def test_rename():
tempdir = os.path.realpath(mkdtemp())
origdir = os.getcwd()
os.chdir(tempdir)
# Test very simple rename
_ = open("file.txt", "w").close()
rn = utility.Rename(in_file="file.txt", format_string="test_file1.txt")
res = rn.run()
outfile = os.path.join(tempdir, "test_file1.txt")
yield assert_equal, res.outputs.out_file, outfile
yield assert_true, os.path.exists(outfile)
# Now a string-formatting version
rn = utility.Rename(in_file="file.txt",
format_string="%(field1)s_file%(field2)d",
keep_ext=True)
# Test .input field creation
yield assert_true, hasattr(rn.inputs, "field1")
yield assert_true, hasattr(rn.inputs, "field2")
# Set the inputs
rn.inputs.field1 = "test"
rn.inputs.field2 = 2
res = rn.run()
outfile = os.path.join(tempdir, "test_file2.txt")
yield assert_equal, res.outputs.out_file, outfile
yield assert_true, os.path.exists(outfile)
# Clean up
os.chdir(origdir)
shutil.rmtree(tempdir)
def test_rename(tmpdir):
tmpdir.chdir()
# Test very simple rename
_ = open("file.txt", "w").close()
rn = utility.Rename(in_file="file.txt", format_string="test_file1.txt")
res = rn.run()
outfile = tmpdir.join("test_file1.txt").strpath
assert res.outputs.out_file == outfile
assert os.path.exists(outfile)
# Now a string-formatting version
rn = utility.Rename(in_file="file.txt",
format_string="%(field1)s_file%(field2)d",
keep_ext=True)
# Test .input field creation
assert hasattr(rn.inputs, "field1")
assert hasattr(rn.inputs, "field2")
# Set the inputs
rn.inputs.field1 = "test"
rn.inputs.field2 = 2
res = rn.run()
outfile = tmpdir.join("test_file2.txt").strpath
assert res.outputs.out_file == outfile
assert os.path.exists(outfile)
def create_converter_diffusion_pipeline(working_dir,
ds_dir,
name='converter_diffusion'):
# initiate workflow
converter_wf = Workflow(name=name)
converter_wf.base_dir = os.path.join(working_dir, 'LeiCA_resting')
# set fsl output
fsl.FSLCommand.set_default_output_type('NIFTI_GZ')
# inputnode
inputnode = Node(util.IdentityInterface(fields=['dMRI_dicom']),
name='inputnode')
outputnode = Node(util.IdentityInterface(fields=['dMRI']),
name='outputnode')
niftisink = Node(nio.DataSink(), name='niftisink')
niftisink.inputs.base_directory = os.path.join(ds_dir, 'raw_niftis')
#######
converter_dMRI = Node(Dcm2nii(), name="converter_dMRI")
converter_dMRI.inputs.gzip_output = True
converter_dMRI.inputs.nii_output = True
converter_dMRI.inputs.anonymize = False
converter_dMRI.plugin_args = {'submit_specs': 'request_memory = 2000'}
converter_wf.connect(inputnode, 'dMRI_dicom', converter_dMRI,
'source_names')
dMRI_rename = Node(util.Rename(format_string='DTI_mx_137.nii.gz'),
name='dMRI_rename')
converter_wf.connect(converter_dMRI, 'converted_files', dMRI_rename,
'in_file')
bvecs_rename = Node(util.Rename(format_string='DTI_mx_137.bvecs'),
name='bvecs_rename')
converter_wf.connect(converter_dMRI, 'bvecs', bvecs_rename, 'in_file')
bvals_rename = Node(util.Rename(format_string='DTI_mx_137.bvals'),
name='bvals_rename')
converter_wf.connect(converter_dMRI, "bvals", bvals_rename, 'in_file')
# reorient to standard orientation
reor_2_std = Node(fsl.Reorient2Std(), name='reor_2_std')
converter_wf.connect(dMRI_rename, 'out_file', reor_2_std, 'in_file')
converter_wf.connect(reor_2_std, 'out_file', outputnode, 'dMRI')
# save original niftis
converter_wf.connect(reor_2_std, 'out_file', niftisink, 'dMRI.@dwi')
converter_wf.connect(bvals_rename, 'out_file', niftisink, 'dMRI.@bvals')
converter_wf.connect(bvecs_rename, 'out_file', niftisink, 'dMRI.@bvecs')
converter_wf.write_graph(dotfilename='converter_struct',
graph2use='flat',
format='pdf')
return converter_wf
def mask2surf(name='MaskToSurface', use_ras_coord=True):
inputnode = pe.Node(niu.IdentityInterface(
fields=['in_file', 'norm', 'in_filled', 'out_name']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['out_surf']),
name='outputnode')
binarize = pe.Node(fs.Binarize(min=0.1), name='binarize')
fill = pe.Node(FillMask(), name='FillMask')
pretess = pe.Node(fs.MRIPretess(label=1), name='PreTess')
tess = pe.Node(fs.MRITessellate(label_value=1,
use_real_RAS_coordinates=use_ras_coord),
name='tess')
smooth = pe.Node(fs.SmoothTessellation(disable_estimates=True),
name='mris_smooth')
rename = pe.Node(niu.Rename(keep_ext=False), name='rename')
togii = pe.Node(fs.MRIsConvert(out_datatype='gii'), name='toGIFTI')
wf = pe.Workflow(name=name)
wf.connect([
(inputnode, binarize, [('in_file', 'in_file')]),
(inputnode, pretess, [('norm', 'in_norm')]),
(inputnode, fill, [('in_filled', 'in_filled')]),
(inputnode, rename, [('out_name', 'format_string')]),
(binarize, fill, [('binary_file', 'in_file')]),
(fill, pretess, [('out_file', 'in_filled')]),
(pretess, tess, [('out_file', 'in_file')]),
(tess, smooth, [('surface', 'in_file')]),
(smooth, rename, [('surface', 'in_file')]),
(rename, togii, [('out_file', 'in_file')]),
(togii, outputnode, [('converted', 'out_surf')]),
])
return wf
def create_align_to_anatomy_workflow(name='align_to_anatomy',
format_string='inplane_to_anatomy'):
align_to_anatomy = pe.Workflow(name=name)
inputs = pe.Node(interface=util.IdentityInterface(
fields=['inplane_file', 'anatomy_file']),
name='inputs')
strip = pe.Node(interface=fs.ReconAll(),
name='strip') #FIXME: reconall interface barfs if rerun
strip.inputs.directive = 'autorecon1'
strip.inputs.flags = '-nowsgcaatlas'
register = pe.Node(interface=fs.RobustRegister(), name='register')
register.inputs.auto_sens = True
#register.inputs.init_orient = True #FIXME: disabled due to bug in binary
convert_xfm = pe.Node(interface=nmutil.LtaToXfm(), name='convert_xfm')
rename_xfm = pe.Node(interface=util.Rename(format_string),
name='rename_xfm')
rename_xfm.inputs.keep_ext = True
outputs = pe.Node(
interface=util.IdentityInterface(fields=['xfm_file', 'strip_file']),
name='outputs')
align_to_anatomy.connect(inputs, 'inplane_file', strip, 'T1_files')
align_to_anatomy.connect(strip, 'brainmask', register, 'source_file')
align_to_anatomy.connect(inputs, 'anatomy_file', register, 'target_file')
align_to_anatomy.connect(register, 'out_reg_file', convert_xfm, 'in_file')
align_to_anatomy.connect(convert_xfm, 'out_file', rename_xfm, 'in_file')
align_to_anatomy.connect(rename_xfm, 'out_file', outputs, 'xfm_file')
align_to_anatomy.connect(strip, 'brainmask', outputs, 'strip_file')
return align_to_anatomy
def create_extract_functional_workflow(
name='extract_functional',
templates={'functional': 'Raw/Functional/Scan_{scan}/'},
format_string='f%(scan)d'):
extract_functional = pe.Workflow(name=name)
inputs = pe.Node(
interface=util.IdentityInterface(fields=['session_dir', 'scan']),
name='inputs')
get_functional = pe.Node(interface=nio.SelectFiles(templates),
name='get_functional')
functional_to_nii = pe.Node(interface=ds.DcmStack(),
name='functional_to_nii')
functional_to_nii.inputs.embed_meta = True
rename_functional = pe.Node(interface=util.Rename(format_string),
name='rename_functional')
rename_functional.inputs.keep_ext = True
outputs = pe.Node(interface=util.IdentityInterface(fields=['out_file']),
name='outputs')
extract_functional.connect(inputs, 'session_dir', get_functional,
'base_directory')
extract_functional.connect(inputs, 'scan', get_functional, 'scan')
extract_functional.connect(get_functional, 'functional', functional_to_nii,
'dicom_files')
extract_functional.connect(functional_to_nii, 'out_file',
rename_functional, 'in_file')
extract_functional.connect(inputs, 'scan', rename_functional, 'scan')
extract_functional.connect(rename_functional, 'out_file', outputs,
'out_file')
return extract_functional
def create_extract_inplane_workflow(
name='extract_inplane',
templates={'inplane': 'Raw/Anatomy/Inplane{id}/'},
format_string='inplane'):
extract_inplane = pe.Workflow(name=name)
inputs = pe.Node(
interface=util.IdentityInterface(fields=['session_dir', 'ref_vol']),
name='inputs')
get_inplane = pe.Node(interface=nio.SelectFiles(templates),
name='get_inplane')
inplane_to_nii = pe.Node(interface=ds.DcmStack(), name='inplane_to_nii')
inplane_to_nii.inputs.embed_meta = True
rename_inplane = pe.Node(interface=util.Rename(format_string),
name='rename_inplane')
rename_inplane.inputs.keep_ext = True
outputs = pe.Node(interface=util.IdentityInterface(fields=['out_file']),
name='outputs')
extract_inplane.connect(inputs, 'session_dir', get_inplane,
'base_directory')
extract_inplane.connect(inputs, ('ref_vol', ref_vol_to_inplane_id),
get_inplane, 'id')
extract_inplane.connect(get_inplane, 'inplane', inplane_to_nii,
'dicom_files')
extract_inplane.connect(inplane_to_nii, 'out_file', rename_inplane,
'in_file')
extract_inplane.connect(rename_inplane, 'out_file', outputs, 'out_file')
return extract_inplane
def create_normalize_workflow(name="normalize"):
# Define the workflow inputs
inputnode = pe.Node(util.IdentityInterface(
fields=["timeseries", "flirt_affine", "warpfield"]),
name="inputs")
# Define the target space and warp to it
mni152 = fsl.Info.standard_image("avg152T1_brain.nii.gz")
applywarp = pe.MapNode(fsl.ApplyWarp(ref_file=mni152, interp="spline"),
iterfield=["in_file", "premat"],
name="applywarp")
# Rename the timeseries
rename = pe.MapNode(util.Rename(format_string="timeseries_warped",
keep_ext=True),
iterfield=["in_file"],
name="rename")
# Define the outputs
outputnode = pe.Node(util.IdentityInterface(fields=["timeseries"]),
name="outputs")
normalize = pe.Workflow(name=name)
normalize.connect([
(inputnode, applywarp, [("timeseries", "in_file"),
("warpfield", "field_file"),
("flirt_affine", "premat")]),
(applywarp, rename, [("out_file", "in_file")]),
(rename, outputnode, [("out_file", "timeseries")]),
])
return normalize
def renameFile(file_name, node_name, wdir=None, nthreads=1):
renameFile = pe.Node(niu.Rename(format_string="%(subjid)s_%(file_name)s"),
name=node_name)
renameFile.base_dir = wdir
renameFile.inputs.keep_ext = True
renameFile.inputs.file_name = file_name
renameFile.interface.num_threads = nthreads
return renameFile
def _run_interface(self, runtime):
outputs = self._list_outputs()
aff_file = outputs['aff_file']
aff = np.diag(4 * [1])
np.savetxt(aff_file, aff, "%g")
rename = niu.Rename()
rename.inputs.in_file = self.inputs.flo_file
rename.inputs.format_string = outputs['res_file']
result = rename.run()
return result.runtime
def create_workflow(self, flow, inputnode, outputnode):
if self.config.seg_tool == "Freesurfer":
# Converting to .mgz format
fs_mriconvert = pe.Node(interface=fs.MRIConvert(out_type="mgz",
out_file="T1.mgz"),
name="mgz_convert")
if self.config.make_isotropic:
fs_mriconvert.inputs.vox_size = (
self.config.isotropic_vox_size,
self.config.isotropic_vox_size,
self.config.isotropic_vox_size)
fs_mriconvert.inputs.resample_type = self.config.isotropic_interpolation
rename = pe.Node(util.Rename(), name="copy_orig")
orig_dir = os.path.join(self.config.freesurfer_subject_id, "mri",
"orig")
if not os.path.exists(orig_dir):
os.makedirs(orig_dir)
print "Folder not existing; %s created!" % orig_dir
rename.inputs.format_string = os.path.join(orig_dir, "001.mgz")
# ReconAll => named outputnode as we don't want to select a specific output....
fs_reconall = pe.Node(interface=fs.ReconAll(
flags='-no-isrunning -parallel -openmp {}'.format(
self.config.fs_number_of_cores)),
name="reconall")
fs_reconall.inputs.directive = 'all'
#fs_reconall.inputs.args = self.config.freesurfer_args
#fs_reconall.inputs.subjects_dir and fs_reconall.inputs.subject_id set in cmp/pipelines/diffusion/diffusion.py
fs_reconall.inputs.subjects_dir = self.config.freesurfer_subjects_dir
# fs_reconall.inputs.hippocampal_subfields_T1 = self.config.segment_hippocampal_subfields
# fs_reconall.inputs.brainstem = self.config.segment_brainstem
def isavailable(file):
print "T1 is available"
return file
flow.connect([
(inputnode, fs_mriconvert, [(('T1', isavailable), 'in_file')]),
(fs_mriconvert, rename, [('out_file', 'in_file')]),
(rename, fs_reconall, [(("out_file", extract_base_directory),
"subject_id")]),
(fs_reconall, outputnode, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
])
def anat_qc_workflow_dhcp(name='MRIQC_Anat', settings=None):
"""
One-subject-one-session-one-run pipeline to extract the NR-IQMs from
anatomical images
"""
if settings is None:
settings = {}
workflow = pe.Workflow(name=name)
deriv_dir = op.abspath('./derivatives')
if 'work_dir' in settings.keys():
deriv_dir = op.abspath(op.join(settings['work_dir'], 'derivatives'))
if not op.exists(deriv_dir):
os.makedirs(deriv_dir)
# Define workflow, inputs and outputs
inputnode = pe.Node(niu.IdentityInterface(
fields=['bids_root', 'subject_id', 'session_id', 'run_id', 'reorient'
]),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['']), name='outputnode')
# Plot mosaic
plot = pe.Node(PlotMosaic(), name='plot_mosaic')
merg = pe.Node(niu.Merge(3), name='plot_metadata')
# Connect all nodes
workflow.connect([(inputnode, plot, [('reorient', 'in_file')]),
(inputnode, plot, [('subject_id', 'subject')]),
(inputnode, merg, [('session_id', 'in1'),
('run_id', 'in2')]),
(merg, plot, [('out', 'metadata')])])
# Save mosaic to well-formed path
mvplot = pe.Node(niu.Rename(
format_string='anatomical_%(subject_id)s_%(session_id)s_%(run_id)s',
keep_ext=True),
name='rename_plot')
dsplot = pe.Node(nio.DataSink(base_directory=settings['work_dir'],
parameterization=False),
name='ds_plot')
workflow.connect([(inputnode, mvplot, [('subject_id', 'subject_id'),
('session_id', 'session_id'),
('run_id', 'run_id')]),
(plot, mvplot, [('out_file', 'in_file')]),
(mvplot, dsplot, [('out_file', '@mosaic')])])
return workflow
def create_workflow(self, flow, inputnode, outputnode):
if self.config.seg_tool == "Freesurfer":
if self.config.use_existing_freesurfer_data == False:
# Converting to .mgz format
fs_mriconvert = pe.Node(interface=fs.MRIConvert(
out_type="mgz", out_file="T1.mgz"),
name="mgz_convert")
rename = pe.Node(util.Rename(), name="copy_orig")
orig_dir = os.path.join(self.config.freesurfer_subject_id,
"mri", "orig")
if not os.path.exists(orig_dir):
os.makedirs(orig_dir)
print "Folder not existing; %s created!" % orig_dir
rename.inputs.format_string = os.path.join(orig_dir, "001.mgz")
# ReconAll => named outputnode as we don't want to select a specific output....
fs_reconall = pe.Node(
interface=fs.ReconAll(flags='-no-isrunning'),
name="reconall")
fs_reconall.inputs.args = self.config.freesurfer_args
#fs_reconall.inputs.subjects_dir and fs_reconall.inputs.subject_id set in cmp/pipelines/diffusion/diffusion.py
fs_reconall.inputs.subjects_dir = self.config.freesurfer_subjects_dir
def isavailable(file):
print "T1 is available"
return file
flow.connect([
(inputnode, fs_mriconvert, [(('T1', isavailable),
'in_file')]),
(fs_mriconvert, rename, [('out_file', 'in_file')]),
(rename, fs_reconall,
[(("out_file", extract_base_directory), "subject_id")]),
(fs_reconall, outputnode, [('subjects_dir',
'subjects_dir'),
('subject_id', 'subject_id')]),
])
else:
outputnode.inputs.subjects_dir = self.config.freesurfer_subjects_dir
outputnode.inputs.subject_id = self.config.freesurfer_subject_id
elif self.config.seg_tool == "Custom segmentation":
outputnode.inputs.custom_wm_mask = self.config.white_matter_mask
def create_gradient_unwarp_workflow(in_file,
in_coeff,
output_dir,
offsets=[0, 0, 0],
scanner='siemens',
radius=0.225,
interp='CUB',
throughplaneonly=False,
inplaneonly=False):
_, subject_id, _ = split_filename(os.path.basename(in_file))
# Create a workflow to process the images
workflow = pe.Workflow(name='gradwarp_correction')
workflow.base_output_dir = 'gradwarp_correction'
# The gradwarp field is computed.
gradwarp = pe.Node(interface=GradwarpCorrection(), name='gradwarp')
gradwarp.inputs.offset_x = -1 * offsets[0]
gradwarp.inputs.offset_y = -1 * offsets[1]
gradwarp.inputs.offset_z = -1 * offsets[2]
gradwarp.inputs.radius = radius
gradwarp.inputs.scanner_type = scanner
gradwarp.inputs.in_file = in_file
gradwarp.inputs.coeff_file = in_coeff
if throughplaneonly:
gradwarp.inputs.throughplaneonly = True
if inplaneonly:
gradwarp.inputs.inplaneonly = True
# The obtained deformation field is used the resample the input image
resampling = pe.Node(interface=niftyreg.RegResample(inter_val=interp,
ref_file=in_file,
flo_file=in_file),
name='resampling')
workflow.connect(gradwarp, 'out_file', resampling, 'trans_file')
renamer = pe.Node(interface=niu.Rename(format_string=subject_id +
"_gradwarp",
keep_ext=True),
name='renamer')
workflow.connect(resampling, 'out_file', renamer, 'in_file')
# Create a data sink
ds = pe.Node(nio.DataSink(parameterization=False), name='ds')
ds.inputs.base_directory = output_dir
workflow.connect(renamer, 'out_file', ds, '@img')
return workflow
def rename_connections(workflow, datasink, rename_list, sink_node):
ncount = 0
rcount = 0
for rename in rename_list:
din_file = sink_node + '.@' + str(ncount)
if len(rename) == 4:
rename_node = pe.MapNode(interface=util.Rename(),
name=rename[2] + str(rcount),
iterfield=['in_file', 'format_string'])
rename_node.inputs.format_string = rename[3]
workflow.connect(rename[0], rename[1], rename_node, 'in_file')
workflow.connect(rename_node, 'out_file', datasink, din_file)
rcount += 1
else:
workflow.connect(rename[0], rename[1], datasink, din_file)
ncount += 1
def convert_rsfmri_dicom(name='convert_rsfmri_dicom'):
inputnode = pe.Node(
utility.IdentityInterface(
fields=['rsfmri_dicom_pattern']),
name='inputspec')
outputnode = pe.Node(
utility.IdentityInterface(
fields=['rsfmri_file','dicom_pars']),
name='outputspec')
n_dcm2nii = pe.Node(
dcm2nii.Dcm2nii(
gzip_output=True,
id_in_filename=True,
date_in_filename=True,
convert_all_pars=False,
config_file='/home_local/bpinsard/.dcm2nii/dcm2nii.ini',),
name='dcm2nii')
n_rename_rsfmri = pe.Node(
utility.Rename(
format_string='%(site)s_S_%(subj)s_%(data)s_%(time)s_rsfMRI.nii.gz',
parse_string='(?P<data>\d{8})_(?P<time>\d{6})(?P<site>\d{3})S(?P<subj>\d{4})'),
name='rename_rsfmri')
n_get_dicom_par=pe.Node(
utility.Function(
input_names=['dicom_pattern'],
output_names=['dicom_pars'],
function=get_dicom_par),
name='get_dicom_par')
w=pe.Workflow(name=name)
w.connect([
(inputnode, n_dcm2nii, [('rsfmri_dicom_pattern','source_names')]),
(n_dcm2nii, n_rename_rsfmri,[('converted_files','in_file')]),
(inputnode,n_get_dicom_par,[('rsfmri_dicom_pattern','dicom_pattern')]),
(n_rename_rsfmri, outputnode, [('out_file','rsfmri_file')]),
(n_get_dicom_par, outputnode, [('dicom_pars','dicom_pars')]),
])
return w
def legacy(
bids_base,
template,
debug=False,
functional_blur_xy=False,
functional_match={},
keep_work=False,
n_jobs=False,
n_jobs_percentage=0.8,
out_base=None,
realign="time",
registration_mask=False,
sessions=[],
structural_match={},
subjects=[],
tr=1,
workflow_name='legacy',
enforce_dummy_scans=DUMMY_SCANS,
exclude={},
):
'''
Legacy realignment and registration workflow representative of the tweaks and workarounds commonly used in the pre-SAMRI period.
Parameters
----------
bids_base : str
Path to the BIDS data set root.
template : str
Path to the template to register the data to.
debug : bool, optional
Whether to enable nipype debug mode.
This increases logging.
exclude : dict
A dictionary with any combination of "sessions", "subjects", "tasks" as keys and corresponding identifiers as values.
If this is specified matching entries will be excluded in the analysis.
functional_blur_xy : float, optional
Factor by which to smooth data in the xy-plane; if parameter evaluates to false, no smoothing will be applied.
Ideally this value should correspond to the resolution or smoothness in the z-direction (assuing z represents the lower-resolution slice-encoding direction).
functional_match : dict, optional
Dictionary specifying a whitelist to use for functional data inclusion into the workflow; if dictionary is empty no whitelist is present and all data will be considered.
The dictionary should have keys which are 'acquisition', 'task', or 'modality', and values which are lists of acceptable strings for the respective BIDS field.
keep_work : bool, str
Whether to keep the work directory after workflow conclusion (this directory contains all the intermediary processing commands, inputs, and outputs --- it is invaluable for debugging but many times larger in size than the actual output).
n_jobs : int, optional
Number of processors to maximally use for the workflow; if unspecified a best guess will be estimate based on `n_jobs_percentage` and hardware (but not on current load).
n_jobs_percentage : float, optional
Percentage of available processors (as in available hardware, not available free load) to maximally use for the workflow (this is overriden by `n_jobs`).
out_base : str, optional
Output base directory - inside which a directory named `workflow_name` (as well as associated directories) will be created.
realign : {"space","time","spacetime",""}, optional
Parameter that dictates slictiming correction and realignment of slices. "time" (FSL.SliceTimer) is default, since it works safely. Use others only with caution!
registration_mask : str, optional
Mask to use for the registration process.
This mask will constrain the area for similarity metric evaluation, but the data will not be cropped.
sessions : list, optional
A whitelist of sessions to include in the workflow, if the list is empty there is no whitelist and all sessions will be considered.
structural_match : dict, optional
Dictionary specifying a whitelist to use for structural data inclusion into the workflow; if dictionary is empty no whitelist is present and all data will be considered.
The dictionary should have keys which are 'acquisition', or 'modality', and values which are lists of acceptable strings for the respective BIDS field.
subjects : list, optional
A whitelist of subjects to include in the workflow, if the list is empty there is no whitelist and all sessions will be considered.
tr : float, optional
Repetition time, explicitly.
WARNING! This is a parameter waiting for deprecation.
workflow_name : str, optional
Top level name for the output directory.
'''
try:
import nipype.interfaces.ants.legacy as antslegacy
except ModuleNotFoundError:
print('''
The `nipype.interfaces.ants.legacy` was not found on this system.
You may want to downgrade nipype to e.g. 1.1.1, as this module has been removed in more recent versions:
https://github.com/nipy/nipype/issues/3197
''')
bids_base, out_base, out_dir, template, registration_mask, data_selection, functional_scan_types, structural_scan_types, subjects_sessions, func_ind, struct_ind = common_select(
bids_base,
out_base,
workflow_name,
template,
registration_mask,
functional_match,
structural_match,
subjects,
sessions,
exclude,
)
if not n_jobs:
n_jobs = max(int(round(mp.cpu_count() * n_jobs_percentage)), 2)
get_f_scan = pe.Node(name='get_f_scan',
interface=util.Function(
function=get_bids_scan,
input_names=inspect.getargspec(get_bids_scan)[0],
output_names=[
'scan_path', 'scan_type', 'task', 'nii_path',
'nii_name', 'events_name', 'subject_session',
'metadata_filename', 'dict_slice', 'ind_type'
]))
get_f_scan.inputs.ignore_exception = True
get_f_scan.inputs.data_selection = data_selection
get_f_scan.inputs.bids_base = bids_base
get_f_scan.iterables = ("ind_type", func_ind)
dummy_scans = pe.Node(
name='dummy_scans',
interface=util.Function(
function=force_dummy_scans,
input_names=inspect.getargspec(force_dummy_scans)[0],
output_names=['out_file', 'deleted_scans']))
dummy_scans.inputs.desired_dummy_scans = enforce_dummy_scans
events_file = pe.Node(
name='events_file',
interface=util.Function(
function=write_bids_events_file,
input_names=inspect.getargspec(write_bids_events_file)[0],
output_names=['out_file']))
temporal_mean = pe.Node(interface=fsl.MeanImage(), name="temporal_mean")
f_resize = pe.Node(interface=VoxelResize(), name="f_resize")
f_resize.inputs.resize_factors = [10, 10, 10]
f_percentile = pe.Node(interface=fsl.ImageStats(), name="f_percentile")
f_percentile.inputs.op_string = '-p 98'
f_threshold = pe.Node(interface=fsl.Threshold(), name="f_threshold")
f_fast = pe.Node(interface=fsl.FAST(), name="f_fast")
f_fast.inputs.no_pve = True
f_fast.inputs.output_biascorrected = True
f_bet = pe.Node(interface=fsl.BET(), name="f_BET")
f_swapdim = pe.Node(interface=fsl.SwapDimensions(), name="f_swapdim")
f_swapdim.inputs.new_dims = ('x', '-z', '-y')
f_deleteorient = pe.Node(interface=FSLOrient(), name="f_deleteorient")
f_deleteorient.inputs.main_option = 'deleteorient'
datasink = pe.Node(nio.DataSink(), name='datasink')
datasink.inputs.base_directory = out_dir
datasink.inputs.parameterization = False
workflow_connections = [
(get_f_scan, dummy_scans, [('nii_path', 'in_file')]),
(dummy_scans, events_file, [('deleted_scans', 'forced_dummy_scans')]),
(dummy_scans, f_resize, [('out_file', 'in_file')]),
(get_f_scan, events_file, [('nii_path', 'timecourse_file'),
('task', 'task'),
('scan_path', 'scan_dir')]),
(events_file, datasink, [('out_file', 'func.@events')]),
(get_f_scan, events_file, [('events_name', 'out_file')]),
(get_f_scan, datasink, [(('subject_session', ss_to_path), 'container')
]),
(temporal_mean, f_percentile, [('out_file', 'in_file')]),
# here we divide by 10 assuming 10 percent noise
(f_percentile, f_threshold, [(('out_stat', divideby_10), 'thresh')]),
(temporal_mean, f_threshold, [('out_file', 'in_file')]),
(f_threshold, f_fast, [('out_file', 'in_files')]),
(f_fast, f_bet, [('restored_image', 'in_file')]),
(f_resize, f_deleteorient, [('out_file', 'in_file')]),
(f_deleteorient, f_swapdim, [('out_file', 'in_file')]),
]
if realign == "space":
realigner = pe.Node(interface=spm.Realign(), name="realigner")
realigner.inputs.register_to_mean = True
workflow_connections.extend([
(f_swapdim, realigner, [('out_file', 'in_file')]),
])
elif realign == "spacetime":
realigner = pe.Node(interface=nipy.SpaceTimeRealigner(),
name="realigner")
realigner.inputs.slice_times = "asc_alt_2"
realigner.inputs.tr = tr
realigner.inputs.slice_info = 3 #3 for coronal slices (2 for horizontal, 1 for sagittal)
workflow_connections.extend([
(f_swapdim, realigner, [('out_file', 'in_file')]),
])
elif realign == "time":
realigner = pe.Node(interface=fsl.SliceTimer(), name="slicetimer")
realigner.inputs.time_repetition = tr
workflow_connections.extend([
(f_swapdim, realigner, [('out_file', 'in_file')]),
])
f_antsintroduction = pe.Node(interface=antslegacy.antsIntroduction(),
name='ants_introduction')
f_antsintroduction.inputs.dimension = 3
f_antsintroduction.inputs.reference_image = template
#will need updating to `1`
f_antsintroduction.inputs.bias_field_correction = True
f_antsintroduction.inputs.transformation_model = 'GR'
f_antsintroduction.inputs.max_iterations = [8, 15, 8]
f_warp = pe.Node(interface=ants.WarpTimeSeriesImageMultiTransform(),
name='f_warp')
f_warp.inputs.reference_image = template
f_warp.inputs.dimension = 4
f_copysform2qform = pe.Node(interface=FSLOrient(),
name='f_copysform2qform')
f_copysform2qform.inputs.main_option = 'copysform2qform'
warp_merge = pe.Node(util.Merge(2), name='warp_merge')
workflow_connections.extend([
(f_bet, f_antsintroduction, [('out_file', 'input_image')]),
(f_antsintroduction, warp_merge, [('warp_field', 'in1')]),
(f_antsintroduction, warp_merge, [('affine_transformation', 'in2')]),
(warp_merge, f_warp, [('out', 'transformation_series')]),
(f_warp, f_copysform2qform, [('output_image', 'in_file')]),
])
if realign == "space":
workflow_connections.extend([
(realigner, temporal_mean, [('realigned_files', 'in_file')]),
(realigner, f_warp, [('realigned_files', 'input_image')]),
])
elif realign == "spacetime":
workflow_connections.extend([
(realigner, temporal_mean, [('out_file', 'in_file')]),
(realigner, f_warp, [('out_file', 'input_image')]),
])
elif realign == "time":
workflow_connections.extend([
(realigner, temporal_mean, [('slice_time_corrected_file',
'in_file')]),
(realigner, f_warp, [('slice_time_corrected_file', 'input_image')
]),
])
else:
workflow_connections.extend([
(f_resize, temporal_mean, [('out_file', 'in_file')]),
(f_swapdim, f_warp, [('out_file', 'input_image')]),
])
if functional_blur_xy:
blur = pe.Node(interface=afni.preprocess.BlurToFWHM(), name="blur")
blur.inputs.fwhmxy = functional_blur_xy
workflow_connections.extend([
(get_f_scan, blur, [('nii_name', 'out_file')]),
(f_copysform2qform, blur, [('out_file', 'in_file')]),
(blur, datasink, [('out_file', 'func')]),
])
else:
f_rename = pe.Node(util.Rename(), name='f_rename')
workflow_connections.extend([
(get_f_scan, f_rename, [('nii_name', 'format_string')]),
(f_copysform2qform, f_rename, [('out_file', 'in_file')]),
(f_rename, datasink, [('out_file', 'func')]),
])
workflow_config = {
'execution': {
'crashdump_dir': path.join(out_base, 'crashdump'),
}
}
if debug:
workflow_config['logging'] = {
'workflow_level': 'DEBUG',
'utils_level': 'DEBUG',
'interface_level': 'DEBUG',
'filemanip_level': 'DEBUG',
'log_to_file': 'true',
}
workdir_name = workflow_name + "_work"
#this gives the name of the workdir, the output name is passed to the datasink
workflow = pe.Workflow(name=workdir_name)
workflow.connect(workflow_connections)
workflow.base_dir = out_base
workflow.config = workflow_config
try:
workflow.write_graph(dotfilename=path.join(workflow.base_dir,
workdir_name, "graph.dot"),
graph2use="hierarchical",
format="png")
except OSError:
print(
'We could not write the DOT file for visualization (`dot` function from the graphviz package). This is non-critical to the processing, but you should get this fixed.'
)
workflow.run(plugin="MultiProc", plugin_args={'n_procs': n_jobs})
copy_bids_files(bids_base, os.path.join(out_base, workflow_name))
if not keep_work:
workdir = path.join(workflow.base_dir, workdir_name)
try:
shutil.rmtree(workdir)
except OSError as e:
if str(e) == 'Cannot call rmtree on a symbolic link':
print(
'Not deleting top level workdir (`{}`), as it is a symlink. Deleting only contents instead'
.format(workdir))
for file_object in os.listdir(workdir):
file_object_path = os.path.join(workdir, file_object)
if os.path.isfile(file_object_path):
os.unlink(file_object_path)
else:
shutil.rmtree(file_object_path)
else:
raise OSError(str(e))
def init_intramodal_template_wf(inputs_list, t1w_source_file, reportlets_dir, transform="Rigid",
num_iterations=2, mem_gb=3, omp_nthreads=1,
name="intramodal_template_wf"):
"""Create an unbiased intramodal template for a subject. This aligns the b=0 references
from all the scans of a subject. Can be rigid, affine or nonlinear (BSplineSyN).
**Parameters**
inputs_list: list of inputs
List if identifiers for the input b=0 images.
transform: 'Rigid', 'Affine', 'BSplineSyN'
Which transform to ultimately use. If 'BSplineSyN', first 2 iterations of Affine will
be run.
num_iterations: int
Default: 2.
**Inputs**
[workflow_name]_image...
One input for each input image. There is no input called inputs_list
t1w_image
**Outputs**
[workflow_name]_transform
transform files to the intramodal template
intramodal_template_to_t1w_transform
Transform from the b0
"""
workflow = Workflow(name=name)
input_names = [name.replace('-', '_') + '_b0_template' for name in inputs_list]
output_names = [name.replace('-', '_') + '_transform' for name in inputs_list]
inputnode = pe.Node(
niu.IdentityInterface(
fields=input_names + [
't1_brain', 't1_preproc', 't1_mask', 't1_seg', 'subjects_dir', 'subject_id',
't1_aseg', 't1_aparc', 't1_tpms', 't1_2_mni_forward_transform',
'dwi_sampling_grid', 't1_2_fsnative_forward_transform',
't1_2_fsnative_reverse_transform', 't1_2_mni_reverse_transform']),
name='inputnode')
merge_inputs = pe.Node(niu.Merge(len(input_names)), name='merge_inputs')
rename_inputs = pe.MapNode(
niu.Rename(keep_ext=True),
iterfield=['in_file', 'format_string'],
name='rename_inputs')
rename_inputs.inputs.format_string = input_names
rename_inputs.synchronize = True
for input_num, input_name in enumerate(input_names):
workflow.connect(inputnode, input_name, merge_inputs, 'in%d' % (input_num + 1))
outputnode = pe.Node(
niu.IdentityInterface(
fields=output_names + ["intramodal_template",
"intramodal_template_mask",
"intramodal_template_to_t1_affine",
"intramodal_template_to_t1_warp"]),
name='outputnode')
split_outputs = pe.Node(niu.Split(splits=[1] * len(input_names), squeeze=True),
name='split_outputs')
for output_num, output_name in enumerate(output_names):
workflow.connect(split_outputs, 'out%d' % (output_num + 1), outputnode, output_name)
runtime_opts = {'num_cores': 1, 'parallel_control': 0}
if omp_nthreads > 1:
runtime_opts = {'num_cores': omp_nthreads, 'parallel_control': 2}
ants_mvtc2 = pe.Node(MultivariateTemplateConstruction2(dimension=3, **runtime_opts),
name='ants_mvtc2')
intramodal_template_mask = init_skullstrip_b0_wf(name="intramodal_template_mask")
workflow.connect([
(merge_inputs, rename_inputs, [('out', 'in_file')]),
(rename_inputs, ants_mvtc2, [('out_file', 'input_images')]),
(intramodal_template_mask, outputnode, [
('outputnode.mask_file', 'intramodal_template_mask')]),
(ants_mvtc2, intramodal_template_mask, [
('templates', 'inputnode.in_file')]),
(ants_mvtc2, split_outputs, [
('forward_transforms', 'inlist')]),
(ants_mvtc2, outputnode, [
('templates', 'intramodal_template')])
])
# calculate dwi registration to T1w
b0_coreg_wf = init_b0_to_anat_registration_wf(omp_nthreads=omp_nthreads,
mem_gb=mem_gb,
write_report=True)
workflow.connect([
(inputnode, b0_coreg_wf, [
('t1_brain', 'inputnode.t1_brain'),
('t1_seg', 'inputnode.t1_seg'),
('subjects_dir', 'inputnode.subjects_dir'),
('subject_id', 'inputnode.subject_id'),
('t1_2_fsnative_reverse_transform',
'inputnode.t1_2_fsnative_reverse_transform')]),
(ants_mvtc2, b0_coreg_wf, [
('templates', 'inputnode.ref_b0_brain')]),
(b0_coreg_wf, outputnode, [
('outputnode.itk_b0_to_t1', 'intramodal_template_to_t1_affine')])
])
# Fill-in datasinks of reportlets seen so far
for node in workflow.list_node_names():
if node.split('.')[-1].startswith('ds_report'):
workflow.get_node(node).inputs.base_directory = reportlets_dir
workflow.get_node(node).inputs.source_file = t1w_source_file
return workflow
def create_network_masks_workflow(name="network_masks", smm_threshold=0.5):
network_masks = Workflow(name=name)
# Input node
inputspec = Node(utility.IdentityInterface(fields=['actmaps', 'networks']),
name='inputspec')
# Binarise results
actmaps2binmasks = MapNode(
fsl.ImageMaths(op_string='-thr {0} -bin'.format(smm_threshold)),
iterfield=['in_file'],
name='actmaps2binmasks')
# Split main masks from exclusive masks
mainmasks = Node(SplitMaps(), name='mainmasks')
# Combine exclusive masks
exclusivemasks = MapNode(fsl.ImageMaths(),
iterfield=['in_file', 'op_string'],
name='exclusivemasks')
# Rename main masks
mainmasks_rename = MapNode(utility.Rename(),
iterfield=['in_file', 'format_string'],
name='mainmasks_rename')
mainmasks_rename.inputs.keep_ext = True
# Rename exclusive masks
exclusivemasks_rename = MapNode(utility.Rename(),
iterfield=['in_file', 'format_string'],
name='exclusivemasks_rename')
exclusivemasks_rename.inputs.keep_ext = True
# Output Node
outputspec = Node(
utility.IdentityInterface(fields=['main_masks', 'exclusive_masks']),
name='outputspec')
# Helper functions
def get_names(x):
return [y['name'] for y in x]
network_masks.connect(inputspec, 'actmaps', actmaps2binmasks, 'in_file')
network_masks.connect(actmaps2binmasks, 'out_file', mainmasks, 'in_files')
network_masks.connect(inputspec, 'networks', mainmasks, 'in_networks')
network_masks.connect(mainmasks, 'out_mains', mainmasks_rename, 'in_file')
network_masks.connect(inputspec, ('networks', get_names), mainmasks_rename,
'format_string')
network_masks.connect(mainmasks, 'out_firsts', exclusivemasks, 'in_file')
network_masks.connect(mainmasks, 'out_opstrings', exclusivemasks,
'op_string')
network_masks.connect(exclusivemasks, 'out_file', exclusivemasks_rename,
'in_file')
network_masks.connect(inputspec, ('networks', get_names),
exclusivemasks_rename, 'format_string')
network_masks.connect(mainmasks_rename, 'out_file', outputspec,
'main_masks')
network_masks.connect(exclusivemasks_rename, 'out_file', outputspec,
'exclusive_masks')
return network_masks
'inlist')
def chooseindex(roi):
return {
'isotropic_voxel': list(range(0, 4)),
'anisotropic_voxel': list(range(4, 8)),
'isotropic_surface': list(range(8, 12))
}[roi]
preprocessing.connect(iter_smoothing_method, ("smoothing_method", chooseindex),
select_smoothed_files, 'index')
rename = pe.MapNode(
util.Rename(format_string="%(orig)s"),
name="rename",
iterfield=['in_file'])
rename.inputs.parse_string = "(?P<orig>.*)"
preprocessing.connect(select_smoothed_files, 'out', rename, 'in_file')
specify_model = pe.Node(interface=model.SpecifyModel(), name="specify_model")
specify_model.inputs.input_units = 'secs'
specify_model.inputs.time_repetition = 3.
specify_model.inputs.high_pass_filter_cutoff = 120
specify_model.inputs.subject_info = [
Bunch(
conditions=['Task-Odd', 'Task-Even'],
onsets=[list(range(15, 240, 60)),
list(range(45, 240, 60))],
def create_reg_and_label_wf(name="reg_wf", manual_seg_rois=False):
inputfields = [
"subject_id", "aparc_aseg", "fa", "wm_mask", "termination_mask"
]
if manual_seg_rois:
inputfields.append("manual_seg_rois")
inputnode = pe.Node(interface=util.IdentityInterface(fields=inputfields),
name="inputnode")
outputnode = pe.Node(interface=util.IdentityInterface(fields=[
"dwi_to_t1_matrix", "t1_to_dwi_matrix", "rois_to_dwi", "rois",
"wmmask_to_dwi", "termmask_to_dwi", "highres_t1_to_dwi_matrix"
]),
name="outputnode")
dmn_labels_if = util.Function(input_names=["in_file", "out_filename"],
output_names=["out_file"],
function=dmn_labels_combined)
dmn_labelling = pe.Node(interface=dmn_labels_if, name='dmn_labelling')
align_wmmask_to_dwi = coreg_without_resample("align_wmmask_to_fa")
align_wmmask_to_dwi.inputs.inputnode.interp = "nearestneighbour"
rois_to_dwi = pe.Node(interface=fsl.ApplyXfm(), name='rois_to_dwi')
rois_to_dwi.inputs.interp = "nearestneighbour"
threshold_fa = pe.Node(interface=fsl.ImageMaths(), name='threshold_fa')
threshold_fa.inputs.op_string = "-thr 0.2 -bin"
multiply_rois_by_termmask = pe.Node(interface=fsl.MultiImageMaths(),
name='multiply_rois_by_termmask')
multiply_rois_by_termmask.inputs.op_string = "-mul %s"
termmask_to_dwi = rois_to_dwi.clone("termmask_to_dwi")
invertxfm = pe.Node(interface=fsl.ConvertXFM(), name='invertxfm')
invertxfm.inputs.invert_xfm = True
'''
Define renaming nodes
'''
rename_t1_to_dwi_mat = pe.Node(
interface=util.Rename(format_string="%(subject_id)s_t1_to_dwi_matrix"),
name='rename_t1_to_dwi_mat')
rename_t1_to_dwi_mat.inputs.keep_ext = True
rename_dwi_to_t1_mat = pe.Node(
interface=util.Rename(format_string="%(subject_id)s_dwi_to_t1_matrix"),
name='rename_dwi_to_t1_mat')
rename_dwi_to_t1_mat.inputs.keep_ext = True
rename_rois_dwi = pe.Node(
interface=util.Rename(format_string="%(subject_id)s_rois_dwi"),
name='rename_rois_dwi')
rename_rois_dwi.inputs.keep_ext = True
rename_rois = pe.Node(
interface=util.Rename(format_string="%(subject_id)s_rois"),
name='rename_rois')
rename_rois.inputs.keep_ext = True
rename_termmask_dwi = pe.Node(
interface=util.Rename(format_string="%(subject_id)s_term_mask_dwi"),
name='rename_termmask_dwi')
rename_termmask_dwi.inputs.keep_ext = True
rename_wmmask_dwi = pe.Node(
interface=util.Rename(format_string="%(subject_id)s_wm_mask_dwi"),
name='rename_wmmask_dwi')
rename_wmmask_dwi.inputs.keep_ext = True
rename_highres_matrix_file = pe.Node(interface=util.Rename(
format_string="%(subject_id)s_t1_to_dwi_NoResample"),
name='rename_highres_matrix_file')
rename_highres_matrix_file.inputs.keep_ext = True
workflow = pe.Workflow(name=name)
workflow.connect([(inputnode, align_wmmask_to_dwi,
[("wm_mask", "inputnode.moving_image")])])
workflow.connect([(inputnode, threshold_fa, [("fa", "in_file")])])
workflow.connect([(threshold_fa, align_wmmask_to_dwi,
[("out_file", "inputnode.fixed_image")])])
if manual_seg_rois:
workflow.connect([(inputnode, rois_to_dwi, [("manual_seg_rois",
"in_file")])])
workflow.connect([(inputnode, rois_to_dwi, [("manual_seg_rois",
"reference")])])
workflow.connect([(inputnode, outputnode, [("manual_seg_rois", "rois")
])])
else:
workflow.connect([(inputnode, dmn_labelling, [
(('subject_id', add_subj_name_to_rois), 'out_filename')
])])
workflow.connect([(inputnode, dmn_labelling, [("aparc_aseg", "in_file")
])])
workflow.connect([(dmn_labelling, multiply_rois_by_termmask,
[("out_file", "in_file")])])
workflow.connect([(inputnode, multiply_rois_by_termmask,
[("termination_mask", "operand_files")])])
workflow.connect([(multiply_rois_by_termmask, rename_rois,
[("out_file", "in_file")])])
workflow.connect([(inputnode, rename_rois, [("subject_id",
"subject_id")])])
workflow.connect([(rename_rois, rois_to_dwi, [("out_file", "in_file")])
])
workflow.connect([(rename_rois, rois_to_dwi, [("out_file", "reference")
])])
workflow.connect([(rename_rois, outputnode, [("out_file", "rois")])])
workflow.connect([(align_wmmask_to_dwi, rois_to_dwi, [
("outputnode.highres_matrix_file", "in_matrix_file")
])])
workflow.connect([(inputnode, termmask_to_dwi, [("termination_mask",
"in_file")])])
workflow.connect([(inputnode, termmask_to_dwi, [("termination_mask",
"reference")])])
workflow.connect([(align_wmmask_to_dwi, termmask_to_dwi, [
("outputnode.highres_matrix_file", "in_matrix_file")
])])
workflow.connect([(align_wmmask_to_dwi, invertxfm,
[("outputnode.lowres_matrix_file", "in_file")])])
workflow.connect([(inputnode, rename_t1_to_dwi_mat, [("subject_id",
"subject_id")])])
workflow.connect([(align_wmmask_to_dwi, rename_t1_to_dwi_mat,
[("outputnode.lowres_matrix_file", "in_file")])])
workflow.connect([(rename_t1_to_dwi_mat, outputnode,
[("out_file", "t1_to_dwi_matrix")])])
workflow.connect([(inputnode, rename_dwi_to_t1_mat, [("subject_id",
"subject_id")])])
workflow.connect([(invertxfm, rename_dwi_to_t1_mat, [("out_file",
"in_file")])])
workflow.connect([(rename_dwi_to_t1_mat, outputnode,
[("out_file", "dwi_to_t1_matrix")])])
workflow.connect([(inputnode, rename_rois_dwi, [("subject_id",
"subject_id")])])
workflow.connect([(rois_to_dwi, rename_rois_dwi, [("out_file", "in_file")])
])
workflow.connect([(rename_rois_dwi, outputnode, [("out_file",
"rois_to_dwi")])])
workflow.connect([(inputnode, rename_termmask_dwi, [("subject_id",
"subject_id")])])
workflow.connect([(termmask_to_dwi, rename_termmask_dwi, [("out_file",
"in_file")])])
workflow.connect([(rename_termmask_dwi, outputnode,
[("out_file", "termmask_to_dwi")])])
workflow.connect([(inputnode, rename_wmmask_dwi, [("subject_id",
"subject_id")])])
workflow.connect([(align_wmmask_to_dwi, rename_wmmask_dwi,
[("outputnode.out_file", "in_file")])])
workflow.connect([(rename_wmmask_dwi, outputnode, [("out_file",
"wmmask_to_dwi")])])
workflow.connect([(inputnode, rename_highres_matrix_file,
[("subject_id", "subject_id")])])
workflow.connect([(align_wmmask_to_dwi, rename_highres_matrix_file,
[("outputnode.highres_matrix_file", "in_file")])])
workflow.connect([(rename_highres_matrix_file, outputnode,
[("out_file", "highres_t1_to_dwi_matrix")])])
return workflow
def init_asl_surf_wf(mem_gb,
surface_spaces,
medial_surface_nan,
name='asl_surf_wf'):
"""
Sample functional images to FreeSurfer surfaces.
For each vertex, the cortical ribbon is sampled at six points (spaced 20% of thickness apart)
and averaged.
Outputs are in GIFTI format.
Workflow Graph
.. workflow::
:graph2use: colored
:simple_form: yes
from aslprep.workflows.asl import init_asl_surf_wf
wf = init_asl_surf_wf(mem_gb=0.1,
surface_spaces=['fsnative', 'fsaverage5'],
medial_surface_nan=False)
Parameters
----------
surface_spaces : :obj:`list`
List of FreeSurfer surface-spaces (either ``fsaverage{3,4,5,6,}`` or ``fsnative``)
the functional images are to be resampled to.
For ``fsnative``, images will be resampled to the individual subject's
native surface.
medial_surface_nan : :obj:`bool`
Replace medial wall values with NaNs on functional GIFTI files
Inputs
------
source_file
Motion-corrected ASL series in T1 space
t1w_preproc
Bias-corrected structural template image
subjects_dir
FreeSurfer SUBJECTS_DIR
subject_id
FreeSurfer subject ID
t1w2fsnative_xfm
LTA-style affine matrix translating from T1w to FreeSurfer-conformed subject space
Outputs
-------
surfaces
ASL series, resampled to FreeSurfer surfaces
"""
from nipype.interfaces.io import FreeSurferSource
from ...niworkflows.engine.workflows import LiterateWorkflow as Workflow
from ...niworkflows.interfaces.surf import GiftiSetAnatomicalStructure
workflow = Workflow(name=name)
workflow.__desc__ = """\
The ASL time-series were resampled onto the following surfaces
(FreeSurfer reconstruction nomenclature):
{out_spaces}.
""".format(out_spaces=', '.join(['*%s*' % s for s in surface_spaces]))
inputnode = pe.Node(niu.IdentityInterface(fields=[
'source_file', 'subject_id', 'subjects_dir', 't1w2fsnative_xfm'
]),
name='inputnode')
itersource = pe.Node(niu.IdentityInterface(fields=['target']),
name='itersource')
itersource.iterables = [('target', surface_spaces)]
get_fsnative = pe.Node(FreeSurferSource(),
name='get_fsnative',
run_without_submitting=True)
def select_target(subject_id, space):
"""Get the target subject ID, given a source subject ID and a target space."""
return subject_id if space == 'fsnative' else space
targets = pe.Node(niu.Function(function=select_target),
name='targets',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
# Rename the source file to the output space to simplify naming later
rename_src = pe.Node(niu.Rename(format_string='%(subject)s',
keep_ext=True),
name='rename_src',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
itk2lta = pe.Node(niu.Function(function=_itk2lta),
name="itk2lta",
run_without_submitting=True)
sampler = pe.MapNode(fs.SampleToSurface(
cortex_mask=True,
interp_method='trilinear',
out_type='gii',
override_reg_subj=True,
sampling_method='average',
sampling_range=(0, 1, 0.2),
sampling_units='frac',
),
iterfield=['hemi'],
name='sampler',
mem_gb=mem_gb * 3)
sampler.inputs.hemi = ['lh', 'rh']
update_metadata = pe.MapNode(GiftiSetAnatomicalStructure(),
iterfield=['in_file'],
name='update_metadata',
mem_gb=DEFAULT_MEMORY_MIN_GB)
outputnode = pe.JoinNode(
niu.IdentityInterface(fields=['surfaces', 'target']),
joinsource='itersource',
name='outputnode')
workflow.connect([
(inputnode, get_fsnative, [('subject_id', 'subject_id'),
('subjects_dir', 'subjects_dir')]),
(inputnode, targets, [('subject_id', 'subject_id')]),
(inputnode, rename_src, [('source_file', 'in_file')]),
(inputnode, itk2lta, [('source_file', 'src_file'),
('t1w2fsnative_xfm', 'in_file')]),
(get_fsnative, itk2lta, [('T1', 'dst_file')]),
(inputnode, sampler, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(itersource, targets, [('target', 'space')]),
(itersource, rename_src, [('target', 'subject')]),
(itk2lta, sampler, [('out', 'reg_file')]),
(targets, sampler, [('out', 'target_subject')]),
(rename_src, sampler, [('out_file', 'source_file')]),
(update_metadata, outputnode, [('out_file', 'surfaces')]),
(itersource, outputnode, [('target', 'target')]),
])
if not medial_surface_nan:
workflow.connect(sampler, 'out_file', update_metadata, 'in_file')
return workflow
from ...niworkflows.interfaces.freesurfer import MedialNaNs
# Refine if medial vertices should be NaNs
medial_nans = pe.MapNode(MedialNaNs(),
iterfield=['in_file'],
name='medial_nans',
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, medial_nans, [('subjects_dir', 'subjects_dir')]),
(sampler, medial_nans, [('out_file', 'in_file')]),
(medial_nans, update_metadata, [('out_file', 'in_file')]),
])
return workflow
def create_workflow(self, flow, inputnode, outputnode):
if self.config.seg_tool == "Freesurfer":
if self.config.use_existing_freesurfer_data is False:
# Converting to .mgz format
fs_mriconvert = pe.Node(interface=fs.MRIConvert(
out_type="mgz", out_file="T1.mgz"),
name="mgzConvert")
if self.config.make_isotropic:
fs_mriconvert.inputs.vox_size = (
self.config.isotropic_vox_size,
self.config.isotropic_vox_size,
self.config.isotropic_vox_size)
fs_mriconvert.inputs.resample_type = self.config.isotropic_interpolation
rename = pe.Node(util.Rename(), name='copyOrig')
orig_dir = os.path.join(self.config.freesurfer_subject_id,
"mri", "orig")
if not os.path.exists(orig_dir):
os.makedirs(orig_dir)
print("INFO : Folder not existing; %s created!" % orig_dir)
rename.inputs.format_string = os.path.join(orig_dir, "001.mgz")
if self.config.brain_mask_extraction_tool == "Freesurfer":
# ReconAll => named outputnode as we don't want to select a specific output....
fs_reconall = pe.Node(interface=fs.ReconAll(
flags='-no-isrunning -parallel -openmp {}'.format(
self.config.number_of_threads)),
name='reconall')
fs_reconall.inputs.directive = 'all'
fs_reconall.inputs.args = self.config.freesurfer_args
# fs_reconall.inputs.subjects_dir and fs_reconall.inputs.subject_id set in cmp/pipelines/diffusion/diffusion.py
fs_reconall.inputs.subjects_dir = self.config.freesurfer_subjects_dir
# fs_reconall.inputs.hippocampal_subfields_T1 = self.config.segment_hippocampal_subfields
# fs_reconall.inputs.brainstem = self.config.segment_brainstem
def isavailable(file):
# print "T1 is available"
return file
flow.connect([
(inputnode, fs_mriconvert, [(('T1', isavailable),
'in_file')]),
(fs_mriconvert, rename, [('out_file', 'in_file')]),
(rename, fs_reconall,
[(("out_file", extract_base_directory), "subject_id")
]),
(fs_reconall, outputnode,
[('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
])
else:
# ReconAll => named outputnode as we don't want to select a specific output....
fs_autorecon1 = pe.Node(interface=fs.ReconAll(
flags='-no-isrunning -parallel -openmp {}'.format(
self.config.number_of_threads)),
name='autorecon1')
fs_autorecon1.inputs.directive = 'autorecon1'
# if self.config.brain_mask_extraction_tool == "Custom" or self.config.brain_mask_extraction_tool == "ANTs":
if self.config.brain_mask_extraction_tool == "ANTs":
fs_autorecon1.inputs.flags = '-no-isrunning -noskullstrip -parallel -openmp {}'.format(
self.config.number_of_threads)
fs_autorecon1.inputs.args = self.config.freesurfer_args
# fs_reconall.inputs.subjects_dir and fs_reconall.inputs.subject_id set in cmp/pipelines/diffusion/diffusion.py
fs_autorecon1.inputs.subjects_dir = self.config.freesurfer_subjects_dir
def isavailable(file):
# print "Is available"
return file
flow.connect([(inputnode, fs_mriconvert, [
(('T1', isavailable), 'in_file')
]), (fs_mriconvert, rename, [('out_file', 'in_file')]),
(rename, fs_autorecon1,
[(("out_file", extract_base_directory),
"subject_id")])])
fs_source = pe.Node(interface=FreeSurferSource(),
name='fsSource')
fs_mriconvert_nu = pe.Node(interface=fs.MRIConvert(
out_type="niigz", out_file="nu.nii.gz"),
name='niigzConvert')
flow.connect([(fs_autorecon1, fs_source, [
('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')
]), (fs_source, fs_mriconvert_nu, [('nu', 'in_file')])])
fs_mriconvert_brainmask = pe.Node(
interface=fs.MRIConvert(out_type="mgz",
out_file="brainmask.mgz"),
name='fsMriconvertBETbrainmask')
if self.config.brain_mask_extraction_tool == "BET":
fsl_bet = pe.Node(interface=fsl.BET(
out_file='brain.nii.gz',
mask=True,
skull=True,
robust=True),
name='fsl_bet')
flow.connect([(fs_mriconvert_nu, fsl_bet,
[('out_file', 'in_file')]),
(fsl_bet, fs_mriconvert_brainmask,
[('out_file', 'in_file')])])
elif self.config.brain_mask_extraction_tool == "ANTs":
# templatefile =
# pkg_resources.resource_filename('cmtklib', os.path.join('data', 'segmentation',
# 'ants_template_IXI', 'T_template2_BrainCerebellum.nii.gz'))
# probmaskfile = pkg_resources.resource_filename('cmtklib',
# os.path.join('data', 'segmentation', 'ants_template_IXI',
# 'T_template_BrainCerebellumProbabilityMask.nii.gz'))
ants_bet = pe.Node(interface=ants.BrainExtraction(
out_prefix='ants_bet_'),
name='antsBET')
ants_bet.inputs.brain_template = self.config.ants_templatefile
ants_bet.inputs.brain_probability_mask = self.config.ants_probmaskfile
ants_bet.inputs.extraction_registration_mask = self.config.ants_regmaskfile
ants_bet.inputs.num_threads = self.config.number_of_threads
flow.connect([(fs_mriconvert_nu, ants_bet,
[('out_file', 'anatomical_image')]),
(ants_bet, fs_mriconvert_brainmask,
[('BrainExtractionBrain', 'in_file')])])
# elif self.config.brain_mask_extraction_tool == "Custom":
# fs_mriconvert_brainmask.inputs.in_file = os.path.abspath(
# self.config.brain_mask_path)
# copy_brainmask_to_fs = pe.Node(interface=copyFileToFreesurfer(),name='copy_brainmask_to_fs')
# copy_brainmask_to_fs.inputs.out_file =
# os.path.join(self.config.freesurfer_subject_id,"mri","brainmask.mgz")
# copy_brainmaskauto_to_fs = pe.Node(interface=copyFileToFreesurfer(),name='copy_brainmaskauto_to_fs')
# copy_brainmaskauto_to_fs.inputs.out_file =
# os.path.join(self.config.freesurfer_subject_id,"mri","brainmask.auto.mgz")
# flow.connect([
# (fs_mriconvert_brainmask,copy_brainmask_to_fs,[('out_file','in_file')]),
# (fs_mriconvert_brainmask,copy_brainmaskauto_to_fs,[('out_file','in_file')])
# ])
copy_brainmask_to_fs = pe.Node(
interface=copyBrainMaskToFreesurfer(),
name='copyBrainmaskTofs')
flow.connect([(rename, copy_brainmask_to_fs, [
(("out_file", extract_base_directory), "subject_dir")
]),
(fs_mriconvert_brainmask,
copy_brainmask_to_fs, [('out_file',
'in_file')])])
# flow.connect([
# (fs_source,fs_mriconvert_nu,[('nu','in_file')])
# ])
def get_freesurfer_subject_id(file):
# print("Extract reconall base dir : %s" % file[:-18])
return file[:-18]
fs_reconall23 = pe.Node(interface=fs.ReconAll(
flags='-no-isrunning -parallel -openmp {}'.format(
self.config.number_of_threads)),
name='reconall23')
fs_reconall23.inputs.directive = 'autorecon2'
fs_reconall23.inputs.args = self.config.freesurfer_args
fs_reconall23.inputs.flags = '-autorecon3'
# fs_reconall.inputs.subjects_dir and fs_reconall.inputs.subject_id set in cmp/pipelines/diffusion/diffusion.py
fs_reconall23.inputs.subjects_dir = self.config.freesurfer_subjects_dir
# fs_reconall.inputs.hippocampal_subfields_T1 = self.config.segment_hippocampal_subfields
# fs_reconall.inputs.brainstem = self.config.segment_brainstem
flow.connect([(copy_brainmask_to_fs, fs_reconall23, [
(("out_brainmask_file", get_freesurfer_subject_id),
"subject_id")
]),
(fs_reconall23, outputnode,
[('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')])])
else:
outputnode.inputs.subjects_dir = self.config.freesurfer_subjects_dir
outputnode.inputs.subject_id = self.config.freesurfer_subject_id
name='level1design')
level1design.inputs.timing_units = modelspec.inputs.output_units
level1design.inputs.interscan_interval = modelspec.inputs.time_repetition
level1design.inputs.bases = {'hrf': {'derivs': [0, 0]}}
level1design.inputs.model_serial_correlations = 'AR(1)'
workflow.connect(modelspec, 'session_info', level1design, 'session_info')
workflow.connect(mean_mask, 'out_file', level1design, 'mask_image')
# plot the design matrix
plot_design_matrix = pe.Node(niu.Function(input_names=['mat_file'],
output_names=['out_file'],
function=_plot_design_matrix),
name='plot_design_matrix')
workflow.connect(level1design, 'spm_mat_file', plot_design_matrix, 'mat_file')
plot_design_matrix_rename = pe.Node(
niu.Rename(format_string='%(subject_id)s_task-olfactoryperception_bold_dmatrix.png'),
name='plot_design_matrix_rename')
workflow.connect(plot_design_matrix, 'out_file', plot_design_matrix_rename, 'in_file')
workflow.connect(infosource, 'subject_id', plot_design_matrix_rename, 'subject_id')
workflow.connect(plot_design_matrix_rename, 'out_file', datasink, 'report.@dmatrix')
# plot the correlation between regressors
plot_regressors_correlation = pe.Node(niu.Function(input_names=['mat_file'],
output_names=['out_file'],
function=_plot_regressors_correlation),
name='plot_regressors_correlation')
workflow.connect(level1design, 'spm_mat_file', plot_regressors_correlation, 'mat_file')
plot_regressors_correlation_rename = pe.Node(
niu.Rename(format_string='%(subject_id)s_task_olfactory_perception_bold_ccmatrix.png'),
name='plot_regressors_correlation_rename')
workflow.connect(plot_regressors_correlation, 'out_file', plot_regressors_correlation_rename, 'in_file')
def create_fsl_flame_wf(ftest=False, wf_name='groupAnalysis'):
"""
FSL `FEAT <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEAT>`_
BASED Group Analysis
Parameters
----------
ftest : boolean, optional(default=False)
Ftest help investigate several contrasts at the same time
for example to see whether any of them (or any combination of them) is
significantly non-zero. Also, the F-test allows you to compare the
contribution of each contrast to the model and decide on significant
and non-significant ones
wf_name : string
Workflow name
Returns
-------
grp_analysis : workflow object
Group Analysis workflow object
Notes
-----
`Source <https://github.com/openconnectome/C-PAC/blob/master/CPAC/group_analysis/group_analysis_preproc.py>`_
Workflow Inputs::
inputspec.mat_file : string (existing file)
Mat file containing matrix for design
inputspec.con_file : string (existing file)
Contrast file containing contrast vectors
inputspec.grp_file : string (existing file)
file containing matrix specifying the groups the covariance is split into
inputspec.zmap_files : string (existing nifti file)
derivative or the zmap file for which the group analysis is to be run
inputspec.z_threshold : float
Z Statistic threshold value for cluster thresholding. It is used to
determine what level of activation would be statistically significant.
Increasing this will result in higher estimates of required effect.
inputspec.p_threshold : float
Probability threshold for cluster thresholding.
inputspec.fts_file : string (existing file)
file containing matrix specifying f-contrasts
inputspec.paramerters : string (tuple)
tuple containing which MNI and FSLDIR path information
Workflow Outputs::
outputspec.merged : string (nifti file)
4D volume file after merging all the derivative
files from each specified subject.
outputspec.zstats : list (nifti files)
Z statistic image for each t contrast
outputspec.zfstats : list (nifti files)
Z statistic image for each f contrast
outputspec.fstats : list (nifti files)
F statistic for each contrast
outputspec.cluster_threshold : list (nifti files)
the thresholded Z statistic image for each t contrast
outputspec.cluster_index : list (nifti files)
image of clusters for each t contrast; the values
in the clusters are the index numbers as used
in the cluster list.
outputspec.cluster_localmax_txt : list (text files)
local maxima text file for each t contrast,
defines the coordinates of maximum value in the cluster
outputspec.overlay_threshold : list (nifti files)
3D color rendered stats overlay image for t contrast
After reloading this image, use the Statistics Color
Rendering GUI to reload the color look-up-table
outputspec.overlay_rendered_image : list (nifti files)
2D color rendered stats overlay picture for each t contrast
outputspec.cluster_threshold_zf : list (nifti files)
the thresholded Z statistic image for each f contrast
outputspec.cluster_index_zf : list (nifti files)
image of clusters for each f contrast; the values
in the clusters are the index numbers as used
in the cluster list.
outputspec.cluster_localmax_txt_zf : list (text files)
local maxima text file for each f contrast,
defines the coordinates of maximum value in the cluster
outputspec.overlay_threshold_zf : list (nifti files)
3D color rendered stats overlay image for f contrast
After reloading this image, use the Statistics Color
Rendering GUI to reload the color look-up-table
outputspec.overlay_rendered_image_zf : list (nifti files)
2D color rendered stats overlay picture for each f contrast
Order of commands:
- Merge all the Z-map 3D images into 4D image file. For details see `fslmerge <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Fslutils>`_::
fslmerge -t sub01/sca/seed1/sca_Z_FWHM_merged.nii
sub02/sca/seed1/sca_Z_FWHM.nii.gz ....
merge.nii.gz
arguments
-t : concatenate images in time
- Create mask specific for analysis. For details see `fslmaths <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Fslutils>`_::
fslmaths merged.nii.gz
-abs -Tmin -bin mean_mask.nii.gz
arguments
-Tmin : min across time
-abs : absolute value
-bin : use (current image>0) to binarise
- FSL FLAMEO to perform higher level analysis. For details see `flameo <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEAT>`_::
flameo --copefile = merged.nii.gz --covsplitfile = anova_with_meanFD.grp --designfile = anova_with_meanFD.mat
--fcontrastsfile = anova_with_meanFD.fts --ld=stats --maskfile = mean_mask.nii.gz --runmode=ols
--tcontrastsfile = anova_with_meanFD.con
arguments
--copefile : cope regressor data file
--designfile : design matrix file
--maskfile : mask file
--tcontrastsfile : file containing an ASCII matrix specifying the t contrasts
--fcontrastsfile : file containing an ASCII matrix specifying the f contrasts
--runmode : Interference to perform (mixed effects - OLS)
- Run FSL Easy thresh
Easy thresh is a simple script for carrying out cluster-based thresholding and colour activation overlaying::
easythresh <raw_zstat> <brain_mask> <z_thresh> <prob_thresh> <background_image> <output_root> [--mm]
A seperate workflow called easythresh is called to run easythresh steps.
.. exec::
from CPAC.group_analysis import create_fsl_flame_wf
wf = create_fsl_flame_wf()
wf.write_graph(
graph2use='orig',
dotfilename='./images/generated/group_analysis.dot'
)
High Level Workflow Graph:
.. image:: ../../images/generated/group_analysis.png
:width: 800
Detailed Workflow Graph:
.. image:: ../../images/generated/group_analysis_detailed.png
:width: 800
Examples
--------
>>> from group_analysis_preproc import create_group_analysis
>>> preproc = create_group_analysis()
>>> preproc.inputs.inputspec.mat_file = '../group_models/anova_with_meanFD/anova_with_meanFD.mat'
>>> preproc.inputs.inputspec.con_file = '../group_models/anova_with_meanFD/anova_with_meanFD.con'
>>> preproc.inputs.inputspec.grp_file = '../group_models/anova_with_meanFD/anova_with_meanFD.grp'
>>> preproc.inputs.inputspec.zmap_files = ['subjects/sub01/seeds_rest_Dickstein_DLPFC/sca_Z_FWHM.nii.gz',
'subjects/sub02/seeds_rest_Dickstein_DLPFC/sca_Z_FWHM.nii.gz']
>>> preproc.inputs.inputspec.z_threshold = 2.3
>>> preproc.inputs.inputspec.p_threshold = 0.05
>>> preproc.inputs.inputspec.parameters = ('/usr/local/fsl/', 'MNI152')
>>> preproc.run() -- SKIP doctest
"""
grp_analysis = pe.Workflow(name=wf_name)
inputnode = pe.Node(util.IdentityInterface(fields=[
'merged_file', 'merge_mask', 'mat_file', 'con_file', 'grp_file',
'fts_file', 'z_threshold', 'p_threshold', 'parameters'
]),
name='inputspec')
outputnode = pe.Node(util.IdentityInterface(fields=[
'merged', 'zstats', 'zfstats', 'fstats', 'cluster_threshold',
'cluster_index', 'cluster_localmax_txt', 'overlay_threshold',
'rendered_image', 'cluster_localmax_txt_zf', 'cluster_threshold_zf',
'cluster_index_zf', 'overlay_threshold_zf', 'rendered_image_zf'
]),
name='outputspec')
'''
merge_to_4d = pe.Node(interface=fsl.Merge(),
name='merge_to_4d')
merge_to_4d.inputs.dimension = 't'
### create analysis specific mask
#-Tmin: min across time
# -abs: absolute value
#-bin: use (current image>0) to binarise
merge_mask = pe.Node(interface=fsl.ImageMaths(),
name='merge_mask')
merge_mask.inputs.op_string = '-abs -Tmin -bin'
'''
fsl_flameo = pe.Node(interface=fsl.FLAMEO(), name='fsl_flameo')
fsl_flameo.inputs.run_mode = 'ols'
# rename the FLAME zstat outputs after the contrast string labels for
# easier interpretation
label_zstat_imports = ["import os"]
label_zstat = pe.Node(util.Function(input_names=['zstat_list', 'con_file'],
output_names=['new_zstat_list'],
function=label_zstat_files,
imports=label_zstat_imports),
name='label_zstat')
rename_zstats = pe.MapNode(interface=util.Rename(),
name='rename_zstats',
iterfield=['in_file', 'format_string'])
rename_zstats.inputs.keep_ext = True
# create analysis specific mask
# fslmaths merged.nii.gz -abs -bin -Tmean -mul volume out.nii.gz
# -Tmean: mean across time
# create group_reg file
# this file can provide an idea of how well the subjects
# in our analysis overlay with each other and the MNI brain.
# e.g., maybe there is one subject with limited coverage.
# not attached to sink currently
merge_mean_mask = pe.Node(interface=fsl.ImageMaths(),
name='merge_mean_mask')
# function node to get the operation string for fslmaths command
get_opstring = pe.Node(util.Function(input_names=['in_file'],
output_names=['out_file'],
function=get_operation),
name='get_opstring')
# connections
'''
grp_analysis.connect(inputnode, 'zmap_files',
merge_to_4d, 'in_files')
grp_analysis.connect(merge_to_4d, 'merged_file',
merge_mask, 'in_file')
'''
grp_analysis.connect(inputnode, 'merged_file', fsl_flameo, 'cope_file')
grp_analysis.connect(inputnode, 'merge_mask', fsl_flameo, 'mask_file')
grp_analysis.connect(inputnode, 'mat_file', fsl_flameo, 'design_file')
grp_analysis.connect(inputnode, 'con_file', fsl_flameo, 't_con_file')
grp_analysis.connect(inputnode, 'grp_file', fsl_flameo, 'cov_split_file')
grp_analysis.connect(fsl_flameo, 'zstats', label_zstat, 'zstat_list')
grp_analysis.connect(inputnode, 'con_file', label_zstat, 'con_file')
grp_analysis.connect(fsl_flameo, 'zstats', rename_zstats, 'in_file')
grp_analysis.connect(label_zstat, 'new_zstat_list', rename_zstats,
'format_string')
if ftest:
grp_analysis.connect(inputnode, 'fts_file', fsl_flameo, 'f_con_file')
easy_thresh_zf = easy_thresh('easy_thresh_zf')
grp_analysis.connect(fsl_flameo, 'zfstats', easy_thresh_zf,
'inputspec.z_stats')
grp_analysis.connect(inputnode, 'merge_mask', easy_thresh_zf,
'inputspec.merge_mask')
grp_analysis.connect(inputnode, 'z_threshold', easy_thresh_zf,
'inputspec.z_threshold')
grp_analysis.connect(inputnode, 'p_threshold', easy_thresh_zf,
'inputspec.p_threshold')
grp_analysis.connect(inputnode, 'parameters', easy_thresh_zf,
'inputspec.parameters')
grp_analysis.connect(easy_thresh_zf, 'outputspec.cluster_threshold',
outputnode, 'cluster_threshold_zf')
grp_analysis.connect(easy_thresh_zf, 'outputspec.cluster_index',
outputnode, 'cluster_index_zf')
grp_analysis.connect(easy_thresh_zf, 'outputspec.cluster_localmax_txt',
outputnode, 'cluster_localmax_txt_zf')
grp_analysis.connect(easy_thresh_zf, 'outputspec.overlay_threshold',
outputnode, 'overlay_threshold_zf')
grp_analysis.connect(easy_thresh_zf, 'outputspec.rendered_image',
outputnode, 'rendered_image_zf')
# calling easythresh for zstats files
easy_thresh_z = easy_thresh('easy_thresh_z')
grp_analysis.connect(rename_zstats, 'out_file', easy_thresh_z,
'inputspec.z_stats')
grp_analysis.connect(inputnode, 'merge_mask', easy_thresh_z,
'inputspec.merge_mask')
grp_analysis.connect(inputnode, 'z_threshold', easy_thresh_z,
'inputspec.z_threshold')
grp_analysis.connect(inputnode, 'p_threshold', easy_thresh_z,
'inputspec.p_threshold')
grp_analysis.connect(inputnode, 'parameters', easy_thresh_z,
'inputspec.parameters')
grp_analysis.connect(inputnode, 'merged_file', get_opstring, 'in_file')
grp_analysis.connect(inputnode, 'merged_file', merge_mean_mask, 'in_file')
grp_analysis.connect(get_opstring, 'out_file', merge_mean_mask,
'op_string')
grp_analysis.connect(fsl_flameo, 'zfstats', outputnode, 'zfstats')
grp_analysis.connect(fsl_flameo, 'fstats', outputnode, 'fstats')
grp_analysis.connect(inputnode, 'merged_file', outputnode, 'merged')
grp_analysis.connect(rename_zstats, 'out_file', outputnode, 'zstats')
grp_analysis.connect(easy_thresh_z, 'outputspec.cluster_threshold',
outputnode, 'cluster_threshold')
grp_analysis.connect(easy_thresh_z, 'outputspec.cluster_index', outputnode,
'cluster_index')
grp_analysis.connect(easy_thresh_z, 'outputspec.cluster_localmax_txt',
outputnode, 'cluster_localmax_txt')
grp_analysis.connect(easy_thresh_z, 'outputspec.overlay_threshold',
outputnode, 'overlay_threshold')
grp_analysis.connect(easy_thresh_z, 'outputspec.rendered_image',
outputnode, 'rendered_image')
return grp_analysis
datagrabber.inputs.template_args['dwi'] = [[
'subject_id', ['session_1/DTI_mx_137/*.dcm']
]]
datagrabber.inputs.sort_filelist = True
datagrabber.inputs.raise_on_empty = False
wf.connect(subjects_infosource, "subject_id", datagrabber, "subject_id")
dcm2nii_dwi = pe.Node(Dcm2nii(), name="dcm2nii_dwi")
dcm2nii_dwi.inputs.gzip_output = True
dcm2nii_dwi.inputs.nii_output = True
dcm2nii_dwi.inputs.anonymize = False
dcm2nii_dwi.plugin_args = {'submit_specs': 'request_memory = 2000'}
wf.connect(datagrabber, "dwi", dcm2nii_dwi, "source_names")
dwi_rename = pe.Node(util.Rename(format_string="DTI_mx_137.nii.gz"),
name="dwi_rename")
wf.connect(dcm2nii_dwi, "converted_files", dwi_rename, "in_file")
bvecs_rename = pe.Node(util.Rename(format_string="DTI_mx_137.bvecs"),
name="bvecs_rename")
wf.connect(dcm2nii_dwi, "bvecs", bvecs_rename, "in_file")
bvals_rename = pe.Node(util.Rename(format_string="DTI_mx_137.bvals"),
name="bvals_rename")
wf.connect(dcm2nii_dwi, "bvals", bvals_rename, "in_file")
ds = pe.Node(nio.DataSink(), name="dwi_datasink")
ds.inputs.base_directory = '/scr/kalifornien1/data/nki_enhanced/'
ds.inputs.substitutions = [('_subject_id_', '')]
ds.inputs.regexp_substitutions = [('_others_rename[0-9]*/', '')]
datasource_dartel = pe.MapNode(nio.DataGrabber(infields=['subject_id'],
outfields=['struct']),
name='datasource_dartel',
iterfield=['subject_id'])
datasource_dartel.inputs.template = 'nipype-tutorial/data/%s/%s.nii'
datasource_dartel.inputs.template_args = dict(
struct=[['subject_id', 'struct']])
datasource_dartel.inputs.sort_filelist = True
datasource_dartel.inputs.subject_id = subject_list
"""Here we make sure that struct files have names corresponding to the subject ids.
This way we will be able to pick the right field flows later.
"""
rename_dartel = pe.MapNode(
niu.Rename(format_string="subject_id_%(subject_id)s_struct"),
iterfield=['in_file', 'subject_id'],
name='rename_dartel')
rename_dartel.inputs.subject_id = subject_list
rename_dartel.inputs.keep_ext = True
dartel_workflow = spm_wf.create_DARTEL_template(name='dartel_workflow')
dartel_workflow.inputs.inputspec.template_prefix = "template"
"""This function will allow to pick the right field flow for each subject
"""
def pickFieldFlow(dartel_flow_fields, subject_id):
from nipype.utils.filemanip import split_filename
for f in dartel_flow_fields:
_, name, _ = split_filename(f)
merge_smoothed_files, 'in3')
select_smoothed_files = pe.Node(interface=util.Select(),
name="select_smoothed_files")
preprocessing.connect(merge_smoothed_files, 'out', select_smoothed_files,
'inlist')
def chooseindex(roi):
return {'isotropic_voxel': list(range(0, 4)), 'anisotropic_voxel': list(range(4, 8)),
'isotropic_surface': list(range(8, 12))}[roi]
preprocessing.connect(iter_smoothing_method, ("smoothing_method", chooseindex),
select_smoothed_files, 'index')
rename = pe.MapNode(util.Rename(format_string="%(orig)s"), name="rename",
iterfield=['in_file'])
rename.inputs.parse_string = "(?P<orig>.*)"
preprocessing.connect(select_smoothed_files, 'out', rename, 'in_file')
specify_model = pe.Node(interface=model.SpecifyModel(), name="specify_model")
specify_model.inputs.input_units = 'secs'
specify_model.inputs.time_repetition = 3.
specify_model.inputs.high_pass_filter_cutoff = 120
specify_model.inputs.subject_info = [Bunch(conditions=['Task-Odd', 'Task-Even'],
onsets=[list(range(15, 240, 60)),
list(range(45, 240, 60))],
durations=[[15], [15]])] * 4
level1design = pe.Node(interface=spm.Level1Design(), name="level1design")
def init_bold_surf_wf(mem_gb,
output_spaces,
medial_surface_nan,
name='bold_surf_wf'):
"""
This workflow samples functional images to FreeSurfer surfaces
For each vertex, the cortical ribbon is sampled at six points (spaced 20% of thickness apart)
and averaged.
Outputs are in GIFTI format.
.. workflow::
:graph2use: colored
:simple_form: yes
from fmriprep.workflows.bold import init_bold_surf_wf
wf = init_bold_surf_wf(mem_gb=0.1,
output_spaces=['T1w', 'fsnative',
'template', 'fsaverage5'],
medial_surface_nan=False)
**Parameters**
output_spaces : list
List of output spaces functional images are to be resampled to
Target spaces beginning with ``fs`` will be selected for resampling,
such as ``fsaverage`` or related template spaces
If the list contains ``fsnative``, images will be resampled to the
individual subject's native surface
medial_surface_nan : bool
Replace medial wall values with NaNs on functional GIFTI files
**Inputs**
source_file
Motion-corrected BOLD series in T1 space
t1_preproc
Bias-corrected structural template image
subjects_dir
FreeSurfer SUBJECTS_DIR
subject_id
FreeSurfer subject ID
t1_2_fsnative_forward_transform
LTA-style affine matrix translating from T1w to FreeSurfer-conformed subject space
**Outputs**
surfaces
BOLD series, resampled to FreeSurfer surfaces
"""
# Ensure volumetric spaces do not sneak into this workflow
spaces = [space for space in output_spaces if space.startswith('fs')]
workflow = Workflow(name=name)
if spaces:
workflow.__desc__ = """\
The BOLD time-series, were resampled to surfaces on the following
spaces: {out_spaces}.
""".format(out_spaces=', '.join(['*%s*' % s for s in spaces]))
inputnode = pe.Node(niu.IdentityInterface(fields=[
'source_file', 't1_preproc', 'subject_id', 'subjects_dir',
't1_2_fsnative_forward_transform'
]),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['surfaces']),
name='outputnode')
def select_target(subject_id, space):
""" Given a source subject ID and a target space, get the target subject ID """
return subject_id if space == 'fsnative' else space
targets = pe.MapNode(niu.Function(function=select_target),
iterfield=['space'],
name='targets',
mem_gb=DEFAULT_MEMORY_MIN_GB)
targets.inputs.space = spaces
# Rename the source file to the output space to simplify naming later
rename_src = pe.MapNode(niu.Rename(format_string='%(subject)s',
keep_ext=True),
iterfield='subject',
name='rename_src',
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
rename_src.inputs.subject = spaces
resampling_xfm = pe.Node(LTAConvert(in_lta='identity.nofile',
out_lta=True),
name='resampling_xfm')
set_xfm_source = pe.Node(ConcatenateLTA(out_type='RAS2RAS'),
name='set_xfm_source')
sampler = pe.MapNode(fs.SampleToSurface(sampling_method='average',
sampling_range=(0, 1, 0.2),
sampling_units='frac',
interp_method='trilinear',
cortex_mask=True,
override_reg_subj=True,
out_type='gii'),
iterfield=['source_file', 'target_subject'],
iterables=('hemi', ['lh', 'rh']),
name='sampler',
mem_gb=mem_gb * 3)
medial_nans = pe.MapNode(MedialNaNs(),
iterfield=['in_file', 'target_subject'],
name='medial_nans',
mem_gb=DEFAULT_MEMORY_MIN_GB)
merger = pe.JoinNode(niu.Merge(1, ravel_inputs=True),
name='merger',
joinsource='sampler',
joinfield=['in1'],
run_without_submitting=True,
mem_gb=DEFAULT_MEMORY_MIN_GB)
update_metadata = pe.MapNode(GiftiSetAnatomicalStructure(),
iterfield='in_file',
name='update_metadata',
mem_gb=DEFAULT_MEMORY_MIN_GB)
workflow.connect([
(inputnode, targets, [('subject_id', 'subject_id')]),
(inputnode, rename_src, [('source_file', 'in_file')]),
(inputnode, resampling_xfm, [('source_file', 'source_file'),
('t1_preproc', 'target_file')]),
(inputnode, set_xfm_source, [('t1_2_fsnative_forward_transform',
'in_lta2')]),
(resampling_xfm, set_xfm_source, [('out_lta', 'in_lta1')]),
(inputnode, sampler, [('subjects_dir', 'subjects_dir'),
('subject_id', 'subject_id')]),
(set_xfm_source, sampler, [('out_file', 'reg_file')]),
(targets, sampler, [('out', 'target_subject')]),
(rename_src, sampler, [('out_file', 'source_file')]),
(merger, update_metadata, [('out', 'in_file')]),
(update_metadata, outputnode, [('out_file', 'surfaces')]),
])
if medial_surface_nan:
workflow.connect([
(inputnode, medial_nans, [('subjects_dir', 'subjects_dir')]),
(sampler, medial_nans, [('out_file', 'in_file')]),
(targets, medial_nans, [('out', 'target_subject')]),
(medial_nans, merger, [('out_file', 'in1')]),
])
else:
workflow.connect(sampler, 'out_file', merger, 'in1')
return workflow
def _run_interface(self, runtime):
# Loading required packages
from additional_interfaces import AdditionalDTIMeasures
from additional_interfaces import DipyDenoise
import nipype.interfaces.fsl as fsl
import nipype.interfaces.io as nio
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as util
import os
# ==============================================================
# Processing of diffusion-weighted data
# Extract b0 image
fslroi = pe.Node(interface=fsl.ExtractROI(), name='extract_b0')
fslroi.inputs.in_file = self.inputs.dwi
fslroi.inputs.t_min = 0
fslroi.inputs.t_size = 1
# Create a brain mask
bet = pe.Node(interface=fsl.BET(
frac=0.3, robust=False, mask=True, no_output=False), name='bet')
# Eddy-current and motion correction
eddy = pe.Node(interface=fsl.epi.Eddy(args='-v'), name='eddy')
eddy.inputs.in_acqp = self.inputs.acqparams
eddy.inputs.in_bvec = self.inputs.bvecs
eddy.inputs.in_bval = self.inputs.bvals
eddy.inputs.in_file = self.inputs.dwi
eddy.inputs.in_index = self.inputs.index_file
# Denoising
dwi_denoise = pe.Node(interface=DipyDenoise(), name='dwi_denoise')
dwi_denoise.inputs.in_file = self.inputs.dwi
# Fitting the diffusion tensor model
dtifit = pe.Node(interface=fsl.DTIFit(), name='dtifit')
dtifit.inputs.base_name = self.inputs.subject_id
dtifit.inputs.dwi = self.inputs.dwi
dtifit.inputs.bvecs = self.inputs.bvecs
dtifit.inputs.bvals = self.inputs.bvals
# Getting AD and RD
get_rd = pe.Node(interface=AdditionalDTIMeasures(), name='get_rd')
# DataSink
datasink = pe.Node(interface=nio.DataSink(), name='datasink')
datasink.inputs.parameterization = False
datasink.inputs.base_directory = self.inputs.out_directory + '/_subject_id_' + self.inputs.subject_id + '/dwi_preproc/'
# Renaming the outputs for consistency
AD_rename = pe.Node(interface=util.Rename(keep_ext = True), name='AD_rename')
AD_rename.inputs.format_string = self.inputs.subject_id + '_AD'
b0_rename = pe.Node(interface=util.Rename(keep_ext = True), name='b0_rename')
b0_rename.inputs.format_string = self.inputs.subject_id + '_b0'
dwi_rename = pe.Node(interface=util.Rename(keep_ext = True), name='dwi_rename')
dwi_rename.inputs.format_string = self.inputs.subject_id + '_dwi'
mask_rename = pe.Node(interface=util.Rename(keep_ext = True), name='mask_rename')
mask_rename.inputs.format_string = self.inputs.subject_id + '_mask'
RD_rename = pe.Node(interface=util.Rename(keep_ext = True), name='RD_rename')
RD_rename.inputs.format_string = self.inputs.subject_id + '_RD'
# ==============================================================
# Setting up the workflow
dwi_preproc = pe.Workflow(name='dwi_preproc')
# Diffusion data
# Preprocessing
dwi_preproc.connect(fslroi, 'roi_file', bet, 'in_file')
dwi_preproc.connect(bet, 'mask_file', eddy, 'in_mask')
dwi_preproc.connect(eddy, 'out_corrected', dwi_denoise, 'in_file')
# Calculate diffusion measures
dwi_preproc.connect(dwi_denoise, 'out_file', dtifit, 'dwi')
dwi_preproc.connect(bet, 'mask_file', dtifit, 'mask')
dwi_preproc.connect(dtifit, 'L1', get_rd, 'L1')
dwi_preproc.connect(dtifit, 'L2', get_rd, 'L2')
dwi_preproc.connect(dtifit, 'L3', get_rd, 'L3')
# Renaming same outputs
dwi_preproc.connect(dwi_denoise, 'out_file', dwi_rename, 'in_file')
dwi_preproc.connect(bet, 'out_file', b0_rename, 'in_file')
dwi_preproc.connect(bet, 'mask_file', mask_rename, 'in_file')
dwi_preproc.connect(get_rd, 'AD', AD_rename, 'in_file')
dwi_preproc.connect(get_rd, 'RD', RD_rename, 'in_file')
# Connecting to the datasink
dwi_preproc.connect(dwi_rename, 'out_file', datasink, 'preprocessed.@dwi')
dwi_preproc.connect(b0_rename, 'out_file', datasink, 'preprocessed.@b0')
dwi_preproc.connect(mask_rename, 'out_file', datasink, 'preprocessed.@mask')
dwi_preproc.connect(dtifit, 'FA', datasink, 'preprocessed.@FA')
dwi_preproc.connect(dtifit, 'MD', datasink, 'preprocessed.@MD')
dwi_preproc.connect(AD_rename, 'out_file', datasink, 'preprocessed.@AD')
dwi_preproc.connect(RD_rename, 'out_file', datasink, 'preprocessed.@RD')
# ==============================================================
# Running the workflow
dwi_preproc.base_dir = os.path.abspath(self.inputs.out_directory + '_subject_id_' + self.inputs.subject_id)
dwi_preproc.write_graph()
dwi_preproc.write_graph()
dwi_preproc.run()
return runtime
