def __init__(self, in_files=['path'], **options):
import nipype.interfaces.spm as spm
smooth = spm.Smooth()
smooth.inputs.in_files = in_files
for ef in options:
setattr(smooth.inputs, ef, options[ef])
self.res = smooth.run()
def spm_smooth(infiles, fwhm=8):
startdir = os.getcwd()
basedir = os.path.split(infiles[0])[0]
os.chdir(basedir)
smth = spm.Smooth(matlab_cmd='matlab-spm8')
smth.inputs.in_files = infiles
smth.inputs.fwhm = fwhm
#smth.inputs.ignore_exception = True
sout = smth.run()
os.chdir(startdir)
return sout
def __init__(self, experiment_dir, output_dir, working_dir, func_source,
struct_source, datasink):
self.experiment_dir = experiment_dir
self.output_dir = output_dir
self.working_dir = working_dir
# specify input and output nodes
self.func_source = func_source
self.struct_source = struct_source
self.datasink = datasink
# specify workflow instance
self.workflow = pe.Workflow(name='workflow')
# specify nodes
self.realign = pe.Node(interface=spm.Realign(), name='realign')
self.coregister = pe.Node(interface=spm.Coregister(),
name="coregister")
self.coregister.inputs.jobtype = 'estimate'
self.segment = pe.Node(interface=spm.Segment(), name="segment")
self.normalize_func = pe.Node(interface=spm.Normalize(),
name="normalize_func")
self.normalize_func.inputs.jobtype = "write"
self.normalize_struc = pe.Node(interface=spm.Normalize(),
name="normalize_struc")
self.normalize_struc.inputs.jobtype = "write"
self.smooth = pe.Node(interface=spm.Smooth(), name="smooth")
# connect the nodes to complete the workflow
self.workflow.connect([
(self.func_source, self.realign, [('outfiles', 'in_files')]),
(self.struct_source, self.coregister, [('outfiles', 'source')]),
(self.realign, self.coregister, [('mean_image', 'target')]),
(self.coregister, self.segment, [('coregistered_source', 'data')]),
(self.segment, self.normalize_func, [('transformation_mat',
'parameter_file')]),
(self.realign, self.normalize_func, [('realigned_files',
'apply_to_files')]),
(self.normalize_func, self.smooth, [('normalized_files',
'in_files')]),
#(self.realign, self.datasink, [('realigned_files', 'realign')]),
#(self.realign, self.datasink, [('mean_image', 'mean')]),
(self.normalize_func, self.datasink, [('normalized_files', 'norm')]
),
(self.smooth, self.datasink, [('smoothed_files', 'smooth')])
])
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 __init__(self):
super(Smooth, self).__init__()
self.requirement = ['matlab', 'spm']
# Inputs description
in_files_desc = 'List of files to smooth. A list of items which are an existing, uncompressed file (valid extensions: [.img, .nii, .hdr]).'
fwhm_desc = 'Full-width at half maximum (FWHM) of the Gaussian smoothing kernel in mm. A list of 3 items which are a float of fwhm for each dimension.'
data_type_desc = 'Data type of the output images (an integer [int or long]).'
implicit_masking_desc = 'A mask implied by a particular voxel value (a boolean).'
out_prefix_desc = 'Specify the string to be prepended to the filenames of the smoothed image file(s) (a string).'
# Outputs description
smoothed_files_desc = 'The smoothed files (a list of items which are an existing file name).'
# Input traits
self.add_trait(
"in_files",
InputMultiPath(ImageFileSPM(),
copyfile=False,
output=False,
desc=in_files_desc))
self.add_trait(
"fwhm",
traits.List([6, 6, 6], output=False, optional=True,
desc=fwhm_desc))
self.add_trait(
"data_type",
traits.Int(output=False, optional=True, desc=data_type_desc))
self.add_trait(
"implicit_masking",
traits.Bool(output=False,
optional=True,
desc=implicit_masking_desc))
self.add_trait(
"out_prefix",
traits.String('s',
usedefault=True,
output=False,
optional=True,
desc=out_prefix_desc))
# Output traits
self.add_trait(
"smoothed_files",
OutputMultiPath(File(), output=True, desc=smoothed_files_desc))
self.process = spm.Smooth()
self.change_dir = True
def test_smooth():
yield assert_equal, spm.Smooth._jobtype, 'spatial'
yield assert_equal, spm.Smooth._jobname, 'smooth'
input_map = dict(
data_type=dict(field='dtype', ),
fwhm=dict(field='fwhm', ),
in_files=dict(
copyfile=False,
mandatory=True,
field='data',
),
)
instance = spm.Smooth()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(instance.inputs.traits()[key],
metakey), value
def smooth_images(write_dir, **template_dict):
"""This function runs smoothing on input images. Ex: modulated images"""
from nipype.interfaces import spm
from nipype.interfaces.io import DataSink
smooth = pe.Node(interface=spm.Smooth(), name='smooth')
smooth.inputs.paths = template_dict['spm_path']
smooth.inputs.implicit_masking = template_dict['implicit_masking']
smooth.inputs.in_files = glob.glob(os.path.join(write_dir, 'mwc*.nii'))
smooth.inputs.fwhm = template_dict['FWHM_SMOOTH']
vbm_smooth_modulated_images = pe.Workflow(
name="vbm_smooth_modulated_images")
datasink = pe.Node(interface=DataSink(), name='datasink')
datasink.inputs.base_directory = write_dir
vbm_smooth_modulated_images.connect([(smooth, datasink, [('smoothed_files',
write_dir)])])
with stdchannel_redirected(sys.stderr, os.devnull):
vbm_smooth_modulated_images.run()
def analysis_steps(self):
self.analysis = type('', (), {})()
# Get files
subj_list = [
subj.split('_')[:-1] for subj in next(os.walk(self.proj_dir))[1]
]
# TODO limit the subj_list to those without sw processed files.
# for parallelization by subject, use idnetityInterface
self.analysis.infosource = Node(
IdentityInterface(fields=['subj_id', 'task']), name="infosource")
self.analysis.infosource.iterables = [('subject_id', subj_list),
('task', self.task_names)]
templates = {
'anat': '{subj_id}/t1/{subj_id}_t1*.nii',
'func': '{subj_id}/{task}*/{subj_id}_{task}*.nii'
}
self.analysis.sf = Node(SelectFiles(templates), name='selectfiles')
self.analysis.sf.inputs.base_directory = self.proj_dir
# Realign
self.analysis.realign = Node(spm.Realign(register_to_mean=True,
fwhm=self.opts.fwhm),
name='realign')
# Coregister
self.analysis.coreg = Node(spm.Coregister(), name='coregistration')
# Normalize
self.analysis.norm12 = Node(spm.Normalize12(
bias_regularization=1e-05, affine_regularization_type='mni'),
name='normalize')
#Smooth
self.analysis.smooth = Node(spm.Smooth(), name='smooth')
#smooth.inputs.in_files = 'functional.nii'
self.analysis.smooth.inputs.fwhm = self.opts.smooth_fwhm
def build_core_nodes(self):
"""Build and connect an output node to the pipeline."""
import nipype.interfaces.spm as spm
import nipype.interfaces.spm.utils as spmutils
import nipype.interfaces.utility as nutil
import nipype.pipeline.engine as npe
from nipype.interfaces.petpvc import PETPVC
from clinica.utils.filemanip import unzip_nii
from clinica.utils.spm import spm_standalone_is_available, use_spm_standalone
from .pet_volume_utils import (
apply_binary_mask,
atlas_statistics,
create_binary_mask,
create_pvc_mask,
get_from_list,
init_input_node,
normalize_to_reference,
pet_pvc_name,
)
if spm_standalone_is_available():
use_spm_standalone()
# Initialize pipeline
# ===================
init_node = npe.Node(
interface=nutil.Function(
input_names=["pet_nii"],
output_names=["pet_nii"],
function=init_input_node,
),
name="init_pipeline",
)
# Unzipping
# =========
unzip_pet_image = npe.Node(
nutil.Function(input_names=["in_file"],
output_names=["out_file"],
function=unzip_nii),
name="unzip_pet_image",
)
unzip_t1_image_native = npe.Node(
nutil.Function(input_names=["in_file"],
output_names=["out_file"],
function=unzip_nii),
name="unzip_t1_image_native",
)
unzip_flow_fields = npe.Node(
nutil.Function(input_names=["in_file"],
output_names=["out_file"],
function=unzip_nii),
name="unzip_flow_fields",
)
unzip_dartel_template = npe.Node(
nutil.Function(input_names=["in_file"],
output_names=["out_file"],
function=unzip_nii),
name="unzip_dartel_template",
)
unzip_reference_mask = npe.Node(
nutil.Function(input_names=["in_file"],
output_names=["out_file"],
function=unzip_nii),
name="unzip_reference_mask",
)
unzip_mask_tissues = npe.MapNode(
nutil.Function(input_names=["in_file"],
output_names=["out_file"],
function=unzip_nii),
name="unzip_mask_tissues",
iterfield=["in_file"],
)
# Coregister PET into T1 native space
# ===================================
coreg_pet_t1 = npe.Node(spm.Coregister(), name="coreg_pet_t1")
# Spatially normalize PET into MNI
# ================================
dartel_mni_reg = npe.Node(spm.DARTELNorm2MNI(), name="dartel_mni_reg")
dartel_mni_reg.inputs.modulate = False
dartel_mni_reg.inputs.fwhm = 0
# Reslice reference region mask into PET
# ======================================
reslice = npe.Node(spmutils.Reslice(), name="reslice")
# Normalize PET values according to reference region
# ==================================================
norm_to_ref = npe.Node(
nutil.Function(
input_names=["pet_image", "region_mask"],
output_names=["suvr_pet_path"],
function=normalize_to_reference,
),
name="norm_to_ref",
)
# Create binary mask from segmented tissues
# =========================================
binary_mask = npe.Node(
nutil.Function(
input_names=["tissues", "threshold"],
output_names=["out_mask"],
function=create_binary_mask,
),
name="binary_mask",
)
binary_mask.inputs.threshold = self.parameters["mask_threshold"]
# Mask PET image
# ==============
apply_mask = npe.Node(
nutil.Function(
input_names=["image", "binary_mask"],
output_names=["masked_image_path"],
function=apply_binary_mask,
),
name="apply_mask",
)
# Smoothing
# =========
if self.parameters["smooth"] is not None and len(
self.parameters["smooth"]) > 0:
smoothing_node = npe.MapNode(spm.Smooth(),
name="smoothing_node",
iterfield=["fwhm", "out_prefix"])
smoothing_node.inputs.fwhm = [[x, x, x]
for x in self.parameters["smooth"]]
smoothing_node.inputs.out_prefix = [
"fwhm-" + str(x) + "mm_" for x in self.parameters["smooth"]
]
# fmt: off
self.connect([
(apply_mask, smoothing_node, [("masked_image_path", "in_files")
]),
(smoothing_node, self.output_node,
[("smoothed_files", "pet_suvr_masked_smoothed")]),
])
# fmt: on
else:
self.output_node.inputs.pet_suvr_masked_smoothed = [[]]
# Atlas Statistics
# ================
atlas_stats_node = npe.MapNode(
nutil.Function(
input_names=["in_image", "in_atlas_list"],
output_names=["atlas_statistics"],
function=atlas_statistics,
),
name="atlas_stats_node",
iterfield=["in_image"],
)
atlas_stats_node.inputs.in_atlas_list = self.parameters["atlases"]
# Connection
# ==========
# fmt: off
self.connect([
(self.input_node, init_node, [("pet_image", "pet_nii")]),
(init_node, unzip_pet_image, [("pet_nii", "in_file")]),
(self.input_node, unzip_t1_image_native, [("t1_image_native",
"in_file")]),
(self.input_node, unzip_flow_fields, [("flow_fields", "in_file")]),
(self.input_node, unzip_dartel_template, [("dartel_template",
"in_file")]),
(self.input_node, unzip_reference_mask, [("reference_mask",
"in_file")]),
(self.input_node, unzip_mask_tissues, [("mask_tissues", "in_file")
]),
(unzip_pet_image, coreg_pet_t1, [("out_file", "source")]),
(unzip_t1_image_native, coreg_pet_t1, [("out_file", "target")]),
(unzip_flow_fields, dartel_mni_reg, [("out_file",
"flowfield_files")]),
(unzip_dartel_template, dartel_mni_reg, [("out_file",
"template_file")]),
(unzip_reference_mask, reslice, [("out_file", "in_file")]),
(unzip_mask_tissues, binary_mask, [("out_file", "tissues")]),
(coreg_pet_t1, dartel_mni_reg, [("coregistered_source",
"apply_to_files")]),
(dartel_mni_reg, reslice, [("normalized_files", "space_defining")
]),
(dartel_mni_reg, norm_to_ref, [("normalized_files", "pet_image")]),
(reslice, norm_to_ref, [("out_file", "region_mask")]),
(norm_to_ref, apply_mask, [("suvr_pet_path", "image")]),
(binary_mask, apply_mask, [("out_mask", "binary_mask")]),
(norm_to_ref, atlas_stats_node, [("suvr_pet_path", "in_image")]),
(coreg_pet_t1, self.output_node, [("coregistered_source",
"pet_t1_native")]),
(dartel_mni_reg, self.output_node, [("normalized_files", "pet_mni")
]),
(norm_to_ref, self.output_node, [("suvr_pet_path", "pet_suvr")]),
(binary_mask, self.output_node, [("out_mask", "binary_mask")]),
(apply_mask, self.output_node, [("masked_image_path",
"pet_suvr_masked")]),
(atlas_stats_node, self.output_node, [("atlas_statistics",
"atlas_statistics")]),
])
# fmt: on
# PVC
# ==========
if self.parameters["apply_pvc"]:
# Unzipping
# =========
unzip_pvc_mask_tissues = npe.MapNode(
nutil.Function(
input_names=["in_file"],
output_names=["out_file"],
function=unzip_nii,
),
name="unzip_pvc_mask_tissues",
iterfield=["in_file"],
)
# Creating Mask to use in PVC
# ===========================
pvc_mask = npe.Node(
nutil.Function(
input_names=["tissues"],
output_names=["out_mask"],
function=create_pvc_mask,
),
name="pvc_mask",
)
# PET PVC
# =======
petpvc = npe.Node(PETPVC(), name="pvc")
petpvc.inputs.pvc = "RBV"
petpvc.inputs.out_file = "pvc.nii"
# Spatially normalize PET into MNI
# ================================
dartel_mni_reg_pvc = npe.Node(spm.DARTELNorm2MNI(),
name="dartel_mni_reg_pvc")
dartel_mni_reg_pvc.inputs.modulate = False
dartel_mni_reg_pvc.inputs.fwhm = 0
# Reslice reference region mask into PET
# ======================================
reslice_pvc = npe.Node(spmutils.Reslice(), name="reslice_pvc")
# Normalize PET values according to reference region
# ==================================================
norm_to_ref_pvc = npe.Node(
nutil.Function(
input_names=["pet_image", "region_mask"],
output_names=["suvr_pet_path"],
function=normalize_to_reference,
),
name="norm_to_ref_pvc",
)
# Mask PET image
# ==============
apply_mask_pvc = npe.Node(
nutil.Function(
input_names=["image", "binary_mask"],
output_names=["masked_image_path"],
function=apply_binary_mask,
),
name="apply_mask_pvc",
)
# Smoothing
# =========
if (self.parameters["smooth"] is not None
and len(self.parameters["smooth"]) > 0):
smoothing_pvc = npe.MapNode(spm.Smooth(),
name="smoothing_pvc",
iterfield=["fwhm", "out_prefix"])
smoothing_pvc.inputs.fwhm = [[x, x, x]
for x in self.parameters["smooth"]
]
smoothing_pvc.inputs.out_prefix = [
"fwhm-" + str(x) + "mm_" for x in self.parameters["smooth"]
]
# fmt: off
self.connect([
(apply_mask_pvc, smoothing_pvc, [("masked_image_path",
"in_files")]),
(smoothing_pvc, self.output_node,
[("smoothed_files", "pet_pvc_suvr_masked_smoothed")]),
])
# fmt: on
else:
self.output_node.inputs.pet_pvc_suvr_masked_smoothed = [[]]
# Atlas Statistics
# ================
atlas_stats_pvc = npe.MapNode(
nutil.Function(
input_names=["in_image", "in_atlas_list"],
output_names=["atlas_statistics"],
function=atlas_statistics,
),
name="atlas_stats_pvc",
iterfield=["in_image"],
)
atlas_stats_pvc.inputs.in_atlas_list = self.parameters["atlases"]
# Connection
# ==========
# fmt: off
self.connect([
(self.input_node, unzip_pvc_mask_tissues, [("pvc_mask_tissues",
"in_file")]),
(unzip_pvc_mask_tissues, pvc_mask, [("out_file", "tissues")]),
(unzip_flow_fields, dartel_mni_reg_pvc, [("out_file",
"flowfield_files")]),
(unzip_dartel_template, dartel_mni_reg_pvc,
[("out_file", "template_file")]),
(unzip_reference_mask, reslice_pvc, [("out_file", "in_file")]),
(coreg_pet_t1, petpvc, [("coregistered_source", "in_file"),
(("coregistered_source", pet_pvc_name,
"RBV"), "out_file")]),
(pvc_mask, petpvc, [("out_mask", "mask_file")]),
(self.input_node, petpvc,
[(("psf", get_from_list, 0), "fwhm_x"),
(("psf", get_from_list, 1), "fwhm_y"),
(("psf", get_from_list, 2), "fwhm_z")]),
(petpvc, dartel_mni_reg_pvc, [("out_file", "apply_to_files")]),
(dartel_mni_reg_pvc, reslice_pvc, [("normalized_files",
"space_defining")]),
(dartel_mni_reg_pvc, norm_to_ref_pvc, [("normalized_files",
"pet_image")]),
(reslice_pvc, norm_to_ref_pvc, [("out_file", "region_mask")]),
(norm_to_ref_pvc, apply_mask_pvc, [("suvr_pet_path", "image")
]),
(binary_mask, apply_mask_pvc, [("out_mask", "binary_mask")]),
(norm_to_ref_pvc, atlas_stats_pvc, [("suvr_pet_path",
"in_image")]),
(petpvc, self.output_node, [("out_file", "pet_pvc")]),
(dartel_mni_reg_pvc, self.output_node, [("normalized_files",
"pet_pvc_mni")]),
(norm_to_ref_pvc, self.output_node, [("suvr_pet_path",
"pet_pvc_suvr")]),
(apply_mask_pvc, self.output_node, [("masked_image_path",
"pet_pvc_suvr_masked")]),
(atlas_stats_pvc, self.output_node,
[("atlas_statistics", "pvc_atlas_statistics")]),
])
# fmt: on
else:
self.output_node.inputs.pet_pvc = [[]]
self.output_node.inputs.pet_pvc_mni = [[]]
self.output_node.inputs.pet_pvc_suvr = [[]]
self.output_node.inputs.pet_pvc_suvr_masked = [[]]
self.output_node.inputs.pvc_atlas_statistics = [[]]
self.output_node.inputs.pet_pvc_suvr_masked_smoothed = [[]]
body registration of the functional data to the structural data.
"""
coregister = pe.Node(spm.Coregister(), name="coregister")
coregister.inputs.jobtype = 'estimate'
"""Warp functional and structural data to SPM's T1 template using
:class:`nipype.interfaces.spm.Normalize`. The tutorial data set
includes the template image, T1.nii.
"""
normalize = pe.Node(spm.Normalize(), name="normalize")
"""Smooth the functional data using
:class:`nipype.interfaces.spm.Smooth`.
"""
smooth = pe.Node(spm.Smooth(), name="smooth")
fwhmlist = [4]
smooth.iterables = ('fwhm', fwhmlist)
preproc.connect([
(inputnode, normalize, [(('in_data', _template_path), 'template')]),
(realign, coregister, [('mean_image', 'source'),
('realigned_files', 'apply_to_files')]),
(coregister, normalize, [('coregistered_files', 'apply_to_files')]),
(normalize, smooth, [('normalized_files', 'in_files')]),
(normalize, skullstrip, [('normalized_source', 'in_file')]),
(realign, art, [('realignment_parameters', 'realignment_parameters')]),
(normalize, art, [('normalized_files', 'realigned_files')]),
(skullstrip, art, [('mask_file', 'mask_file')]),
])
"""
iter_fwhm = pe.Node(
interface=util.IdentityInterface(fields=["fwhm"]), name="iter_fwhm")
iter_fwhm.iterables = [('fwhm', [4, 8])]
iter_smoothing_method = pe.Node(
interface=util.IdentityInterface(fields=["smoothing_method"]),
name="iter_smoothing_method")
iter_smoothing_method.iterables = [('smoothing_method', [
'isotropic_voxel', 'anisotropic_voxel', 'isotropic_surface'
])]
realign = pe.Node(interface=spm.Realign(), name="realign")
realign.inputs.register_to_mean = True
isotropic_voxel_smooth = pe.Node(
interface=spm.Smooth(), name="isotropic_voxel_smooth")
preprocessing.connect(realign, "realigned_files", isotropic_voxel_smooth,
"in_files")
preprocessing.connect(iter_fwhm, "fwhm", isotropic_voxel_smooth, "fwhm")
compute_mask = pe.Node(interface=nipy.ComputeMask(), name="compute_mask")
preprocessing.connect(realign, "mean_image", compute_mask, "mean_volume")
anisotropic_voxel_smooth = fsl_wf.create_susan_smooth(
name="anisotropic_voxel_smooth", separate_masks=False)
anisotropic_voxel_smooth.inputs.smooth.output_type = 'NIFTI'
preprocessing.connect(realign, "realigned_files", anisotropic_voxel_smooth,
"inputnode.in_files")
preprocessing.connect(iter_fwhm, "fwhm", anisotropic_voxel_smooth,
"inputnode.fwhm")
preprocessing.connect(compute_mask, "brain_mask", anisotropic_voxel_smooth,
def build_core_nodes(self):
"""Build and connect the core nodes of the pipelines.
"""
import os
import nipype.interfaces.spm as spm
import nipype.interfaces.matlab as mlab
import nipype.pipeline.engine as npe
import nipype.interfaces.utility as nutil
from clinica.utils.io import unzip_nii
from clinica.pipelines.t1_volume_dartel2mni.t1_volume_dartel2mni_utils import prepare_flowfields, join_smoothed_files, atlas_statistics, select_gm_images
spm_home = os.getenv("SPM_HOME")
mlab_home = os.getenv("MATLABCMD")
mlab.MatlabCommand.set_default_matlab_cmd(mlab_home)
mlab.MatlabCommand.set_default_paths(spm_home)
if 'SPMSTANDALONE_HOME' in os.environ:
if 'MCR_HOME' in os.environ:
matlab_cmd = os.path.join(os.environ['SPMSTANDALONE_HOME'],
'run_spm12.sh') \
+ ' ' + os.environ['MCR_HOME'] \
+ ' script'
spm.SPMCommand.set_mlab_paths(matlab_cmd=matlab_cmd,
use_mcr=True)
version = spm.SPMCommand().version
else:
raise EnvironmentError(
'MCR_HOME variable not in environnement. Althought, ' +
'SPMSTANDALONE_HOME has been found')
else:
version = spm.Info.getinfo()
if version:
if isinstance(version, dict):
spm_path = version['path']
if version['name'] == 'SPM8':
print(
'You are using SPM version 8. The recommended version to use with Clinica is SPM 12. '
+ 'Please upgrade your SPM toolbox.')
tissue_map = os.path.join(spm_path, 'toolbox/Seg/TPM.nii')
elif version['name'] == 'SPM12':
tissue_map = os.path.join(spm_path, 'tpm/TPM.nii')
else:
raise RuntimeError(
'SPM version 8 or 12 could not be found. Please upgrade your SPM toolbox.'
)
if isinstance(version, str):
if float(version) >= 12.7169:
tissue_map = os.path.join(
str(spm_home), 'spm12_mcr/spm/spm12/tpm/TPM.nii')
else:
raise RuntimeError(
'SPM standalone version not supported. Please upgrade SPM standalone.'
)
else:
raise RuntimeError(
'SPM could not be found. Please verify your SPM_HOME environment variable.'
)
# Unzipping
# =========
unzip_tissues_node = npe.MapNode(nutil.Function(
input_names=['in_file'],
output_names=['out_file'],
function=unzip_nii),
name='unzip_tissues_node',
iterfield=['in_file'])
unzip_flowfields_node = npe.MapNode(nutil.Function(
input_names=['in_file'],
output_names=['out_file'],
function=unzip_nii),
name='unzip_flowfields_node',
iterfield=['in_file'])
unzip_template_node = npe.Node(nutil.Function(
input_names=['in_file'],
output_names=['out_file'],
function=unzip_nii),
name='unzip_template_node')
# DARTEL2MNI Registration
# =======================
dartel2mni_node = npe.MapNode(
spm.DARTELNorm2MNI(),
name='dartel2MNI',
iterfield=['apply_to_files', 'flowfield_files'])
if self.parameters['bounding_box'] is not None:
dartel2mni_node.inputs.bounding_box = self.parameters[
'bounding_box']
if self.parameters['voxel_size'] is not None:
dartel2mni_node.inputs.voxel_size = self.parameters['voxel_size']
dartel2mni_node.inputs.modulate = self.parameters['modulation']
dartel2mni_node.inputs.fwhm = 0
# Smoothing
# =========
if self.parameters['fwhm'] is not None and len(
self.parameters['fwhm']) > 0:
smoothing_node = npe.MapNode(spm.Smooth(),
name='smoothing_node',
iterfield=['in_files'])
smoothing_node.iterables = [
('fwhm', [[x, x, x] for x in self.parameters['fwhm']]),
('out_prefix',
['fwhm-' + str(x) + 'mm_' for x in self.parameters['fwhm']])
]
smoothing_node.synchronize = True
join_smoothing_node = npe.JoinNode(
interface=nutil.Function(
input_names=['smoothed_normalized_files'],
output_names=['smoothed_normalized_files'],
function=join_smoothed_files),
joinsource='smoothing_node',
joinfield='smoothed_normalized_files',
name='join_smoothing_node')
self.connect([(dartel2mni_node, smoothing_node,
[('normalized_files', 'in_files')]),
(smoothing_node, join_smoothing_node,
[('smoothed_files', 'smoothed_normalized_files')]),
(join_smoothing_node, self.output_node,
[('smoothed_normalized_files',
'smoothed_normalized_files')])])
else:
self.output_node.inputs.smoothed_normalized_files = []
# Atlas Statistics
# ================
atlas_stats_node = npe.MapNode(nutil.Function(
input_names=['in_image', 'in_atlas_list'],
output_names=['atlas_statistics'],
function=atlas_statistics),
name='atlas_stats_node',
iterfield=['in_image'])
atlas_stats_node.inputs.in_atlas_list = self.parameters['atlas_list']
# Connection
# ==========
self.connect([(self.input_node, unzip_tissues_node, [('apply_to_files',
'in_file')]),
(self.input_node, unzip_flowfields_node,
[('flowfield_files', 'in_file')]),
(self.input_node, unzip_template_node, [('template_file',
'in_file')]),
(unzip_tissues_node, dartel2mni_node,
[('out_file', 'apply_to_files')]),
(unzip_flowfields_node, dartel2mni_node,
[(('out_file', prepare_flowfields,
self.parameters['tissues']), 'flowfield_files')]),
(unzip_template_node, dartel2mni_node,
[('out_file', 'template_file')]),
(dartel2mni_node, self.output_node,
[('normalized_files', 'normalized_files')]),
(dartel2mni_node, atlas_stats_node,
[(('normalized_files', select_gm_images), 'in_image')]),
(atlas_stats_node, self.output_node,
[('atlas_statistics', 'atlas_statistics')])])
# roi_files.append(roi_file)
# return roi_files
# extract = pe.Node(util.Function(
# input_names = ['in_files'],
# function = extract_all, output_names = ['roi_files']),
# name = 'extract')
extract = pe.MapNode(fsl.ExtractROI(), name="extract", iterfield = ['in_file'])
extract.inputs.t_min = del_scan
extract.inputs.t_size = -1
extract.inputs.output_type='NIFTI'
# smoothing
smooth = Node(spm.Smooth(), name="smooth", fwhm = fwhm)
# set contrasts, depend on the condition
cond_names = ['Med_amb', 'Med_ambxMed_amb_reward_prob^1', 'Med_risk', 'Med_riskxMed_risk_reward_prob^1',
'Mon_amb', 'Mon_ambxMon_amb_reward_prob^1', 'Mon_risk', 'Mon_riskxMon_risk_reward_prob^1']
# general activation
cont1 = ['Med_Amb', 'T', cond_names, [1, 0, 0, 0, 0, 0, 0, 0]]
cont2 = ['Med_Risk', 'T', cond_names, [0, 0, 1, 0, 0, 0, 0, 0]]
cont3 = ['Med_Amb>Risk', 'T', cond_names, [1, 0, -1, 0, 0, 0, 0, 0]]
cont4 = ['Mon_Amb', 'T', cond_names, [0, 0, 0, 0, 1, 0, 0, 0]]
cont5 = ['Mon_Risk', 'T', cond_names, [0, 0, 0, 0, 0, 0, 1, 0]]
cont6 = ['Mon_Amb>Risk', 'T', cond_names, [0, 0, 0, 0, 1, 0, -1, 0]]
cont7 = ['Med>Mon_Amb', 'T', cond_names, [1, 0, 0, 0, -1, 0, 0, 0]]
preprocessing = pe.Workflow(name="preprocessing")
iter_fwhm = pe.Node(interface=util.IdentityInterface(fields=["fwhm"]),
name="iter_fwhm")
iter_fwhm.iterables = [('fwhm', [4, 8])]
iter_smoothing_method = pe.Node(interface=util.IdentityInterface(fields=["smoothing_method"]),
name="iter_smoothing_method")
iter_smoothing_method.iterables = [('smoothing_method', ['isotropic_voxel',
'anisotropic_voxel',
'isotropic_surface'])]
realign = pe.Node(interface=spm.Realign(), name="realign")
realign.inputs.register_to_mean = True
isotropic_voxel_smooth = pe.Node(interface=spm.Smooth(),
name="isotropic_voxel_smooth")
preprocessing.connect(realign, "realigned_files", isotropic_voxel_smooth,
"in_files")
preprocessing.connect(iter_fwhm, "fwhm", isotropic_voxel_smooth, "fwhm")
compute_mask = pe.Node(interface=nipy.ComputeMask(), name="compute_mask")
preprocessing.connect(realign, "mean_image", compute_mask, "mean_volume")
anisotropic_voxel_smooth = fsl_wf.create_susan_smooth(name="anisotropic_voxel_smooth",
separate_masks=False)
anisotropic_voxel_smooth.inputs.smooth.output_type = 'NIFTI'
preprocessing.connect(realign, "realigned_files", anisotropic_voxel_smooth,
"inputnode.in_files")
preprocessing.connect(iter_fwhm, "fwhm", anisotropic_voxel_smooth,
"inputnode.fwhm")
def create_spm_preproc(c, name='preproc'):
"""Create an spm preprocessing workflow with freesurfer registration and
artifact detection.
The workflow realigns and smooths and registers the functional images with
the subject's freesurfer space.
Example
-------
>>> preproc = create_spm_preproc()
>>> preproc.base_dir = '.'
>>> preproc.inputs.inputspec.fwhm = 6
>>> preproc.inputs.inputspec.subject_id = 's1'
>>> preproc.inputs.inputspec.subjects_dir = '.'
>>> preproc.inputs.inputspec.functionals = ['f3.nii', 'f5.nii']
>>> preproc.inputs.inputspec.norm_threshold = 1
>>> preproc.inputs.inputspec.zintensity_threshold = 3
Inputs::
inputspec.functionals : functional runs use 4d nifti
inputspec.subject_id : freesurfer subject id
inputspec.subjects_dir : freesurfer subjects dir
inputspec.fwhm : smoothing fwhm
inputspec.norm_threshold : norm threshold for outliers
inputspec.zintensity_threshold : intensity threshold in z-score
Outputs::
outputspec.realignment_parameters : realignment parameter files
outputspec.smoothed_files : smoothed functional files
outputspec.outlier_files : list of outliers
outputspec.outlier_stats : statistics of outliers
outputspec.outlier_plots : images of outliers
outputspec.mask_file : binary mask file in reference image space
outputspec.reg_file : registration file that maps reference image to
freesurfer space
outputspec.reg_cost : cost of registration (useful for detecting misalignment)
"""
from nipype.workflows.smri.freesurfer.utils import create_getmask_flow
import nipype.algorithms.rapidart as ra
import nipype.interfaces.spm as spm
import nipype.interfaces.utility as niu
import nipype.pipeline.engine as pe
import nipype.interfaces.io as nio
"""
Initialize the workflow
"""
workflow = pe.Workflow(name=name)
"""
Define the inputs to this workflow
"""
inputnode = pe.Node(niu.IdentityInterface(fields=[
'functionals', 'subject_id', 'subjects_dir', 'fwhm', 'norm_threshold',
'zintensity_threshold', 'tr', 'do_slicetime', 'sliceorder', 'node',
'csf_prob', 'wm_prob', 'gm_prob'
]),
name='inputspec')
"""
Setup the processing nodes and create the mask generation and coregistration
workflow
"""
poplist = lambda x: x.pop()
#realign = pe.Node(spm.Realign(), name='realign')
sym_func = pe.Node(niu.Function(input_names=['in_file'],
output_names=['out_link'],
function=do_symlink),
name='func_symlink')
realign = pe.Node(niu.Function(
input_names=['node', 'in_file', 'tr', 'do_slicetime', 'sliceorder'],
output_names=['out_file', 'par_file'],
function=mod_realign),
name="mod_realign")
mean = art_mean_workflow()
workflow.connect(realign, 'out_file', mean, 'inputspec.realigned_files')
workflow.connect(realign, 'par_file', mean,
'inputspec.realignment_parameters')
mean.inputs.inputspec.parameter_source = 'FSL' # Modular realign puts it in FSL format for consistency
#workflow.connect(inputnode, 'functionals', realign, 'in_file')
workflow.connect(inputnode, 'functionals', sym_func, 'in_file')
workflow.connect(sym_func, 'out_link', realign, 'in_file')
workflow.connect(inputnode, 'tr', realign, 'tr')
workflow.connect(inputnode, 'do_slicetime', realign, 'do_slicetime')
workflow.connect(inputnode, 'sliceorder', realign, 'sliceorder')
workflow.connect(inputnode, 'node', realign, 'node')
maskflow = create_getmask_flow()
workflow.connect([(inputnode, maskflow,
[('subject_id', 'inputspec.subject_id'),
('subjects_dir', 'inputspec.subjects_dir')])])
maskflow.inputs.inputspec.contrast_type = 't2'
workflow.connect(mean, 'outputspec.mean_image', maskflow,
'inputspec.source_file')
smooth = pe.Node(spm.Smooth(), name='smooth')
normalize = pe.Node(spm.Normalize(jobtype='write'), name='normalize')
normalize_struct = normalize.clone('normalize_struct')
segment = pe.Node(spm.Segment(csf_output_type=[True, True, False],
gm_output_type=[True, True, False],
wm_output_type=[True, True, False]),
name='segment')
mergefunc = lambda in1, in2, in3: [in1, in2, in3]
# merge = pe.Node(niu.Merge(),name='merge')
merge = pe.Node(niu.Function(input_names=['in1', 'in2', 'in3'],
output_names=['out'],
function=mergefunc),
name='merge')
workflow.connect(inputnode, 'csf_prob', merge, 'in3')
workflow.connect(inputnode, 'wm_prob', merge, 'in2')
workflow.connect(inputnode, 'gm_prob', merge, 'in1')
#workflow.connect(merge,'out', segment,'tissue_prob_maps')
sym_prob = sym_func.clone('sym_prob')
workflow.connect(merge, 'out', sym_prob, 'in_file')
workflow.connect(sym_prob, 'out_link', segment, 'tissue_prob_maps')
workflow.connect(maskflow, ('outputspec.mask_file', pickfirst), segment,
'mask_image')
workflow.connect(inputnode, 'fwhm', smooth, 'fwhm')
#sym_brain = sym_func.clone('sym_brain')
#workflow.connect(realign, 'mean_image', normalize, 'source')
#workflow.connect(maskflow,'fssource.brain',segment,'data')
fssource = maskflow.get_node('fssource')
import nipype.interfaces.freesurfer as fs
convert_brain = pe.Node(interface=fs.ApplyVolTransform(inverse=True),
name='convert')
workflow.connect(fssource, 'brain', convert_brain, 'target_file')
workflow.connect(maskflow, ('outputspec.reg_file', pickfirst),
convert_brain, 'reg_file')
workflow.connect(mean, 'outputspec.mean_image', convert_brain,
'source_file')
convert2nii = pe.Node(fs.MRIConvert(in_type='mgz', out_type='nii'),
name='convert2nii')
workflow.connect(convert_brain, 'transformed_file', convert2nii, 'in_file')
workflow.connect(convert2nii, 'out_file', segment, 'data')
workflow.connect(segment, 'transformation_mat', normalize,
'parameter_file')
workflow.connect(segment, 'transformation_mat', normalize_struct,
'parameter_file')
workflow.connect(convert2nii, 'out_file', normalize_struct,
'apply_to_files')
workflow.connect(realign, 'out_file', normalize, 'apply_to_files')
#normalize.inputs.template='/software/spm8/templates/EPI.nii'
workflow.connect(normalize, 'normalized_files', smooth, 'in_files')
#workflow.connect(realign, 'realigned_files', smooth, 'in_files')
artdetect = pe.Node(ra.ArtifactDetect(mask_type='file',
parameter_source='FSL',
use_differences=[True, False],
use_norm=True,
save_plot=True),
name='artdetect')
workflow.connect([(inputnode, artdetect,
[('norm_threshold', 'norm_threshold'),
('zintensity_threshold', 'zintensity_threshold')])])
workflow.connect([(realign, artdetect, [('out_file', 'realigned_files'),
('par_file',
'realignment_parameters')])])
workflow.connect(maskflow, ('outputspec.mask_file', poplist), artdetect,
'mask_file')
"""
Define the outputs of the workflow and connect the nodes to the outputnode
"""
outputnode = pe.Node(niu.IdentityInterface(fields=[
"realignment_parameters", "smoothed_files", "mask_file", "mean_image",
"reg_file", "reg_cost", 'outlier_files', 'outlier_stats',
'outlier_plots', 'norm_components', 'mod_csf', 'unmod_csf', 'mod_wm',
'unmod_wm', 'mod_gm', 'unmod_gm', 'mean', 'normalized_struct',
'struct_in_functional_space', 'normalization_parameters',
'reverse_normalize_parameters'
]),
name="outputspec")
workflow.connect([
(maskflow, outputnode, [("outputspec.reg_file", "reg_file")]),
(maskflow, outputnode, [("outputspec.reg_cost", "reg_cost")]),
(maskflow, outputnode, [(("outputspec.mask_file", poplist),
"mask_file")]),
(realign, outputnode, [('par_file', 'realignment_parameters')]),
(smooth, outputnode, [('smoothed_files', 'smoothed_files')]),
(artdetect, outputnode, [('outlier_files', 'outlier_files'),
('statistic_files', 'outlier_stats'),
('plot_files', 'outlier_plots'),
('norm_files', 'norm_components')])
])
workflow.connect(segment, 'modulated_csf_image', outputnode, 'mod_csf')
workflow.connect(segment, 'modulated_wm_image', outputnode, 'mod_wm')
workflow.connect(segment, 'modulated_gm_image', outputnode, 'mod_gm')
workflow.connect(segment, 'normalized_csf_image', outputnode, 'unmod_csf')
workflow.connect(segment, 'normalized_wm_image', outputnode, 'unmod_wm')
workflow.connect(segment, 'normalized_gm_image', outputnode, 'unmod_gm')
workflow.connect(mean, 'outputspec.mean_image', outputnode, 'mean')
workflow.connect(normalize_struct, 'normalized_files', outputnode,
'normalized_struct')
workflow.connect(segment, 'transformation_mat', outputnode,
'normalization_parameters')
workflow.connect(segment, 'inverse_transformation_mat', outputnode,
'reverse_normalize_parameters')
workflow.connect(convert2nii, 'out_file', outputnode,
'struct_in_functional_space')
workflow.inputs.inputspec.fwhm = c.fwhm
workflow.inputs.inputspec.subjects_dir = c.surf_dir
workflow.inputs.inputspec.norm_threshold = c.norm_thresh
workflow.inputs.inputspec.zintensity_threshold = c.z_thresh
workflow.inputs.inputspec.node = c.motion_correct_node
workflow.inputs.inputspec.tr = c.TR
workflow.inputs.inputspec.do_slicetime = c.do_slicetiming
workflow.inputs.inputspec.sliceorder = c.SliceOrder
workflow.inputs.inputspec.csf_prob = c.csf_prob
workflow.inputs.inputspec.gm_prob = c.grey_prob
workflow.inputs.inputspec.wm_prob = c.white_prob
workflow.base_dir = c.working_dir
workflow.config = {'execution': {'crashdump_dir': c.crash_dir}}
datagrabber = get_dataflow(c)
workflow.connect(datagrabber, 'func', inputnode, 'functionals')
infosource = pe.Node(niu.IdentityInterface(fields=['subject_id']),
name='subject_names')
if not c.test_mode:
infosource.iterables = ('subject_id', c.subjects)
else:
infosource.iterables = ('subject_id', c.subjects[:1])
workflow.connect(infosource, 'subject_id', inputnode, 'subject_id')
workflow.connect(infosource, 'subject_id', datagrabber, 'subject_id')
sub = lambda x: [('_subject_id_%s' % x, '')]
sinker = pe.Node(nio.DataSink(), name='sinker')
workflow.connect(infosource, 'subject_id', sinker, 'container')
workflow.connect(infosource, ('subject_id', sub), sinker, 'substitutions')
sinker.inputs.base_directory = c.sink_dir
outputspec = workflow.get_node('outputspec')
workflow.connect(outputspec, 'realignment_parameters', sinker,
'spm_preproc.realignment_parameters')
workflow.connect(outputspec, 'smoothed_files', sinker,
'spm_preproc.smoothed_outputs')
workflow.connect(outputspec, 'outlier_files', sinker,
'spm_preproc.art.@outlier_files')
workflow.connect(outputspec, 'outlier_stats', sinker,
'spm_preproc.art.@outlier_stats')
workflow.connect(outputspec, 'outlier_plots', sinker,
'spm_preproc.art.@outlier_plots')
workflow.connect(outputspec, 'norm_components', sinker,
'spm_preproc.art.@norm')
workflow.connect(outputspec, 'reg_file', sinker,
'spm_preproc.bbreg.@reg_file')
workflow.connect(outputspec, 'reg_cost', sinker,
'spm_preproc.bbreg.@reg_cost')
workflow.connect(outputspec, 'mask_file', sinker,
'spm_preproc.mask.@mask_file')
workflow.connect(outputspec, 'mod_csf', sinker,
'spm_preproc.segment.mod.@csf')
workflow.connect(outputspec, 'mod_wm', sinker,
'spm_preproc.segment.mod.@wm')
workflow.connect(outputspec, 'mod_gm', sinker,
'spm_preproc.segment.mod.@gm')
workflow.connect(outputspec, 'unmod_csf', sinker,
'spm_preproc.segment.unmod.@csf')
workflow.connect(outputspec, 'unmod_wm', sinker,
'spm_preproc.segment.unmod.@wm')
workflow.connect(outputspec, 'unmod_gm', sinker,
'spm_preproc.segment.unmod.@gm')
workflow.connect(outputspec, 'mean', sinker, 'spm_preproc.mean')
workflow.connect(outputspec, 'normalized_struct', sinker,
'spm_preproc.normalized_struct')
workflow.connect(outputspec, 'normalization_parameters', sinker,
'spm_preproc.normalization_parameters.@forward')
workflow.connect(outputspec, 'reverse_normalize_parameters', sinker,
'spm_preproc.normalization_parameters.@reverse')
workflow.connect(outputspec, 'struct_in_functional_space', sinker,
'spm_preproc.struct_in_func_space')
return workflow
def create_spm_preproc_func_pipeline(data_dir=None,
subject_id=None,
task_list=None):
###############################
## Set up Nodes
###############################
ds = Node(nio.DataGrabber(infields=['subject_id', 'task_id'],
outfields=['func', 'struc']),
name='datasource')
ds.inputs.base_directory = os.path.abspath(data_dir + '/' + subject_id)
ds.inputs.template = '*'
ds.inputs.sort_filelist = True
ds.inputs.template_args = {'func': [['task_id']], 'struc': []}
ds.inputs.field_template = {
'func': 'Functional/Raw/%s/func.nii',
'struc': 'Structural/SPGR/spgr.nii'
}
ds.inputs.subject_id = subject_id
ds.inputs.task_id = task_list
ds.iterables = ('task_id', task_list)
# ds.run().outputs #show datafiles
# #Setup Data Sinker for writing output files
# datasink = Node(nio.DataSink(), name='sinker')
# datasink.inputs.base_directory = '/path/to/output'
# workflow.connect(realigner, 'realignment_parameters', datasink, 'motion.@par')
# datasink.inputs.substitutions = [('_variable', 'variable'),('file_subject_', '')]
#Get Timing Acquisition for slice timing
tr = 2
ta = Node(interface=util.Function(input_names=['tr', 'n_slices'],
output_names=['ta'],
function=get_ta),
name="ta")
ta.inputs.tr = tr
#Slice Timing: sequential ascending
slice_timing = Node(interface=spm.SliceTiming(), name="slice_timing")
slice_timing.inputs.time_repetition = tr
slice_timing.inputs.ref_slice = 1
#Realignment - 6 parameters - realign to first image of very first series.
realign = Node(interface=spm.Realign(), name="realign")
realign.inputs.register_to_mean = True
#Plot Realignment
plot_realign = Node(interface=PlotRealignmentParameters(),
name="plot_realign")
#Artifact Detection
art = Node(interface=ra.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'
#Coregister - 12 parameters, cost function = 'nmi', fwhm 7, interpolate, don't mask
#anatomical to functional mean across all available data.
coregister = Node(interface=spm.Coregister(), name="coregister")
coregister.inputs.jobtype = 'estimate'
# Segment structural, gray/white/csf,mni,
segment = Node(interface=spm.Segment(), name="segment")
segment.inputs.save_bias_corrected = True
#Normalize - structural to MNI - then apply this to the coregistered functionals
normalize = Node(interface=spm.Normalize(), name="normalize")
normalize.inputs.template = os.path.abspath(t1_template_file)
#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")
fwhmlist = [0, 5, 8]
smooth.iterables = ('fwhm', fwhmlist)
#Create Covariate matrix
make_covariates = Node(interface=Create_Covariates(),
name="make_covariates")
###############################
## Create Pipeline
###############################
Preprocessed = Workflow(name="Preprocessed")
Preprocessed.base_dir = os.path.abspath(data_dir + '/' + subject_id +
'/Functional')
Preprocessed.connect([
(ds, ta, [(('func', get_n_slices), "n_slices")]),
(ta, slice_timing, [("ta", "time_acquisition")]),
(ds, slice_timing, [
('func', 'in_files'),
(('func', get_n_slices), "num_slices"),
(('func', get_slice_order), "slice_order"),
]),
(slice_timing, realign, [('timecorrected_files', 'in_files')]),
(realign, compute_mask, [('mean_image', 'mean_volume')]),
(realign, coregister, [('mean_image', 'target')]),
(ds, coregister, [('struc', 'source')]),
(coregister, segment, [('coregistered_source', 'data')]),
(segment, normalize, [
('transformation_mat', 'parameter_file'),
('bias_corrected_image', 'source'),
]),
(realign, normalize, [('realigned_files', 'apply_to_files'),
(('realigned_files', get_vox_dims),
'write_voxel_sizes')]),
(normalize, smooth, [('normalized_files', 'in_files')]),
(compute_mask, art, [('brain_mask', 'mask_file')]),
(realign, art, [('realignment_parameters', 'realignment_parameters')]),
(realign, art, [('realigned_files', 'realigned_files')]),
(realign, plot_realign, [('realignment_parameters',
'realignment_parameters')]),
(normalize, plot_normalization_check, [('normalized_files', 'wra_img')
]),
(realign, make_covariates, [('realignment_parameters',
'realignment_parameters')]),
(art, make_covariates, [('outlier_files', 'spike_id')]),
])
return Preprocessed
def build_core_nodes(self):
"""Build and connect the core nodes of the pipeline."""
import nipype.interfaces.spm as spm
import nipype.pipeline.engine as npe
import nipype.interfaces.utility as nutil
from clinica.utils.filemanip import unzip_nii
from ..t1_volume_dartel2mni import t1_volume_dartel2mni_utils as dartel2mni_utils
# Unzipping
# =========
unzip_tissues_node = npe.MapNode(nutil.Function(
input_names=['in_file'],
output_names=['out_file'],
function=unzip_nii),
name='unzip_tissues_node',
iterfield=['in_file'])
unzip_flowfields_node = npe.MapNode(nutil.Function(
input_names=['in_file'],
output_names=['out_file'],
function=unzip_nii),
name='unzip_flowfields_node',
iterfield=['in_file'])
unzip_template_node = npe.Node(nutil.Function(
input_names=['in_file'],
output_names=['out_file'],
function=unzip_nii),
name='unzip_template_node')
# DARTEL2MNI Registration
# =======================
dartel2mni_node = npe.MapNode(
spm.DARTELNorm2MNI(),
name='dartel2MNI',
iterfield=['apply_to_files', 'flowfield_files'])
if self.parameters['voxel_size'] is not None:
dartel2mni_node.inputs.voxel_size = tuple(
self.parameters['voxel_size'])
dartel2mni_node.inputs.modulate = self.parameters['modulate']
dartel2mni_node.inputs.fwhm = 0
# Smoothing
# =========
if self.parameters['smooth'] is not None and len(
self.parameters['smooth']) > 0:
smoothing_node = npe.MapNode(spm.Smooth(),
name='smoothing_node',
iterfield=['in_files'])
smoothing_node.iterables = [
('fwhm', [[x, x, x] for x in self.parameters['smooth']]),
('out_prefix',
['fwhm-' + str(x) + 'mm_' for x in self.parameters['smooth']])
]
smoothing_node.synchronize = True
join_smoothing_node = npe.JoinNode(
interface=nutil.Function(
input_names=['smoothed_normalized_files'],
output_names=['smoothed_normalized_files'],
function=dartel2mni_utils.join_smoothed_files),
joinsource='smoothing_node',
joinfield='smoothed_normalized_files',
name='join_smoothing_node')
self.connect([(dartel2mni_node, smoothing_node,
[('normalized_files', 'in_files')]),
(smoothing_node, join_smoothing_node,
[('smoothed_files', 'smoothed_normalized_files')]),
(join_smoothing_node, self.output_node,
[('smoothed_normalized_files',
'smoothed_normalized_files')])])
else:
self.output_node.inputs.smoothed_normalized_files = []
# Connection
# ==========
self.connect([(self.input_node, unzip_tissues_node,
[('native_segmentations', 'in_file')]),
(self.input_node, unzip_flowfields_node,
[('flowfield_files', 'in_file')]),
(self.input_node, unzip_template_node, [('template_file',
'in_file')]),
(unzip_tissues_node, dartel2mni_node,
[('out_file', 'apply_to_files')]),
(unzip_flowfields_node, dartel2mni_node,
[(('out_file', dartel2mni_utils.prepare_flowfields,
self.parameters['tissues']), 'flowfield_files')]),
(unzip_template_node, dartel2mni_node,
[('out_file', 'template_file')]),
(dartel2mni_node, self.output_node,
[('normalized_files', 'normalized_files')])])
def create_spm_preproc(name='preproc'):
"""Create an spm preprocessing workflow with freesurfer registration and
artifact detection.
The workflow realigns and smooths and registers the functional images with
the subject's freesurfer space.
Example
-------
>>> preproc = create_spm_preproc()
>>> preproc.base_dir = '.'
>>> preproc.inputs.inputspec.fwhm = 6
>>> preproc.inputs.inputspec.subject_id = 's1'
>>> preproc.inputs.inputspec.subjects_dir = '.'
>>> preproc.inputs.inputspec.functionals = ['f3.nii', 'f5.nii']
>>> preproc.inputs.inputspec.norm_threshold = 1
>>> preproc.inputs.inputspec.zintensity_threshold = 3
Inputs::
inputspec.functionals : functional runs use 4d nifti
inputspec.subject_id : freesurfer subject id
inputspec.subjects_dir : freesurfer subjects dir
inputspec.fwhm : smoothing fwhm
inputspec.norm_threshold : norm threshold for outliers
inputspec.zintensity_threshold : intensity threshold in z-score
Outputs::
outputspec.realignment_parameters : realignment parameter files
outputspec.smoothed_files : smoothed functional files
outputspec.outlier_files : list of outliers
outputspec.outlier_stats : statistics of outliers
outputspec.outlier_plots : images of outliers
outputspec.mask_file : binary mask file in reference image space
outputspec.reg_file : registration file that maps reference image to
freesurfer space
outputspec.reg_cost : cost of registration (useful for detecting misalignment)
"""
"""
Initialize the workflow
"""
workflow = pe.Workflow(name=name)
"""
Define the inputs to this workflow
"""
inputnode = pe.Node(niu.IdentityInterface(fields=[
'functionals', 'subject_id', 'subjects_dir', 'fwhm', 'norm_threshold',
'zintensity_threshold'
]),
name='inputspec')
"""
Setup the processing nodes and create the mask generation and coregistration
workflow
"""
poplist = lambda x: x.pop()
realign = pe.Node(spm.Realign(), name='realign')
workflow.connect(inputnode, 'functionals', realign, 'in_files')
maskflow = create_getmask_flow()
workflow.connect([(inputnode, maskflow,
[('subject_id', 'inputspec.subject_id'),
('subjects_dir', 'inputspec.subjects_dir')])])
maskflow.inputs.inputspec.contrast_type = 't2'
workflow.connect(realign, 'mean_image', maskflow, 'inputspec.source_file')
smooth = pe.Node(spm.Smooth(), name='smooth')
workflow.connect(inputnode, 'fwhm', smooth, 'fwhm')
workflow.connect(realign, 'realigned_files', smooth, 'in_files')
artdetect = pe.Node(ra.ArtifactDetect(mask_type='file',
parameter_source='SPM',
use_differences=[True, False],
use_norm=True,
save_plot=True),
name='artdetect')
workflow.connect([(inputnode, artdetect,
[('norm_threshold', 'norm_threshold'),
('zintensity_threshold', 'zintensity_threshold')])])
workflow.connect([(realign, artdetect, [
('realigned_files', 'realigned_files'),
('realignment_parameters', 'realignment_parameters')
])])
workflow.connect(maskflow, ('outputspec.mask_file', poplist), artdetect,
'mask_file')
"""
Define the outputs of the workflow and connect the nodes to the outputnode
"""
outputnode = pe.Node(niu.IdentityInterface(fields=[
"realignment_parameters", "smoothed_files", "mask_file", "reg_file",
"reg_cost", 'outlier_files', 'outlier_stats', 'outlier_plots'
]),
name="outputspec")
workflow.connect([
(maskflow, outputnode, [("outputspec.reg_file", "reg_file")]),
(maskflow, outputnode, [("outputspec.reg_cost", "reg_cost")]),
(maskflow, outputnode, [(("outputspec.mask_file", poplist),
"mask_file")]),
(realign, outputnode, [('realignment_parameters',
'realignment_parameters')]),
(smooth, outputnode, [('smoothed_files', 'smoothed_files')]),
(artdetect, outputnode, [('outlier_files', 'outlier_files'),
('statistic_files', 'outlier_stats'),
('plot_files', 'outlier_plots')])
])
return workflow
# contrasts of interest
contrasts_of_interest = pe.Node(niu.Function(inputnames=['subject_id',
'conditions'],
output_names=['contrasts'],
function=_specify_contrast),
name='contrasts_of_interest')
contrasts_of_interest.inputs.conditions = CONDITIONS
workflow.connect(infosource, 'subject_id', contrasts_of_interest, 'subject_id')
# fmri model specifications
unzip_source = pe.MapNode(misc.Gunzip(),
iterfield=['in_file'],
name='unzip_source')
workflow.connect(datasource, 'func', unzip_source, 'in_file')
smooth = pe.Node(interface=spm.Smooth(fwhm=[8, 8, 8]),
name='smooth')
workflow.connect(unzip_source, 'out_file', smooth, 'in_files')
modelspec = pe.Node(interface=modelgen.SpecifySPMModel(),
name='modelspec')
modelspec.inputs.input_units = 'secs'
modelspec.inputs.output_units = 'secs'
modelspec.inputs.time_repetition = TR
modelspec.inputs.high_pass_filter_cutoff = HIGHPASS_CUTOFF
workflow.connect(get_session_informations, 'informations', modelspec, 'subject_info')
workflow.connect(smooth, 'smoothed_files', modelspec, 'functional_runs')
# merge runs's masks
merge_masks = pe.Node(interface=fsl.Merge(dimension='t'),
name='merge_masks')
def create_spm_preproc(c, name='preproc'):
"""
"""
from nipype.workflows.smri.freesurfer.utils import create_getmask_flow
import nipype.algorithms.rapidart as ra
import nipype.interfaces.spm as spm
import nipype.interfaces.utility as niu
import nipype.pipeline.engine as pe
import nipype.interfaces.io as nio
import nipype.interfaces.freesurfer as fs
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=[
'functionals', 'subject_id', 'subjects_dir', 'fwhm', 'norm_threshold',
'zintensity_threshold', 'tr', 'do_slicetime', 'sliceorder',
'parameters', 'node', 'csf_prob', 'wm_prob', 'gm_prob'
]),
name='inputspec')
poplist = lambda x: x.pop()
sym_func = pe.Node(niu.Function(input_names=['in_file'],
output_names=['out_link'],
function=do_symlink),
name='func_symlink')
# REALIGN
realign = pe.Node(niu.Function(
input_names=[
'node', 'in_file', 'tr', 'do_slicetime', 'sliceorder', 'parameters'
],
output_names=['out_file', 'par_file', 'parameter_source'],
function=mod_realign),
name="mod_realign")
workflow.connect(inputnode, 'parameters', realign, 'parameters')
workflow.connect(inputnode, 'functionals', realign, 'in_file')
workflow.connect(inputnode, 'tr', realign, 'tr')
workflow.connect(inputnode, 'do_slicetime', realign, 'do_slicetime')
workflow.connect(inputnode, 'sliceorder', realign, 'sliceorder')
workflow.connect(inputnode, 'node', realign, 'node')
# TAKE MEAN IMAGE
mean = art_mean_workflow()
workflow.connect(realign, 'out_file', mean, 'inputspec.realigned_files')
workflow.connect(realign, 'par_file', mean,
'inputspec.realignment_parameters')
workflow.connect(realign, 'parameter_source', mean,
'inputspec.parameter_source')
# CREATE BRAIN MASK
maskflow = create_getmask_flow()
workflow.connect([(inputnode, maskflow,
[('subject_id', 'inputspec.subject_id'),
('subjects_dir', 'inputspec.subjects_dir')])])
maskflow.inputs.inputspec.contrast_type = 't2'
workflow.connect(mean, 'outputspec.mean_image', maskflow,
'inputspec.source_file')
# SEGMENT
segment = pe.Node(spm.Segment(csf_output_type=[True, True, False],
gm_output_type=[True, True, False],
wm_output_type=[True, True, False]),
name='segment')
mergefunc = lambda in1, in2, in3: [in1, in2, in3]
merge = pe.Node(niu.Function(input_names=['in1', 'in2', 'in3'],
output_names=['out'],
function=mergefunc),
name='merge')
workflow.connect(inputnode, 'csf_prob', merge, 'in3')
workflow.connect(inputnode, 'wm_prob', merge, 'in2')
workflow.connect(inputnode, 'gm_prob', merge, 'in1')
sym_prob = sym_func
workflow.connect(merge, 'out', sym_prob, 'in_file')
workflow.connect(sym_prob, 'out_link', segment, 'tissue_prob_maps')
xform_mask = pe.Node(fs.ApplyVolTransform(fs_target=True),
name='transform_mask')
workflow.connect(maskflow, ('outputspec.reg_file', pickfirst), xform_mask,
'reg_file')
workflow.connect(maskflow, ('outputspec.mask_file', pickfirst), xform_mask,
'source_file')
workflow.connect(xform_mask, "transformed_file", segment, 'mask_image')
fssource = maskflow.get_node('fssource')
convert2nii = pe.Node(fs.MRIConvert(in_type='mgz', out_type='nii'),
name='convert2nii')
workflow.connect(fssource, 'brain', convert2nii, 'in_file')
workflow.connect(convert2nii, 'out_file', segment, 'data')
# NORMALIZE
normalize = pe.MapNode(spm.Normalize(jobtype='write'),
name='normalize',
iterfield=['apply_to_files'])
normalize_struct = normalize.clone('normalize_struct')
normalize_mask = normalize.clone('normalize_mask')
workflow.connect(segment, 'transformation_mat', normalize,
'parameter_file')
workflow.connect(segment, 'transformation_mat', normalize_mask,
'parameter_file')
workflow.connect(segment, 'transformation_mat', normalize_struct,
'parameter_file')
workflow.connect(convert2nii, 'out_file', normalize_struct,
'apply_to_files')
workflow.connect(xform_mask, "transformed_file", normalize_mask,
'apply_to_files')
xform_image = pe.MapNode(fs.ApplyVolTransform(fs_target=True),
name='xform_image',
iterfield=['source_file'])
workflow.connect(maskflow, ('outputspec.reg_file', pickfirst), xform_image,
'reg_file')
workflow.connect(realign, 'out_file', xform_image, "source_file")
workflow.connect(xform_image, "transformed_file", normalize,
"apply_to_files")
#SMOOTH
smooth = pe.Node(spm.Smooth(), name='smooth')
workflow.connect(inputnode, 'fwhm', smooth, 'fwhm')
workflow.connect(normalize, 'normalized_files', smooth, 'in_files')
# ART
artdetect = pe.Node(ra.ArtifactDetect(mask_type='file',
use_differences=[True, False],
use_norm=True,
save_plot=True),
name='artdetect')
workflow.connect(realign, 'parameter_source', artdetect,
'parameter_source')
workflow.connect([(inputnode, artdetect,
[('norm_threshold', 'norm_threshold'),
('zintensity_threshold', 'zintensity_threshold')])])
workflow.connect([(realign, artdetect, [('out_file', 'realigned_files'),
('par_file',
'realignment_parameters')])])
workflow.connect(maskflow, ('outputspec.mask_file', poplist), artdetect,
'mask_file')
# OUTPUTS
outputnode = pe.Node(niu.IdentityInterface(fields=[
"realignment_parameters", "smoothed_files", "mask_file", "mean_image",
"reg_file", "reg_cost", 'outlier_files', 'outlier_stats',
'outlier_plots', 'norm_components', 'mod_csf', 'unmod_csf', 'mod_wm',
'unmod_wm', 'mod_gm', 'unmod_gm', 'mean', 'normalized_struct',
'normalization_parameters', 'reverse_normalize_parameters'
]),
name="outputspec")
workflow.connect([
(maskflow, outputnode, [("outputspec.reg_file", "reg_file")]),
(maskflow, outputnode, [("outputspec.reg_cost", "reg_cost")]),
(realign, outputnode, [('par_file', 'realignment_parameters')]),
(smooth, outputnode, [('smoothed_files', 'smoothed_files')]),
(artdetect, outputnode, [('outlier_files', 'outlier_files'),
('statistic_files', 'outlier_stats'),
('plot_files', 'outlier_plots'),
('norm_files', 'norm_components')])
])
workflow.connect(normalize_mask, "normalized_files", outputnode,
"mask_file")
workflow.connect(segment, 'modulated_csf_image', outputnode, 'mod_csf')
workflow.connect(segment, 'modulated_wm_image', outputnode, 'mod_wm')
workflow.connect(segment, 'modulated_gm_image', outputnode, 'mod_gm')
workflow.connect(segment, 'normalized_csf_image', outputnode, 'unmod_csf')
workflow.connect(segment, 'normalized_wm_image', outputnode, 'unmod_wm')
workflow.connect(segment, 'normalized_gm_image', outputnode, 'unmod_gm')
workflow.connect(mean, 'outputspec.mean_image', outputnode, 'mean')
workflow.connect(normalize_struct, 'normalized_files', outputnode,
'normalized_struct')
workflow.connect(segment, 'transformation_mat', outputnode,
'normalization_parameters')
workflow.connect(segment, 'inverse_transformation_mat', outputnode,
'reverse_normalize_parameters')
# CONNECT TO CONFIG
workflow.inputs.inputspec.fwhm = c.fwhm
workflow.inputs.inputspec.subjects_dir = c.surf_dir
workflow.inputs.inputspec.norm_threshold = c.norm_thresh
workflow.inputs.inputspec.zintensity_threshold = c.z_thresh
workflow.inputs.inputspec.node = c.motion_correct_node
workflow.inputs.inputspec.tr = c.TR
workflow.inputs.inputspec.do_slicetime = c.do_slicetiming
workflow.inputs.inputspec.sliceorder = c.SliceOrder
workflow.inputs.inputspec.csf_prob = c.csf_prob
workflow.inputs.inputspec.gm_prob = c.grey_prob
workflow.inputs.inputspec.wm_prob = c.white_prob
workflow.inputs.inputspec.parameters = {"order": c.order}
workflow.base_dir = c.working_dir
workflow.config = {'execution': {'crashdump_dir': c.crash_dir}}
datagrabber = get_dataflow(c)
workflow.connect(datagrabber, 'func', inputnode, 'functionals')
infosource = pe.Node(niu.IdentityInterface(fields=['subject_id']),
name='subject_names')
if not c.test_mode:
infosource.iterables = ('subject_id', c.subjects)
else:
infosource.iterables = ('subject_id', c.subjects[:1])
workflow.connect(infosource, 'subject_id', inputnode, 'subject_id')
workflow.connect(infosource, 'subject_id', datagrabber, 'subject_id')
sub = lambda x: [('_subject_id_%s' % x, '')]
sinker = pe.Node(nio.DataSink(), name='sinker')
workflow.connect(infosource, 'subject_id', sinker, 'container')
workflow.connect(infosource, ('subject_id', sub), sinker, 'substitutions')
sinker.inputs.base_directory = c.sink_dir
outputspec = workflow.get_node('outputspec')
workflow.connect(outputspec, 'realignment_parameters', sinker,
'spm_preproc.realignment_parameters')
workflow.connect(outputspec, 'smoothed_files', sinker,
'spm_preproc.smoothed_outputs')
workflow.connect(outputspec, 'outlier_files', sinker,
'spm_preproc.art.@outlier_files')
workflow.connect(outputspec, 'outlier_stats', sinker,
'spm_preproc.art.@outlier_stats')
workflow.connect(outputspec, 'outlier_plots', sinker,
'spm_preproc.art.@outlier_plots')
workflow.connect(outputspec, 'norm_components', sinker,
'spm_preproc.art.@norm')
workflow.connect(outputspec, 'reg_file', sinker,
'spm_preproc.bbreg.@reg_file')
workflow.connect(outputspec, 'reg_cost', sinker,
'spm_preproc.bbreg.@reg_cost')
workflow.connect(outputspec, 'mask_file', sinker,
'spm_preproc.mask.@mask_file')
workflow.connect(outputspec, 'mod_csf', sinker,
'spm_preproc.segment.mod.@csf')
workflow.connect(outputspec, 'mod_wm', sinker,
'spm_preproc.segment.mod.@wm')
workflow.connect(outputspec, 'mod_gm', sinker,
'spm_preproc.segment.mod.@gm')
workflow.connect(outputspec, 'unmod_csf', sinker,
'spm_preproc.segment.unmod.@csf')
workflow.connect(outputspec, 'unmod_wm', sinker,
'spm_preproc.segment.unmod.@wm')
workflow.connect(outputspec, 'unmod_gm', sinker,
'spm_preproc.segment.unmod.@gm')
workflow.connect(outputspec, 'mean', sinker, 'spm_preproc.mean')
workflow.connect(outputspec, 'normalized_struct', sinker,
'spm_preproc.normalized_struct')
workflow.connect(outputspec, 'normalization_parameters', sinker,
'spm_preproc.normalization_parameters.@forward')
workflow.connect(outputspec, 'reverse_normalize_parameters', sinker,
'spm_preproc.normalization_parameters.@reverse')
return workflow
def build_core_nodes(self):
"""Build and connect an output node to the pipeline."""
import nipype.interfaces.spm as spm
import nipype.interfaces.spm.utils as spmutils
from nipype.interfaces.petpvc import PETPVC
import nipype.interfaces.utility as nutil
import nipype.pipeline.engine as npe
from clinica.utils.filemanip import unzip_nii
from clinica.utils.spm import spm_standalone_is_available, use_spm_standalone
import clinica.pipelines.pet_volume.pet_volume_utils as utils
if spm_standalone_is_available():
use_spm_standalone()
# Initialize pipeline
# ===================
init_node = npe.Node(interface=nutil.Function(
input_names=['pet_nii'],
output_names=['pet_nii'],
function=utils.init_input_node),
name='init_pipeline')
# Unzipping
# =========
unzip_pet_image = npe.Node(nutil.Function(input_names=['in_file'],
output_names=['out_file'],
function=unzip_nii),
name='unzip_pet_image')
unzip_t1_image_native = npe.Node(nutil.Function(
input_names=['in_file'],
output_names=['out_file'],
function=unzip_nii),
name='unzip_t1_image_native')
unzip_flow_fields = npe.Node(nutil.Function(input_names=['in_file'],
output_names=['out_file'],
function=unzip_nii),
name='unzip_flow_fields')
unzip_dartel_template = npe.Node(nutil.Function(
input_names=['in_file'],
output_names=['out_file'],
function=unzip_nii),
name='unzip_dartel_template')
unzip_reference_mask = npe.Node(nutil.Function(
input_names=['in_file'],
output_names=['out_file'],
function=unzip_nii),
name='unzip_reference_mask')
unzip_mask_tissues = npe.MapNode(nutil.Function(
input_names=['in_file'],
output_names=['out_file'],
function=unzip_nii),
name='unzip_mask_tissues',
iterfield=['in_file'])
# Coregister PET into T1 native space
# ===================================
coreg_pet_t1 = npe.Node(spm.Coregister(), name='coreg_pet_t1')
# Spatially normalize PET into MNI
# ================================
dartel_mni_reg = npe.Node(spm.DARTELNorm2MNI(), name='dartel_mni_reg')
dartel_mni_reg.inputs.modulate = False
dartel_mni_reg.inputs.fwhm = 0
# Reslice reference region mask into PET
# ======================================
reslice = npe.Node(spmutils.Reslice(), name='reslice')
# Normalize PET values according to reference region
# ==================================================
norm_to_ref = npe.Node(nutil.Function(
input_names=['pet_image', 'region_mask'],
output_names=['suvr_pet_path'],
function=utils.normalize_to_reference),
name='norm_to_ref')
# Create binary mask from segmented tissues
# =========================================
binary_mask = npe.Node(nutil.Function(
input_names=['tissues', 'threshold'],
output_names=['out_mask'],
function=utils.create_binary_mask),
name='binary_mask')
binary_mask.inputs.threshold = self.parameters['mask_threshold']
# Mask PET image
# ==============
apply_mask = npe.Node(nutil.Function(
input_names=['image', 'binary_mask'],
output_names=['masked_image_path'],
function=utils.apply_binary_mask),
name='apply_mask')
# Smoothing
# =========
if self.parameters['smooth'] is not None and len(
self.parameters['smooth']) > 0:
smoothing_node = npe.MapNode(spm.Smooth(),
name='smoothing_node',
iterfield=['fwhm', 'out_prefix'])
smoothing_node.inputs.fwhm = [[x, x, x]
for x in self.parameters['smooth']]
smoothing_node.inputs.out_prefix = [
'fwhm-' + str(x) + 'mm_' for x in self.parameters['smooth']
]
self.connect([(apply_mask, smoothing_node, [('masked_image_path',
'in_files')]),
(smoothing_node, self.output_node,
[('smoothed_files', 'pet_suvr_masked_smoothed')])])
else:
self.output_node.inputs.pet_suvr_masked_smoothed = [[]]
# Atlas Statistics
# ================
atlas_stats_node = npe.MapNode(nutil.Function(
input_names=['in_image', 'in_atlas_list'],
output_names=['atlas_statistics'],
function=utils.atlas_statistics),
name='atlas_stats_node',
iterfield=['in_image'])
atlas_stats_node.inputs.in_atlas_list = self.parameters['atlases']
# Connection
# ==========
self.connect([
(self.input_node, init_node, [('pet_image', 'pet_nii')]),
(init_node, unzip_pet_image, [('pet_nii', 'in_file')]),
(self.input_node, unzip_t1_image_native, [('t1_image_native',
'in_file')]),
(self.input_node, unzip_flow_fields, [('flow_fields', 'in_file')]),
(self.input_node, unzip_dartel_template, [('dartel_template',
'in_file')]),
(self.input_node, unzip_reference_mask, [('reference_mask',
'in_file')]),
(self.input_node, unzip_mask_tissues, [('mask_tissues', 'in_file')
]),
(unzip_pet_image, coreg_pet_t1, [('out_file', 'source')]),
(unzip_t1_image_native, coreg_pet_t1, [('out_file', 'target')]),
(unzip_flow_fields, dartel_mni_reg, [('out_file',
'flowfield_files')]),
(unzip_dartel_template, dartel_mni_reg, [('out_file',
'template_file')]),
(unzip_reference_mask, reslice, [('out_file', 'in_file')]),
(unzip_mask_tissues, binary_mask, [('out_file', 'tissues')]),
(coreg_pet_t1, dartel_mni_reg, [('coregistered_source',
'apply_to_files')]),
(dartel_mni_reg, reslice, [('normalized_files', 'space_defining')
]),
(dartel_mni_reg, norm_to_ref, [('normalized_files', 'pet_image')]),
(reslice, norm_to_ref, [('out_file', 'region_mask')]),
(norm_to_ref, apply_mask, [('suvr_pet_path', 'image')]),
(binary_mask, apply_mask, [('out_mask', 'binary_mask')]),
(norm_to_ref, atlas_stats_node, [('suvr_pet_path', 'in_image')]),
(coreg_pet_t1, self.output_node, [('coregistered_source',
'pet_t1_native')]),
(dartel_mni_reg, self.output_node, [('normalized_files', 'pet_mni')
]),
(norm_to_ref, self.output_node, [('suvr_pet_path', 'pet_suvr')]),
(binary_mask, self.output_node, [('out_mask', 'binary_mask')]),
(apply_mask, self.output_node, [('masked_image_path',
'pet_suvr_masked')]),
(atlas_stats_node, self.output_node, [('atlas_statistics',
'atlas_statistics')])
])
# PVC
# ==========
if self.parameters['apply_pvc']:
# Unzipping
# =========
unzip_pvc_mask_tissues = npe.MapNode(nutil.Function(
input_names=['in_file'],
output_names=['out_file'],
function=unzip_nii),
name='unzip_pvc_mask_tissues',
iterfield=['in_file'])
# Creating Mask to use in PVC
# ===========================
pvc_mask = npe.Node(nutil.Function(input_names=['tissues'],
output_names=['out_mask'],
function=utils.create_pvc_mask),
name='pvc_mask')
# PET PVC
# =======
petpvc = npe.Node(PETPVC(), name='pvc')
petpvc.inputs.pvc = 'RBV'
petpvc.inputs.out_file = 'pvc.nii'
# Spatially normalize PET into MNI
# ================================
dartel_mni_reg_pvc = npe.Node(spm.DARTELNorm2MNI(),
name='dartel_mni_reg_pvc')
dartel_mni_reg_pvc.inputs.modulate = False
dartel_mni_reg_pvc.inputs.fwhm = 0
# Reslice reference region mask into PET
# ======================================
reslice_pvc = npe.Node(spmutils.Reslice(), name='reslice_pvc')
# Normalize PET values according to reference region
# ==================================================
norm_to_ref_pvc = npe.Node(nutil.Function(
input_names=['pet_image', 'region_mask'],
output_names=['suvr_pet_path'],
function=utils.normalize_to_reference),
name='norm_to_ref_pvc')
# Mask PET image
# ==============
apply_mask_pvc = npe.Node(nutil.Function(
input_names=['image', 'binary_mask'],
output_names=['masked_image_path'],
function=utils.apply_binary_mask),
name='apply_mask_pvc')
# Smoothing
# =========
if self.parameters['smooth'] is not None and len(
self.parameters['smooth']) > 0:
smoothing_pvc = npe.MapNode(spm.Smooth(),
name='smoothing_pvc',
iterfield=['fwhm', 'out_prefix'])
smoothing_pvc.inputs.fwhm = [[x, x, x]
for x in self.parameters['smooth']
]
smoothing_pvc.inputs.out_prefix = [
'fwhm-' + str(x) + 'mm_' for x in self.parameters['smooth']
]
self.connect([(apply_mask_pvc, smoothing_pvc,
[('masked_image_path', 'in_files')]),
(smoothing_pvc, self.output_node,
[('smoothed_files',
'pet_pvc_suvr_masked_smoothed')])])
else:
self.output_node.inputs.pet_pvc_suvr_masked_smoothed = [[]]
# Atlas Statistics
# ================
atlas_stats_pvc = npe.MapNode(nutil.Function(
input_names=['in_image', 'in_atlas_list'],
output_names=['atlas_statistics'],
function=utils.atlas_statistics),
name='atlas_stats_pvc',
iterfield=['in_image'])
atlas_stats_pvc.inputs.in_atlas_list = self.parameters['atlases']
# Connection
# ==========
self.connect([
(self.input_node, unzip_pvc_mask_tissues, [('pvc_mask_tissues',
'in_file')]),
(unzip_pvc_mask_tissues, pvc_mask, [('out_file', 'tissues')]),
(unzip_flow_fields, dartel_mni_reg_pvc, [('out_file',
'flowfield_files')]),
(unzip_dartel_template, dartel_mni_reg_pvc,
[('out_file', 'template_file')]),
(unzip_reference_mask, reslice_pvc, [('out_file', 'in_file')]),
(coreg_pet_t1, petpvc, [('coregistered_source', 'in_file'),
(('coregistered_source',
utils.pet_pvc_name, 'RBV'),
'out_file')]),
(pvc_mask, petpvc, [('out_mask', 'mask_file')]),
(self.input_node, petpvc,
[(('psf', utils.get_from_list, 0), 'fwhm_x'),
(('psf', utils.get_from_list, 1), 'fwhm_y'),
(('psf', utils.get_from_list, 2), 'fwhm_z')]),
(petpvc, dartel_mni_reg_pvc, [('out_file', 'apply_to_files')]),
(dartel_mni_reg_pvc, reslice_pvc, [('normalized_files',
'space_defining')]),
(dartel_mni_reg_pvc, norm_to_ref_pvc, [('normalized_files',
'pet_image')]),
(reslice_pvc, norm_to_ref_pvc, [('out_file', 'region_mask')]),
(norm_to_ref_pvc, apply_mask_pvc, [('suvr_pet_path', 'image')
]),
(binary_mask, apply_mask_pvc, [('out_mask', 'binary_mask')]),
(norm_to_ref_pvc, atlas_stats_pvc, [('suvr_pet_path',
'in_image')]),
(petpvc, self.output_node, [('out_file', 'pet_pvc')]),
(dartel_mni_reg_pvc, self.output_node, [('normalized_files',
'pet_pvc_mni')]),
(norm_to_ref_pvc, self.output_node, [('suvr_pet_path',
'pet_pvc_suvr')]),
(apply_mask_pvc, self.output_node, [('masked_image_path',
'pet_pvc_suvr_masked')]),
(atlas_stats_pvc, self.output_node, [('atlas_statistics',
'pvc_atlas_statistics')])
])
else:
self.output_node.inputs.pet_pvc = [[]]
self.output_node.inputs.pet_pvc_mni = [[]]
self.output_node.inputs.pet_pvc_suvr = [[]]
self.output_node.inputs.pet_pvc_suvr_masked = [[]]
self.output_node.inputs.pvc_atlas_statistics = [[]]
self.output_node.inputs.pet_pvc_suvr_masked_smoothed = [[]]
# Start at the slice-time corrected image base_fname = 'afds114_sub009_t2r1.nii' structural_fname = 'ds114_sub009_highres.nii' # Realign realign = spm.Realign() realign.inputs.in_files = base_fname # Do not write resliced files, do write mean image realign.inputs.write_which = [0, 1] realign.run() # Coregistration coreg = spm.Coregister() # Coregister structural to mean image from realignment coreg.inputs.target = 'mean' + base_fname coreg.inputs.source = structural_fname coreg.run() # Normalization / resampling with normalization + realign params seg_norm = spm.Normalize12() seg_norm.inputs.image_to_align = structural_fname seg_norm.inputs.apply_to_files = base_fname seg_norm.run() # Smoothing smooth = spm.Smooth() smooth.inputs.in_files = 'w' + base_fname smooth.inputs.fwhm = [8, 8, 8] smooth.run()
def build_core_nodes(self):
"""Build and connect the core nodes of the pipelines.
"""
import os
import platform
import nipype.interfaces.spm as spm
import nipype.interfaces.matlab as mlab
import nipype.pipeline.engine as npe
import nipype.interfaces.utility as nutil
import clinica.pipelines.t1_volume_tissue_segmentation.t1_volume_tissue_segmentation_utils as seg_utils
import clinica.pipelines.t1_volume_create_dartel.t1_volume_create_dartel_utils as dartel_utils
import clinica.pipelines.t1_volume_dartel2mni.t1_volume_dartel2mni_utils as dartel2mni_utils
from clinica.utils.io import unzip_nii
spm_home = os.getenv("SPM_HOME")
mlab_home = os.getenv("MATLABCMD")
mlab.MatlabCommand.set_default_matlab_cmd(mlab_home)
mlab.MatlabCommand.set_default_paths(spm_home)
if 'SPMSTANDALONE_HOME' in os.environ:
if 'MCR_HOME' in os.environ:
matlab_cmd = os.path.join(os.environ['SPMSTANDALONE_HOME'],
'run_spm12.sh') \
+ ' ' + os.environ['MCR_HOME'] \
+ ' script'
spm.SPMCommand.set_mlab_paths(matlab_cmd=matlab_cmd, use_mcr=True)
version = spm.SPMCommand().version
else:
raise EnvironmentError('MCR_HOME variable not in environnement. Althought, '
+ 'SPMSTANDALONE_HOME has been found')
else:
version = spm.Info.getinfo()
if version:
if isinstance(version, dict):
spm_path = version['path']
if version['name'] == 'SPM8':
print('You are using SPM version 8. The recommended version to use with Clinica is SPM 12. '
+ 'Please upgrade your SPM toolbox.')
tissue_map = os.path.join(spm_path, 'toolbox/Seg/TPM.nii')
elif version['name'] == 'SPM12':
tissue_map = os.path.join(spm_path, 'tpm/TPM.nii')
else:
raise RuntimeError('SPM version 8 or 12 could not be found. Please upgrade your SPM toolbox.')
if isinstance(version, str):
if float(version) >= 12.7169:
if platform.system() == 'Darwin':
tissue_map = os.path.join(str(spm_home), 'spm12.app/Contents/MacOS/spm12_mcr/spm12/spm12/tpm/TPM.nii')
else:
tissue_map = os.path.join(str(spm_home), 'spm12_mcr/spm/spm12/tpm/TPM.nii')
else:
raise RuntimeError('SPM standalone version not supported. Please upgrade SPM standalone.')
else:
raise RuntimeError('SPM could not be found. Please verify your SPM_HOME environment variable.')
# Unzipping
# ===============================
unzip_node = npe.MapNode(nutil.Function(input_names=['in_file'],
output_names=['out_file'],
function=unzip_nii),
name='unzip_node', iterfield=['in_file'])
# Unified Segmentation
# ===============================
new_segment = npe.MapNode(spm.NewSegment(),
name='new_segment',
iterfield=['channel_files'])
if self.parameters['affine_regularization'] is not None:
new_segment.inputs.affine_regularization = self.parameters['affine_regularization']
if self.parameters['channel_info'] is not None:
new_segment.inputs.channel_info = self.parameters['channel_info']
if self.parameters['sampling_distance'] is not None:
new_segment.inputs.sampling_distance = self.parameters['sampling_distance']
if self.parameters['warping_regularization'] is not None:
new_segment.inputs.warping_regularization = self.parameters['warping_regularization']
# Check if we need to save the forward transformation for registering the T1 to the MNI space
if self.parameters['save_t1_mni'] is not None and self.parameters['save_t1_mni']:
if self.parameters['write_deformation_fields'] is not None:
self.parameters['write_deformation_fields'][1] = True
else:
self.parameters['write_deformation_fields'] = [False, True]
if self.parameters['write_deformation_fields'] is not None:
new_segment.inputs.write_deformation_fields = self.parameters['write_deformation_fields']
if self.parameters['tpm'] is not None:
tissue_map = self.parameters['tpm']
new_segment.inputs.tissues = seg_utils.get_tissue_tuples(tissue_map,
self.parameters['tissue_classes'],
self.parameters['dartel_tissues'],
self.parameters['save_warped_unmodulated'],
self.parameters['save_warped_modulated'])
# Apply segmentation deformation to T1 (into MNI space)
# ========================================================
if self.parameters['save_t1_mni'] is not None and self.parameters['save_t1_mni']:
t1_to_mni = npe.MapNode(seg_utils.ApplySegmentationDeformation(),
name='t1_to_mni',
iterfield=['deformation_field', 'in_files'])
self.connect([
(unzip_node, t1_to_mni, [('out_file', 'in_files')]),
(new_segment, t1_to_mni, [('forward_deformation_field', 'deformation_field')]),
(t1_to_mni, self.output_node, [('out_files', 't1_mni')])
])
# DARTEL template
# ===============================
dartel_template = npe.Node(spm.DARTEL(),
name='dartel_template')
if self.parameters['iteration_parameters'] is not None:
dartel_template.inputs.iteration_parameters = self.parameters['iteration_parameters']
if self.parameters['optimization_parameters'] is not None:
dartel_template.inputs.optimization_parameters = self.parameters['optimization_parameters']
if self.parameters['regularization_form'] is not None:
dartel_template.inputs.regularization_form = self.parameters['regularization_form']
# DARTEL2MNI Registration
# =======================
dartel2mni_node = npe.MapNode(spm.DARTELNorm2MNI(),
name='dartel2MNI',
iterfield=['apply_to_files', 'flowfield_files'])
if self.parameters['bounding_box'] is not None:
dartel2mni_node.inputs.bounding_box = self.parameters['bounding_box']
if self.parameters['voxel_size'] is not None:
dartel2mni_node.inputs.voxel_size = self.parameters['voxel_size']
dartel2mni_node.inputs.modulate = self.parameters['modulation']
dartel2mni_node.inputs.fwhm = 0
# Smoothing
# =========
if self.parameters['fwhm'] is not None and len(self.parameters['fwhm']) > 0:
smoothing_node = npe.MapNode(spm.Smooth(),
name='smoothing_node',
iterfield=['in_files'])
smoothing_node.iterables = [('fwhm', [[x, x, x] for x in self.parameters['fwhm']]),
('out_prefix', ['fwhm-' + str(x) + 'mm_' for x in self.parameters['fwhm']])]
smoothing_node.synchronize = True
join_smoothing_node = npe.JoinNode(interface=nutil.Function(input_names=['smoothed_normalized_files'],
output_names=['smoothed_normalized_files'],
function=dartel2mni_utils.join_smoothed_files),
joinsource='smoothing_node',
joinfield='smoothed_normalized_files',
name='join_smoothing_node')
self.connect([
(dartel2mni_node, smoothing_node, [('normalized_files', 'in_files')]),
(smoothing_node, join_smoothing_node, [('smoothed_files', 'smoothed_normalized_files')]),
(join_smoothing_node, self.output_node, [('smoothed_normalized_files', 'smoothed_normalized_files')])
])
else:
self.output_node.inputs.smoothed_normalized_files = []
# Atlas Statistics
# ================
atlas_stats_node = npe.MapNode(nutil.Function(input_names=['in_image',
'in_atlas_list'],
output_names=['atlas_statistics'],
function=dartel2mni_utils.atlas_statistics),
name='atlas_stats_node',
iterfield=['in_image'])
atlas_stats_node.inputs.in_atlas_list = self.parameters['atlas_list']
# Connection
# ==========
self.connect([
(self.input_node, unzip_node, [('input_images', 'in_file')]),
(unzip_node, new_segment, [('out_file', 'channel_files')]),
(new_segment, self.output_node, [('bias_corrected_images', 'bias_corrected_images'),
('bias_field_images', 'bias_field_images'),
('dartel_input_images', 'dartel_input_images'),
('forward_deformation_field', 'forward_deformation_field'),
('inverse_deformation_field', 'inverse_deformation_field'),
('modulated_class_images', 'modulated_class_images'),
('native_class_images', 'native_class_images'),
('normalized_class_images', 'normalized_class_images'),
('transformation_mat', 'transformation_mat')]),
(new_segment, dartel_template, [(('dartel_input_images', dartel_utils.get_class_images,
self.parameters['dartel_tissues']), 'image_files')]),
(dartel_template, self.output_node, [('dartel_flow_fields', 'dartel_flow_fields'),
('final_template_file', 'final_template_file'),
('template_files', 'template_files')]),
(new_segment, dartel2mni_node, [(('native_class_images', seg_utils.group_nested_images_by_subject),
'apply_to_files')]),
(dartel_template, dartel2mni_node, [(('dartel_flow_fields', dartel2mni_utils.prepare_flowfields,
self.parameters['tissue_classes']), 'flowfield_files')]),
(dartel_template, dartel2mni_node, [('final_template_file', 'template_file')]),
(dartel2mni_node, self.output_node, [('normalized_files', 'normalized_files')]),
(dartel2mni_node, atlas_stats_node, [(('normalized_files', dartel2mni_utils.select_gm_images),
'in_image')]),
(atlas_stats_node, self.output_node, [('atlas_statistics', 'atlas_statistics')])
])
#Slice timing corrected (gets timing from header) st_corr = Node(spm.SliceTiming(), name='slicetiming_correction') st_corr.inputs.ref_slice = 1 #Outputs: timecorrected_files #Realignment using SPM <--- Maybe just estimate and apply all transforms at the end? realign = Node(spm.Realign(), name='realign') realign.inputs.register_to_mean = False realign.inputs.quality = 1.0 #Outputs: realignment_parameters, reliced epi images (motion corrected) tsnr = Node(misc.TSNR(), name='tsnr') tsnr.inputs.regress_poly = 2 #Outputs: detrended_file, mean_file, stddev_file, tsnr_file smooth = Node(spm.Smooth(), name='smooth') smooth.inputs.fwhm = fwhm ####Anatomical preprocessing#### #dcmstack - Convert dicoms to nii (with embeded metadata) anat_stack = Node(dcmstack.DcmStack(), name='anatstack') anat_stack.inputs.embed_meta = True anat_stack.inputs.out_format = 'anat' anat_stack.inputs.out_ext = '.nii' #Outputs: out_file #Coregisters FLAIR & mask to T1 (NOTE: settings taken from Clinical Toolbox) flaircoreg = Node(spm.Coregister(), name='coreg2anat') flaircoreg.inputs.cost_function = 'nmi' flaircoreg.inputs.separation = [4, 2]
plt.plot(abs_disp)
plt.plot(rel_disp)
plt.legend(['abs', 'rel'])
plt.savefig('Motion')
Plot_Motion = Node(name = 'Plot_Motion',
interface = Function(input_names = ['motion_par','abs_disp','rel_disp'],
function = Plot_Motion))
# In[12]:
#-----------------------------------------------------------------------------------------------------
#Use spm smoothin, because, as you know, fsl does not support anisotropic smoothing
Spm_Smoothing = Node(spm.Smooth(), name = 'Smoothing')
#I tried all these kernels and this one is most reasonable one
Spm_Smoothing.inputs.fwhm = [5.75, 5.75, 8]
#Spm_Smoothing.iterables = ('fwhm', [[5,5,8],[5.75,5.75,8],[5.75,5.75,10], [5.75,5.75,16]])
#-----------------------------------------------------------------------------------------------------
#Getting median intensity
Median_Intensity = Node(fsl.ImageStats(), name = 'Median_Intensity')
#Put -k before -p 50
Median_Intensity.inputs.op_string = '-k %s -p 50'
#Scale median intensity
def Scale_Median_Intensity (median_intensity):
scaling = 10000/median_intensity
return scaling
def build_core_nodes(self):
"""Build and connect the core nodes of the pipelines.
"""
import fmri_preprocessing_workflows as utils
import nipype.interfaces.utility as nutil
import nipype.interfaces.spm as spm
import nipype.pipeline.engine as npe
from clinica.utils.filemanip import zip_nii, unzip_nii
# Zipping
# =======
unzip_node = npe.MapNode(name='Unzipping',
iterfield=['in_file'],
interface=nutil.Function(
input_names=['in_file'],
output_names=['out_file'],
function=unzip_nii))
unzip_T1w = unzip_node.clone('UnzippingT1w')
unzip_phasediff = unzip_node.clone('UnzippingPhasediff')
unzip_bold = unzip_node.clone('UnzippingBold')
unzip_magnitude1 = unzip_node.clone('UnzippingMagnitude1')
# FieldMap calculation
# ====================
if self.parameters['unwarping']:
fm_node = npe.MapNode(name="FieldMapCalculation",
iterfield=[
'phase', 'magnitude', 'epi', 'et',
'blipdir', 'tert'
],
interface=spm.FieldMap())
# Slice timing correction
# =======================
st_node = npe.MapNode(name="SliceTimingCorrection",
iterfield=[
'in_files', 'time_repetition', 'slice_order',
'num_slices', 'ref_slice', 'time_acquisition'
],
interface=spm.SliceTiming())
# Motion correction and unwarping
# ===============================
if self.parameters['unwarping']:
mc_node = npe.MapNode(name="MotionCorrectionUnwarping",
iterfield=["scans", "pmscan"],
interface=spm.RealignUnwarp())
mc_node.inputs.register_to_mean = True
mc_node.inputs.reslice_mask = False
else:
mc_node = npe.MapNode(name="MotionCorrection",
iterfield=["in_files"],
interface=spm.Realign())
mc_node.inputs.register_to_mean = True
# Brain extraction
# ================
import os.path as path
from nipype.interfaces.freesurfer import MRIConvert
if self.parameters['freesurfer_brain_mask']:
brain_masks = [
path.join(self.caps_directory, 'subjects', self.subjects[i],
self.sessions[i], 't1/freesurfer_cross_sectional',
self.subjects[i] + '_' + self.sessions[i],
'mri/brain.mgz') for i in range(len(self.subjects))
]
conv_brain_masks = [
str(self.subjects[i] + '_' + self.sessions[i] + '.nii')
for i in range(len(self.subjects))
]
bet_node = npe.MapNode(interface=MRIConvert(),
iterfield=["in_file", "out_file"],
name="BrainConversion")
bet_node.inputs.in_file = brain_masks
bet_node.inputs.out_file = conv_brain_masks
bet_node.inputs.out_type = 'nii'
else:
bet_node = utils.BrainExtractionWorkflow(name="BrainExtraction")
# Registration
# ============
reg_node = npe.MapNode(
interface=spm.Coregister(),
iterfield=["apply_to_files", "source", "target"],
name="Registration")
# Normalization
# =============
norm_node = npe.MapNode(interface=spm.Normalize12(),
iterfield=['image_to_align', 'apply_to_files'],
name='Normalization')
# Smoothing
# =========
smooth_node = npe.MapNode(interface=spm.Smooth(),
iterfield=['in_files'],
name='Smoothing')
smooth_node.inputs.fwhm = self.parameters['full_width_at_half_maximum']
# Zipping
# =======
zip_node = npe.MapNode(name='Zipping',
iterfield=['in_file'],
interface=nutil.Function(
input_names=['in_file'],
output_names=['out_file'],
function=zip_nii))
zip_bet_node = zip_node.clone('ZippingBET')
zip_mc_node = zip_node.clone('ZippingMC')
zip_reg_node = zip_node.clone('ZippingRegistration')
zip_norm_node = zip_node.clone('ZippingNormalization')
zip_smooth_node = zip_node.clone('ZippingSmoothing')
# Connections
# ===========
if self.parameters['freesurfer_brain_mask']:
self.connect([
# Brain extraction
(bet_node, reg_node, [('out_file', 'target')]),
(bet_node, zip_bet_node, [('out_file', 'in_file')]),
])
else:
self.connect([
# Brain extraction
(unzip_T1w, bet_node, [('out_file', 'Segmentation.data')]),
(unzip_T1w, bet_node, [('out_file', 'ApplyMask.in_file')]),
(bet_node, reg_node, [('ApplyMask.out_file', 'target')]),
(bet_node, zip_bet_node, [('Fill.out_file', 'in_file')]),
])
if self.parameters['unwarping']:
self.connect([
# FieldMap calculation
(self.input_node, fm_node, [('et', 'et')]),
(self.input_node, fm_node, [('blipdir', 'blipdir')]),
(self.input_node, fm_node, [('tert', 'tert')]),
(self.input_node, unzip_phasediff, [('phasediff', 'in_file')]),
(self.input_node, unzip_magnitude1, [('magnitude1', 'in_file')
]),
(unzip_magnitude1, fm_node, [('out_file', 'magnitude')]),
(unzip_phasediff, fm_node, [('out_file', 'phase')]),
(unzip_bold, fm_node, [('out_file', 'epi')]),
# Motion correction and unwarping
(st_node, mc_node, [('timecorrected_files', 'scans')]),
(fm_node, mc_node, [('vdm', 'pmscan')]),
(mc_node, reg_node, [('realigned_unwarped_files',
'apply_to_files')]),
(mc_node, zip_mc_node, [('realigned_unwarped_files', 'in_file')
]),
])
else:
self.connect([
# Motion correction and unwarping
(st_node, mc_node, [('timecorrected_files', 'in_files')]),
(mc_node, reg_node, [('realigned_files', 'apply_to_files')]),
(mc_node, zip_mc_node, [('realigned_files', 'in_file')]),
])
self.connect([
# Unzipping
(self.input_node, unzip_T1w, [('T1w', 'in_file')]),
(self.input_node, unzip_bold, [('bold', 'in_file')]),
# Slice timing correction
(unzip_bold, st_node, [('out_file', 'in_files')]),
(self.input_node, st_node, [('time_repetition', 'time_repetition')
]),
(self.input_node, st_node, [('num_slices', 'num_slices')]),
(self.input_node, st_node, [('slice_order', 'slice_order')]),
(self.input_node, st_node, [('ref_slice', 'ref_slice')]),
(self.input_node, st_node, [('time_acquisition',
'time_acquisition')]),
# Registration
(mc_node, reg_node, [('mean_image', 'source')]),
# Normalization
(unzip_T1w, norm_node, [('out_file', 'image_to_align')]),
(reg_node, norm_node, [('coregistered_files', 'apply_to_files')]),
# Smoothing
(norm_node, smooth_node, [('normalized_files', 'in_files')]),
# Zipping
(reg_node, zip_reg_node, [('coregistered_files', 'in_file')]),
(norm_node, zip_norm_node, [('normalized_files', 'in_file')]),
(smooth_node, zip_smooth_node, [('smoothed_files', 'in_file')]),
# Returning output
(zip_bet_node, self.output_node, [('out_file', 't1_brain_mask')]),
(mc_node, self.output_node, [('realignment_parameters',
'mc_params')]),
(zip_mc_node, self.output_node, [('out_file', 'native_fmri')]),
(zip_reg_node, self.output_node, [('out_file', 't1_fmri')]),
(zip_norm_node, self.output_node, [('out_file', 'mni_fmri')]),
(zip_smooth_node, self.output_node, [('out_file',
'mni_smoothed_fmri')]),
])
def create_workflow(files,
target_file,
subject_id,
TR,
slice_times,
norm_threshold=1,
num_components=5,
vol_fwhm=None,
surf_fwhm=None,
lowpass_freq=-1,
highpass_freq=-1,
subjects_dir=None,
sink_directory=os.getcwd(),
target_subject=['fsaverage3', 'fsaverage4'],
name='resting'):
wf = Workflow(name=name)
# Rename files in case they are named identically
name_unique = MapNode(Rename(format_string='rest_%(run)02d'),
iterfield=['in_file', 'run'],
name='rename')
name_unique.inputs.keep_ext = True
name_unique.inputs.run = list(range(1, len(files) + 1))
name_unique.inputs.in_file = files
realign = Node(interface=spm.Realign(), name="realign")
realign.inputs.jobtype = 'estwrite'
num_slices = len(slice_times)
slice_timing = Node(interface=spm.SliceTiming(), name="slice_timing")
slice_timing.inputs.num_slices = num_slices
slice_timing.inputs.time_repetition = TR
slice_timing.inputs.time_acquisition = TR - TR / float(num_slices)
slice_timing.inputs.slice_order = (np.argsort(slice_times) + 1).tolist()
slice_timing.inputs.ref_slice = int(num_slices / 2)
# Comute TSNR on realigned data regressing polynomials upto order 2
tsnr = MapNode(TSNR(regress_poly=2), iterfield=['in_file'], name='tsnr')
wf.connect(slice_timing, 'timecorrected_files', tsnr, 'in_file')
# Compute the median image across runs
calc_median = Node(Function(input_names=['in_files'],
output_names=['median_file'],
function=median,
imports=imports),
name='median')
wf.connect(tsnr, 'detrended_file', calc_median, 'in_files')
"""Segment and Register
"""
registration = create_reg_workflow(name='registration')
wf.connect(calc_median, 'median_file', registration,
'inputspec.mean_image')
registration.inputs.inputspec.subject_id = subject_id
registration.inputs.inputspec.subjects_dir = subjects_dir
registration.inputs.inputspec.target_image = target_file
"""Use :class:`nipype.algorithms.rapidart` to determine which of the
images in the functional series are outliers based on deviations in
intensity or movement.
"""
art = Node(interface=ArtifactDetect(), name="art")
art.inputs.use_differences = [True, True]
art.inputs.use_norm = True
art.inputs.norm_threshold = norm_threshold
art.inputs.zintensity_threshold = 9
art.inputs.mask_type = 'spm_global'
art.inputs.parameter_source = 'SPM'
"""Here we are connecting all the nodes together. Notice that we add the merge node only if you choose
to use 4D. Also `get_vox_dims` function is passed along the input volume of normalise to set the optimal
voxel sizes.
"""
wf.connect([
(name_unique, realign, [('out_file', 'in_files')]),
(realign, slice_timing, [('realigned_files', 'in_files')]),
(slice_timing, art, [('timecorrected_files', 'realigned_files')]),
(realign, art, [('realignment_parameters', 'realignment_parameters')]),
])
def selectindex(files, idx):
import numpy as np
from nipype.utils.filemanip import filename_to_list, list_to_filename
return list_to_filename(
np.array(filename_to_list(files))[idx].tolist())
mask = Node(fsl.BET(), name='getmask')
mask.inputs.mask = True
wf.connect(calc_median, 'median_file', mask, 'in_file')
# get segmentation in normalized functional space
def merge_files(in1, in2):
out_files = filename_to_list(in1)
out_files.extend(filename_to_list(in2))
return out_files
# filter some noise
# Compute motion regressors
motreg = Node(Function(
input_names=['motion_params', 'order', 'derivatives'],
output_names=['out_files'],
function=motion_regressors,
imports=imports),
name='getmotionregress')
wf.connect(realign, 'realignment_parameters', motreg, 'motion_params')
# Create a filter to remove motion and art confounds
createfilter1 = Node(Function(
input_names=['motion_params', 'comp_norm', 'outliers', 'detrend_poly'],
output_names=['out_files'],
function=build_filter1,
imports=imports),
name='makemotionbasedfilter')
createfilter1.inputs.detrend_poly = 2
wf.connect(motreg, 'out_files', createfilter1, 'motion_params')
wf.connect(art, 'norm_files', createfilter1, 'comp_norm')
wf.connect(art, 'outlier_files', createfilter1, 'outliers')
filter1 = MapNode(fsl.GLM(out_f_name='F_mcart.nii',
out_pf_name='pF_mcart.nii',
demean=True),
iterfield=['in_file', 'design', 'out_res_name'],
name='filtermotion')
wf.connect(slice_timing, 'timecorrected_files', filter1, 'in_file')
wf.connect(slice_timing, ('timecorrected_files', rename, '_filtermotart'),
filter1, 'out_res_name')
wf.connect(createfilter1, 'out_files', filter1, 'design')
createfilter2 = MapNode(Function(input_names=[
'realigned_file', 'mask_file', 'num_components', 'extra_regressors'
],
output_names=['out_files'],
function=extract_noise_components,
imports=imports),
iterfield=['realigned_file', 'extra_regressors'],
name='makecompcorrfilter')
createfilter2.inputs.num_components = num_components
wf.connect(createfilter1, 'out_files', createfilter2, 'extra_regressors')
wf.connect(filter1, 'out_res', createfilter2, 'realigned_file')
wf.connect(registration,
('outputspec.segmentation_files', selectindex, [0, 2]),
createfilter2, 'mask_file')
filter2 = MapNode(fsl.GLM(out_f_name='F.nii',
out_pf_name='pF.nii',
demean=True),
iterfield=['in_file', 'design', 'out_res_name'],
name='filter_noise_nosmooth')
wf.connect(filter1, 'out_res', filter2, 'in_file')
wf.connect(filter1, ('out_res', rename, '_cleaned'), filter2,
'out_res_name')
wf.connect(createfilter2, 'out_files', filter2, 'design')
wf.connect(mask, 'mask_file', filter2, 'mask')
bandpass = Node(Function(
input_names=['files', 'lowpass_freq', 'highpass_freq', 'fs'],
output_names=['out_files'],
function=bandpass_filter,
imports=imports),
name='bandpass_unsmooth')
bandpass.inputs.fs = 1. / TR
bandpass.inputs.highpass_freq = highpass_freq
bandpass.inputs.lowpass_freq = lowpass_freq
wf.connect(filter2, 'out_res', bandpass, 'files')
"""Smooth the functional data using
:class:`nipype.interfaces.spm.Smooth`.
"""
smooth = Node(interface=spm.Smooth(), name="smooth")
smooth.inputs.fwhm = vol_fwhm
wf.connect(bandpass, 'out_files', smooth, 'in_files')
collector = Node(Merge(2), name='collect_streams')
wf.connect(smooth, 'smoothed_files', collector, 'in1')
wf.connect(bandpass, 'out_files', collector, 'in2')
"""
Transform the remaining images. First to anatomical and then to target
"""
warpall = MapNode(ants.ApplyTransforms(),
iterfield=['input_image'],
name='warpall')
warpall.inputs.input_image_type = 3
warpall.inputs.interpolation = 'Linear'
warpall.inputs.invert_transform_flags = [False, False]
warpall.inputs.terminal_output = 'file'
warpall.inputs.reference_image = target_file
warpall.inputs.args = '--float'
warpall.inputs.num_threads = 1
# transform to target
wf.connect(collector, 'out', warpall, 'input_image')
wf.connect(registration, 'outputspec.transforms', warpall, 'transforms')
mask_target = Node(fsl.ImageMaths(op_string='-bin'), name='target_mask')
wf.connect(registration, 'outputspec.anat2target', mask_target, 'in_file')
maskts = MapNode(fsl.ApplyMask(), iterfield=['in_file'], name='ts_masker')
wf.connect(warpall, 'output_image', maskts, 'in_file')
wf.connect(mask_target, 'out_file', maskts, 'mask_file')
# map to surface
# extract aparc+aseg ROIs
# extract subcortical ROIs
# extract target space ROIs
# combine subcortical and cortical rois into a single cifti file
#######
# Convert aparc to subject functional space
# Sample the average time series in aparc ROIs
sampleaparc = MapNode(
freesurfer.SegStats(default_color_table=True),
iterfield=['in_file', 'summary_file', 'avgwf_txt_file'],
name='aparc_ts')
sampleaparc.inputs.segment_id = ([8] + list(range(10, 14)) +
[17, 18, 26, 47] + list(range(49, 55)) +
[58] + list(range(1001, 1036)) +
list(range(2001, 2036)))
wf.connect(registration, 'outputspec.aparc', sampleaparc,
'segmentation_file')
wf.connect(collector, 'out', sampleaparc, 'in_file')
def get_names(files, suffix):
"""Generate appropriate names for output files
"""
from nipype.utils.filemanip import (split_filename, filename_to_list,
list_to_filename)
out_names = []
for filename in files:
_, name, _ = split_filename(filename)
out_names.append(name + suffix)
return list_to_filename(out_names)
wf.connect(collector, ('out', get_names, '_avgwf.txt'), sampleaparc,
'avgwf_txt_file')
wf.connect(collector, ('out', get_names, '_summary.stats'), sampleaparc,
'summary_file')
# Sample the time series onto the surface of the target surface. Performs
# sampling into left and right hemisphere
target = Node(IdentityInterface(fields=['target_subject']), name='target')
target.iterables = ('target_subject', filename_to_list(target_subject))
samplerlh = MapNode(freesurfer.SampleToSurface(),
iterfield=['source_file'],
name='sampler_lh')
samplerlh.inputs.sampling_method = "average"
samplerlh.inputs.sampling_range = (0.1, 0.9, 0.1)
samplerlh.inputs.sampling_units = "frac"
samplerlh.inputs.interp_method = "trilinear"
samplerlh.inputs.smooth_surf = surf_fwhm
# samplerlh.inputs.cortex_mask = True
samplerlh.inputs.out_type = 'niigz'
samplerlh.inputs.subjects_dir = subjects_dir
samplerrh = samplerlh.clone('sampler_rh')
samplerlh.inputs.hemi = 'lh'
wf.connect(collector, 'out', samplerlh, 'source_file')
wf.connect(registration, 'outputspec.out_reg_file', samplerlh, 'reg_file')
wf.connect(target, 'target_subject', samplerlh, 'target_subject')
samplerrh.set_input('hemi', 'rh')
wf.connect(collector, 'out', samplerrh, 'source_file')
wf.connect(registration, 'outputspec.out_reg_file', samplerrh, 'reg_file')
wf.connect(target, 'target_subject', samplerrh, 'target_subject')
# Combine left and right hemisphere to text file
combiner = MapNode(Function(input_names=['left', 'right'],
output_names=['out_file'],
function=combine_hemi,
imports=imports),
iterfield=['left', 'right'],
name="combiner")
wf.connect(samplerlh, 'out_file', combiner, 'left')
wf.connect(samplerrh, 'out_file', combiner, 'right')
# Sample the time series file for each subcortical roi
ts2txt = MapNode(Function(
input_names=['timeseries_file', 'label_file', 'indices'],
output_names=['out_file'],
function=extract_subrois,
imports=imports),
iterfield=['timeseries_file'],
name='getsubcortts')
ts2txt.inputs.indices = [8] + list(range(10, 14)) + [17, 18, 26, 47] +\
list(range(49, 55)) + [58]
ts2txt.inputs.label_file = \
os.path.abspath(('OASIS-TRT-20_jointfusion_DKT31_CMA_labels_in_MNI152_'
'2mm_v2.nii.gz'))
wf.connect(maskts, 'out_file', ts2txt, 'timeseries_file')
######
substitutions = [('_target_subject_', ''),
('_filtermotart_cleaned_bp_trans_masked', ''),
('_filtermotart_cleaned_bp', '')]
regex_subs = [
('_ts_masker.*/sar', '/smooth/'),
('_ts_masker.*/ar', '/unsmooth/'),
('_combiner.*/sar', '/smooth/'),
('_combiner.*/ar', '/unsmooth/'),
('_aparc_ts.*/sar', '/smooth/'),
('_aparc_ts.*/ar', '/unsmooth/'),
('_getsubcortts.*/sar', '/smooth/'),
('_getsubcortts.*/ar', '/unsmooth/'),
('series/sar', 'series/smooth/'),
('series/ar', 'series/unsmooth/'),
('_inverse_transform./', ''),
]
# Save the relevant data into an output directory
datasink = Node(interface=DataSink(), name="datasink")
datasink.inputs.base_directory = sink_directory
datasink.inputs.container = subject_id
datasink.inputs.substitutions = substitutions
datasink.inputs.regexp_substitutions = regex_subs # (r'(/_.*(\d+/))', r'/run\2')
wf.connect(realign, 'realignment_parameters', datasink,
'resting.qa.motion')
wf.connect(art, 'norm_files', datasink, 'resting.qa.art.@norm')
wf.connect(art, 'intensity_files', datasink, 'resting.qa.art.@intensity')
wf.connect(art, 'outlier_files', datasink, 'resting.qa.art.@outlier_files')
wf.connect(registration, 'outputspec.segmentation_files', datasink,
'resting.mask_files')
wf.connect(registration, 'outputspec.anat2target', datasink,
'resting.qa.ants')
wf.connect(mask, 'mask_file', datasink, 'resting.mask_files.@brainmask')
wf.connect(mask_target, 'out_file', datasink, 'resting.mask_files.target')
wf.connect(filter1, 'out_f', datasink, 'resting.qa.compmaps.@mc_F')
wf.connect(filter1, 'out_pf', datasink, 'resting.qa.compmaps.@mc_pF')
wf.connect(filter2, 'out_f', datasink, 'resting.qa.compmaps')
wf.connect(filter2, 'out_pf', datasink, 'resting.qa.compmaps.@p')
wf.connect(bandpass, 'out_files', datasink,
'resting.timeseries.@bandpassed')
wf.connect(smooth, 'smoothed_files', datasink,
'resting.timeseries.@smoothed')
wf.connect(createfilter1, 'out_files', datasink,
'resting.regress.@regressors')
wf.connect(createfilter2, 'out_files', datasink,
'resting.regress.@compcorr')
wf.connect(maskts, 'out_file', datasink, 'resting.timeseries.target')
wf.connect(sampleaparc, 'summary_file', datasink,
'resting.parcellations.aparc')
wf.connect(sampleaparc, 'avgwf_txt_file', datasink,
'resting.parcellations.aparc.@avgwf')
wf.connect(ts2txt, 'out_file', datasink,
'resting.parcellations.grayo.@subcortical')
datasink2 = Node(interface=DataSink(), name="datasink2")
datasink2.inputs.base_directory = sink_directory
datasink2.inputs.container = subject_id
datasink2.inputs.substitutions = substitutions
datasink2.inputs.regexp_substitutions = regex_subs # (r'(/_.*(\d+/))', r'/run\2')
wf.connect(combiner, 'out_file', datasink2,
'resting.parcellations.grayo.@surface')
return wf
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")
# 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=','),
# segment.inputs.gaussians_per_class = [1, 1, 1, 4]
"""Warp functional and structural data to SPM's T1 template using
:class:`nipype.interfaces.spm.Normalize`. The tutorial data set
includes the template image, T1.nii.
"""
normalize_func = pe.Node(interface=spm.Normalize(), name="normalize_func")
normalize_func.inputs.jobtype = "write"
normalize_struc = pe.Node(interface=spm.Normalize(), name="normalize_struc")
normalize_struc.inputs.jobtype = "write"
"""Smooth the functional data using
:class:`nipype.interfaces.spm.Smooth`.
"""
smooth = pe.Node(interface=spm.Smooth(), name="smooth")
"""`write_voxel_sizes` is the input of the normalize interface that is recommended to be set to
the voxel sizes of the target volume. There is no need to set it manually since we van infer it from data
using the following function:
"""
def get_vox_dims(volume):
import nibabel as nb
from nipype.utils import NUMPY_MMAP
if isinstance(volume, list):
volume = volume[0]
nii = nb.load(volume, mmap=NUMPY_MMAP)
hdr = nii.header
voxdims = hdr.get_zooms()
return [float(voxdims[0]), float(voxdims[1]), float(voxdims[2])]
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
