def spm_slicetime(
in_files=traits.Undefined,
out_prefix=traits.Undefined,
num_slices=0,
time_repetition=-1,
time_acquisition=-1,
ref_slice=traits.Undefined,
slice_order=None,
):
""" Return a nipype interface to the SPM SliceTiming.
Parameters
----------
in_files: str or list of str
Path to the input file(s).
out_prefix: str
Prefix to the output file.
Default: 'a'
num_slices: int
time_repetition: int or str
The time repetition (TR) of the input dataset in seconds
Default: 0
If left to default will read the TR from the nifti image header.
time_acquisition: int
Time of volume acquisition. usually calculated as TR-(TR/num_slices)
ref_slice: int
Index of the reference slice
slice_order: list of int
Returns
-------
stc: nipype interface
"""
import nipype.interfaces.spm as spm
stc = spm.SliceTiming()
stc.inputs.in_files = in_files
stc.inputs.out_prefix = 'a' if not isdefined(out_prefix) else out_prefix
stc.inputs.slice_order = slice_order
stc.inputs.ref_slice = ref_slice
stc.inputs.time_repetition = time_repetition
stc.inputs.time_acquisition = time_acquisition
stc.inputs.num_slices = num_slices
#res = stc.run()
return stc
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 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 slicetime(new_in_file, sliceorder):
print "running slice time correction"
img = nib.load(new_in_file[0])
num_slices = img.shape[2]
st = spm.SliceTiming()
st.inputs.in_files = new_in_file
st.inputs.num_slices = num_slices
st.inputs.time_repetition = tr
st.inputs.time_acquisition = tr - tr / num_slices
st.inputs.slice_order = (np.asarray(sliceorder) +
1).astype(int).tolist()
st.inputs.ref_slice = 1
res_st = st.run()
file_to_realign = res_st.outputs.timecorrected_files
return file_to_realign
def test_slicetiming():
yield assert_equal, spm.SliceTiming._jobtype, 'temporal'
yield assert_equal, spm.SliceTiming._jobname, 'st'
input_map = dict(in_files=dict(field='scans',
mandatory=True,
copyfile=False),
num_slices=dict(field='nslices'),
time_repetition=dict(field='tr'),
time_acquisition=dict(field='ta'),
slice_order=dict(field='so'),
ref_slice=dict(field='refslice'))
st = spm.SliceTiming()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(st.inputs.traits()[key],
metakey), value
def test_slicetiming_list_outputs():
filelist, outdir, cwd = create_files_in_directory()
st = spm.SliceTiming(in_files=filelist[0])
yield assert_equal, st._list_outputs()['timecorrected_files'][0][0], 'a'
clean_directory(outdir, cwd)
def test_slicetiming_list_outputs(create_files_in_directory):
filelist, outdir, cwd = create_files_in_directory
st = spm.SliceTiming(in_files=filelist[0])
assert st._list_outputs()['timecorrected_files'][0][0] == 'a'
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 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')]),
])
####EPI preprocessing#### #Convert EPI dicoms to nii (with embeded metadata) epi_stack = Node(dcmstack.DcmStack(), name='epistack') epi_stack.inputs.embed_meta = True epi_stack.inputs.out_format = 'epi' epi_stack.inputs.out_ext = '.nii' #Outputs: out_file #Despiking using afni (position based on Jo et al. (2013)). despike = Node(afni.Despike(), name='despike') despike.inputs.outputtype = 'NIFTI' #Outputs: out_file #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
def __init__(self, **template_dict):
self.node = pe.Node(interface=spm.SliceTiming(), name='slicetiming')
self.node.inputs.paths = template_dict['spm_path']
return list(odd) + list(even)
order_func = ascending_interleaved
# Drop dummy volumes
img = nib.load(base_fname)
dropped_img = nib.Nifti1Image(img.get_data()[..., n_dummies:], img.affine,
img.header)
nib.save(dropped_img, 'f' + base_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 = 'f' + base_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()
# Realign
realign = spm.Realign()
realign.inputs.in_files = 'af' + base_fname
# Do not write resliced files, do write mean image
realign.inputs.write_which = write_which
realign.run()
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
from __future__ import division from builtins import range import nipype.pipeline.engine as pe from nipype.interfaces import spm from nipype.interfaces import fsl from nipype.algorithms.misc import Gunzip import os in_file = "feeds/data/fmri.nii.gz" split = pe.Node(fsl.Split(dimension="t", output_type="NIFTI"), name="split") split.inputs.in_file = os.path.abspath(in_file) stc = pe.Node(interface=spm.SliceTiming(), name='stc') stc.inputs.num_slices = 21 stc.inputs.time_repetition = 1.0 stc.inputs.time_acquisition = 2. - 2. / 32 stc.inputs.slice_order = list(range(21, 0, -1)) stc.inputs.ref_slice = 10 realign_estimate = pe.Node(interface=spm.Realign(), name='realign_estimate') realign_estimate.inputs.jobtype = "estimate" realign_write = pe.Node(interface=spm.Realign(), name='realign_write') realign_write.inputs.jobtype = "write" realign_estwrite = pe.Node(interface=spm.Realign(), name='realign_estwrite') realign_estwrite.inputs.jobtype = "estwrite" realign_estwrite.inputs.register_to_mean = True smooth = pe.Node(interface=spm.Smooth(), name='smooth')
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')])
])
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
def mod_realign(node, in_file, tr, do_slicetime, sliceorder, parameters={}):
import nipype.interfaces.fsl as fsl
import nipype.interfaces.spm as spm
import nipype.interfaces.nipy as nipy
import os
parameter_source = "FSL"
keys = parameters.keys()
if node == "nipy":
realign = nipy.FmriRealign4d()
realign.inputs.in_file = in_file
realign.inputs.tr = tr
if "loops" in keys:
realign.inputs.loops = parameters["loops"]
if "speedup" in keys:
realign.inputs.speedup = parameters["speedup"]
if "between_loops" in keys:
realign.inputs.between_loops = parameters["between_loops"]
if do_slicetime:
realign.inputs.slice_order = sliceorder
realign.inputs.time_interp = True
res = realign.run()
out_file = res.outputs.out_file
par_file = res.outputs.par_file
elif node == "fsl":
if not isinstance(in_file, list):
in_file = [in_file]
out_file = []
par_file = []
# get the first volume of first run as ref file
if not do_slicetime:
extract = fsl.ExtractROI()
extract.inputs.t_min = 0
extract.inputs.t_size = 1
extract.inputs.in_file = in_file[0]
ref_vol = extract.run().outputs.roi_file
for idx, file in enumerate(in_file):
if do_slicetime:
slicetime = fsl.SliceTimer()
slicetime.inputs.in_file = file
sliceorder_file = os.path.abspath('FSL_custom_order.txt')
with open(sliceorder_file, 'w') as custom_order_fp:
for t in sliceorder:
custom_order_fp.write('%d\n' % (t + 1))
slicetime.inputs.custom_order = sliceorder_file
slicetime.inputs.time_repetition = tr
res = slicetime.run()
file_to_realign = res.outputs.slice_time_corrected_file
if not idx:
extract = fsl.ExtractROI()
extract.inputs.t_min = 0
extract.inputs.t_size = 1
extract.inputs.in_file = file_to_realign
ref_vol = extract.run().outputs.roi_file
else:
file_to_realign = file
realign = fsl.MCFLIRT(interpolation='spline', ref_file=ref_vol)
realign.inputs.save_plots = True
realign.inputs.mean_vol = True
realign.inputs.in_file = file_to_realign
realign.inputs.out_file = 'fsl_corr_' + \
os.path.split(file_to_realign)[1]
Realign_res = realign.run()
out_file.append(Realign_res.outputs.out_file)
par_file.append(Realign_res.outputs.par_file)
elif node == 'spm':
import numpy as np
import nibabel as nib
import nipype.interfaces.freesurfer as fs
if not isinstance(in_file, list):
in_file = [in_file]
new_in_file = []
for f in in_file:
if f.endswith('.nii.gz'):
convert = fs.MRIConvert()
convert.inputs.in_file = f
convert.inputs.out_type = 'nii'
convert.inputs.in_type = 'niigz'
f = convert.run().outputs.out_file
new_in_file.append(f)
else:
new_in_file.append(f)
if do_slicetime:
img = nib.load(new_in_file[0])
num_slices = img.shape[2]
st = spm.SliceTiming()
st.inputs.in_files = new_in_file
st.inputs.num_slices = num_slices
st.inputs.time_repetition = tr
st.inputs.time_acquisition = tr - tr / num_slices
st.inputs.slice_order = (np.asarray(sliceorder) +
1).astype(int).tolist()
st.inputs.ref_slice = 1
res_st = st.run()
file_to_realign = res_st.outputs.timecorrected_files
else:
file_to_realign = new_in_file
par_file = []
realign = spm.Realign()
realign.inputs.in_files = file_to_realign
#realign.inputs.out_prefix = 'spm_corr_'
res = realign.run()
parameters = res.outputs.realignment_parameters
if not isinstance(parameters, list):
parameters = [parameters]
par_file = parameters
parameter_source = 'SPM'
fsl.ImageMaths(in_file=res.outputs.realigned_files,
out_file=res.outputs.realigned_files,
op_string='-nan').run()
out_file = res.outputs.realigned_files
elif node == 'afni':
import nipype.interfaces.afni as afni
import nibabel as nib
import numpy as np
if not isinstance(in_file, list):
in_file = [in_file]
img = nib.load(in_file[0])
Nz = img.shape[2]
out_file = []
par_file = []
# get the first volume of first run as ref file
if not do_slicetime:
extract = fsl.ExtractROI()
extract.inputs.t_min = 0
extract.inputs.t_size = 1
extract.inputs.in_file = in_file[0]
ref_vol = extract.run().outputs.roi_file
for idx, file in enumerate(in_file):
if do_slicetime:
slicetime = afni.TShift()
slicetime.inputs.in_file = file
custom_order = open(
os.path.abspath('afni_custom_order_file.txt'), 'w')
tpattern = []
for i in xrange(len(sliceorder)):
tpattern.append((i * tr / float(Nz), sliceorder[i]))
tpattern.sort(key=lambda x: x[1])
for i, t in enumerate(tpattern):
print '%f\n' % (t[0])
custom_order.write('%f\n' % (t[0]))
custom_order.close()
slicetime.inputs.args = '-tpattern @%s' % os.path.abspath(
'afni_custom_order_file.txt')
slicetime.inputs.tr = str(tr) + 's'
slicetime.inputs.outputtype = 'NIFTI_GZ'
res = slicetime.run()
file_to_realign = res.outputs.out_file
if not idx:
extract = fsl.ExtractROI()
extract.inputs.t_min = 0
extract.inputs.t_size = 1
extract.inputs.in_file = file_to_realign
ref_vol = extract.run().outputs.roi_file
else:
file_to_realign = file
realign = afni.Volreg()
realign.inputs.in_file = file_to_realign
realign.inputs.out_file = "afni_corr_" + os.path.split(
file_to_realign)[1]
realign.inputs.oned_file = "afni_realignment_parameters.par"
realign.inputs.basefile = ref_vol
Realign_res = realign.run()
out_file.append(Realign_res.outputs.out_file)
parameters = Realign_res.outputs.oned_file
if not isinstance(parameters, list):
parameters = [parameters]
for i, p in enumerate(parameters):
foo = np.genfromtxt(p)
boo = foo[:, [1, 2, 0, 4, 5, 3]]
boo[:, :3] = boo[:, :3] * np.pi / 180
np.savetxt(os.path.abspath('realignment_parameters_%d.par' %
i),
boo,
delimiter='\t')
par_file.append(
os.path.abspath('realignment_parameters_%d.par' % i))
#par_file.append(Realign_res.outputs.oned_file)
return out_file, par_file, parameter_source
Setup preprocessing workflow ---------------------------- This is a generic preprocessing workflow that can be used by different analyses """ 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
name='camino_VtkStreamlines')
#Wraps the executable command ``dt2nii``.
camino_DT2NIfTI = pe.Node(interface=camino.DT2NIfTI(), name='camino_DT2NIfTI')
#Wraps the executable command ``erode``.
mrtrix_Erode = pe.Node(interface=mrtrix.Erode(), name='mrtrix_Erode')
#Converts separate b-values and b-vectors from text files (FSL style) into a
mrtrix_FSL2MRTrix = pe.Node(interface=mrtrix.FSL2MRTrix(),
name='mrtrix_FSL2MRTrix')
#Wraps the executable command ``mrconvert``.
mrtrix_MRConvert = pe.Node(interface=mrtrix.MRConvert(),
name='mrtrix_MRConvert')
#Use spm to perform slice timing correction.
spm_SliceTiming = pe.Node(interface=spm.SliceTiming(), name='spm_SliceTiming')
#uses spm_reslice to resample in_file into space of space_defining
spm_Reslice = pe.Node(interface=spm.Reslice(), name='spm_Reslice')
#Create a workflow to connect all those nodes
analysisflow = nipype.Workflow('MyWorkflow')
#Run the workflow
plugin = 'MultiProc' #adjust your desired plugin here
plugin_args = {'n_procs': 1} #adjust to your number of cores
analysisflow.write_graph(graph2use='flat', format='png', simple_form=False)
analysisflow.run(plugin=plugin, plugin_args=plugin_args)
