def get_tr_and_sliceorder(dicom_files, convention="french"):
import numpy as np
import dcmstack, dicom
from dcmstack.dcmmeta import NiftiWrapper
nii_wrp = NiftiWrapper.from_filename(dicom_files)
if convention == "french":
sliceorder = np.argsort(np.argsort(nii_wrp.meta_ext.get_values('CsaImage.MosaicRefAcqTimes')[0])).tolist()
elif convention == "SPM":
sliceorder = np.argsort(nii_wrp.meta_ext.get_values('CsaImage.MosaicRefAcqTimes')[0]).tolist()
tr = nii_wrp.meta_ext.get_values('RepetitionTime')
return tr/1000.,sliceorder
get_meta = pe.Node(util.Function(input_names=['dicom_files', 'convention'], output_names=['tr', 'sliceorder'], function=get_tr_and_sliceorder), name="get_meta")
get_meta.inputs.convention = "french"
wf.connect(datagrabber, "resting_nifti", get_meta, "dicom_files")
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"}
# 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', [5])
preproc.get_node('take_mean_art').get_node('strict_artifact_detect').inputs.save_plot = True
#preproc.get_node('take_mean_art').get_node('strict_artifact_detect').overwrite=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.highpass_freq = 0.01
# preproc.inputs.inputspec.lowpass_freq = 0.1
#[motion_params, composite_norm, compcorr_components, global_signal, art_outliers, motion derivatives]
preproc.inputs.inputspec.reg_params = [True, True, True, False, True, False]
preproc.inputs.inputspec.fssubject_dir = freesurferdir
wf.connect(get_meta, "tr", preproc, "inputspec.tr")
wf.connect(get_meta, "sliceorder", preproc, "inputspec.sliceorder")
wf.connect(subject_id_infosource, "subject_id", preproc, 'inputspec.fssubject_id')
wf.connect(datagrabber, "resting_nifti", preproc, "inputspec.func")
# report_wf = create_preproc_report_wf(resultsdir + "/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")
ds = pe.Node(nio.DataSink(), name="datasink", overwrite=True)
ds.inputs.base_directory = os.path.join(resultsdir, "volumes")
wf.connect(preproc, 'bandpass_filter.out_file', ds, "preprocessed_resting")
wf.connect(preproc, 'getmask.register.out_fsl_file', ds, "func2anat_transform")
wf.connect(preproc, 'outputspec.mask', ds, "epi_mask")
wf.write_graph()
wf.run(plugin="MultiProc")
import dcmstack, dicom
from dcmstack.dcmmeta import NiftiWrapper
nii_wrp = NiftiWrapper.from_filename(dicom_files)
sliceorder = np.argsort(nii_wrp.meta_ext.get_values("CsaImage.MosaicRefAcqTimes")[0]).tolist()
tr = nii_wrp.meta_ext.get_values("RepetitionTime")
return tr / 1000.0, sliceorder
get_meta = pe.Node(
util.Function(input_names=["dicom_files"], output_names=["tr", "sliceorder"], function=get_tr_and_sliceorder),
name="get_meta",
)
wf.connect(datagrabber, "resting_nifti", get_meta, "dicom_files")
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"}
# inputs
preproc.inputs.inputspec.motion_correct_node = "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
def get_tr_and_sliceorder(dicom_files, convention="french"):
import numpy as np
import dcmstack, dicom
from dcmstack.dcmmeta import NiftiWrapper
nii_wrp = NiftiWrapper.from_filename(dicom_files)
if convention == "french":
sliceorder = np.argsort(
np.argsort(
nii_wrp.meta_ext.get_values('CsaImage.MosaicRefAcqTimes')
[0])).tolist()
elif convention == "SPM":
sliceorder = np.argsort(
nii_wrp.meta_ext.get_values('CsaImage.MosaicRefAcqTimes')
[0]).tolist()
tr = nii_wrp.meta_ext.get_values('RepetitionTime')
return tr / 1000., sliceorder
get_meta = pe.Node(util.Function(input_names=['dicom_files', 'convention'],
output_names=['tr', 'sliceorder'],
function=get_tr_and_sliceorder),
name="get_meta")
get_meta.inputs.convention = "french"
wf.connect(datagrabber, "resting_nifti", get_meta, "dicom_files")
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"}
# 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', [5])
preproc.get_node('take_mean_art').get_node(
'strict_artifact_detect').inputs.save_plot = True
#preproc.get_node('take_mean_art').get_node('strict_artifact_detect').overwrite=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.highpass_freq = 0.01
# preproc.inputs.inputspec.lowpass_freq = 0.1
#[motion_params, composite_norm, compcorr_components, global_signal, art_outliers, motion derivatives]
preproc.inputs.inputspec.reg_params = [
True, True, True, False, True, False
]
preproc.inputs.inputspec.fssubject_dir = freesurferdir
wf.connect(get_meta, "tr", preproc, "inputspec.tr")
wf.connect(get_meta, "sliceorder", preproc, "inputspec.sliceorder")
wf.connect(subject_id_infosource, "subject_id", preproc,
'inputspec.fssubject_id')
wf.connect(datagrabber, "resting_nifti", preproc, "inputspec.func")
# report_wf = create_preproc_report_wf(resultsdir + "/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")
ds = pe.Node(nio.DataSink(), name="datasink", overwrite=True)
ds.inputs.base_directory = os.path.join(resultsdir, "volumes")
wf.connect(preproc, 'bandpass_filter.out_file', ds, "preprocessed_resting")
wf.connect(preproc, 'getmask.register.out_fsl_file', ds,
"func2anat_transform")
wf.connect(preproc, 'outputspec.mask', ds, "epi_mask")
wf.write_graph()
wf.run(plugin="MultiProc")
elif convention == "SPM":
sliceorder = np.argsort(
nii_wrp.meta_ext.get_values('CsaImage.MosaicRefAcqTimes')
[0]).tolist()
tr = nii_wrp.meta_ext.get_values('RepetitionTime')
return tr / 1000., sliceorder
get_meta = pe.Node(util.Function(input_names=['dicom_files', 'convention'],
output_names=['tr', 'sliceorder'],
function=get_tr_and_sliceorder),
name="get_meta")
get_meta.inputs.convention = "french"
wf.connect(datagrabber, "resting_nifti", get_meta, "dicom_files")
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"}
# 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]
