sessions)
infosource = Node(IdentityInterface(fields=['subject_id']), name="infosource")
infosource.iterables = [('subject_id', subject_list)]
# SelectFiles - uses glob and regex to find your files
templates = dict(struct='{subject_id}/structural/structural.nii.gz',
func='{subject_id}/functional/*.nii.gz')
selectfiles = Node(SelectFiles(templates), "selectfiles")
selectfiles.inputs.base_directory = DATA_DIR
# Node which might come in handy when piping data to interfaces that are incompatible with gzipped format
gunzip_struct = Node(Gunzip(), name="gunzip_struct")
# Reorient images to match approximate orientation of the standard template images (MNI152)
reorient_func = Node(fsl.Reorient2Std(output_type='NIFTI_GZ'),
name='reorient_func')
reorient_struct = Node(fsl.Reorient2Std(output_type='NIFTI_GZ'),
name='reorient_struct')
# Convert functional images to float representation (FLOAT32)
img2float = Node(fsl.ImageMaths(out_data_type='float',
op_string='',
suffix='_dtype'),
name='img2float')
# Return the volume index of a file
def spm_anat_preprocessing(wf_name="spm_anat_preproc"):
""" Run the T1 pre-processing workflow against the anat_hc files in `data_dir`.
It does:
- N4BiasFieldCorrection
- SPM12 New Segment
- SPM12 Warp of MPRAGE to MNI
[Optional: from config]
Nipype Inputs
-------------
anat_input.in_file: traits.File
path to the anatomical image
Nipype Outputs
--------------
anat_output.anat_mni: traits.File
The bias-field normalized to MNI anatomical image.
anat_output.tissues_warped: traits.File
The tissue segmentation in MNI space from SPM.
anat_output.tissues_native: traits.File
The tissue segmentation in native space from SPM
anat_output.affine_transform: traits.File
The affine transformation file.
anat_output.warp_forward: traits.File
The forward (anat to MNI) warp field from SPM.
anat_output.warp_inverse: traits.File
The inverse (MNI to anat) warp field from SPM.
anat_output.anat_biascorr: traits.File
The bias-field corrected anatomical image
anat_output.atlas_anat: traits.File
The atlas file warped to anatomical space,
if do_atlas and the atlas file is set in configuration.
anat_output.brain_mask: traits.File
A brain mask file in anatomical space.
This is calculated by summing up the maps of segmented tissues
(CSF, WM, GM) and then binarised.
Returns
-------
wf: nipype Workflow
"""
# Create the workflow object
wf = pe.Workflow(name=wf_name)
# specify input and output fields
in_fields = ["in_file"]
out_fields = [
"anat_mni",
"tissues_warped",
"tissues_native",
"affine_transform",
"warp_forward",
"warp_inverse",
"anat_biascorr",
"brain_mask",
]
do_atlas, atlas_file = check_atlas_file()
if do_atlas:
in_fields += ["atlas_file"]
out_fields += ["atlas_anat"]
# input node
anat_input = pe.Node(IdentityInterface(fields=in_fields,
mandatory_inputs=True),
name="anat_input")
# atlas registration
if do_atlas:
anat_input.inputs.set(atlas_file=atlas_file)
# T1 preprocessing nodes
biascor = setup_node(biasfield_correct(), name="bias_correction")
gunzip_anat = setup_node(Gunzip(), name="gunzip_anat")
segment = setup_node(spm_segment(), name="new_segment")
warp_anat = setup_node(spm_apply_deformations(), name="warp_anat")
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()
# calculate brain mask from tissue maps
tissues = setup_node(IdentityInterface(fields=["gm", "wm", "csf"],
mandatory_inputs=True),
name="tissues")
brain_mask = setup_node(Function(
function=math_img,
input_names=["formula", "out_file", "gm", "wm", "csf"],
output_names=["out_file"],
imports=['from 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"
# output node
anat_output = pe.Node(IdentityInterface(fields=out_fields),
name="anat_output")
# Connect the nodes
wf.connect([
# input
(anat_input, biascor, [("in_file", "input_image")]),
# new segment
(biascor, gunzip_anat, [("output_image", "in_file")]),
(gunzip_anat, segment, [("out_file", "channel_files")]),
# Normalize12
(segment, warp_anat, [("forward_deformation_field", "deformation_file")
]),
(segment, warp_anat, [("bias_corrected_images", "apply_to_files")]),
(tpm_bbox, warp_anat, [("bbox", "write_bounding_box")]),
# brain mask from tissues
(segment, tissues, [
(("native_class_images", selectindex, [0]), "gm"),
(("native_class_images", selectindex, [1]), "wm"),
(("native_class_images", selectindex, [2]), "csf"),
]),
(tissues, brain_mask, [
("gm", "gm"),
("wm", "wm"),
("csf", "csf"),
]),
# output
(warp_anat, anat_output, [("normalized_files", "anat_mni")]),
(segment, anat_output, [("modulated_class_images", "tissues_warped"),
("native_class_images", "tissues_native"),
("transformation_mat", "affine_transform"),
("forward_deformation_field", "warp_forward"),
("inverse_deformation_field", "warp_inverse"),
("bias_corrected_images", "anat_biascorr")]),
(brain_mask, anat_output, [("out_file", "brain_mask")]),
])
# atlas warping nodes
if do_atlas:
gunzip_atlas = pe.Node(Gunzip(), name="gunzip_atlas")
warp_atlas = setup_node(spm_apply_deformations(), name="warp_atlas")
anat_bbox = setup_node(Function(function=get_bounding_box,
input_names=["in_file"],
output_names=["bbox"]),
name="anat_bbox")
# set the warping interpolation to nearest neighbour.
warp_atlas.inputs.write_interp = 0
# connect the atlas registration nodes
wf.connect([
(anat_input, gunzip_atlas, [("atlas_file", "in_file")]),
(gunzip_anat, anat_bbox, [("out_file", "in_file")]),
(gunzip_atlas, warp_atlas, [("out_file", "apply_to_files")]),
(segment, warp_atlas, [("inverse_deformation_field",
"deformation_file")]),
(anat_bbox, warp_atlas, [("bbox", "write_bounding_box")]),
(warp_atlas, anat_output, [("normalized_files", "atlas_anat")]),
])
return wf
def petpvc_workflow(wf_name="petpvc"):
""" Run the PET pre-processing workflow against the gunzip_pet.in_file files.
It coregisters the reference_file and tissues to PET space, then applies PVC and grey matter normalization.
It does:
- SPM12 Coregister T1 and tisues to PET
- PVC the PET image in PET space
Parameters
----------
wf_name: str
Name of the workflow.
Nipype Inputs
-------------
pvc_input.in_file: traits.File
The raw NIFTI_GZ PET image file
pvc_input.reference_file: traits.File
The anatomical image in its native space. For registration reference.
pvc_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
--------------
pvc_output.coreg_ref: existing file
The coregistered reference_file image in PET space.
pvc_output.coreg_others: list of existing files
List of coregistered files from coreg_pet.apply_to_files
pvc_output.pvc_out: existing file
The output of the PETPVC process.
pvc_output.petpvc_mask: existing file
The mask built for the PETPVC.
pvc_output.brain_mask: existing file
A brain mask calculated with the tissues file.
pvc_output.gm_norm: existing file
The output of the grey matter intensity normalization process.
This is the last step in the PET signal correction.
Returns
-------
wf: nipype Workflow
"""
# fixed parameters of the NUK mMR
psf_fwhm = (4.3, 4.3, 4.3)
# specify input and output fields
in_fields = [
"in_file",
"reference_file",
"tissues",
]
out_fields = [
"coreg_ref",
"coreg_others",
"pvc_out",
"petpvc_mask",
"brain_mask",
"gm_norm",
]
# input
pet_input = setup_node(IdentityInterface(fields=in_fields,
mandatory_inputs=True),
name="pvc_input")
flat_list = pe.Node(Function(input_names=['list_of_lists'],
output_names=['out'],
function=flatten_list),
name='flatten_tissue_list')
# coreg pet
gunzip_pet = setup_node(Gunzip(), name="gunzip_pet")
coreg_pet = setup_node(spm_coregister(cost_function="mi"),
name="coreg_pet")
tissues_sel = setup_node(Select(index=[0, 1, 2]), name="tissues")
select_gm = setup_node(Select(index=[0]), name="select_gm")
pvc = setup_node(petpvc_cmd(fwhm_mm=psf_fwhm, pvc_method='RBV'),
name="pvc")
# output
pvc_output = setup_node(IdentityInterface(fields=out_fields),
name="pvc_output")
# workflow to create the mask
mask_wf = petpvc_mask(wf_name="petpvc_mask")
# workflow for intensity normalization
norm_wf = intensity_norm(wf_name="intensity_norm_gm")
# Create the workflow object
wf = pe.Workflow(name=wf_name)
wf.connect([
# inputs
(pet_input, gunzip_pet, [("in_file", "in_file")]),
(pet_input, tissues_sel, [("tissues", "inlist")]),
])
# 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, coreg_pet, [("reference_file", "source")]),
# unzip to coregister the reference file (anatomical image) to PET space.
(gunzip_pet, coreg_pet, [("out_file", "target")]),
(tissues_sel, flat_list, [("out", "list_of_lists")]),
(flat_list, coreg_pet, [("out", "apply_to_files")]),
# the list of tissues to the mask wf and the GM for PET intensity normalization
(coreg_pet, select_gm, [("coregistered_files", "inlist")]),
(coreg_pet, mask_wf, [("coregistered_files",
"pvcmask_input.tissues")]),
# the PET in native space to PVC correction
(gunzip_pet, pvc, [("out_file", "in_file")]),
# the merged file with 4 tissues to PCV correction
(mask_wf, pvc, [("pvcmask_output.petpvc_mask", "mask_file")]),
# normalize voxel values of PET PVCed by demeaning it entirely by GM PET voxel values
(pvc, norm_wf, [("out_file", "intnorm_input.source")]),
(select_gm, norm_wf, [("out", "intnorm_input.mask")]),
# output
(coreg_pet, pvc_output, [("coregistered_source", "coreg_ref")]),
(coreg_pet, pvc_output, [("coregistered_files", "coreg_others")]),
(pvc, pvc_output, [("out_file", "pvc_out")]),
(mask_wf, pvc_output, [("pvcmask_output.brain_mask", "brain_mask")
]),
(mask_wf, pvc_output, [("pvcmask_output.petpvc_mask",
"petpvc_mask")]),
(norm_wf, pvc_output, [("intnorm_output.out_file", "gm_norm")]),
])
else: # PET to ANAT
wf.connect([
# inputs
(pet_input, coreg_pet, [("reference_file", "target")]),
# unzip PET image and set as a source to register it to anatomical space.
(gunzip_pet, coreg_pet, [("out_file", "source")]),
(tissues_sel, flat_list, [("out", "list_of_lists")]),
(flat_list, coreg_pet, [("out", "apply_to_files")]),
# the list of tissues to the mask wf and the GM for PET intensity normalization
(tissues_sel, select_gm, [("out", "inlist")]),
(flat_list, mask_wf, [("out", "pvcmask_input.tissues")]),
# the PET in ANAT space to PVC correction
(coreg_pet, pvc, [("coregistered_source", "in_file")]),
# the merged file with 4 tissues to PCV correction
(mask_wf, pvc, [("pvcmask_output.petpvc_mask", "mask_file")]),
# normalize voxel values of PET PVCed by demeaning it entirely by GM PET voxel values
(pvc, norm_wf, [("out_file", "intnorm_input.source")]),
(select_gm, norm_wf, [("out", "intnorm_input.mask")]),
# output
# TODO: coreg_ref should have a different name in this case
(coreg_pet, pvc_output, [("coregistered_source", "coreg_ref")]),
(coreg_pet, pvc_output, [("coregistered_files", "coreg_others")]),
(pvc, pvc_output, [("out_file", "pvc_out")]),
(mask_wf, pvc_output, [("pvcmask_output.brain_mask", "brain_mask")
]),
(mask_wf, pvc_output, [("pvcmask_output.petpvc_mask",
"petpvc_mask")]),
(norm_wf, pvc_output, [("intnorm_output.out_file", "gm_norm")]),
])
return wf
from nilearn.plotting import plot_glass_brain
experiment_dir = '/Volumes/INTENSO/DPX_EEG_fMRI/fMRI/output'
output_dir = '/Volumes/INTENSO/DPX_EEG_fMRI/fMRI/data'
working_dir = '/Volumes/INTENSO/DPX_EEG_fMRI/fMRI/workingdir'
# Smoothing withds used during preprocessing
fwhm = 5
# Which contrasts to use for the 2nd-level analysis
contrast_list = ['con_0001', 'con_0002', 'con_0003', 'con_0004', 'con_0005']
mask = "/usr/local/fsl/data/standard/MNI152_T1_1mm_brain_mask_dil.nii.gz"
# Gunzip - unzip the mask image
gunzip = Node(Gunzip(in_file=mask), name="gunzip")
# OneSampleTTestDesign - with only two cues being tested for differences, a one sample T-Test Design
# is sufficient
onesamplettestdes = Node(OneSampleTTestDesign(),
name="onesampttestdes")
# EstimateModel - estimates the model
level2estimate = Node(EstimateModel(estimation_method={'Classical': 1}),
name="level2estimate")
# EstimateContrast - estimates group contrast
level2conestimate = Node(EstimateContrast(group_contrast=True),
name="level2conestimate")
cont1 = ['Group', 'T', ['mean'], [1]]
level2conestimate.inputs.contrasts = [cont1]
def spm_anat_preprocessing(wf_name="spm_anat_preproc"):
""" Run the T1 pre-processing workflow against the anat_hc
files in `data_dir`.
It does:
- N4BiasFieldCorrection
- SPM12 New Segment
- SPM12 Warp of MPRAGE to MNI
[Optional: from config]
- Atlas file warping to MPRAGE
- Cortical thickness (SPM+DiReCT)
Nipype Inputs
-------------
anat_input.in_file: traits.File
Path to the anatomical image.
anat_input.atlas_file: traits.File
Path to an atlas file in MNI space to be
warped to the anatomical space.
Can also be set through the configuration
setting `atlas_file`.
Nipype Outputs
--------------
anat_output.anat_mni: traits.File
The bias-field normalized to MNI anatomical image.
anat_output.tissues_warped: traits.File
The tissue segmentation in MNI space from SPM.
anat_output.tissues_native: traits.File
The tissue segmentation in native space from SPM.
anat_output.affine_transform: traits.File
The affine transformation file.
anat_output.warp_forward: traits.File
The forward (anat to MNI) warp field from SPM.
anat_output.warp_inverse: traits.File
The inverse (MNI to anat) warp field from SPM.
anat_output.anat_biascorr: traits.File
The bias-field corrected anatomical image.
anat_output.brain_mask: traits.File
A brain mask file in anatomical space.
This is calculated by summing up the maps of
segmented tissues (CSF, WM, GM) and then binarised.
anat_output.atlas_anat: traits.File
If `atlas_file` is an existing file in MNI space.
The atlas file warped to anatomical space,
if do_atlas and the atlas file is set in configuration.
anat_output.cortical_thickness: traits.File
If `anat_preproc.do_cortical_thickness` is True.
The cortical thickness estimations calculated with the
SPM+DiReCT method (KellyKapowski).
anat_output.warped_white_matter: warped_white_matter
If `anat_preproc.do_cortical_thickness` is True.
The warped white matter image calculated with the
SPM+DiReCT method (KellyKapowski).
Returns
-------
wf: nipype Workflow
"""
# Create the workflow object
wf = pe.Workflow(name=wf_name)
# specify input and output fields
in_fields = ["in_file"]
out_fields = [
"anat_mni",
"tissues_warped",
"tissues_native",
"affine_transform",
"warp_forward",
"warp_inverse",
"anat_biascorr",
"brain_mask",
]
# check if we have to warp an atlas files too.
do_atlas, atlas_file = check_atlas_file()
if do_atlas:
in_fields += ["atlas_file"]
out_fields += ["atlas_anat"]
# check if we have to do cortical thickness (SPM+DiReCT) method.
do_cortical_thickness = get_config_setting(
'anat_preproc.do_cortical_thickness', False)
if do_cortical_thickness:
out_fields += [
"cortical_thickness",
"warped_white_matter",
]
# input node
anat_input = pe.Node(IdentityInterface(fields=in_fields,
mandatory_inputs=True),
name="anat_input")
# atlas registration
if do_atlas and not isdefined(anat_input.inputs.atlas_file):
anat_input.inputs.set(atlas_file=atlas_file)
# T1 preprocessing nodes
biascor = setup_node(biasfield_correct(), name="bias_correction")
gunzip_anat = setup_node(Gunzip(), name="gunzip_anat")
segment = setup_node(spm_segment(), name="new_segment")
warp_anat = setup_node(spm_apply_deformations(), name="warp_anat")
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()
# calculate brain mask from tissue maps
tissues = setup_node(IdentityInterface(fields=["gm", "wm", "csf"],
mandatory_inputs=True),
name="tissues")
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"
# output node
anat_output = pe.Node(IdentityInterface(fields=out_fields),
name="anat_output")
# Connect the nodes
wf.connect([
# input to biasfieldcorrection
(anat_input, biascor, [("in_file", "input_image")]),
# new segment
(biascor, gunzip_anat, [("output_image", "in_file")]),
(gunzip_anat, segment, [("out_file", "channel_files")]),
# Normalize12
(segment, warp_anat, [("forward_deformation_field", "deformation_file")
]),
(segment, warp_anat, [("bias_corrected_images", "apply_to_files")]),
(tpm_bbox, warp_anat, [("bbox", "write_bounding_box")]),
# brain mask from tissues
(segment, tissues, [
(("native_class_images", selectindex, 0), "gm"),
(("native_class_images", selectindex, 1), "wm"),
(("native_class_images", selectindex, 2), "csf"),
]),
(tissues, brain_mask, [
("gm", "gm"),
("wm", "wm"),
("csf", "csf"),
]),
# output
(warp_anat, anat_output, [("normalized_files", "anat_mni")]),
(segment, anat_output, [("modulated_class_images", "tissues_warped"),
("native_class_images", "tissues_native"),
("transformation_mat", "affine_transform"),
("forward_deformation_field", "warp_forward"),
("inverse_deformation_field", "warp_inverse"),
("bias_corrected_images", "anat_biascorr")]),
(brain_mask, anat_output, [("out_file", "brain_mask")]),
])
# atlas warping nodes
if do_atlas:
gunzip_atlas = pe.Node(Gunzip(), name="gunzip_atlas")
warp_atlas = setup_node(spm_apply_deformations(), name="warp_atlas")
anat_bbox = setup_node(Function(function=get_bounding_box,
input_names=["in_file"],
output_names=["bbox"]),
name="anat_bbox")
# set the warping interpolation to nearest neighbour.
warp_atlas.inputs.write_interp = 0
# connect the atlas registration nodes
wf.connect([
(anat_input, gunzip_atlas, [("atlas_file", "in_file")]),
(gunzip_anat, anat_bbox, [("out_file", "in_file")]),
(gunzip_atlas, warp_atlas, [("out_file", "apply_to_files")]),
(segment, warp_atlas, [("inverse_deformation_field",
"deformation_file")]),
(anat_bbox, warp_atlas, [("bbox", "write_bounding_box")]),
(warp_atlas, anat_output, [("normalized_files", "atlas_anat")]),
])
# cortical thickness (SPM+DiReCT) method
if do_cortical_thickness:
from ..interfaces.ants import KellyKapowski
segm_img = setup_node(Function(
function=math_img,
input_names=["formula", "out_file", "gm", "wm"],
output_names=["out_file"],
imports=['from neuro_pypes.interfaces.nilearn import ni2file']),
name='gm-wm_image')
segm_img.inputs.out_file = "gm_wm.nii.gz"
segm_img.inputs.formula = '((gm >= 0.5)*2 + (wm > 0.5)*3).astype(np.uint8)'
# copy the header from the GM tissue image to the result from `gm-wm_image`.
# this is necessary because the `gm-wm_image` operation sometimes modifies the
# offset of the image, which will provoke an ANTs exception due to
# ITK tolerance in ImageToImageFilter
# https://github.com/stnava/ANTs/issues/74
cp_hdr = setup_node(Function(
function=copy_header,
input_names=["in_file", "data_file"],
output_names=["out_file"],
imports=['from neuro_pypes.interfaces.nilearn import ni2file']),
name='copy_header')
kk = setup_node(KellyKapowski(), name='direct')
kk.inputs.cortical_thickness = 'direct_cortical_thickness.nii.gz'
kk.inputs.warped_white_matter = 'direct_warped_white_matter.nii.gz'
# connect the cortical thickness (SPM+DiReCT) method
wf.connect([
# create segmentation GM+WM file
(tissues, segm_img, [("gm", "gm"), ("wm", "wm")]),
(segm_img, cp_hdr, [("out_file", "data_file")]),
(tissues, cp_hdr, [("gm", "in_file")]),
# kellykapowski
(cp_hdr, kk, [("out_file", "segmentation_image")]),
(tissues, kk, [("gm", "gray_matter_prob_image"),
("wm", "white_matter_prob_image")]),
(kk, anat_output, [("cortical_thickness", "cortical_thickness"),
("warped_white_matter", "warped_white_matter")
]),
])
return wf
def spm_register_to_template_wf(wf_name="spm_registration_to_template"):
"""Return a workflow that registers each reg_input.in_file to the file in reg_input.template.
For now this does not do atlas registration.
It does:
- SPM12 Warp input image to the given template
Parameters
----------
wf_name: str
Name of the workflow.
Nipype Inputs
-------------
reg_input.in_file: traits.File
The raw NIFTI_GZ subject image file.
reg_input.template: list of traits.File
The template file for inter-subject registration reference.
Nipype outputs
--------------
reg_output.warped: existing file
Image normalized to the given template.
reg_output.warp_field: existing files
Spatial normalization parameters .mat file.
Returns
-------
wf: nipype Workflow
"""
# specify input and output fields
in_fields = [
"in_file",
"template",
]
out_fields = [
"warped",
"warp_field",
]
# input
reg_input = setup_node(IdentityInterface(fields=in_fields,
mandatory_inputs=True),
name="reg_input")
# warp each subject to the group template
gunzip_template = setup_node(
Gunzip(),
name="gunzip_template",
)
gunzip_input = setup_node(
Gunzip(),
name="gunzip_input",
)
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
reg_output = setup_node(IdentityInterface(fields=out_fields),
name="reg_output")
# Create the workflow object
wf = pe.Workflow(name=wf_name)
wf.connect([
# get template bounding box to apply to results
(reg_input, get_bbox, [("template", "in_file")]),
# gunzip some inputs
(reg_input, gunzip_input, [("in_file", "in_file")]),
(reg_input, gunzip_template, [("template", "in_file")]),
# prepare the target parameters of the warp to template
(gunzip_template, warp2template, [("out_file", "template")]),
(get_bbox, warp2template, [("bbox", "write_bounding_box")]),
# directly warp pet to the template
(gunzip_input, warp2template, [("out_file", "source")]),
# output
(warp2template, reg_output, [
("normalization_parameters", "warp_field"),
("normalized_source", "warped"),
]),
])
return wf
outDirSubj = os.path.join(outDirSite, iSubj)
# if the directory doesn't exist, create it
if not os.path.exists(outDirSubj):
os.makedirs(outDirSubj)
# finally, output directory for the results in MNI space
outDir = outDirSubj
# if the directory doesn't exist, create it
if not os.path.exists(outDir):
os.makedirs(outDir)
#
# T1 Normalization nodes
#
# gunzip node
gunzip_T1w = Node(Gunzip(in_file=imageT1), name="gunzip_T1w")
# Segmentation, native space
segNative = Node(spm.NewSegment(), name='segNative')
# Normalize - normalizes structural images to the MNI template
normalizeT1 = Node(spm.Normalize12(jobtype='estwrite',
tpm=fTPM,
write_bounding_box=[[-90, -120, -70],
[90, 90, 105]]),
name="normalizeT1")
# Segmentation, template space
segMNI = Node(spm.NewSegment(), name='segMNI')
#
def genNormalizeDwiWF(
name='NormalizeDwi',
base_dir=op.abspath('.'),
input_dir=None,
input_temp='%s/%s/*%s',
input_temp_args={
'ref_T1': [['subject_id', 'bias_corrected_images', '_mT1.nii.gz']],
'forward_deformation_field':
[['subject_id', 'forward_deformation_field', '_y_T1.nii.gz']],
'denoised_dwi':
[['subject_id', 'denoised_dwi_series', '_dwi_denoised.nii.gz']],
'bval': [['subject_id', 'raw_bvals', '_bval.gz']],
'bvec': [['subject_id', 'processed_bvecs', '_bvecs.gz']],
'apply_to': [[
'subject_id', 'apply_to_files',
['_ICVF.nii.gz', '_ISOVF.nii.gz', '_OD.nii.gz']
]]
},
subjects=None,
spm_standalone=None,
mcr=None):
# Generate WF
wf = Workflow(name=name)
wf.base_dir = base_dir
#Node: subject List
subjectList = Node(IdentityInterface(fields=['subject_id'],
mandatory_inputs=True),
name="subjectList")
if subjects:
subjectList.iterables = ('subject_id', subjects)
else:
subjectList.iterables = ('subject_id', [
pth for pth in os.listdir(input_dir)
if os.path.isdir(op.join(input_dir, pth))
])
print subjectList.iterables
scanList = Node(DataGrabber(infields=['subject_id'],
outfields=[
'ref_T1', 'forward_deformation_field',
'denoised_dwi', 'bval', 'bvec', 'apply_to'
]),
name="scanList")
scanList.inputs.base_directory = input_dir
scanList.inputs.ignore_exception = False
scanList.inputs.raise_on_empty = True
scanList.inputs.sort_filelist = False
scanList.inputs.template = input_temp
scanList.inputs.template_args = input_temp_args
wf.connect(subjectList, "subject_id", scanList, "subject_id")
# Unzip everythin for spm
gunzipT1 = Node(Gunzip(), name='gunzipT1')
wf.connect(scanList, "ref_T1", gunzipT1, "in_file")
gunzipDF = Node(Gunzip(), name='gunzipDF')
wf.connect(scanList, "forward_deformation_field", gunzipDF, "in_file")
gunzipbval = Node(Gunzip(), name='gunzipbval')
wf.connect(scanList, "bval", gunzipbval, "in_file")
gunzipbvec = Node(Gunzip(), name='gunzipbvec')
wf.connect(scanList, "bvec", gunzipbvec, "in_file")
gunzipApplyTo = MapNode(Gunzip(),
iterfield=["in_file"],
name='gunzipApplyTo')
wf.connect(scanList, "apply_to", gunzipApplyTo, "in_file")
# Extract b=0 frames from denoised DWI and average them to make a ref_dwi
dwib0 = Node(DWIExtract(), name="dwib0")
dwib0.inputs.bzero = True
dwib0.inputs.out_file = "dwib0.nii.gz"
wf.connect(scanList, "denoised_dwi", dwib0, "in_file")
wf.connect(gunzipbval, "out_file", dwib0, "in_bval")
wf.connect(gunzipbvec, "out_file", dwib0, "in_bvec")
# Make an average image
avgb0 = Node(MeanImage(), name="avgb0")
avgb0.inputs.nan2zeros = True
avgb0.inputs.output_type = "NIFTI"
avgb0.inputs.out_file = "avg_dwib0.nii"
avgb0.inputs.dimension = "T"
wf.connect(dwib0, "out_file", avgb0, "in_file")
# spm Normalize WF
spmNormProc = genSpmNormalizeDwiWF(name="spmNormProc",
spm_standalone=spm_standalone,
mcr=mcr)
wf.connect(gunzipT1, "out_file", spmNormProc, "inputNode.ref_T1")
wf.connect(gunzipDF, "out_file", spmNormProc,
"inputNode.forward_deformation_field")
wf.connect(avgb0, "out_file", spmNormProc, "inputNode.ref_dwi")
wf.connect(gunzipApplyTo, "out_file", spmNormProc, "inputNode.apply_to")
# Datasink
datasink = Node(DataSink(base_directory=base_dir,
container='%sSink' % name),
name='Datasink')
wf.connect(spmNormProc, "outputNode.normalized_files", datasink,
"normalized_files")
return wf
def matlab_noddi_processing(caps_directory,
num_cores,
bStep,
name='NoddiMatlab'):
"""
This is a function to fit NODDI onto the multiple shells diffusion data based on this paper: NODDI: Practical in vivo neurite orientation
dispersion and density imaging of the human brain, Neuroimage, 2012, Gary Zhang.
TODO: hard code to find the path to matlab script.
TODO: for the matlab script, using multiple cores to run one subject has a bug for python wrapper, but ok with one core fitting, improve this in the future
TODO: deal with how to delete the original nii file in the output folder of NODDI
Args:
caps_directory: CAPS directory
tsv: the tsv file containing the participant_id and session_id
bStep: the bvalue to round
working_directory: the path to working_directory
num_cores: number of cores that fit Noddi model
name: the name of the pipeline
Returns:
"""
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as niu
from nipype.algorithms.misc import Gunzip
import os
import clinica.pipelines as clp
inputnode = pe.Node(niu.IdentityInterface(fields=[
'subject_id_list', 'noddi_preprocessed_dwi', 'noddi_preprocessed_bvec',
'noddi_preprocessed_bval', 'noddi_preprocessed_mask',
'noddi_toolbox_dir', 'nifti_matlib_dir'
]),
name='inputnode')
capsnode = pe.MapNode(
name='capsnode',
interface=niu.Function(
input_names=['subject_id_list', 'caps_directory'],
output_names=['temp_folder'],
function=make_processing_caps),
iterfield=['subject_id_list'])
capsnode.inputs.caps_directory = caps_directory
path_to_matscript = os.path.join(os.path.dirname(clp.__path__[0]),
'lib/noddi')
# output node
outputnode = pe.Node(
niu.IdentityInterface(fields=['fit_icvf', 'fit_isovf', 'fit_od']),
name='outputnode')
# gzip the nii.gz img to nii img
# Gunzip - unzip functional
gunzip_dwi = pe.MapNode(Gunzip(), name="gunzipdwi", iterfield=['in_file'])
gunzip_mask = pe.MapNode(Gunzip(),
name="gunzipmask",
iterfield=['in_file'])
# Node to wrap noddi matlab toolbox script.
nodditoolbox = pe.MapNode(name='nodditoolbox',
interface=niu.Function(input_names=[
'output_dir', 'noddi_img', 'brain_mask',
'roi_mask', 'bval', 'bvec', 'prefix',
'bStep', 'num_cores', 'path_to_matscript',
'noddi_toolbox_dir', 'nifti_matlib_dir'
],
output_names=[
'fit_icvf',
'fit_isovf', 'fit_od'
],
function=runmatlab),
iterfield=[
'output_dir', 'noddi_img', 'brain_mask',
'roi_mask', 'bval', 'bvec', 'prefix'
])
nodditoolbox.inputs.path_to_matscript = path_to_matscript
nodditoolbox.inputs.num_cores = num_cores
nodditoolbox.inputs.bStep = bStep
# zip the result imgs
zip_icvf = pe.MapNode(name='zip_icvf',
interface=niu.Function(input_names=['in_file'],
output_names=['out_file'],
function=compress_nii),
iterfield=['in_file'])
zip_isovf = pe.MapNode(name='zip_isovf',
interface=niu.Function(input_names=['in_file'],
output_names=['out_file'],
function=compress_nii),
iterfield=['in_file'])
zip_odi = pe.MapNode(name='zip_odi',
interface=niu.Function(input_names=['in_file'],
output_names=['out_file'],
function=compress_nii),
iterfield=['in_file'])
# workflow
nodditoolbox_wf = pe.Workflow(name=name)
# unzip
nodditoolbox_wf.connect(inputnode, 'noddi_preprocessed_dwi', gunzip_dwi,
'in_file')
nodditoolbox_wf.connect(inputnode, 'noddi_preprocessed_mask', gunzip_mask,
'in_file')
# fit matlab toolbox
nodditoolbox_wf.connect(gunzip_dwi, 'out_file', nodditoolbox, 'noddi_img')
nodditoolbox_wf.connect(gunzip_mask, 'out_file', nodditoolbox,
'brain_mask')
nodditoolbox_wf.connect(gunzip_mask, 'out_file', nodditoolbox, 'roi_mask')
nodditoolbox_wf.connect(inputnode, 'noddi_preprocessed_bvec', nodditoolbox,
'bvec')
nodditoolbox_wf.connect(inputnode, 'noddi_preprocessed_bval', nodditoolbox,
'bval')
nodditoolbox_wf.connect(inputnode, 'subject_id_list', nodditoolbox,
'prefix')
# nodditoolbox_wf.connect(inputnode, 'bStep', nodditoolbox, 'bStep')
nodditoolbox_wf.connect(inputnode, 'noddi_toolbox_dir', nodditoolbox,
'noddi_toolbox_dir')
nodditoolbox_wf.connect(inputnode, 'nifti_matlib_dir', nodditoolbox,
'nifti_matlib_dir')
nodditoolbox_wf.connect(inputnode, 'subject_id_list', capsnode,
'subject_id_list')
nodditoolbox_wf.connect(capsnode, 'temp_folder', nodditoolbox,
'output_dir')
nodditoolbox_wf.connect(nodditoolbox, 'fit_icvf', zip_icvf, 'in_file')
nodditoolbox_wf.connect(nodditoolbox, 'fit_isovf', zip_isovf, 'in_file')
nodditoolbox_wf.connect(nodditoolbox, 'fit_od', zip_odi, 'in_file')
# output node
nodditoolbox_wf.connect(zip_icvf, 'out_file', outputnode, 'fit_icvf')
nodditoolbox_wf.connect(zip_isovf, 'out_file', outputnode, 'fit_isovf')
nodditoolbox_wf.connect(zip_odi, 'out_file', outputnode, 'fit_od')
return nodditoolbox_wf
input_names=['in_file', 'events_file', 'regressors_file', 'regressors_names'],
function=_bids2nipypeinfo, output_names=['info', 'realign_file']),
name='runinfo')
# Set the column names to be used from the confounds file
runinfo.inputs.regressors_names = ['dvars', 'framewise_displacement'] + \
['a_comp_cor_%02d' % i for i in range(6)] + ['cosine%02d' % i for i in range(4)]
#%%
cont1 = ['Trauma>Sad', 'T', ['trauma', 'sad'], [1, -1]]
cont2 = ['Trauma>Relax', 'T', ['trauma', 'relax'], [1, -1]]
cont3 = ['Sad>Relax', 'T', ['sad', 'relax'], [1, -1]]
cont4 = ['Trauma', 'T', ['trauma'], [1]]
cont5 = ['Sad', 'T', ['sad'], [1]]
contrasts = [cont1, cont2, cont3, cont4, cont5]
#%%
gunzip = MapNode(Gunzip(), name='gunzip',
iterfield=['in_file'])
#%% Addinf simple denozining procedures (remove dummy scans, smoothing, art detection)
#extract = Node(fsl.ExtractROI(t_min=4, t_size=-1, output_type='NIFTI'),
# name="extract")
smooth = Node(spm.Smooth(), name="smooth", fwhm = fwhm)
# Artifact Detection - determines outliers in functional images
#art = Node(ArtifactDetect(norm_threshold=2,
# zintensity_threshold=3,
# mask_type='spm_global',
# parameter_source='FSL',
# use_differences=[True, False],
# plot_type='svg'),
# name="art")
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
'/media/Data/FromHPC/output/fmriprep/sub-%s/ses-1/func/sub-*_ses-1_task-%s_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
}
#selectfiles = Node(SelectFiles(templates,
# base_directory=data_dir),
# name="selectfiles")
datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'],
outfields=['func']),
name='datasource')
datasource.inputs.base_directory = data_dir
datasource.inputs.template = '/media/Data/FromHPC/output/fmriprep/sub-%s/ses-1/func/sub-*_ses-1_task-%s_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz'
datasource.inputs.template_args = info
datasource.inputs.sort_filelist = True
# add unzip node
# Gunzip - unzip functional
gunzip = MapNode(Gunzip(), name='gunzip', iterfield=['in_file'])
#os.chdir('/media/Data/work')
###########################
def subjectinfo(subject_id):
from readConditionFiles_r_aPTSD import loadmat, _check_keys, _todict, readConditions
import scipy.io as spio
import os, json, glob, sys
###############################################################################################
# Define experiment things (data_dir = filder where data files are present. )
data_dir = '/media/Data/FromHPC/output/fmriprep'
from bids.grabbids import BIDSLayout
layout = BIDSLayout(data_dir)
def init_brain_extraction_wf(name='brain_extraction_wf',
in_template='OASIS30ANTs',
template_spec=None,
use_float=True,
normalization_quality='precise',
omp_nthreads=None,
mem_gb=3.0,
bids_suffix='T1w',
atropos_refine=True,
atropos_use_random_seed=True,
atropos_model=None,
use_laplacian=True,
bspline_fitting_distance=200):
"""
Build a workflow for atlas-based brain extraction on anatomical MRI data.
A Nipype implementation of the official ANTs' ``antsBrainExtraction.sh``
workflow (only for 3D images).
The official workflow is built as follows (and this implementation
follows the same organization):
1. Step 1 performs several clerical tasks (adding padding, calculating
the Laplacian of inputs, affine initialization) and the core
spatial normalization.
2. Maps the brain mask into target space using the normalization
calculated in 1.
3. Superstep 1b: smart binarization of the brain mask
4. Superstep 6: apply ATROPOS and massage its outputs
5. Superstep 7: use results from 4 to refine the brain mask
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from niworkflows.anat.ants import init_brain_extraction_wf
wf = init_brain_extraction_wf()
Parameters
----------
in_template : str
Name of the skull-stripping template ('OASIS30ANTs', 'NKI', or
path).
The brain template from which regions will be projected
Anatomical template created using e.g. LPBA40 data set with
``buildtemplateparallel.sh`` in ANTs.
The workflow will automatically search for a brain probability
mask created using e.g. LPBA40 data set which have brain masks
defined, and warped to anatomical template and
averaged resulting in a probability image.
use_float : bool
Whether single precision should be used
normalization_quality : str
Use more precise or faster registration parameters
(default: ``precise``, other possible values: ``testing``)
omp_nthreads : int
Maximum number of threads an individual process may use
mem_gb : float
Estimated peak memory consumption of the most hungry nodes
in the workflow
bids_suffix : str
Sequence type of the first input image. For a list of acceptable values
see https://bids-specification.readthedocs.io/en/latest/\
04-modality-specific-files/01-magnetic-resonance-imaging-data.html#anatomy-imaging-data
atropos_refine : bool
Enables or disables the whole ATROPOS sub-workflow
atropos_use_random_seed : bool
Whether ATROPOS should generate a random seed based on the
system's clock
atropos_model : tuple or None
Allows to specify a particular segmentation model, overwriting
the defaults based on ``bids_suffix``
use_laplacian : bool
Enables or disables alignment of the Laplacian as an additional
criterion for image registration quality (default: True)
bspline_fitting_distance : float
The size of the b-spline mesh grid elements, in mm (default: 200)
name : str, optional
Workflow name (default: antsBrainExtraction)
Inputs
------
in_files : list
List of input anatomical images to be brain-extracted,
typically T1-weighted.
If a list of anatomical images is provided, subsequently
specified images are used during the segmentation process.
However, only the first image is used in the registration
of priors.
Our suggestion would be to specify the T1w as the first image.
in_mask : list, optional
Mask used for registration to limit the metric
computation to a specific region.
Outputs
-------
out_file : str
Skull-stripped and :abbr:`INU (intensity non-uniformity)`-corrected ``in_files``
out_mask : str
Calculated brain mask
bias_corrected : str
The ``in_files`` input images, after :abbr:`INU (intensity non-uniformity)`
correction, before skull-stripping.
bias_image : str
The :abbr:`INU (intensity non-uniformity)` field estimated for each
input in ``in_files``
out_segm : str
Output segmentation by ATROPOS
out_tpms : str
Output :abbr:`TPMs (tissue probability maps)` by ATROPOS
"""
from templateflow.api import get as get_template
wf = pe.Workflow(name)
template_spec = template_spec or {}
# suffix passed via spec takes precedence
template_spec['suffix'] = template_spec.get('suffix', bids_suffix)
tpl_target_path, common_spec = get_template_specs(
in_template, template_spec=template_spec)
# Get probabilistic brain mask if available
tpl_mask_path = get_template(
in_template, label='brain', suffix='probseg', **common_spec) or \
get_template(in_template, desc='brain', suffix='mask', **common_spec)
if omp_nthreads is None or omp_nthreads < 1:
omp_nthreads = cpu_count()
inputnode = pe.Node(niu.IdentityInterface(fields=['in_files', 'in_mask']),
name='inputnode')
# Try to find a registration mask, set if available
tpl_regmask_path = get_template(in_template,
desc='BrainCerebellumExtraction',
suffix='mask',
**common_spec)
if tpl_regmask_path:
inputnode.inputs.in_mask = str(tpl_regmask_path)
outputnode = pe.Node(niu.IdentityInterface(fields=[
'out_file', 'out_mask', 'bias_corrected', 'bias_image', 'out_segm',
'out_tpms'
]),
name='outputnode')
copy_xform = pe.Node(CopyXForm(
fields=['out_file', 'out_mask', 'bias_corrected', 'bias_image']),
name='copy_xform',
run_without_submitting=True)
trunc = pe.MapNode(ImageMath(operation='TruncateImageIntensity',
op2='0.01 0.999 256'),
name='truncate_images',
iterfield=['op1'])
# inu_n4 = pe.MapNode(
# N4BiasFieldCorrection(
# dimension=3, save_bias=False, copy_header=True,
# n_iterations=[50] * 4, convergence_threshold=1e-7, shrink_factor=4,
# bspline_fitting_distance=bspline_fitting_distance),
# n_procs=omp_nthreads, name='inu_n4', iterfield=['input_image'])
gunzip_4n4 = pe.MapNode(Gunzip(), name="gunzip_con", iterfield=['in_file'])
inu_n4 = pe.MapNode(
Segment(gm_output_type=[False, False, False],
wm_output_type=[False, False, False],
csf_output_type=[False, False, False],
clean_masks="thorough",
save_bias_corrected=True,
bias_regularization=0.001,
bias_fwhm=30,
sampling_distance=3,
use_mcr=True,
affine_regularization="mni"),
iterfield=['data'],
name='inu_n4',
mem_gb=32,
serial=True,
run_without_submitting=True) # n_procs=1 for reproducibility
res_tmpl = pe.Node(ResampleImageBySpacing(out_spacing=(4, 4, 4),
apply_smoothing=True),
name='res_tmpl')
res_tmpl.inputs.input_image = tpl_target_path
res_target = pe.Node(ResampleImageBySpacing(out_spacing=(4, 4, 4),
apply_smoothing=True),
name='res_target')
lap_tmpl = pe.Node(ImageMath(operation='Laplacian', op2='1.5 1'),
name='lap_tmpl')
lap_tmpl.inputs.op1 = tpl_target_path
lap_target = pe.Node(ImageMath(operation='Laplacian', op2='1.5 1'),
name='lap_target')
mrg_tmpl = pe.Node(niu.Merge(2), name='mrg_tmpl')
mrg_tmpl.inputs.in1 = tpl_target_path
mrg_target = pe.Node(niu.Merge(2), name='mrg_target')
# Initialize transforms with antsAI
init_aff = pe.Node(AI(metric=('Mattes', 32, 'Regular', 0.25),
transform=('Affine', 0.1),
search_factor=(15, 0.1),
principal_axes=False,
convergence=(10, 1e-6, 10),
verbose=True),
name='init_aff',
n_procs=omp_nthreads)
# Tolerate missing ANTs at construction time
_ants_version = Registration().version
if _ants_version and parseversion(_ants_version) >= Version('2.3.0'):
init_aff.inputs.search_grid = (40, (0, 40, 40))
# Set up spatial normalization
settings_file = 'antsBrainExtraction_%s.json' if use_laplacian \
else 'antsBrainExtractionNoLaplacian_%s.json'
norm = pe.Node(
Registration(from_file=pkgr_fn('niworkflows.data', settings_file %
normalization_quality)),
name='norm',
n_procs=omp_nthreads,
mem_gb=mem_gb)
norm.inputs.float = use_float
fixed_mask_trait = 'fixed_image_mask'
if _ants_version and parseversion(_ants_version) >= Version('2.2.0'):
fixed_mask_trait += 's'
map_brainmask = pe.Node(ApplyTransforms(interpolation='Gaussian',
float=True),
name='map_brainmask',
mem_gb=1)
map_brainmask.inputs.input_image = str(tpl_mask_path)
thr_brainmask = pe.Node(ThresholdImage(dimension=3,
th_low=0.5,
th_high=1.0,
inside_value=1,
outside_value=0),
name='thr_brainmask')
# Morphological dilation, radius=2
dil_brainmask = pe.Node(ImageMath(operation='MD', op2='2'),
name='dil_brainmask')
# Get largest connected component
get_brainmask = pe.Node(ImageMath(operation='GetLargestComponent'),
name='get_brainmask')
# Refine INU correction
inu_n4_final = pe.MapNode(N4BiasFieldCorrection(
dimension=3,
save_bias=True,
copy_header=True,
n_iterations=[50] * 5,
convergence_threshold=1e-7,
shrink_factor=4,
bspline_fitting_distance=bspline_fitting_distance),
n_procs=omp_nthreads,
name='inu_n4_final',
iterfield=['input_image'])
if _ants_version and parseversion(_ants_version) >= Version('2.1.0'):
inu_n4_final.inputs.rescale_intensities = True
else:
warn(
"""\
Found ANTs version %s, which is too old. Please consider upgrading to 2.1.0 or \
greater so that the --rescale-intensities option is available with \
N4BiasFieldCorrection.""" % _ants_version, DeprecationWarning)
# Apply mask
apply_mask = pe.MapNode(ApplyMask(),
iterfield=['in_file'],
name='apply_mask')
wf.connect([
(inputnode, trunc, [('in_files', 'op1')]),
(inputnode, copy_xform, [(('in_files', _pop), 'hdr_file')]),
(inputnode, inu_n4_final, [('in_files', 'input_image')]),
(inputnode, init_aff, [('in_mask', 'fixed_image_mask')]),
(inputnode, norm, [('in_mask', fixed_mask_trait)]),
(inputnode, map_brainmask, [(('in_files', _pop), 'reference_image')]),
(trunc, gunzip_4n4, [('output_image', 'in_file')]),
(gunzip_4n4, inu_n4, [('out_file', 'data')]),
(inu_n4, res_target, [(('bias_corrected_image', _pop), 'input_image')
]),
(res_tmpl, init_aff, [('output_image', 'fixed_image')]),
(res_target, init_aff, [('output_image', 'moving_image')]),
(init_aff, norm, [('output_transform', 'initial_moving_transform')]),
(norm, map_brainmask, [('reverse_transforms', 'transforms'),
('reverse_invert_flags',
'invert_transform_flags')]),
(map_brainmask, thr_brainmask, [('output_image', 'input_image')]),
(thr_brainmask, dil_brainmask, [('output_image', 'op1')]),
(dil_brainmask, get_brainmask, [('output_image', 'op1')]),
(inu_n4_final, apply_mask, [('output_image', 'in_file')]),
(get_brainmask, apply_mask, [('output_image', 'mask_file')]),
(get_brainmask, copy_xform, [('output_image', 'out_mask')]),
(apply_mask, copy_xform, [('out_file', 'out_file')]),
(inu_n4_final, copy_xform, [('output_image', 'bias_corrected'),
('bias_image', 'bias_image')]),
(copy_xform, outputnode, [('out_file', 'out_file'),
('out_mask', 'out_mask'),
('bias_corrected', 'bias_corrected'),
('bias_image', 'bias_image')]),
])
if use_laplacian:
lap_tmpl = pe.Node(ImageMath(operation='Laplacian', op2='1.5 1'),
name='lap_tmpl')
lap_tmpl.inputs.op1 = tpl_target_path
lap_target = pe.Node(ImageMath(operation='Laplacian', op2='1.5 1'),
name='lap_target')
mrg_tmpl = pe.Node(niu.Merge(2), name='mrg_tmpl')
mrg_tmpl.inputs.in1 = tpl_target_path
mrg_target = pe.Node(niu.Merge(2), name='mrg_target')
wf.connect([
(inu_n4, lap_target, [(('bias_corrected_image', _pop), 'op1')]),
(lap_tmpl, mrg_tmpl, [('output_image', 'in2')]),
(inu_n4, mrg_target, [('bias_corrected_image', 'in1')]),
(lap_target, mrg_target, [('output_image', 'in2')]),
(mrg_tmpl, norm, [('out', 'fixed_image')]),
(mrg_target, norm, [('out', 'moving_image')]),
])
else:
norm.inputs.fixed_image = tpl_target_path
wf.connect([
(inu_n4, norm, [(('bias_corrected_image', _pop), 'moving_image')]),
])
if atropos_refine:
atropos_model = atropos_model or list(
ATROPOS_MODELS[bids_suffix].values())
atropos_wf = init_atropos_wf(
use_random_seed=atropos_use_random_seed,
omp_nthreads=omp_nthreads,
mem_gb=mem_gb,
in_segmentation_model=atropos_model,
)
sel_wm = pe.Node(niu.Select(index=atropos_model[-1] - 1),
name='sel_wm',
run_without_submitting=True)
wf.disconnect([
(get_brainmask, apply_mask, [('output_image', 'mask_file')]),
(copy_xform, outputnode, [('out_mask', 'out_mask')]),
])
wf.connect([
(inu_n4, atropos_wf, [('bias_corrected_image',
'inputnode.in_files')]),
(thr_brainmask, atropos_wf, [('output_image', 'inputnode.in_mask')
]),
(get_brainmask, atropos_wf, [('output_image',
'inputnode.in_mask_dilated')]),
(atropos_wf, sel_wm, [('outputnode.out_tpms', 'inlist')]),
(sel_wm, inu_n4_final, [('out', 'weight_image')]),
(atropos_wf, apply_mask, [('outputnode.out_mask', 'mask_file')]),
(atropos_wf, outputnode, [('outputnode.out_mask', 'out_mask'),
('outputnode.out_segm', 'out_segm'),
('outputnode.out_tpms', 'out_tpms')]),
])
return wf
def setup_DARTEL_warp_wf(subj_list, data_template, warp_template, work_dir,
out_dir):
'''
subj_list: list of strings for each subject
e.g. ['sub-001', 'sub-002', 'sub-003']
data_template: string to identify all data files (using glob).
e.g. template = '/home/neuro/data/rest1_AROMA/nosmooth/sub-*/model/sub-*/_modelestimate0/res4d.nii.gz'
The template can identify a larger set of files, and the subject_list will grab a subset.
e.g. The template may grab sub-001, sub-002, sub-003 ...
But if the subject_list only includes sub-001, then only sub-001 will be used.
This means the template can overgeneralize, but specific subjects can be easily excluded (e.g. for movement)
warp_template: string to identify all dartel flowfield files (using glob).
same as above.
Dartel flowfield files are made by create_DARTEL_wf,
also see jtnipyutil.fsmap.make_PAG_masks, and jtnipyutil.fsmap.create_aqueduct_template
work_dir: string naming directory to store work.
out_dir: string naming directory for output.
'''
import os
import nibabel as nib
import numpy as np
import nipype.pipeline.engine as pe
from nipype import IdentityInterface
from nipype.interfaces.io import DataSink
from nipype.interfaces.utility.wrappers import Function
from nipype.interfaces.spm.preprocess import CreateWarped
from jtnipyutil.util import files_from_template
# create working directory if necessary.
if not os.path.isdir(work_dir):
os.makedirs(work_dir)
if not os.path.isdir(out_dir):
os.makedirs(out_dir)
# set up data warp workflow
apply_warp_wf = pe.Workflow(name='apply_warp_wf')
apply_warp_wf.base_dir = work_dir
# set up file lists
inputspec = pe.Node(IdentityInterface(fields=['file_list', 'warp_list']),
name='inputspec')
inputspec.inputs.file_list = files_from_template(subj_list, data_template)
inputspec.inputs.warp_list = files_from_template(subj_list, warp_template)
# rename files, as names are often indistinguishable (e.g. res4d.nii.gz)
def rename_list(in_list):
import nibabel as nib
import os
out_list = []
for file in in_list:
file_in = nib.load(file)
nib.save(file_in,
os.path.join(os.getcwd(), '_'.join(file.split('/')[-3:])))
out_list.append(
os.path.join(os.getcwd(), '_'.join(file.split('/')[-3:])))
return out_list
rename = pe.Node(Function(input_names=['in_list'],
output_names=['out_list'],
function=rename_list),
name='rename')
# dartel warping node.
warp_data = pe.Node(interface=CreateWarped(), name='warp_data')
# warp_data.inputs.image_files = # from inputspec OR gunzip
# warp_data.inputs.flowfield_files = # from inputspec
sinker = pe.Node(DataSink(), name='sinker')
sinker.inputs.base_directory = out_dir
# check if unzipping is necessary.
apply_warp_wf.connect([
(inputspec, rename, [('file_list', 'in_list')]),
(inputspec, warp_data, [('warp_list', 'flowfield_files')]),
(warp_data, sinker, [('warped_files', 'warped_files')])
])
if any('nii.gz' in file
for file in files_from_template(subj_list, data_template)):
from nipype.algorithms.misc import Gunzip
gunzip = pe.MapNode(interface=Gunzip(),
name='gunzip',
iterfield=['in_file'])
apply_warp_wf.connect([(rename, gunzip, [('out_list', 'in_file')]),
(gunzip, warp_data, [('out_file', 'image_files')
])])
else:
apply_warp_wf.connect([(rename, warp_data, [('out_list', 'image_files')
])])
return apply_warp_wf
def init_n4_only_wf(atropos_model=None,
atropos_refine=True,
atropos_use_random_seed=True,
bids_suffix='T1w',
mem_gb=3.0,
name='n4_only_wf',
omp_nthreads=None):
"""
Build a workflow to sidetrack brain extraction on skull-stripped datasets.
An alternative workflow to "init_brain_extraction_wf", for anatomical
images which have already been brain extracted.
1. Creates brain mask assuming all zero voxels are outside the brain
2. Applies N4 bias field correction
3. (Optional) apply ATROPOS and massage its outputs
4. Use results from 3 to refine N4 bias field correction
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from niworkflows.anat.ants import init_n4_only_wf
wf = init_n4_only_wf()
Parameters
----------
omp_nthreads : int
Maximum number of threads an individual process may use
mem_gb : float
Estimated peak memory consumption of the most hungry nodes
bids_suffix : str
Sequence type of the first input image. For a list of acceptable values see
https://bids-specification.readthedocs.io/en/latest/04-modality-specific-files/01-magnetic-resonance-imaging-data.html#anatomy-imaging-data
atropos_refine : bool
Enables or disables the whole ATROPOS sub-workflow
atropos_use_random_seed : bool
Whether ATROPOS should generate a random seed based on the
system's clock
atropos_model : tuple or None
Allows to specify a particular segmentation model, overwriting
the defaults based on ``bids_suffix``
name : str, optional
Workflow name (default: ``'n4_only_wf'``).
Inputs
------
in_files
List of input anatomical images to be bias corrected,
typically T1-weighted.
If a list of anatomical images is provided, subsequently
specified images are used during the segmentation process.
However, only the first image is used in the registration
of priors.
Our suggestion would be to specify the T1w as the first image.
Outputs
-------
out_file
:abbr:`INU (intensity non-uniformity)`-corrected ``in_files``
out_mask
Calculated brain mask
bias_corrected
Same as "out_file", provided for consistency with brain extraction
bias_image
The :abbr:`INU (intensity non-uniformity)` field estimated for each
input in ``in_files``
out_segm
Output segmentation by ATROPOS
out_tpms
Output :abbr:`TPMs (tissue probability maps)` by ATROPOS
"""
wf = pe.Workflow(name)
inputnode = pe.Node(niu.IdentityInterface(fields=['in_files', 'in_mask']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=[
'out_file', 'out_mask', 'bias_corrected', 'bias_image', 'out_segm',
'out_tpms'
]),
name='outputnode')
# Create brain mask
thr_brainmask = pe.Node(Binarize(thresh_low=2), name='binarize')
# INU correction
inu_n4_final = pe.MapNode(N4BiasFieldCorrection(
dimension=3,
save_bias=True,
copy_header=True,
n_iterations=[50] * 5,
convergence_threshold=1e-7,
shrink_factor=4,
bspline_fitting_distance=200),
n_procs=omp_nthreads,
name='inu_n4_final',
iterfield=['input_image'])
# Check ANTs version
try:
inu_n4_final.inputs.rescale_intensities = True
except ValueError:
warn(
"The installed ANTs version too old. Please consider upgrading to "
"2.1.0 or greater.", DeprecationWarning)
wf.connect([(inputnode, inu_n4_final, [('in_files', 'input_image')]),
(inputnode, thr_brainmask, [(('in_files', _pop), 'in_file')]),
(thr_brainmask, outputnode, [('out_mask', 'out_mask')]),
(inu_n4_final, outputnode, [('output_image', 'out_file')]),
(inu_n4_final, outputnode, [('output_image', 'bias_corrected')
]),
(inu_n4_final, outputnode, [('bias_image', 'bias_image')])])
# If atropos refine, do in4 twice
if atropos_refine:
# Morphological dilation, radius=2
dil_brainmask = pe.Node(ImageMath(operation='MD', op2='2'),
name='dil_brainmask')
# Get largest connected component
get_brainmask = pe.Node(ImageMath(operation='GetLargestComponent'),
name='get_brainmask')
atropos_model = atropos_model or list(
ATROPOS_MODELS[bids_suffix].values())
atropos_wf = init_atropos_wf(
use_random_seed=atropos_use_random_seed,
omp_nthreads=omp_nthreads,
mem_gb=mem_gb,
in_segmentation_model=atropos_model,
)
sel_wm = pe.Node(niu.Select(index=atropos_model[-1] - 1),
name='sel_wm',
run_without_submitting=True)
gunzip_4n4 = pe.MapNode(Gunzip(),
name="gunzip_con",
iterfield=['in_file'])
inu_n4 = pe.MapNode(
Segment(gm_output_type=[False, False, False],
wm_output_type=[False, False, False],
csf_output_type=[False, False, False],
clean_masks="thorough",
save_bias_corrected=True,
bias_regularization=0.001,
bias_fwhm=30,
sampling_distance=3,
use_mcr=True,
affine_regularization="mni"),
iterfield=['data'],
name='inu_n4',
mem_gb=32,
serial=True,
run_without_submitting=True) # n_procs=1 for reproducibility
wf.connect([
(inputnode, gunzip_4n4, [('in_files', 'in_file')]),
(gunzip_4n4, inu_n4, [('out_file', 'data')]),
(inu_n4, atropos_wf, [('bias_corrected_image',
'inputnode.in_files')]),
(thr_brainmask, atropos_wf, [('out_mask', 'inputnode.in_mask')]),
(thr_brainmask, dil_brainmask, [('out_mask', 'op1')]),
(dil_brainmask, get_brainmask, [('output_image', 'op1')]),
(get_brainmask, atropos_wf, [('output_image',
'inputnode.in_mask_dilated')]),
(atropos_wf, sel_wm, [('outputnode.out_tpms', 'inlist')]),
(sel_wm, inu_n4_final, [('out', 'weight_image')]),
(atropos_wf, outputnode, [('outputnode.out_segm', 'out_segm'),
('outputnode.out_tpms', 'out_tpms')]),
])
return wf
def auto_spm_slicetime(in_file=traits.Undefined,
out_prefix='stc',
num_slices=traits.Undefined,
time_repetition=traits.Undefined,
time_acquisition=traits.Undefined,
ref_slice=traits.Undefined,
slice_order=traits.Undefined,
wf_name='auto_spm_slicetime'):
""" A workflow that tries to automatically read the slice timing correction parameters
from the input file and passes them to a spm.SliceTiming node.
Parameters
----------
in_files: str
Path to the input file.
out_prefix: str
Prefix to the output file.
Default: 'a'
num_slices: int
Number of slices of `in_files`.
time_repetition: int or str
The time repetition (TR) of the input dataset in seconds
Default: 0
If left to default will read the TR from the nifti image header.
time_acquisition: int
Time of volume acquisition. usually calculated as TR-(TR/num_slices)
ref_slice: int
Index of the reference slice
slice_order: list of int
List of integers with the order in which slices are acquired
wf_name: str
Name of the workflow
Nipype Inputs
-------------
## Mandatory:
stc_input.in_files:
## Optional:
stc_input.num_slices
stc_input.slice_order
stc_input.time_repetition
stc_input.time_acquisition
stc_input.ref_slice
stc_input.slice_mode
Nipype Outputs
--------------
stc_output.timecorrected_files
stc_output.time_repetition
Returns
-------
auto_spm_stc: nipype Workflow
SPM slice timing correction workflow with automatic
parameters detection.
"""
# helper functions
def _sum_one_to_each(slice_order): # SPM starts count from 1
return [i + 1 for i in slice_order]
def _sum_one(num):
return num + 1
def _pick_first(sequence):
return sequence[0]
# the input and output nodes
stc_input = setup_node(IdentityInterface(fields=[
"in_file",
"num_slices",
"slice_order",
"time_repetition",
"time_acquisition",
"ref_slice",
"slice_mode",
]),
name="stc_input")
stc_output = setup_node(IdentityInterface(fields=[
"timecorrected_files",
"time_repetition",
]),
name="stc_output")
# Declare the processing nodes
params = setup_node(STCParametersInterface(in_files=in_file),
name='stc_params')
gunzip = setup_node(Gunzip(), name="gunzip")
stc = setup_node(spm_slicetime(out_prefix=out_prefix,
num_slices=num_slices,
time_repetition=time_repetition,
time_acquisition=time_acquisition,
ref_slice=ref_slice,
slice_order=slice_order),
name='slice_timer')
# Create the workflow object
wf = pe.Workflow(name=wf_name)
# Connect the nodes
wf.connect([
# input node
(stc_input, params, [
("in_file", "in_files"),
("num_slices", "num_slices"),
("slice_order", "slice_order"),
("time_repetition", "time_repetition"),
("time_acquisition", "time_acquisition"),
("ref_slice", "ref_slice"),
("slice_mode", "slice_mode"),
]),
# processing nodes
(params, gunzip, [(("in_files", _pick_first), "in_file")]),
(params, stc, [
(("slice_order", _sum_one_to_each), "slice_order"),
(("ref_slice", _sum_one), "ref_slice"),
("num_slices", "num_slices"),
("time_acquisition", "time_acquisition"),
("time_repetition", "time_repetition"),
]),
(gunzip, stc, [("out_file", "in_files")]),
# output node
(params, stc_output, [("time_repetition", "time_repetition")]),
(stc, stc_output, [("timecorrected_files", "timecorrected_files")]),
])
return wf
def run(base_dir):
template = '/home/brainlab/Desktop/Rudas/Data/Parcellation/TPM.nii'
matlab_cmd = '/home/brainlab/Desktop/Rudas/Tools/spm12_r7487/spm12/run_spm12.sh /home/brainlab/Desktop/Rudas/Tools/MCR/v713/ script'
spm.SPMCommand.set_mlab_paths(matlab_cmd=matlab_cmd, use_mcr=True)
print('SPM version: ' + str(spm.SPMCommand().version))
structural_dir = '/home/brainlab/Desktop/Rudas/Data/Propofol/Structurals/'
experiment_dir = opj(base_dir, 'output/')
output_dir = 'datasink'
working_dir = 'workingdir'
'''
subject_list = ['2014_05_02_02CB',
'2014_05_16_16RA',
'2014_05_30_30AQ',
'2014_07_04_04HD']
'''
subject_list = [
'2014_05_02_02CB', '2014_05_16_16RA', '2014_05_30_30AQ',
'2014_07_04_04HD', '2014_07_04_04SG', '2014_08_13_13CA',
'2014_10_08_08BC', '2014_10_08_08VR', '2014_10_22_22CY',
'2014_10_22_22TK', '2014_11_17_17EK', '2014_11_17_17NA',
'2014_11_19_19SA', '2014_11_19_AK', '2014_11_25.25JK',
'2014_11_27_27HF', '2014_12_10_10JR'
]
# list of subject identifiers
fwhm = 8 # Smoothing widths to apply (Gaussian kernel size)
TR = 2 # Repetition time
init_volume = 0 # Firts volumen identification which will use in the pipeline
iso_size = 2 # Isometric resample of functional images to voxel size (in mm)
# ExtractROI - skip dummy scans
extract = Node(ExtractROI(t_min=init_volume,
t_size=-1,
output_type='NIFTI'),
name="extract")
# MCFLIRT - motion correction
mcflirt = Node(MCFLIRT(mean_vol=True, save_plots=True,
output_type='NIFTI'),
name="motion_correction")
# SliceTimer - correct for slice wise acquisition
slicetimer = Node(SliceTimer(index_dir=False,
interleaved=True,
output_type='NIFTI',
time_repetition=TR),
name="slice_timing_correction")
# Smooth - image smoothing
smooth = Node(spm.Smooth(fwhm=fwhm), name="smooth")
n4bias = Node(N4Bias(out_file='t1_n4bias.nii.gz'), name='n4bias')
descomposition = Node(Descomposition(n_components=20,
low_pass=0.1,
high_pass=0.01,
tr=TR),
name='descomposition')
# Artifact Detection - determines outliers in functional images
art = Node(ArtifactDetect(norm_threshold=2,
zintensity_threshold=3,
mask_type='spm_global',
parameter_source='FSL',
use_differences=[True, False],
plot_type='svg'),
name="artifact_detection")
extract_confounds_ws_csf = Node(
ExtractConfounds(out_file='ev_without_gs.csv'),
name='extract_confounds_ws_csf')
extract_confounds_gs = Node(ExtractConfounds(out_file='ev_with_gs.csv',
delimiter=','),
name='extract_confounds_global_signal')
signal_extraction = Node(SignalExtraction(
time_series_out_file='time_series.csv',
correlation_matrix_out_file='correlation_matrix.png',
atlas_identifier='cort-maxprob-thr25-2mm',
tr=TR,
plot=True),
name='signal_extraction')
art_remotion = Node(ArtifacRemotion(out_file='fmri_art_removed.nii'),
name='artifact_remotion')
# BET - Skullstrip anatomical anf funtional images
bet_t1 = Node(BET(frac=0.5, robust=True, mask=True,
output_type='NIFTI_GZ'),
name="bet_t1")
# FAST - Image Segmentation
segmentation = Node(FAST(output_type='NIFTI'), name="segmentation")
# Normalize - normalizes functional and structural images to the MNI template
normalize_fmri = Node(Normalize12(jobtype='estwrite',
tpm=template,
write_voxel_sizes=[2, 2, 2],
write_bounding_box=[[-90, -126, -72],
[90, 90, 108]]),
name="normalize_fmri")
gunzip = Node(Gunzip(), name="gunzip")
normalize_t1 = Node(Normalize12(
jobtype='estwrite',
tpm=template,
write_voxel_sizes=[iso_size, iso_size, iso_size],
write_bounding_box=[[-90, -126, -72], [90, 90, 108]]),
name="normalize_t1")
normalize_masks = Node(Normalize12(
jobtype='estwrite',
tpm=template,
write_voxel_sizes=[iso_size, iso_size, iso_size],
write_bounding_box=[[-90, -126, -72], [90, 90, 108]]),
name="normalize_masks")
# Threshold - Threshold WM probability image
threshold = Node(Threshold(thresh=0.5, args='-bin',
output_type='NIFTI_GZ'),
name="wm_mask_threshold")
# FLIRT - pre-alignment of functional images to anatomical images
coreg_pre = Node(FLIRT(dof=6, output_type='NIFTI_GZ'),
name="linear_warp_estimation")
# FLIRT - coregistration of functional images to anatomical images with BBR
coreg_bbr = Node(FLIRT(dof=6,
cost='bbr',
schedule=opj(os.getenv('FSLDIR'),
'etc/flirtsch/bbr.sch'),
output_type='NIFTI_GZ'),
name="nonlinear_warp_estimation")
# Apply coregistration warp to functional images
applywarp = Node(FLIRT(interp='spline',
apply_isoxfm=iso_size,
output_type='NIFTI'),
name="registration_fmri")
# Apply coregistration warp to mean file
applywarp_mean = Node(FLIRT(interp='spline',
apply_isoxfm=iso_size,
output_type='NIFTI_GZ'),
name="registration_mean_fmri")
# Infosource - a function free node to iterate over the list of subject names
infosource = Node(IdentityInterface(fields=['subject_id']),
name="infosource")
infosource.iterables = [('subject_id', subject_list)]
# SelectFiles - to grab the data (alternativ to DataGrabber)
anat_file = opj(structural_dir, '{subject_id}', 't1.nii')
func_file = opj('{subject_id}', 'fmri.nii')
templates = {'anat': anat_file, 'func': func_file}
selectfiles = Node(SelectFiles(templates, base_directory=base_dir),
name="selectfiles")
# Datasink - creates output folder for important outputs
datasink = Node(DataSink(base_directory=experiment_dir,
container=output_dir),
name="datasink")
# Create a coregistration workflow
coregwf = Workflow(name='coreg_fmri_to_t1')
coregwf.base_dir = opj(experiment_dir, working_dir)
# Create a preprocessing workflow
preproc = Workflow(name='preproc')
preproc.base_dir = opj(experiment_dir, working_dir)
# Connect all components of the coregistration workflow
coregwf.connect([
(bet_t1, n4bias, [('out_file', 'in_file')]),
(n4bias, segmentation, [('out_file', 'in_files')]),
(segmentation, threshold, [(('partial_volume_files', get_latest),
'in_file')]),
(n4bias, coreg_pre, [('out_file', 'reference')]),
(threshold, coreg_bbr, [('out_file', 'wm_seg')]),
(coreg_pre, coreg_bbr, [('out_matrix_file', 'in_matrix_file')]),
(coreg_bbr, applywarp, [('out_matrix_file', 'in_matrix_file')]),
(n4bias, applywarp, [('out_file', 'reference')]),
(coreg_bbr, applywarp_mean, [('out_matrix_file', 'in_matrix_file')]),
(n4bias, applywarp_mean, [('out_file', 'reference')]),
])
## Use the following DataSink output substitutions
substitutions = [('_subject_id_', 'sub-')]
# ('_fwhm_', 'fwhm-'),
# ('_roi', ''),
# ('_mcf', ''),
# ('_st', ''),
# ('_flirt', ''),
# ('.nii_mean_reg', '_mean'),
# ('.nii.par', '.par'),
# ]
#subjFolders = [('fwhm-%s/' % f, 'fwhm-%s_' % f) for f in fwhm]
#substitutions.extend(subjFolders)
datasink.inputs.substitutions = substitutions
# Connect all components of the preprocessing workflow
preproc.connect([
(infosource, selectfiles, [('subject_id', 'subject_id')]),
(selectfiles, extract, [('func', 'in_file')]),
(extract, mcflirt, [('roi_file', 'in_file')]),
(mcflirt, slicetimer, [('out_file', 'in_file')]),
(selectfiles, coregwf, [('anat', 'bet_t1.in_file'),
('anat', 'nonlinear_warp_estimation.reference')
]),
(mcflirt, coregwf, [('mean_img', 'linear_warp_estimation.in_file'),
('mean_img', 'nonlinear_warp_estimation.in_file'),
('mean_img', 'registration_mean_fmri.in_file')]),
(slicetimer, coregwf, [('slice_time_corrected_file',
'registration_fmri.in_file')]),
(coregwf, art, [('registration_fmri.out_file', 'realigned_files')]),
(mcflirt, art, [('par_file', 'realignment_parameters')]),
(art, art_remotion, [('outlier_files', 'outlier_files')]),
(coregwf, art_remotion, [('registration_fmri.out_file', 'in_file')]),
(coregwf, gunzip, [('n4bias.out_file', 'in_file')]),
(selectfiles, normalize_fmri, [('anat', 'image_to_align')]),
(art_remotion, normalize_fmri, [('out_file', 'apply_to_files')]),
(selectfiles, normalize_t1, [('anat', 'image_to_align')]),
(gunzip, normalize_t1, [('out_file', 'apply_to_files')]),
(selectfiles, normalize_masks, [('anat', 'image_to_align')]),
(coregwf, normalize_masks, [(('segmentation.partial_volume_files',
get_wm_csf), 'apply_to_files')]),
(normalize_fmri, smooth, [('normalized_files', 'in_files')]),
(smooth, extract_confounds_ws_csf, [('smoothed_files', 'in_file')]),
(normalize_masks, extract_confounds_ws_csf, [('normalized_files',
'list_mask')]),
(mcflirt, extract_confounds_ws_csf, [('par_file', 'file_concat')]),
#(smooth, extract_confounds_gs, [('smoothed_files', 'in_file')]),
#(normalize_t1, extract_confounds_gs, [(('normalized_files',change_to_list), 'list_mask')]),
#(extract_confounds_ws_csf, extract_confounds_gs, [('out_file', 'file_concat')]),
(smooth, signal_extraction, [('smoothed_files', 'in_file')]),
#(extract_confounds_gs, signal_extraction, [('out_file', 'confounds_file')]),
(extract_confounds_ws_csf, signal_extraction, [('out_file',
'confounds_file')]),
#(smooth, descomposition, [('smoothed_files', 'in_file')]),
#(extract_confounds_ws_csf, descomposition, [('out_file', 'confounds_file')]),
#(extract_confounds_gs, datasink, [('out_file', 'preprocessing.@confounds_with_gs')]),
(extract_confounds_ws_csf, datasink,
[('out_file', 'preprocessing.@confounds_without_gs')]),
(smooth, datasink, [('smoothed_files', 'preprocessing.@smoothed')]),
(normalize_fmri, datasink, [('normalized_files',
'preprocessing.@fmri_normalized')]),
(normalize_t1, datasink, [('normalized_files',
'preprocessing.@t1_normalized')]),
(normalize_masks, datasink, [('normalized_files',
'preprocessing.@masks_normalized')]),
(signal_extraction, datasink, [('time_series_out_file',
'preprocessing.@time_serie')]),
(signal_extraction, datasink, [('correlation_matrix_out_file',
'preprocessing.@correlation_matrix')]),
(signal_extraction, datasink,
[('fmri_cleaned_out_file', 'preprocessing.@fmri_cleaned_out_file')]),
#(descomposition, datasink, [('out_file', 'preprocessing.@descomposition')]),
#(descomposition, datasink, [('plot_files', 'preprocessing.@descomposition_plot_files')])
])
preproc.write_graph(graph2use='colored', format='png', simple_form=True)
preproc.run()
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
ref_slice=number_of_slices // 2),
name="sliceTiming")
# Realign - correct for motion
realign = Node(Realign(register_to_mean=True), name="realign")
# Artifact Detection - determine which of the images in the functional series
# are outliers. This is based on deviation in intensity or movement.
art = Node(ArtifactDetect(norm_threshold=1,
zintensity_threshold=3,
mask_type='file',
parameter_source='SPM',
use_differences=[True, False]),
name="art")
#Gunzip - unzip anatomical
gunzip2 = Node(Gunzip(), name="gunzip2")
gunzip = Node(Gunzip(), name="gunzip")
sliceTiming = Node(SliceTiming(num_slices=number_of_slices,
time_repetition=TR,
time_acquisition=TR - TR / number_of_slices,
slice_order=interleaved_order,
ref_slice=19),
name="sliceTiming")
# Realign - correct for motion
realign = Node(Realign(register_to_mean=True), name="realign")
# Artifact Detection - determine which of the images in the functional series
# are outliers. This is based on deviation in intensity or movement.
