def create_extract_inplane_workflow(
name='extract_inplane',
templates={'inplane': 'Raw/Anatomy/Inplane{id}/'},
format_string='inplane'):
extract_inplane = pe.Workflow(name=name)
inputs = pe.Node(
interface=util.IdentityInterface(fields=['session_dir', 'ref_vol']),
name='inputs')
get_inplane = pe.Node(interface=nio.SelectFiles(templates),
name='get_inplane')
inplane_to_nii = pe.Node(interface=ds.DcmStack(), name='inplane_to_nii')
inplane_to_nii.inputs.embed_meta = True
rename_inplane = pe.Node(interface=util.Rename(format_string),
name='rename_inplane')
rename_inplane.inputs.keep_ext = True
outputs = pe.Node(interface=util.IdentityInterface(fields=['out_file']),
name='outputs')
extract_inplane.connect(inputs, 'session_dir', get_inplane,
'base_directory')
extract_inplane.connect(inputs, ('ref_vol', ref_vol_to_inplane_id),
get_inplane, 'id')
extract_inplane.connect(get_inplane, 'inplane', inplane_to_nii,
'dicom_files')
extract_inplane.connect(inplane_to_nii, 'out_file', rename_inplane,
'in_file')
extract_inplane.connect(rename_inplane, 'out_file', outputs, 'out_file')
return extract_inplane
def create_extract_functional_workflow(
name='extract_functional',
templates={'functional': 'Raw/Functional/Scan_{scan}/'},
format_string='f%(scan)d'):
extract_functional = pe.Workflow(name=name)
inputs = pe.Node(
interface=util.IdentityInterface(fields=['session_dir', 'scan']),
name='inputs')
get_functional = pe.Node(interface=nio.SelectFiles(templates),
name='get_functional')
functional_to_nii = pe.Node(interface=ds.DcmStack(),
name='functional_to_nii')
functional_to_nii.inputs.embed_meta = True
rename_functional = pe.Node(interface=util.Rename(format_string),
name='rename_functional')
rename_functional.inputs.keep_ext = True
outputs = pe.Node(interface=util.IdentityInterface(fields=['out_file']),
name='outputs')
extract_functional.connect(inputs, 'session_dir', get_functional,
'base_directory')
extract_functional.connect(inputs, 'scan', get_functional, 'scan')
extract_functional.connect(get_functional, 'functional', functional_to_nii,
'dicom_files')
extract_functional.connect(functional_to_nii, 'out_file',
rename_functional, 'in_file')
extract_functional.connect(inputs, 'scan', rename_functional, 'scan')
extract_functional.connect(rename_functional, 'out_file', outputs,
'out_file')
return extract_functional
####Nipype script begins below####
#Set up iteration over subjects
infosource = Node(IdentityInterface(fields=['subject_id']), name='infosource')
infosource.iterables = ('subject_id', subject_list)
selectfiles = Node(SelectFiles(template), name='selectfiles')
selectfiles.inputs.base_directory = rawdir
selectfiles.inputs.sort_files = True
#Outputs: anat, epi, flair, mask, wm_noise, csf_noise, mni_template
####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
def get_wf():
import numpy as np
wf = pe.Workflow(name="main_workflow")
wf.base_dir = os.path.join(workingdir, "rs_preprocessing/")
wf.config['execution']['crashdump_dir'] = wf.base_dir + "crash_files/"
##Infosource##
subject_id_infosource = pe.Node(
util.IdentityInterface(fields=['subject_id']),
name="subject_id_infosource")
subject_id_infosource.iterables = ('subject_id', subjects)
session_infosource = pe.Node(util.IdentityInterface(fields=['session']),
name="session_infosource")
session_infosource.iterables = ('session', sessions)
hemi_infosource = pe.Node(util.IdentityInterface(fields=['hemi']),
name="hemi_infosource")
hemi_infosource.iterables = ('hemi', hemispheres)
fs_infosource = pe.Node(util.IdentityInterface(fields=['fs']),
name="fs_infosource")
fs_infosource.iterables = ('fs', fsaverage)
##Datagrabber##
datagrabber = pe.Node(nio.DataGrabber(
infields=['subject_id'],
outfields=['resting_nifti', 't1_nifti', 'dicoms']),
name="datagrabber",
overwrite=True)
datagrabber.inputs.base_directory = '/'
datagrabber.inputs.template = '*'
datagrabber.inputs.field_template = rs_preprocessing_dg_template
datagrabber.inputs.template_args = rs_preprocessing_dg_args
datagrabber.inputs.sort_filelist = True
wf.connect(subject_id_infosource, 'subject_id', datagrabber, 'subject_id')
wf.connect(session_infosource, 'session', datagrabber, 'session')
##DcmStack & MetaData##
dicom_filedir = pe.Node(name='dicom_filedir',
interface=util.Function(
input_names=['subject_id', 'session'],
output_names=['filedir'],
function=construct_dicomfiledir))
stack = pe.Node(dcm.DcmStack(), name='stack')
stack.inputs.embed_meta = True
tr_lookup = pe.Node(dcm.LookupMeta(), name='tr_lookup')
tr_lookup.inputs.meta_keys = {'RepetitionTime': 'TR'}
wf.connect(subject_id_infosource, 'subject_id', dicom_filedir,
'subject_id')
wf.connect(session_infosource, 'session', dicom_filedir, 'session')
wf.connect(dicom_filedir, 'filedir', stack, 'dicom_files')
wf.connect(stack, 'out_file', tr_lookup, 'in_file')
##Preproc from BIPs##
preproc = create_rest_prep(name="bips_resting_preproc", fieldmap=False)
zscore = preproc.get_node('z_score')
preproc.remove_nodes([zscore])
mod_realign = preproc.get_node('mod_realign')
mod_realign.plugin_args = {'submit_specs': 'request_memory=4000\n'}
#workaround for realignment crashing in multiproc environment
mod_realign.run_without_submitting = True
# inputs
preproc.inputs.inputspec.motion_correct_node = 'nipy'
ad = preproc.get_node('artifactdetect')
preproc.disconnect(mod_realign, 'parameter_source', ad, 'parameter_source')
ad.inputs.parameter_source = 'NiPy'
preproc.inputs.inputspec.realign_parameters = {
"loops": [5],
"speedup": [5]
}
preproc.inputs.inputspec.do_whitening = False
preproc.inputs.inputspec.timepoints_to_remove = 4
preproc.inputs.inputspec.smooth_type = 'susan'
preproc.inputs.inputspec.do_despike = False
preproc.inputs.inputspec.surface_fwhm = 0.0
preproc.inputs.inputspec.num_noise_components = 6
preproc.inputs.inputspec.regress_before_PCA = False
preproc.get_node('fwhm_input').iterables = ('fwhm', [0, 5])
preproc.get_node('take_mean_art').get_node(
'strict_artifact_detect').inputs.save_plot = True
preproc.inputs.inputspec.ad_normthresh = 1
preproc.inputs.inputspec.ad_zthresh = 3
preproc.inputs.inputspec.do_slicetime = True
preproc.inputs.inputspec.compcor_select = [True, True]
preproc.inputs.inputspec.filter_type = 'fsl'
preproc.get_node('bandpass_filter').iterables = [('highpass_freq', [0.01]),
('lowpass_freq', [0.1])]
preproc.inputs.inputspec.reg_params = [
True, True, True, False, True, False
]
preproc.inputs.inputspec.fssubject_dir = freesurferdir
#preproc.inputs.inputspec.tr = 1400/1000
#preproc.inputs.inputspec.motion_correct_node = 'afni'
#preproc.inputs.inputspec.sliceorder = slicetime_file
#preproc.inputs.inputspec.sliceorder = list(np.linspace(0,1.4,64))
def convert_units(tr):
mstr = (tr * .001)
return tr
def get_sliceorder(in_file):
import nipype.interfaces.dcmstack as dcm
import numpy as np
nii_wrp = dcm.NiftiWrapper.from_filename(in_file)
sliceorder = np.argsort(
np.argsort(
nii_wrp.meta_ext.get_values('CsaImage.MosaicRefAcqTimes')
[0])).tolist()
return sliceorder
wf.connect(tr_lookup, ("TR", convert_units), preproc, "inputspec.tr")
wf.connect(stack, ('out_file', get_sliceorder), preproc,
"inputspec.sliceorder")
wf.connect(subject_id_infosource, 'subject_id', preproc,
"inputspec.fssubject_id")
wf.connect(datagrabber, "resting_nifti", preproc, "inputspec.func")
##Sampler##
sampler = pe.Node(fs.SampleToSurface(), name='sampler')
sampler.inputs.sampling_method = 'average'
sampler.inputs.sampling_range = (0.2, 0.8, 0.1)
sampler.inputs.sampling_units = 'frac'
sampler.inputs.interp_method = 'nearest'
sampler.inputs.out_type = 'nii'
wf.connect(preproc, ('bandpass_filter.out_file', list_to_filename),
sampler, 'source_file')
wf.connect(preproc, ('getmask.register.out_reg_file', list_to_filename),
sampler, 'reg_file')
wf.connect(hemi_infosource, 'hemi', sampler, 'hemi')
##SXFM##
sxfm = pe.Node(fs.SurfaceTransform(), name='sxfm')
sxfm.inputs.args = '--cortex --fwhm-src 5 --noreshape'
sxfm.inputs.target_type = 'nii'
wf.connect(sampler, 'out_file', sxfm, 'source_file')
wf.connect(subject_id_infosource, 'subject_id', sxfm, 'source_subject')
wf.connect(hemi_infosource, 'hemi', sxfm, 'hemi')
wf.connect(fs_infosource, 'fs', sxfm, 'target_subject')
###########
#report_wf = create_preproc_report_wf(os.path.join(preprocdir, "reports"))
#report_wf.inputs.inputspec.fssubjects_dir = preproc.inputs.inputspec.fssubject_dir
def pick_full_brain_ribbon(l):
import os
for path in l:
if os.path.split(path)[1] == "ribbon.mgz":
return path
#wf.connect(preproc,"artifactdetect.plot_files", report_wf, "inputspec.art_detect_plot")
#wf.connect(preproc,"take_mean_art.weighted_mean.mean_image", report_wf, "inputspec.mean_epi")
#wf.connect(preproc,("getmask.register.out_reg_file", list_to_filename), report_wf, "inputspec.reg_file")
#wf.connect(preproc,("getmask.fssource.ribbon",pick_full_brain_ribbon), report_wf, "inputspec.ribbon")
#wf.connect(preproc,("CompCor.tsnr.tsnr_file", list_to_filename), report_wf, "inputspec.tsnr_file")
#wf.connect(subject_id_infosource, 'subject_id', report_wf, "inputspec.subject_id")
##Datasink##
ds = pe.Node(nio.DataSink(), name="datasink")
ds.inputs.base_directory = os.path.join(preprocdir, "aimivolumes")
wf.connect(preproc, 'bandpass_filter.out_file', ds, "preprocessed_resting")
wf.connect(preproc, 'getmask.register.out_fsl_file', ds,
"func2anat_transform")
wf.connect(sampler, 'out_file', ds, 'sampledtosurf')
wf.connect(sxfm, 'out_file', ds, 'sxfmout')
#wf.write_graph()
return wf
infosource.iterables = [('subject_id', subject_list)]
#Select files
template = {
'anat': data_dir + '{subject_id}/T1_MPRAGE_SAG_ISO_0_7*/*.IMA',
'epi_s1': data_dir + '{subject_id}/*PACE_MOCO_P2-1*/*.IMA',
'epi_s2': data_dir + '{subject_id}/*PACE_MOCO_P2-2*/*.IMA',
'template': template_dir + 't1_3mm_brain.nii',
'mask': template_dir + 't1_3mm_mask.nii'
}
select_files = Node(SelectFiles(template), name='select_files')
select_files.inputs.base_directory = h_dir
select_files.inputs.sort_files = True
anat_stack = Node(dcmstack.DcmStack(), name='anat_stack')
anat_stack.inputs.embed_meta = True
anat_stack.inputs.out_format = 'anat'
anat_stack.inputs.out_ext = '.nii'
epi_s1_stack = Node(dcmstack.DcmStack(), name='epi_s1_stack')
epi_s1_stack.inputs.embed_meta = True
epi_s1_stack.inputs.out_format = 'epi1'
epi_s1_stack.inputs.out_ext = '.nii'
epi_s2_stack = Node(dcmstack.DcmStack(), name='epi_s2_stack')
epi_s2_stack.inputs.embed_meta = True
epi_s2_stack.inputs.out_format = 'epi2'
epi_s2_stack.inputs.out_ext = '.nii'
st_corr = Node(spm.SliceTiming(), name='slicetiming_correction')