def test_coregister_list_outputs():
filelist, outdir, cwd = create_files_in_directory()
coreg = spm.Coregister(source=filelist[0])
yield assert_true, coreg._list_outputs(
)['coregistered_source'][0].startswith('r')
coreg = spm.Coregister(source=filelist[0], apply_to_files=filelist[1])
yield assert_true, coreg._list_outputs(
)['coregistered_files'][0].startswith('r')
clean_directory(outdir, cwd)
def epi_normalize(name='epi_normalize'):
inputnode = pe.Node(
utility.IdentityInterface(
fields=['fmri_mean','t1','t1_to_mni','t1_mask']),
name='inputspec')
outputnode = pe.Node(
utility.IdentityInterface(
fields=['epi2t1_warp','coregistered_fmri_mean','epi2mni_warp',
't1_to_epi_warp','epi_mask']),
name='outputspec')
n_spm_coregister = pe.Node(
spm.Coregister(jobtype='estimate'),
name='spm_coregister')
# n_epi2mni = pe.Node(
# spm.preprocess.ApplyDeformations(
# reference_volume='/coconut/applis/src/spm8_x64/toolbox/Seg/TPM.nii'),
# name='epi2mni')
n_flirt_epi2t1 = pe.Node(
fsl.FLIRT(out_matrix_file='flirt_epi2t1.mat',
out_file='%s_flirt',
cost='normmi', # as in fcon1000 preproc, why??
searchr_x=[-10,10],searchr_y=[-10,10],searchr_z=[-10,10],
dof=6),
name='flirt_epi2t1')
n_t1_to_epi = pe.Node(
fsl.ConvertXFM(invert_xfm=True),
name='t1_to_epi')
n_mask_to_epi = pe.Node(
fsl.FLIRT(interp='nearestneighbour',
out_file='%s_epi',
apply_xfm=True,),
name='mask_to_epi')
w=pe.Workflow(name=name)
w.connect([
# (inputnode,n_spm_coregister,[('fmri_mean','source'),
# ('t1','target')]),
(inputnode,n_flirt_epi2t1,[('t1','reference')]),
# (n_spm_coregister,n_epi2mni,[('coregistered_source','in_files')]),
# (inputnode,n_epi2mni,[('t1_to_mni','deformation_field')]),
(inputnode,n_flirt_epi2t1,[('fmri_mean','in_file')]),
(n_flirt_epi2t1,outputnode,[('out_matrix_file','epi2t1_warp')]),
(n_flirt_epi2t1,n_t1_to_epi,[('out_matrix_file','in_file')]),
(n_t1_to_epi,outputnode,[('out_file','t1_to_epi_warp')]),
(inputnode,n_mask_to_epi,[('fmri_mean','reference'),
('t1_mask','in_file')]),
(n_t1_to_epi, n_mask_to_epi,[('out_file','in_matrix_file')]),
(n_mask_to_epi, outputnode, [('out_file','epi_mask')])
# (n_spm_coregister,outputnode,[('coregistered_source',
# 'coregistered_fmri_mean')]),
])
return w
def evaluate_coregister_wf(name='evaluate_coregister'):
w = pe.Workflow(name=name)
n_epi_grey_to_t1 = pe.Node(spm.Coregister(jobtype='write'), name='warp')
w.add_nodes([n_epi_grey_to_t1])
return w
def __init__(self, target='path', source='path', **options):
import nipype.interfaces.spm as spm
coreg = spm.Coregister()
coreg.inputs.target = target
coreg.inputs.source = source
for ef in options:
setattr(coreg.inputs, ef, options[ef])
self.res = coreg.run()
def test_coregister():
yield assert_equal, spm.Coregister._jobtype, 'spatial'
yield assert_equal, spm.Coregister._jobname, 'coreg'
yield assert_equal, spm.Coregister().inputs.jobtype, 'estwrite'
input_map = dict(target=dict(field='ref', mandatory=True, copyfile=False),
source=dict(field='source', copyfile=True),
apply_to_files=dict(field='other', copyfile=True),
cost_function=dict(field='eoptions.cost_fun'),
fwhm=dict(field='eoptions.fwhm'),
separation=dict(field='eoptions.sep'),
tolerance=dict(field='eoptions.tol'),
write_interp=dict(field='roptions.interp'),
write_wrap=dict(field='roptions.wrap'),
write_mask=dict(field='roptions.mask'))
coreg = spm.Coregister()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(coreg.inputs.traits()[key],
metakey), value
def create_visualise_thresholded_overlay(pipeline_name, name): #, contrasts):
inputnode = pe.Node(interface=util.IdentityInterface(fields=[
'background', "overlays", "ggmm_overlays", "contrasts", "task_name"
]),
name="inputnode")
reslice_overlay = pe.Node(interface=spm.Coregister(),
name="reslice_overlay")
reslice_overlay.inputs.jobtype = "write"
reslice_overlay.inputs.write_interp = 0
reslice_ggmm_overlay = reslice_overlay.clone(name="reslice_ggmm_overlay")
plot = pe.MapNode(interface=neuroutils.Overlay(),
name="plot",
iterfield=['overlay', 'title'])
make_titles_fdr = pe.Node(interface=util.Function(
input_names=['task', 'contrasts', 'prefix'],
output_names=['titles'],
function=_make_titles),
name="make_titles_fdr")
make_titles_fdr.inputs.prefix = "Topo FDR "
plot_ggmm = plot.clone("plot_ggmm")
make_titles_ggmm = pe.Node(interface=util.Function(
input_names=['task', 'contrasts', 'prefix'],
output_names=['titles'],
function=_make_titles),
name="make_titles_ggmm")
make_titles_ggmm.inputs.prefix = "Topo GGMM "
#plot = pe.MapNode(interface=neuroutils.Overlay(), name="plot", iterfield=['overlay'])
visualise_overlay = pe.Workflow(name="visualise" + name)
visualise_overlay.connect([
(inputnode, reslice_overlay, [("background", "target"),
("overlays", "source")]),
(inputnode, reslice_ggmm_overlay, [("background", "target"),
("ggmm_overlays", "source")]),
(reslice_overlay, plot, [("coregistered_source", "overlay")]),
(inputnode, plot, [("background", "background")]),
(inputnode, make_titles_fdr, [('task_name', 'task'),
('contrasts', 'contrasts')]),
(make_titles_fdr, plot, [('titles', 'title')]),
(reslice_ggmm_overlay, plot_ggmm, [("coregistered_source", "overlay")
]),
(inputnode, plot_ggmm, [("background", "background")]),
(inputnode, make_titles_ggmm, [('task_name', 'task'),
('contrasts', 'contrasts')]),
(make_titles_ggmm, plot_ggmm, [('titles', 'title')]),
])
return visualise_overlay
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 process_subject(func_fname, anat_fname):
# Drop dummy volumes
img = nib.load(func_fname)
dropped_img = nib.Nifti1Image(img.get_data()[..., n_dummies:], img.affine,
img.header)
fixed_fname = prefix_path('f', degz(func_fname))
nib.save(dropped_img, fixed_fname)
# Ungz anatomical
if anat_fname.endswith('.gz'):
anat_img = nib.load(anat_fname)
anat_fname = degz(anat_fname)
nib.save(anat_img, anat_fname)
# Slice time correction
num_slices = img.shape[2]
time_for_one_slice = TR / num_slices
TA = TR - time_for_one_slice
st = spm.SliceTiming()
st.inputs.in_files = fixed_fname
st.inputs.num_slices = num_slices
st.inputs.time_repetition = TR
st.inputs.time_acquisition = TA
st.inputs.slice_order = order_func(num_slices)
st.inputs.ref_slice = ref_slice
st.run()
fixed_stimed = prefix_path('a', fixed_fname)
# Realign
realign = spm.Realign()
realign.inputs.in_files = fixed_stimed
# Do not write resliced files, do write mean image
realign.inputs.write_which = write_which
realign.run()
# Coregistration
coreg = spm.Coregister()
# Coregister structural to mean image from realignment
coreg.inputs.target = prefix_path('mean', fixed_stimed)
coreg.inputs.source = anat_fname
coreg.run()
# Normalization / resampling with normalization + realign params
seg_norm = spm.Normalize12()
seg_norm.inputs.image_to_align = anat_fname
seg_norm.inputs.apply_to_files = fixed_stimed
seg_norm.inputs.write_bounding_box = bounding_box
seg_norm.inputs.write_voxel_sizes = voxel_sizes
seg_norm.run()
def simple_coregister(target, moving, other=None):
""" uses the basic spm Coregister functionality to move the
moving image to target, and applies to others is any"""
startdir = os.getcwd()
pth, _ = os.path.split(moving)
os.chdir(pth)
corg = spm.Coregister(matlab_cmd='matlab-spm8')
corg.inputs.target = target
corg.inputs.source = moving
corg.inputs.ignore_exception = True
if other is not None:
corg.inputs.apply_to_files = other
corg_out = corg.run()
os.chdir(startdir)
return corg_out
def coregistration_4D(source_file, ref, out_file=None, spm_path=None):
'''
Coregistration with spm + fsl for 4D files.
Why? Nor SPM, nor fsl are able to do this by default
:param source_file: path to input 4D file
:param ref: reference file to co-register the source-file to
:param out_file: output file
:param spm_path: path to spm
:return: path to coregistered file
'''
if spm_path is not None:
mlab.MatlabCommand.set_default_paths(spm_path)
if spm.SPMCommand().version is None:
raise Exception('SPM path not set correctly:', spm_path,
spm.SPMCommand().version)
main_dir, source_file_name = os.path.split(source_file)
if out_file is None:
out_file = os.path.join(main_dir, 'r' + source_file_name)
split_folder = os.path.join(main_dir, '4D_split')
if not os.path.exists(os.path.join(main_dir, '4D_split')):
os.mkdir(split_folder)
split = Split(in_file=source_file, dimension='t')
split.inputs.in_file = source_file
split.inputs.dimension = 't'
split.inputs.out_base_name = os.path.join(split_folder, '4D_vol_')
split.inputs.output_type = 'NIFTI'
split = split.run()
split_files = split.outputs.out_files
index_file = split_files.pop(0)
coreg = spm.Coregister()
coreg.inputs.target = ref
coreg.inputs.source = index_file
coreg.inputs.apply_to_files = split_files
coreg = coreg.run()
merger = Merge()
merger.inputs.in_files = coreg.outputs.coregistered_files
merger.inputs.dimension = 't'
merger.inputs.output_type = 'NIFTI_GZ'
merger.inputs.merged_file = out_file
merger = merger.run()
shutil.rmtree(split_folder)
return merger.outputs.merged_file
def coregister_to_T1(slice_time_dir, T1_dir):
# Function coregisters all EPIs in sequence to ac-pc aligned T1
# Get the name of the ac-pc aligned t1
os.chdir(T1_dir)
for files in os.listdir(T1_dir):
T1name = files
# Go to the dir with the slice time corrected EPI's and iter over
# filenames to coregister each to the ac-pc aligned T1.
os.chdir(slice_time_dir)
for files in os.listdir(slice_time_dir):
if files.startswith("ar"):
fname = files
coreg = spm.Coregister()
coreg.inputs.target = slice_time_dir + '/' + fname
coreg.inputs.source = T1_dir + '/' + T1name
print "<:::Coregistering EPI %s to AC-PC aligned T1:::>" % (fname)
coreg.run()
def spm_coregister(src_img=traits.Undefined,
tgt_img=traits.Undefined,
cost_function='mi'):
"""Use spm_coreg for estimating cross-modality rigid body alignment.
The write_interp option is set to 0 by default.
More info: http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=39
Parameters
----------
src_img: str
Path to the coregistration source image
tgt_img: str
Path to the coregistration target image
cost_function: ('mi' or 'nmi' or 'ecc' or 'ncc')
cost function, one of: 'mi' - Mutual Information,
'nmi' - Normalised Mutual Information,
'ecc' - Entropy Correlation Coefficient,
'ncc' - Normalised Cross Correlation
For more info:
http://www.mit.edu/~satra/nipype-nightly/interfaces/generated/nipype.interfaces.spm.preprocess.html#coregister
Returns
-------
coreg: nipype.interfaces.smp.Coregister
spm.Coregister interface object
"""
coreg = spm.Coregister()
coreg.inputs.source = src_img
coreg.inputs.target = tgt_img
coreg.inputs.cost_function = cost_function
coreg.inputs.jobtype = 'estwrite'
#coreg.run()
return coreg
def create_visualise_masked_overlay(pipeline_name, name): #, contrasts):
inputnode = pe.Node(interface=util.IdentityInterface(
fields=['background', "mask", "overlays", "contrasts", "task_name"]),
name="inputnode")
reslice_mask = pe.Node(interface=spm.Coregister(), name="reslice_mask")
reslice_mask.inputs.jobtype = "write"
reslice_mask.inputs.write_interp = 0
reslice_overlay = reslice_mask.clone(name="reslice_overlay")
plot = pe.MapNode(interface=neuroutils.Overlay(),
name="plot",
iterfield=['overlay', 'title'])
plot.inputs.bbox = True
#plot.inputs.title = [(pipeline_name + ": " + contrast[0]) for contrast in contrasts]
plot.inputs.nrows = 12
make_titles = pe.Node(interface=util.Function(
input_names=['task', 'contrasts'],
output_names=['titles'],
function=_make_titles),
name="make_titles")
visualise_overlay = pe.Workflow(name="visualise" + name)
visualise_overlay.connect([
(inputnode, reslice_overlay, [("background", "target"),
("overlays", "source")]),
(inputnode, reslice_mask, [("background", "target"),
("mask", "source")]),
(inputnode, make_titles, [('task_name', 'task'),
('contrasts', 'contrasts')]),
(make_titles, plot, [('titles', 'title')]),
(reslice_overlay, plot, [("coregistered_source", "overlay")]),
(inputnode, plot, [("background", "background")]),
(reslice_mask, plot, [("coregistered_source", "mask")]),
])
return visualise_overlay
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 test_coregister():
yield assert_equal, spm.Coregister._jobtype, 'spatial'
yield assert_equal, spm.Coregister._jobname, 'coreg'
yield assert_equal, spm.Coregister().inputs.jobtype, 'estwrite'
def test_coregister_list_outputs(create_files_in_directory):
filelist, outdir, cwd = create_files_in_directory
coreg = spm.Coregister(source=filelist[0])
assert coreg._list_outputs()['coregistered_source'][0].startswith('r')
coreg = spm.Coregister(source=filelist[0], apply_to_files=filelist[1])
assert coreg._list_outputs()['coregistered_files'][0].startswith('r')
def test_coregister():
assert spm.Coregister._jobtype == 'spatial'
assert spm.Coregister._jobname == 'coreg'
assert spm.Coregister().inputs.jobtype == 'estwrite'
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 = [[]]
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 __init__(self, func_source, struct_source, datasink):
# specify input and output nodes
self.func_source = func_source
self.struct_source = struct_source
self.datasink = datasink
# specify nodes
# structual process
self.bet_struct = pe.Node(interface=fsl.BET(),
name='non_brain_removal_BET_struct')
self.bet_struct.inputs.output_type = "NIFTI"
# functional process
self.slice_timer = pe.Node(interface=fsl.SliceTimer(),
name='time_slice_correction')
self.mcflirt = pe.Node(interface=fsl.MCFLIRT(),
name='motion_correction')
self.mcflirt.inputs.output_type = "NIFTI"
self.mcflirt.inputs.mean_vol = True
self.fslsplit = pe.Node(interface=fsl.Split(), name='fslsplit')
self.fslsplit.inputs.dimension = 't'
self.fslsplit.inputs.output_type = "NIFTI"
self.fslmerge = pe.Node(interface=fsl.Merge(), name='fslmerge')
self.fslmerge.inputs.dimension = 't'
self.fslmerge.inputs.output_type = "NIFTI"
self.bet_mean = pe.Node(interface=fsl.BET(),
name='non_brain_removal_BET_mean')
self.bet_mean.inputs.output_type = "NIFTI"
# helper function(s)
def bet_each(in_files):
'''
@param in_files: list of image files
@return out_files: list of image files after applied fsl.BET on it
'''
from nipype.interfaces import fsl
import nipype.pipeline.engine as pe
out_files = list()
step_no = 0
for file_ in in_files:
bet = pe.Node(interface=fsl.BET(),
name='BET_for_step_{}'.format(step_no))
bet.inputs.in_file = file_
bet.inputs.out_file = file_[:len(file_) - 4] + '_bet.nii'
bet.inputs.output_type = "NIFTI"
bet.run()
out_files.append(bet.inputs.out_file)
step_no += 1
return out_files
# bet_func return a list of NIFITI files
self.bet_func = pe.Node(interface=Function(input_names=['in_files'],
output_names=['out_files'],
function=bet_each),
name='non_brain_removal_BET_func')
self.coregister = pe.Node(interface=spm.Coregister(),
name="coregister")
self.coregister.inputs.jobtype = 'estimate'
self.segment = pe.Node(interface=spm.Segment(), name="segment")
self.segment.inputs.affine_regularization = 'mni'
self.normalize_func = pe.Node(interface=spm.Normalize(),
name="normalize_func")
self.normalize_func.inputs.jobtype = "write"
# self.fourier = pe.Node(interface=afni.Fourier(), name='temporal_filtering')
# self.fourier.inputs.highpass = 0.01
# self.fourier.inputs.lowpass = 0.1
self.smooth = pe.Node(interface=spm.Smooth(), name="smooth")
self.smooth.inputs.fwhm = [8, 8, 8]
# specify workflow instance
self.workflow = pe.Workflow(name='preprocessing_workflow')
# connect nodes
self.workflow.connect([
(self.struct_source, self.bet_struct, [('outfiles', 'in_file')]),
(self.func_source, self.slice_timer, [('outfiles', 'in_file')]),
(self.slice_timer, self.mcflirt, [('slice_time_corrected_file',
'in_file')]),
(self.mcflirt, self.bet_mean, [('mean_img', 'in_file')]),
(self.mcflirt, self.fslsplit, [('out_file', 'in_file')]),
(self.fslsplit, self.bet_func, [('out_files', 'in_files')]),
(self.bet_func, self.fslmerge, [('out_files', 'in_files')
]), # intersect
(self.bet_struct, self.coregister, [('out_file', 'source')]),
(self.bet_mean, self.coregister, [('out_file', 'target')]),
(self.coregister, self.segment, [('coregistered_source', 'data')]),
(self.segment, self.normalize_func, [('transformation_mat',
'parameter_file')]),
(self.fslmerge, self.normalize_func, [('merged_file',
'apply_to_files')]),
(self.normalize_func, self.smooth, [('normalized_files',
'in_files')]),
(self.coregister, self.datasink, [('coregistered_source',
'registered_file')]),
(self.normalize_func, self.datasink, [('normalized_files',
'before_smooth')]),
(self.smooth, self.datasink, [('smoothed_files', 'final_out')])
])
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 = [[]]
#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]
flaircoreg.inputs.tolerance = [
0.02, 0.02, 0.02, 0.001, 0.001, 0.001, 0.01, 0.01, 0.01, 0.001, 0.001,
0.001
]
flaircoreg.inputs.fwhm = [7, 7]
flaircoreg.inputs.write_interp = 1
flaircoreg.inputs.write_wrap = [0, 0, 0]
flaircoreg.inputs.write_mask = False
#Coregisters T1, FLAIR + mask to EPI (NOTE: settings taken from Clinical Toolbox)
coreg = MapNode(spm.Coregister(), iterfield='apply_to_files', name='coreg2epi')
coreg.inputs.cost_function = 'nmi'
coreg.inputs.separation = [4, 2]
import nipype.interfaces.spm as spm # 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]
def __init__(self,
subject,
func_source,
struct_source,
datasink,
TR,
num_slices,
dim=2):
self.subject = subject
# specify input and output nodes
self.func_source = func_source
self.struct_source = struct_source
self.datasink = datasink
self.TR = TR
self.num_slices = num_slices
# specify nodes
# structual process
self.bet_struct = pe.Node(interface=fsl.BET(),
name='non_brain_removal_BET_struct')
self.bet_struct.inputs.output_type = "NIFTI"
self.bet_struct.inputs.frac = 0.3
self.coregister_struct = pe.Node(interface=spm.Coregister(),
name="coregister_struct_to_mni")
self.coregister_struct.inputs.target = '/mnt/Program/python/dev/images/MNI152_T1_2mm_brain.nii'
if dim == 1:
self.coregister_struct.inputs.target = '/mnt/Program/python/dev/images/MNI152_T1_1mm_brain.nii'
self.coregister_struct.inputs.write_interp = 7
self.coregister_struct.inputs.separation = [1.0, 1.0]
self.coregister_struct.inputs.jobtype = 'estwrite'
self.segment_struct = pe.Node(interface=spm.Segment(),
name="segment_struct")
# self.segment_struct.inputs.affine_regularization = 'mni'
self.segment_struct.inputs.csf_output_type = [True, True, True]
self.segment_struct.inputs.gm_output_type = [True, True, True]
self.segment_struct.inputs.wm_output_type = [True, True, True]
self.normalize_struct = pe.Node(interface=spm.Normalize(),
name='normalize_struct')
self.normalize_struct.inputs.jobtype = 'write'
self.normalize_struct.inputs.write_bounding_box = [[-90, -126, -72],
[90, 90, 108]
] # 91, 109, 91
if dim == 1:
self.normalize_struct.inputs.write_bounding_box = [[
-91, -126, -72
], [90, 91, 109]] # 182, 218, 182
self.normalize_struct.inputs.write_voxel_sizes = [1, 1, 1]
self.normalize_gm = pe.Node(interface=spm.Normalize(),
name='normalize_gm')
self.normalize_gm.inputs.jobtype = 'write'
self.normalize_gm.inputs.write_bounding_box = [[-90, -126, -72],
[90, 90,
108]] # 91, 109, 91
if dim == 1:
self.normalize_gm.inputs.write_bounding_box = [[-91, -126, -72],
[90, 91, 109]
] # 182, 218, 182
self.normalize_gm.inputs.write_voxel_sizes = [1, 1, 1]
self.normalize_wm = pe.Node(interface=spm.Normalize(),
name='normalize_wm')
self.normalize_wm.inputs.jobtype = 'write'
self.normalize_wm.inputs.write_bounding_box = [[-90, -126, -72],
[90, 90,
108]] # 91, 109, 91
if dim == 1:
self.normalize_wm.inputs.write_bounding_box = [[-91, -126, -72],
[90, 91, 109]
] # 182, 218, 182
self.normalize_wm.inputs.write_voxel_sizes = [1, 1, 1]
self.normalize_csf = pe.Node(interface=spm.Normalize(),
name='normalize_csf')
self.normalize_csf.inputs.jobtype = 'write'
self.normalize_csf.inputs.write_bounding_box = [[-90, -126, -72],
[90, 90,
108]] # 91, 109, 91
if dim == 1:
self.normalize_csf.inputs.write_bounding_box = [[-91, -126, -72],
[90, 91, 109]
] # 182, 218, 182
self.normalize_csf.inputs.write_voxel_sizes = [1, 1, 1]
###################################################################################################
# functional process
self.fslsplit = pe.Node(interface=fsl.Split(), name='fslsplit')
self.fslsplit.inputs.dimension = 't'
self.fslsplit.inputs.output_type = "NIFTI"
self.fslmerge = pe.Node(interface=fsl.Merge(), name='fslmerge')
self.fslmerge.inputs.dimension = 't'
self.fslmerge.inputs.output_type = "NIFTI"
# helper function(s)
def bet_each(in_files, subject_name):
'''
@param in_files: list of image files
@return out_files: list of image files after applied fsl.BET on it
'''
from nipype.interfaces import fsl
import nipype.pipeline.engine as pe
out_files = list()
step_no = 0
for file_ in in_files:
bet = pe.Node(interface=fsl.BET(),
name='BET_for_step_{}_{}'.format(
step_no, subject_name))
bet.inputs.in_file = file_
bet.inputs.out_file = file_[:len(file_) - 4] + '_bet.nii'
bet.inputs.output_type = "NIFTI"
bet.inputs.frac = 0.5
bet.run()
out_files.append(bet.inputs.out_file)
step_no += 1
return out_files
# bet_func return a list of NIFITI files
self.bet_func = pe.Node(interface=Function(
input_names=['in_files', 'subject_name'],
output_names=['out_files'],
function=bet_each),
name='non_brain_removal_BET_func')
self.bet_func.inputs.subject_name = self.subject
self.realign = pe.Node(interface=spm.Realign(),
name='realign_motion_correction')
self.realign.inputs.register_to_mean = True
self.coregister_func = pe.Node(interface=spm.Coregister(),
name="coregister_func_to_mni")
self.coregister_func.inputs.target = '/mnt/Program/python/dev/images/MNI152_T1_2mm_brain.nii'
self.coregister_func.inputs.write_interp = 7
self.coregister_func.inputs.separation = [1.0, 1.0]
self.coregister_func.inputs.jobtype = 'estwrite'
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_func.inputs.write_bounding_box = [[-90, -126, -72],
[90, 90,
108]] # 91, 109, 91
self.smooth = pe.Node(interface=spm.Smooth(), name="smooth")
self.smooth.inputs.fwhm = [8, 8, 8]
# backup node(s)
self.slice_timing = pe.Node(interface=spm.SliceTiming(),
name='time_slice_correction')
self.slice_timing.inputs.time_repetition = self.TR
self.slice_timing.inputs.num_slices = self.num_slices
self.slice_timing.inputs.time_acquisition = self.TR - (self.TR /
self.num_slices)
self.slice_timing.inputs.slice_order = list(
range(self.num_slices, 0, -1))
self.slice_timing.inputs.ref_slice = 1
self.direct_normalize = pe.Node(interface=spm.Normalize12(),
name='direct_normalize')
self.direct_normalize.inputs.image_to_align = 'images/MNI152_T1_2mm_brain.nii'
self.direct_normalize.inputs.affine_regularization_type = 'size'
# specify workflow instance
self.workflow = pe.Workflow(name='preprocess_workflow')
# connect nodes
self.workflow.connect([
(self.struct_source, self.bet_struct, [('outfiles', 'in_file')]),
(self.bet_struct, self.coregister_struct, [('out_file', 'source')
]),
(self.coregister_struct, self.segment_struct,
[('coregistered_source', 'data')]),
(self.segment_struct, self.normalize_struct,
[('transformation_mat', 'parameter_file')]),
(self.coregister_struct, self.normalize_struct,
[('coregistered_source', 'apply_to_files')]),
(self.segment_struct, self.normalize_gm, [('transformation_mat',
'parameter_file')]),
(self.segment_struct, self.normalize_gm, [('native_gm_image',
'apply_to_files')]),
(self.segment_struct, self.normalize_wm, [('transformation_mat',
'parameter_file')]),
(self.segment_struct, self.normalize_wm, [('native_wm_image',
'apply_to_files')]),
(self.segment_struct, self.normalize_csf, [('transformation_mat',
'parameter_file')]),
(self.segment_struct, self.normalize_csf, [('native_csf_image',
'apply_to_files')]),
(self.func_source, self.fslsplit, [('outfiles', 'in_file')]),
(self.fslsplit, self.bet_func, [('out_files', 'in_files')]),
(self.bet_func, self.fslmerge, [('out_files', 'in_files')]),
(self.fslmerge, self.realign, [('merged_file', 'in_files')]),
(self.realign, self.datasink, [('realignment_parameters',
'realignment_parameters')]),
(self.realign, self.coregister_func, [('mean_image', 'source')]),
(self.realign, self.coregister_func, [('realigned_files',
'apply_to_files')]),
(self.coregister_func, self.segment, [('coregistered_source',
'data')]),
(self.segment, self.normalize_func, [('transformation_mat',
'parameter_file')]),
(self.coregister_func, self.normalize_func, [('coregistered_files',
'apply_to_files')]),
(self.normalize_func, self.smooth, [('normalized_files',
'in_files')]), # end
(self.normalize_func, self.datasink, [('normalized_files',
'before_smooth')]),
(self.smooth, self.datasink, [('smoothed_files', 'final_out')]),
(self.normalize_struct, self.datasink,
[('normalized_files', 'standardized_struct_file')]),
# (self.segment_struct, self.datasink, [('native_csf_image', 'csf'), ('native_gm_image', 'grey_matter'), ('native_wm_image', 'white_matter')])
(self.normalize_gm, self.datasink, [('normalized_files',
'grey_matter')]),
(self.normalize_wm, self.datasink, [('normalized_files',
'white_matter')]),
(self.normalize_csf, self.datasink, [('normalized_files', 'csf')]),
])
# backup workflow(s)
self.workflow_only_fmri = pe.Workflow(name='preprocess_workflow')
# connect nodes
self.workflow_only_fmri.connect([
(self.func_source, self.fslsplit, [('outfiles', 'in_file')]),
(self.fslsplit, self.bet_func, [('out_files', 'in_files')]),
(self.bet_func, self.fslmerge, [('out_files', 'in_files')]),
(self.fslmerge, self.realign, [('merged_file', 'in_files')]),
(self.realign, self.direct_normalize, [('realigned_files',
'apply_to_files')]),
(self.direct_normalize, self.smooth, [('normalized_files',
'in_files')]), # end
(self.direct_normalize, self.datasink, [('normalized_files',
'before_smooth')]),
(self.smooth, self.datasink, [('smoothed_files', 'final_out')])
])
# fMRI pre-processing
#
# skip dummy scans
extract = Node(
fsl.ExtractROI(
in_file=imagefMRI, # input image
t_min=4, # first 4 volumes are deleted
t_size=-1,
output_type='NIFTI'), # forces output to be .nii
name="extract")
# motion correction aka realignment
realign = Node(spm.Realign(), name="realign")
# coregistration, fMRI to T1w
coreg = Node(spm.Coregister(cost_function='nmi'), name="coreg")
# estimating affine transform for co-registration
coregEst = Node(spm.CalcCoregAffine(), name="coregEst")
# warping fMRI by applying the warping estimated for T1
normalizefMRI = Node(spm.Normalize12(jobtype='write',
write_bounding_box=[[-90, -120, -70],
[90, 90, 105]],
write_voxel_sizes=voxfMRI),
name="normalizefMRI")
# gunzip node, FSL brain mask
gunzip_mask = Node(Gunzip(in_file=fmask), name="gunzip_mask")
# Reslice the FSL template to match fMRI
def create_preproc_func_pipeline(
): #ref_slice, n_skip=4, n_slices=30, tr=2.5, sparse=False):
# if sparse:
# real_tr = tr/2
# else:
# real_tr = tr
inputnode = pe.Node(interface=util.IdentityInterface(
fields=['func', "struct", "TR", "sparse"]),
name="inputnode")
skip = pe.Node(interface=fsl.ExtractROI(), name="skip")
skip.inputs.t_min = 4 #TODO
skip.inputs.t_size = 100000
realign = pe.Node(interface=spm.Realign(), name="realign")
realign.inputs.register_to_mean = True
tr_convert = pe.Node(interface=util.Function(input_names=['tr', 'sparse'],
output_names=['tr'],
function=get_tr),
name="tr_converter")
ta = pe.Node(interface=util.Function(input_names=['real_tr', 'n_slices'],
output_names=['ta'],
function=get_ta),
name="ta")
slice_timing = pe.Node(interface=spm.SliceTiming(), name="slice_timing")
#slice_timing.inputs.num_slices = n_slices
#slice_timing.inputs.time_repetition = real_tr
#slice_timing.inputs.time_acquisition = real_tr - real_tr/float(n_slices)
#slice_timing.inputs.slice_order = range(1,n_slices+1,2) + range(2,n_slices+1,2)
#slice_timing.inputs.ref_slice = ref_slice
coregister = pe.Node(interface=spm.Coregister(), name="coregister")
coregister.inputs.jobtype = "estimate"
smooth = pe.Node(interface=spm.Smooth(), name="smooth")
smooth.iterables = ('fwhm', [[8, 8, 8], [0, 0, 0]])
art = pe.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'
compute_mask = pe.Node(interface=ComputeMask(), name="compute_mask")
plot_realign = pe.Node(interface=neuroutils.PlotRealignemntParameters(),
name="plot_realign")
preproc_func = pe.Workflow(name="preproc_func")
preproc_func.connect([
(inputnode, skip, [("func", "in_file")]),
(inputnode, coregister, [("struct", "target")]),
(realign, coregister, [('mean_image', 'source'),
('realigned_files', 'apply_to_files')]),
(coregister, compute_mask, [('coregistered_source', 'mean_volume')]),
(skip, slice_timing, [("roi_file", "in_files"),
(('roi_file', get_n_slices), "num_slices"),
(('roi_file', get_slice_order), "slice_order"),
(('roi_file', get_ref_slice), "ref_slice")]),
(inputnode, tr_convert, [("sparse", "sparse"), ("TR", "tr")]),
(tr_convert, slice_timing, [("tr", "time_repetition")]),
(tr_convert, ta, [("tr", "real_tr")]),
(skip, ta, [(('roi_file', get_n_slices), "n_slices")]),
(ta, slice_timing, [("ta", "time_acquisition")]),
(slice_timing, realign, [("timecorrected_files", "in_files")]),
(coregister, smooth, [("coregistered_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')])
])
return preproc_func
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'
"""Skull strip structural images using
:class:`nipype.interfaces.fsl.BET`.
"""
skullstrip = pe.Node(fsl.BET(), name="skullstrip")
skullstrip.inputs.mask = True
"""Use :class:`nipype.interfaces.spm.Coregister` to perform a rigid
body registration of the functional data to the structural data.
"""
coregister = pe.Node(spm.Coregister(), name="coregister")
coregister.inputs.jobtype = 'estimate'
"""Normalize and smooth functional data using DARTEL template
"""
normalize_and_smooth_func = pe.Node(spm.DARTELNorm2MNI(modulate=True),
name='normalize_and_smooth_func')
fwhmlist = [4]
normalize_and_smooth_func.iterables = ('fwhm', fwhmlist)
"""Normalize structural data using DARTEL template
"""
normalize_struct = pe.Node(spm.DARTELNorm2MNI(modulate=True),
name='normalize_struct')
normalize_struct.inputs.fwhm = 2
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')]),
])
""" preproc = pe.Workflow(name='preproc') """Use :class:`nipype.interfaces.spm.Realign` for motion correction and register all images to the mean image. """ realign = pe.Node(interface=spm.Realign(), name="realign") slice_timing = pe.Node(interface=spm.SliceTiming(), name="slice_timing") """Use :class:`nipype.interfaces.spm.Coregister` to perform a rigid body registration of the functional data to the structural data. """ coregister = pe.Node(interface=spm.Coregister(), name="coregister") coregister.inputs.jobtype = 'estimate' segment = pe.Node(interface=spm.Segment(), name="segment") segment.inputs.save_bias_corrected = True """Uncomment the following line for faster execution """ # 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"
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
