def test_vmhc_ants():
test_name = 'test_vmhc_ants'
# get the config and strat for the mock
pipeline_config, strat = configuration_strategy_mock(method='ANTS')
num_strat = 0
workflow = pe.Workflow(name=test_name)
workflow.base_dir = pipeline_config.workingDirectory
workflow.config['execution'] = {
'hash_method': 'timestamp',
'crashdump_dir': os.path.abspath(pipeline_config.crashLogDirectory)
}
nodes = strat.get_nodes_names()
print('nodes {0}'.format(nodes))
workflow, strat = create_vmhc(workflow,
num_strat,
strat,
pipeline_config,
output_name='vmhc_{0}'.format(num_strat))
workflow.run()
def test_registration():
from ..registration import create_nonlinear_register
from ..registration import create_register_func_to_mni
from CPAC.pipeline import nipype_pipeline_engine as pe
import nipype.interfaces.fsl as fsl
func_file = '/home/data/Projects/nuisance_reliability_paper/working_dir_CPAC_order/resting_preproc/nuisance_preproc/_session_id_NYU_TRT_session1_subject_id_sub05676/_csf_threshold_0.4/_gm_threshold_0.2/_wm_threshold_0.66/_run_scrubbing_False/_nc_5/_selector_6.7/regress_nuisance/mapflow/_regress_nuisance0/residual.nii.gz'
anat_skull_file = '/home/data/Projects/nuisance_reliability_paper/working_dir_CPAC_order/resting_preproc/anatpreproc/_session_id_NYU_TRT_session1_subject_id_sub05676/anat_reorient/mprage_anonymized_RPI.nii.gz'
anat_bet_file = '/home/data/Projects/nuisance_reliability_paper/working_dir_CPAC_order/resting_preproc/anatpreproc/_session_id_NYU_TRT_session1_subject_id_sub05676/anat_skullstrip/mprage_anonymized_RPI_3dT.nii.gz'
mni_brain_file = '/usr/share/fsl/4.1/data/standard/MNI152_T1_3mm_brain.nii.gz'
mni_skull_file = '/usr/share/fsl/4.1/data/standard/MNI152_T1_3mm.nii.gz'
mni_workflow = pe.Workflow(name='mni_workflow')
nr = create_nonlinear_register()
nr.inputs.inputspec.input_brain = anat_bet_file
nr.inputs.inputspec.input_skull = anat_skull_file
nr.inputs.inputspec.reference_brain = mni_brain_file
nr.inputs.inputspec.reference_skull = mni_skull_file
nr.inputs.inputspec.fnirt_config = '/usr/share/fsl/4.1/etc/flirtsch/T1_2_MNI152_3mm.cnf'
func2mni = create_register_func_to_mni()
func2mni.inputs.inputspec.func = func_file
func2mni.inputs.inputspec.mni = mni_brain_file
func2mni.inputs.inputspec.anat = anat_bet_file
mni_workflow.connect(nr, 'outputspec.nonlinear_xfm', func2mni,
'inputspec.anat_to_mni_xfm')
mni_workflow.base_dir = './mni_05676_3'
mni_workflow.run()
def create_grp_analysis_dataflow(wf_name='gp_dataflow'):
from CPAC.pipeline import nipype_pipeline_engine as pe
import nipype.interfaces.utility as util
from CPAC.utils.datasource import select_model_files
wf = pe.Workflow(name=wf_name)
inputnode = pe.Node(util.IdentityInterface(
fields=['ftest', 'grp_model', 'model_name'], mandatory_inputs=True),
name='inputspec')
selectmodel = pe.Node(function.Function(
input_names=['model', 'ftest', 'model_name'],
output_names=['fts_file', 'con_file', 'grp_file', 'mat_file'],
function=select_model_files,
as_module=True),
name='selectnode')
wf.connect(inputnode, 'ftest', selectmodel, 'ftest')
wf.connect(inputnode, 'grp_model', selectmodel, 'model')
wf.connect(inputnode, 'model_name', selectmodel, 'model_name')
outputnode = pe.Node(util.IdentityInterface(
fields=['fts', 'grp', 'mat', 'con'], mandatory_inputs=True),
name='outputspec')
wf.connect(selectmodel, 'mat_file', outputnode, 'mat')
wf.connect(selectmodel, 'grp_file', outputnode, 'grp')
wf.connect(selectmodel, 'fts_file', outputnode, 'fts')
wf.connect(selectmodel, 'con_file', outputnode, 'con')
return wf
def test_fsl_apply_transform_func_to_mni_linear_mapnode():
c, strat = configuration_strategy_mock(method='FSL')
strat.append_name('anat_mni_flirt_register_0')
# build the workflow
workflow = pe.Workflow(
name='test_fsl_apply_transform_func_to_mni_linear_mapnode')
workflow.base_dir = c.workingDirectory
workflow.config['execution'] = {
'hash_method': 'timestamp',
'crashdump_dir': os.path.abspath(c.crashLogDirectory)
}
workflow = fsl_apply_transform_func_to_mni(
workflow,
'dr_tempreg_to_standard',
'dr_tempreg_maps_files',
'template_brain_for_func_preproc',
0,
strat,
c.funcRegFSLinterpolation,
map_node=True)
workflow.run()
def create_connectome(name='connectome'):
wf = pe.Workflow(name=name)
inputspec = pe.Node(
util.IdentityInterface(fields=[
'time_series',
'method'
]),
name='inputspec'
)
outputspec = pe.Node(
util.IdentityInterface(fields=[
'connectome',
]),
name='outputspec'
)
node = pe.Node(Function(input_names=['time_series', 'method'],
output_names=['connectome'],
function=compute_correlation,
as_module=True),
name='connectome')
wf.connect([
(inputspec, node, [('time_series', 'time_series')]),
(inputspec, node, [('method', 'method')]),
(node, outputspec, [('connectome', 'connectome')]),
])
return wf
def create_anat_datasource(wf_name='anat_datasource'):
from CPAC.pipeline import nipype_pipeline_engine as pe
import nipype.interfaces.utility as util
wf = pe.Workflow(name=wf_name)
inputnode = pe.Node(util.IdentityInterface(
fields=['subject', 'anat', 'creds_path', 'dl_dir', 'img_type'],
mandatory_inputs=True),
name='inputnode')
check_s3_node = pe.Node(function.Function(
input_names=['file_path', 'creds_path', 'dl_dir', 'img_type'],
output_names=['local_path'],
function=check_for_s3,
as_module=True),
name='check_for_s3')
wf.connect(inputnode, 'anat', check_s3_node, 'file_path')
wf.connect(inputnode, 'creds_path', check_s3_node, 'creds_path')
wf.connect(inputnode, 'dl_dir', check_s3_node, 'dl_dir')
wf.connect(inputnode, 'img_type', check_s3_node, 'img_type')
outputnode = pe.Node(util.IdentityInterface(fields=['subject', 'anat']),
name='outputspec')
wf.connect(inputnode, 'subject', outputnode, 'subject')
wf.connect(check_s3_node, 'local_path', outputnode, 'anat')
# Return the workflow
return wf
def prep_cwas_workflow(c, subject_infos):
print('Preparing CWAS workflow')
p_id, s_ids, scan_ids, s_paths = (list(tup) for tup in zip(*subject_infos))
print('Subjects', s_ids)
wf = pe.Workflow(name='cwas_workflow')
wf.base_dir = c.pipeline_setup['working_directory']['path']
from CPAC.cwas import create_cwas
import numpy as np
regressor = np.loadtxt(c.cwasRegressorFile)
cw = create_cwas()
cw.inputs.inputspec.roi = c.cwasROIFile
cw.inputs.inputspec.subjects = s_paths
cw.inputs.inputspec.regressor = regressor
cw.inputs.inputspec.cols = c.cwasRegressorCols
cw.inputs.inputspec.f_samples = c.cwasFSamples
cw.inputs.inputspec.strata = c.cwasRegressorStrata # will stay None?
cw.inputs.inputspec.parallel_nodes = c.cwasParallelNodes
ds = pe.Node(nio.DataSink(), name='cwas_sink')
out_dir = os.path.dirname(s_paths[0]).replace(s_ids[0], 'cwas_results')
ds.inputs.base_directory = out_dir
ds.inputs.container = ''
wf.connect(cw, 'outputspec.F_map', ds, 'F_map')
wf.connect(cw, 'outputspec.p_map', ds, 'p_map')
wf.run(plugin='MultiProc', plugin_args={'n_procs': c.numCoresPerSubject})
def create_qc_skullstrip(wf_name='qc_skullstrip'):
wf = pe.Workflow(name=wf_name)
input_node = pe.Node(util.IdentityInterface(
fields=['anatomical_brain', 'anatomical_reorient']),
name='inputspec')
output_node = pe.Node(
util.IdentityInterface(fields=['axial_image', 'sagittal_image']),
name='outputspec')
skull_edge = pe.Node(Function(input_names=['in_file'],
output_names=['out_file'],
function=afni_Edge3,
as_module=True),
name='skull_edge')
montage_skull = create_montage('montage_skull',
'red',
'skull_vis',
mapnode=False)
wf.connect(input_node, 'anatomical_reorient', skull_edge, 'in_file')
wf.connect(input_node, 'anatomical_brain', montage_skull,
'inputspec.underlay')
wf.connect(skull_edge, 'out_file', montage_skull, 'inputspec.overlay')
wf.connect(montage_skull, 'outputspec.axial_png', output_node,
'axial_image')
wf.connect(montage_skull, 'outputspec.sagittal_png', output_node,
'sagittal_image')
return wf
def fisher_z_score_standardize(wf_name, label,
input_image_type='func_derivative', opt=None):
wf = pe.Workflow(name=wf_name)
map_node = False
if input_image_type == 'func_derivative_multi':
map_node = True
inputnode = pe.Node(util.IdentityInterface(fields=['correlation_file',
'timeseries_oned']),
name='inputspec')
fisher_z_score_std = get_fisher_zscore(label, map_node,
'fisher_z_score_std')
wf.connect(inputnode, 'correlation_file',
fisher_z_score_std, 'inputspec.correlation_file')
wf.connect(inputnode, 'timeseries_oned',
fisher_z_score_std, 'inputspec.timeseries_one_d')
outputnode = pe.Node(util.IdentityInterface(fields=['out_file']),
name='outputspec')
wf.connect(fisher_z_score_std, 'outputspec.fisher_z_score_img',
outputnode, 'out_file')
return wf
def test_nonlinear_register():
from ..registration import create_nonlinear_register
from CPAC.pipeline import nipype_pipeline_engine as pe
import nipype.interfaces.fsl as fsl
## necessary inputs
## -input_brain
## -input_skull
## -reference_brain
## -reference_skull
## -fnirt_config
## -fnirt_warp_res
## input_brain
anat_bet_file = '/home/data/Projects/nuisance_reliability_paper/working_dir_CPAC_order/resting_preproc/anatpreproc/_session_id_NYU_TRT_session1_subject_id_sub05676/anat_skullstrip/mprage_anonymized_RPI_3dT.nii.gz'
## input_skull
## reference_brain
mni_file = '/usr/share/fsl/4.1/data/standard/MNI152_T1_3mm_brain.nii.gz'
## reference_skull
## fnirt_config
fnirt_config = 'T1_2_MNI152_3mm'
## fnirt_warp_res
fnirt_warp_res = None
#?? what is this for?:
func_file = '/home/data/Projects/nuisance_reliability_paper/working_dir_CPAC_order/resting_preproc/nuisance_preproc/_session_id_NYU_TRT_session1_subject_id_sub05676/_csf_threshold_0.4/_gm_threshold_0.2/_wm_threshold_0.66/_run_scrubbing_False/_nc_5/_selector_6.7/regress_nuisance/mapflow/_regress_nuisance0/residual.nii.gz'
mni_workflow = pe.Workflow(name='mni_workflow')
linear_reg = pe.Node(interface=fsl.FLIRT(), name='linear_reg_0')
linear_reg.inputs.cost = 'corratio'
linear_reg.inputs.dof = 6
linear_reg.inputs.interp = 'nearestneighbour'
linear_reg.inputs.in_file = func_file
linear_reg.inputs.reference = anat_bet_file
#T1 to MNI Node
c = create_nonlinear_register()
c.inputs.inputspec.input = anat_bet_file
c.inputs.inputspec.reference = '/usr/share/fsl/4.1/data/standard/MNI152_T1_3mm_brain.nii.gz'
c.inputs.inputspec.fnirt_config = 'T1_2_MNI152_3mm'
#EPI to MNI warp Node
mni_warp = pe.Node(interface=fsl.ApplyWarp(), name='mni_warp')
mni_warp.inputs.ref_file = '/usr/share/fsl/4.1/data/standard/MNI152_T1_3mm_brain.nii.gz'
mni_warp.inputs.in_file = func_file
mni_workflow.connect(c, 'outputspec.nonlinear_xfm', mni_warp, 'field_file')
mni_workflow.connect(linear_reg, 'out_matrix_file', mni_warp, 'premat')
mni_workflow.base_dir = './'
mni_workflow.run()
def test_ants_apply_warp_func_mni_symm():
test_name = 'test_ants_apply_warps_func_mni_symm'
# get the config and strat for the mock
c, strat = configuration_strategy_mock()
num_strat = 0
node, out = strat['mean_functional']
mean_functional = node.inputs.file
# build the workflow
workflow = pe.Workflow(name=test_name)
workflow.base_dir = c.workingDirectory
workflow.config['execution'] = {
'hash_method': 'timestamp',
'crashdump_dir': os.path.abspath(c.crashLogDirectory)
}
workflow = ants_apply_warps_func_mni(
workflow,
'mean_functional_to_standard_symm',
'mean_functional',
'template_brain_for_func_preproc',
num_strat,
strat,
interpolation_method=c.funcRegANTSinterpolation,
distcor=False,
map_node=False,
inverse=False,
symmetry='symmetric',
input_image_type=0,
num_ants_cores=8)
workflow = ants_apply_warps_func_mni(
workflow,
'mean_functional_standard_to_original_symm',
'mean_functional_to_standard_symm',
'mean_functional',
num_strat,
strat,
interpolation_method=c.funcRegANTSinterpolation,
distcor=False,
map_node=False,
inverse=True,
symmetry='symmetric',
input_image_type=0,
num_ants_cores=1)
retval = workflow.run()
mean_functional_after_transform = os.path.join(
c.workingDirectory, test_name,
'apply_ants_warp_mean_functional_standard_to_original_symm_inverse_0',
'sub-M10978008_ses-NFB3_task-test_bold_calc_tshift_resample_volreg_calc_tstat_antswarp_antswarp.nii.gz'
)
assert (test_utils.pearson_correlation(
mean_functional, mean_functional_after_transform) > .93)
def test_qc():
outputs = Outputs()
c = Configuration({
"workingDirectory": "",
"crashLogDirectory": "",
"outputDirectory":""
})
workflow = pe.Workflow(name='workflow_name')
workflow.base_dir = c.workingDirectory
workflow.config['execution'] = {
'hash_method': 'timestamp',
'crashdump_dir': os.path.abspath(c.crashLogDirectory)
}
strat_initial = Strategy()
strat_list = [strat_initial]
output_df_dct = gather_outputs(
c.outputDirectory,
[
"functional_brain_mask",
"functional_to_anat_linear_xfm",
"anatomical_brain",
"anatomical_reorient",
"mean_functional_in_anat",
"motion_params",
"frame_wise_displacement_power",
"frame_wise_displacement_jenkinson",
],
None,
get_motion=False,
get_raw_score=False,
get_func=True,
derivatives=[
"functional_brain_mask",
"functional_to_anat_linear_xfm",
"anatomical_brain",
"anatomical_reorient",
"mean_functional_in_anat",
"motion_params",
"frame_wise_displacement_power",
"frame_wise_displacement_jenkinson",
],
exts=['nii', 'nii.gz', '1D', 'mat', 'txt']
)
for (resource, _), df in output_df_dct.items():
strat_initial.update_resource_pool({
resource: file_node(df.Filepath[0])
})
qc_montage_id_a, qc_montage_id_s, qc_hist_id, qc_plot_id = \
create_qc_workflow(workflow, c, strat_list, outputs.qc)
def get_normalized_moments(wf_name='normalized_moments'):
"""
Workflow to calculate the normalized moments for skewedness calculations
Parameters
----------
wf_name : string
name of the workflow
Returns
-------
wflow : workflow object
workflow object
Notes
-----
`Source <https://github.com/FCP-INDI/C-PAC/blob/master/CPAC/timeseries/timeseries_analysis.py>`_
Workflow Inputs::
inputspec.spatial_timeseries : string (nifti file)
spatial map timeseries
Workflow Outputs::
outputspec.moments: list
list of moment values
Example
-------
>>> import CPAC.timeseries.timeseries_analysis as t
>>> wf = t.get_normalized_moments()
>>> wf.inputs.inputspec.spatial_timeseries = '/home/data/outputs/SurfaceRegistration/lh_surface_file.nii.gz'
>>> wf.base_dir = './'
>>> wf.run()
"""
wflow = pe.Workflow(name=wf_name)
inputNode = pe.Node(util.IdentityInterface(fields=['spatial_timeseries']),
name='inputspec')
# calculate normalized moments
# output of this node is a list, 'moments'
norm_moments = pe.Node(util.CalculateNormalizedMoments(moment='3'),
name='norm_moments')
outputNode = pe.Node(util.IdentityInterface(fields=['moments_outputs']),
name='outputspec')
wflow.connect(inputNode, 'spatial_timeseries', norm_moments,
'timeseries_file')
wflow.connect(norm_moments, 'moments', outputNode, 'moments_outputs')
return wflow
def run_warp_nipype(inputs,output_dir=None,run=True):
import EPI_DistCorr
warp_workflow = pe.Workflow(name = 'preproc')
if output_dir == None:
output_dir = '/home/nrajamani'
workflow_dir = os.path.join(output_dir,"workflow_output_with_aroma_with_change")
warp_workflow.base_dir = workflow_dir
# taken from QAP files
#resource_pool = {}
num_of_cores = 1
#resource_pool({'epireg': (warp_nipype2.warp_nipype, 'outputspec.epireg')})
t_node = EPI_DistCorr.create_EPI_DistCorr()####
t_node.inputs.inputspec.anat_file= '/Users/nanditharajamani/Downloads/ExBox19/T1.nii.gz'
t_node.inputs.inputspec.func_file= '/Users/nanditharajamani/Downloads/ExBox19/func.nii.gz'
t_node.inputs.inputspec.fmap_pha= '/Users/nanditharajamani/Downloads/ExBox19/fmap_phase.nii.gz'
t_node.inputs.inputspec.fmap_mag= '/Users/nanditharajamani/Downloads/ExBox19/fmap_mag.nii.gz'
t_node.inputs.inputspec.bbr_schedule='/usr/local/fsl/etc/flirtsch/bbr.sch'
t_node.inputs.inputspec.deltaTE = 2.46
t_node.inputs.inputspec.dwellT = 0.0005
t_node.inputs.inputspec.dwell_asym_ratio = 0.93902439
t_node.inputs.inputspec.bet_frac = 0.5
#'home/nrajamani/FieldMap_SubjectExampleData/SubjectData/epi_run2/fMT0160-0015-00003-000003-01_BRAIN.nii.gz',
# for image in inputs:
# if not(image.endswith('.nii') or image.endswith('.nii.gz')):
# raise 'The input image is not the right format'
# try:
# for image in inputs:
# size = image.get_shape()
# assert len(size) == 3
# except:
# if len(size) < 3:
# raise 'input image is not 3D'
# intensity = ImageStats(in_file = t_node.inputs.inputspec.fmap_pha, op_string = '-p 90')
# if intensity < 3686:
# raise 'input phase image does not have the correct range values'
dataSink = pe.Node(nio.DataSink(), name='dataSink_file')
dataSink.inputs.base_directory = workflow_dir
#node, out_file = resource_pool["epireg"]
#warp_workflow.connect(t_node,'outputspec.roi_file',dataSink,'roi_file')
warp_workflow.connect(t_node,'outputspec.fieldmap',dataSink,'fieldmap_file')
warp_workflow.connect(t_node,'outputspec.fmapmagbrain',dataSink,'fmapmagbrain')
warp_workflow.connect(t_node,'outputspec.fieldmapmask',dataSink,'fieldmapmask')
warp_workflow.connect(t_node,'outputspec.fmap_despiked',dataSink,'fmap_despiked')
warp_workflow.connect(t_node,'outputspec.struct',dataSink,'epi2struct')
warp_workflow.connect(t_node,'outputspec.anat_func',dataSink,'anat_func')
if run == True:
warp_workflow.run(plugin='MultiProc', plugin_args ={'n_procs': num_of_cores})
#outpath = glob.glob(os.path.join(workflow_dir, "EPI_DistCorr","*"))[0]
#return outpath
else:
return warp_workflow, warp_workflow.base_dir
def setup_test_wf(s3_prefix, paths_list, test_name, workdirs_to_keep=None):
"""Set up a basic template Nipype workflow for testing single nodes or
small sub-workflows.
"""
import os
import shutil
from CPAC.pipeline import nipype_pipeline_engine as pe
from CPAC.utils.datasource import check_for_s3
from CPAC.utils.interfaces.datasink import DataSink
test_dir = os.path.join(os.getcwd(), test_name)
work_dir = os.path.join(test_dir, "workdir")
out_dir = os.path.join(test_dir, "output")
if os.path.exists(out_dir):
try:
shutil.rmtree(out_dir)
except:
pass
if os.path.exists(work_dir):
for dirname in os.listdir(work_dir):
if workdirs_to_keep:
for keepdir in workdirs_to_keep:
print("{0} --- {1}\n".format(dirname, keepdir))
if keepdir in dirname:
continue
try:
shutil.rmtree(os.path.join(work_dir, dirname))
except:
pass
local_paths = {}
for subpath in paths_list:
s3_path = os.path.join(s3_prefix, subpath)
local_path = check_for_s3(s3_path, dl_dir=test_dir)
local_paths[subpath] = local_path
wf = pe.Workflow(name=test_name)
wf.base_dir = os.path.join(work_dir)
wf.config['execution'] = {
'hash_method': 'timestamp',
'crashdump_dir': os.path.abspath(test_dir)
}
ds = pe.Node(DataSink(), name='sinker_{0}'.format(test_name))
ds.inputs.base_directory = out_dir
ds.inputs.parameterization = True
return (wf, ds, local_paths)
def test_smooth():
test_name = 'test_smooth_nodes'
c, strat = configuration_strategy_mock(method='FSL')
num_strat = 0
# build the workflow
workflow = pe.Workflow(name=test_name)
workflow.base_dir = c.workingDirectory
workflow.config['execution'] = {
'hash_method': 'timestamp',
'crashdump_dir': os.path.abspath(c.crashLogDirectory)
}
spatial_smooth(workflow, 'mean_functional', 'functional_brain_mask',
'mean_functional_smooth'.format(num_strat), strat,
num_strat, c)
func_node, func_output = strat['mean_functional']
mask_node, mask_output = strat['functional_brain_mask']
spatial_smooth(workflow, (func_node, func_output),
(mask_node, mask_output),
'mean_functional_smooth_nodes'.format(num_strat), strat,
num_strat, c)
print(workflow.list_node_names())
workflow.run()
correlations = []
for fwhm in c.fwhm:
out_name1 = os.path.join(
c.workingDirectory, test_name,
'_fwhm_{0}/mean_functional_smooth_0/'.format(fwhm),
'sub-M10978008_ses-NFB3_task-test_bold_calc_tshift_resample_volreg_calc_tstat_maths.nii.gz'
)
out_name2 = os.path.join(
c.workingDirectory, test_name,
'_fwhm_{0}/mean_functional_smooth_nodes_0/'.format(fwhm),
'sub-M10978008_ses-NFB3_task-test_bold_calc_tshift_resample_volreg_calc_tstat_maths.nii.gz'
)
correlations.append(
test_utils.pearson_correlation(out_name1, out_name2) > 0.99)
assert all(correlations)
def initialize_nipype_wf(cfg, sub_data_dct, name=""):
if name:
name = f'_{name}'
workflow_name = f'cpac{name}_{sub_data_dct["subject_id"]}_{sub_data_dct["unique_id"]}'
wf = pe.Workflow(name=workflow_name)
wf.base_dir = cfg.pipeline_setup['working_directory']['path']
wf.config['execution'] = {
'hash_method': 'timestamp',
'crashdump_dir': os.path.abspath(cfg.pipeline_setup['log_directory'][
'path'])
}
return wf
def run_randomize(inputs, output_dir=None, run=True):
from . import pipeline
randomise_workflow = pe.Workflow(name='preproc')
if output_dir == None:
output_dir = ''
workflow_dir = os.path.join(output_dir, "randomise_results")
randomise_workflow.base_dir = workflow_dir
#resource_pool = {}
num_of_cores = 1
t_node = pipeline.create_randomise()
t_node.inputs.inputspec.subjects = ''
t_node.inputs.inputspec.design_matrix_file = ''
t_node.inputs.inputspec.constrast_file = ''
#t_node.inputs.inputspec.f_constrast_file= ''
t_node.inputs.inputspec.permutations = 5000
#t_node.inputs.inputspec.mask = ''#
dataSink = pe.Node(nio.DataSink(), name='dataSink_file')
dataSink.inputs.base_directory = workflow_dir
randomise_workflow.connect(t_node, 'outputspec.index_file', dataSink,
'index_file')
#randomise_workflow.connect(t_node,'outputspec.thresh_out',dataSink,'threshold_file')
randomise_workflow.connect(t_node, 'outputspec.localmax_txt_file',
dataSink, 'localmax_txt_file')
randomise_workflow.connect(t_node, 'outputspec.localmax_vol_file',
dataSink, 'localmax_vol_file')
randomise_workflow.connect(t_node, 'outputspec.max_file', dataSink,
'max_file')
randomise_workflow.connect(t_node, 'outputspec.mean_file', dataSink,
'mean_file')
randomise_workflow.connect(t_node, 'outputspec.pval_file', dataSink,
'pval_file')
randomise_workflow.connect(t_node, 'outputspec.size_file', dataSink,
'size_file')
randomise_workflow.connect(t_node, 'outputspec.tstat_files', dataSink,
'tstat_files')
randomise_workflow.connect(t_node, 'outputspec.t_corrected_p_files',
dataSink, 't_corrected_p_files')
if run == True:
randomise_workflow.run(plugin='MultiProc',
plugin_args={'n_procs': num_of_cores})
else:
return randomise_workflow, randomise_workflow.base_dir
def prep_randomise_workflow(c, subject_infos):
print('Preparing Randomise workflow')
p_id, s_ids, scan_ids, s_paths = (list(tup) for tup in zip(*subject_infos))
print('Subjects', s_ids)
wf = pe.Workflow(name='randomise_workflow')
wf.base_dir = c.pipeline_setup['working_directory']['path']
from CPAC.randomise import create_randomise
rw = create_randomise()
rw.inputs.inputspec.permutations = c.randopermutations
rw.inputs.inputspec.subjects = s_paths
#rw.inputs.inputspec.pipeline_ouput_folder = c.os.path.join(c.outputDirectory,
# 'pipeline_{0}'.format(c.pipelineName))
rw.inputs.inputspec.mask_boolean = c.mask_boolean #TODO pipe from output dir, not the user input
rw.inputs.inputspec.tfce = c.tfce # will stay None?
rw.inputs.inputspec.demean = c.demean
rw.inputs.inputspec.c_thresh = c.c_thresh
ds = pe.Node(nio.DataSink(), name='randomise_sink')
out_dir = os.path.dirname(s_paths[0]).replace(s_ids[0],
'randomise_results')
ds.inputs.base_directory = out_dir
ds.inputs.container = ''
#'tstat_files' ,'t_corrected_p_files','index_file','threshold_file','localmax_txt_file','localmax_vol_file','max_file','mean_file','pval_file','size_file'
wf.connect(rw, 'outputspec.tstat_files', ds, 'tstat_files')
wf.connect(rw, 'outputspec.t_corrected_p_files', ds, 't_corrected_p_files')
wf.connect(rw, 'outputspec.index_file', ds, 'index_file')
wf.connect(rw, 'outputspec.threshold_file', ds, 'threshold_file')
wf.connect(rw, 'outputspec.localmax_vol_file', ds, 'localmax_vol_file')
wf.connect(rw, 'outputspec.localmax_txt_file', ds, 'localmax_txt_file')
wf.connect(rw, 'outputspec.max_file', ds, 'max_file')
wf.connect(rw, 'outputspec.mean_file', ds, 'mean_file')
wf.connect(rw, 'outputspec.max_file', ds, 'max_file')
wf.connect(rw, 'outputspec.pval_file', ds, 'pval_file')
wf.connect(rw, 'outputspec.size_file', ds, 'size_file')
wf.run(plugin='MultiProc', plugin_args={'n_procs': c.numCoresPerSubject})
return wf
def test_output_func_to_standard_FSL_nonlinear():
test_name = 'test_output_func_to_standard_FSL_nonlinear'
# get the config and strat for the mock
c, strat = configuration_strategy_mock(method='FSL')
strat.append_name('anat_mni_fnirt_register_0')
num_strat = 0
# build the workflow
workflow = pe.Workflow(name='test_output_func_to_standard_FSL_nonlinear')
workflow.base_dir = c.workingDirectory
workflow.config['execution'] = {
'hash_method': 'timestamp',
'crashdump_dir': os.path.abspath(c.crashLogDirectory)
}
output_func_to_standard(workflow,
'mean_functional',
'template_brain_for_func_preproc',
'mean_functional_to_standard',
strat, num_strat, c, input_image_type='func_derivative')
out1_name = os.path.join(c.workingDirectory, test_name,
'func_mni_fsl_warp_mean_functional_to_standard_0',
'sub-M10978008_ses-NFB3_task-test_bold_calc_tshift_resample_volreg_calc_tstat_warp.nii.gz')
node, out_file = strat['mean_functional']
output_func_to_standard(workflow,
(node, out_file),
'template_brain_for_func_preproc',
'mean_functional_to_standard_node',
strat, num_strat, c, input_image_type='func_derivative')
out2_name = os.path.join(c.workingDirectory, test_name,
'func_mni_fsl_warp_mean_functional_to_standard_node_0',
'sub-M10978008_ses-NFB3_task-test_bold_calc_tshift_resample_volreg_calc_tstat_warp.nii.gz')
workflow.run()
assert(test_utils.pearson_correlation(out1_name, out2_name) > .99)
def prep_basc_workflow(c, subject_infos):
print('Preparing BASC workflow')
p_id, s_ids, scan_ids, s_paths = (list(tup) for tup in zip(*subject_infos))
print('Subjects', s_ids)
wf = pe.Workflow(name='basc_workflow')
wf.base_dir = c.pipeline_setup['working_directory']['path']
from CPAC.basc import create_basc
b = create_basc()
b.inputs.inputspec.roi = c.bascROIFile
b.inputs.inputspec.subjects = s_paths
b.inputs.inputspec.k_clusters = c.bascClusters
b.inputs.inputspec.dataset_bootstraps = c.bascDatasetBootstraps
b.inputs.inputspec.timeseries_bootstraps = c.bascTimeseriesBootstraps
aff_list = open(c.bascAffinityThresholdFile, 'r').readlines()
aff_list = [float(aff.rstrip('\r\n')) for aff in aff_list]
b.inputs.inputspec.affinity_threshold = aff_list
ds = pe.Node(nio.DataSink(), name='basc_sink')
out_dir = os.path.dirname(s_paths[0]).replace(s_ids[0], 'basc_results')
ds.inputs.base_directory = out_dir
ds.inputs.container = ''
# wf.connect(b, 'outputspec.gsm',
# ds, 'gsm')
# wf.connect(b, 'outputspec.gsclusters',
# ds, 'gsclusters')
# wf.connect(b, 'outputspec.gsmap',
# ds, 'gsmap')
wf.connect(b, 'outputspec.gsclusters_img', ds, 'gsclusters_img')
wf.connect(b, 'outputspec.ismap_imgs', ds, 'ismap_imgs')
wf.run(plugin='MultiProc', plugin_args={'n_procs': c.numCoresPerSubject})
def create_qc_fd(wf_name='qc_fd'):
wf = pe.Workflow(name=wf_name)
input_node = pe.Node(
util.IdentityInterface(fields=['fd', 'excluded_volumes']),
name='inputspec')
output_node = pe.Node(util.IdentityInterface(fields=['fd_histogram_plot']),
name='outputspec')
fd_plot = pe.Node(Function(input_names=['arr', 'measure', 'ex_vol'],
output_names=['hist_path'],
function=gen_plot_png,
as_module=True),
name='fd_plot')
fd_plot.inputs.measure = 'FD'
wf.connect(input_node, 'fd', fd_plot, 'arr')
wf.connect(input_node, 'excluded_volumes', fd_plot, 'ex_vol')
wf.connect(fd_plot, 'hist_path', output_node, 'fd_histogram_plot')
return wf
def create_qc_motion(wf_name='qc_motion'):
wf = pe.Workflow(name=wf_name)
input_node = pe.Node(util.IdentityInterface(fields=['motion_parameters']),
name='inputspec')
output_node = pe.Node(util.IdentityInterface(
fields=['motion_translation_plot', 'motion_rotation_plot']),
name='outputspec')
mov_plot = pe.Node(Function(
input_names=['motion_parameters'],
output_names=['translation_plot', 'rotation_plot'],
function=gen_motion_plt,
as_module=True),
name='motion_plot')
wf.connect(input_node, 'motion_parameters', mov_plot, 'motion_parameters')
wf.connect(mov_plot, 'translation_plot', output_node,
'motion_translation_plot')
wf.connect(mov_plot, 'rotation_plot', output_node, 'motion_rotation_plot')
return wf
def z_score_standardize(wf_name, input_image_type='func_derivative',
opt=None):
wf = pe.Workflow(name=wf_name)
map_node = False
if input_image_type == 'func_derivative_multi':
map_node = True
inputnode = pe.Node(util.IdentityInterface(fields=['in_file',
'mask']),
name='inputspec')
z_score_std = get_zscore(map_node, 'z_score_std')
wf.connect(inputnode, 'in_file', z_score_std, 'inputspec.input_file')
wf.connect(inputnode, 'mask', z_score_std, 'inputspec.mask_file')
outputnode = pe.Node(util.IdentityInterface(fields=['out_file']),
name='outputspec')
wf.connect(z_score_std, 'outputspec.z_score_img', outputnode, 'out_file')
return wf
def create_fsl_flame_wf(ftest=False, wf_name='groupAnalysis'):
"""
FSL `FEAT <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEAT>`_
BASED Group Analysis
Parameters
----------
ftest : boolean, optional(default=False)
Ftest help investigate several contrasts at the same time
for example to see whether any of them (or any combination of them) is
significantly non-zero. Also, the F-test allows you to compare the
contribution of each contrast to the model and decide on significant
and non-significant ones
wf_name : string
Workflow name
Returns
-------
grp_analysis : workflow object
Group Analysis workflow object
Notes
-----
`Source <https://github.com/openconnectome/C-PAC/blob/master/CPAC/group_analysis/group_analysis_preproc.py>`_
Workflow Inputs::
inputspec.mat_file : string (existing file)
Mat file containing matrix for design
inputspec.con_file : string (existing file)
Contrast file containing contrast vectors
inputspec.grp_file : string (existing file)
file containing matrix specifying the groups the covariance is split into
inputspec.zmap_files : string (existing nifti file)
derivative or the zmap file for which the group analysis is to be run
inputspec.z_threshold : float
Z Statistic threshold value for cluster thresholding. It is used to
determine what level of activation would be statistically significant.
Increasing this will result in higher estimates of required effect.
inputspec.p_threshold : float
Probability threshold for cluster thresholding.
inputspec.fts_file : string (existing file)
file containing matrix specifying f-contrasts
inputspec.paramerters : string (tuple)
tuple containing which MNI and FSLDIR path information
Workflow Outputs::
outputspec.merged : string (nifti file)
4D volume file after merging all the derivative
files from each specified subject.
outputspec.zstats : list (nifti files)
Z statistic image for each t contrast
outputspec.zfstats : list (nifti files)
Z statistic image for each f contrast
outputspec.fstats : list (nifti files)
F statistic for each contrast
outputspec.cluster_threshold : list (nifti files)
the thresholded Z statistic image for each t contrast
outputspec.cluster_index : list (nifti files)
image of clusters for each t contrast; the values
in the clusters are the index numbers as used
in the cluster list.
outputspec.cluster_localmax_txt : list (text files)
local maxima text file for each t contrast,
defines the coordinates of maximum value in the cluster
outputspec.overlay_threshold : list (nifti files)
3D color rendered stats overlay image for t contrast
After reloading this image, use the Statistics Color
Rendering GUI to reload the color look-up-table
outputspec.overlay_rendered_image : list (nifti files)
2D color rendered stats overlay picture for each t contrast
outputspec.cluster_threshold_zf : list (nifti files)
the thresholded Z statistic image for each f contrast
outputspec.cluster_index_zf : list (nifti files)
image of clusters for each f contrast; the values
in the clusters are the index numbers as used
in the cluster list.
outputspec.cluster_localmax_txt_zf : list (text files)
local maxima text file for each f contrast,
defines the coordinates of maximum value in the cluster
outputspec.overlay_threshold_zf : list (nifti files)
3D color rendered stats overlay image for f contrast
After reloading this image, use the Statistics Color
Rendering GUI to reload the color look-up-table
outputspec.overlay_rendered_image_zf : list (nifti files)
2D color rendered stats overlay picture for each f contrast
Order of commands:
- Merge all the Z-map 3D images into 4D image file. For details see `fslmerge <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Fslutils>`_::
fslmerge -t sub01/sca/seed1/sca_Z_FWHM_merged.nii
sub02/sca/seed1/sca_Z_FWHM.nii.gz ....
merge.nii.gz
arguments
-t : concatenate images in time
- Create mask specific for analysis. For details see `fslmaths <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Fslutils>`_::
fslmaths merged.nii.gz
-abs -Tmin -bin mean_mask.nii.gz
arguments
-Tmin : min across time
-abs : absolute value
-bin : use (current image>0) to binarise
- FSL FLAMEO to perform higher level analysis. For details see `flameo <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEAT>`_::
flameo --copefile = merged.nii.gz --covsplitfile = anova_with_meanFD.grp --designfile = anova_with_meanFD.mat
--fcontrastsfile = anova_with_meanFD.fts --ld=stats --maskfile = mean_mask.nii.gz --runmode=ols
--tcontrastsfile = anova_with_meanFD.con
arguments
--copefile : cope regressor data file
--designfile : design matrix file
--maskfile : mask file
--tcontrastsfile : file containing an ASCII matrix specifying the t contrasts
--fcontrastsfile : file containing an ASCII matrix specifying the f contrasts
--runmode : Interference to perform (mixed effects - OLS)
- Run FSL Easy thresh
Easy thresh is a simple script for carrying out cluster-based thresholding and colour activation overlaying::
easythresh <raw_zstat> <brain_mask> <z_thresh> <prob_thresh> <background_image> <output_root> [--mm]
A seperate workflow called easythresh is called to run easythresh steps.
.. exec::
from CPAC.group_analysis import create_fsl_flame_wf
wf = create_fsl_flame_wf()
wf.write_graph(
graph2use='orig',
dotfilename='./images/generated/group_analysis.dot'
)
High Level Workflow Graph:
.. image:: ../../images/generated/group_analysis.png
:width: 800
Detailed Workflow Graph:
.. image:: ../../images/generated/group_analysis_detailed.png
:width: 800
Examples
--------
>>> from group_analysis_preproc import create_group_analysis
>>> preproc = create_group_analysis()
>>> preproc.inputs.inputspec.mat_file = '../group_models/anova_with_meanFD/anova_with_meanFD.mat'
>>> preproc.inputs.inputspec.con_file = '../group_models/anova_with_meanFD/anova_with_meanFD.con'
>>> preproc.inputs.inputspec.grp_file = '../group_models/anova_with_meanFD/anova_with_meanFD.grp'
>>> preproc.inputs.inputspec.zmap_files = ['subjects/sub01/seeds_rest_Dickstein_DLPFC/sca_Z_FWHM.nii.gz',
'subjects/sub02/seeds_rest_Dickstein_DLPFC/sca_Z_FWHM.nii.gz']
>>> preproc.inputs.inputspec.z_threshold = 2.3
>>> preproc.inputs.inputspec.p_threshold = 0.05
>>> preproc.inputs.inputspec.parameters = ('/usr/local/fsl/', 'MNI152')
>>> preproc.run() -- SKIP doctest
"""
grp_analysis = pe.Workflow(name=wf_name)
inputnode = pe.Node(util.IdentityInterface(fields=[
'merged_file', 'merge_mask', 'mat_file', 'con_file', 'grp_file',
'fts_file', 'z_threshold', 'p_threshold', 'parameters'
]),
name='inputspec')
outputnode = pe.Node(util.IdentityInterface(fields=[
'merged', 'zstats', 'zfstats', 'fstats', 'cluster_threshold',
'cluster_index', 'cluster_localmax_txt', 'overlay_threshold',
'rendered_image', 'cluster_localmax_txt_zf', 'cluster_threshold_zf',
'cluster_index_zf', 'overlay_threshold_zf', 'rendered_image_zf'
]),
name='outputspec')
'''
merge_to_4d = pe.Node(interface=fsl.Merge(),
name='merge_to_4d')
merge_to_4d.inputs.dimension = 't'
### create analysis specific mask
#-Tmin: min across time
# -abs: absolute value
#-bin: use (current image>0) to binarise
merge_mask = pe.Node(interface=fsl.ImageMaths(),
name='merge_mask')
merge_mask.inputs.op_string = '-abs -Tmin -bin'
'''
fsl_flameo = pe.Node(interface=fsl.FLAMEO(), name='fsl_flameo')
fsl_flameo.inputs.run_mode = 'ols'
# rename the FLAME zstat outputs after the contrast string labels for
# easier interpretation
label_zstat_imports = ["import os"]
label_zstat = pe.Node(util.Function(input_names=['zstat_list', 'con_file'],
output_names=['new_zstat_list'],
function=label_zstat_files,
imports=label_zstat_imports),
name='label_zstat')
rename_zstats = pe.MapNode(interface=util.Rename(),
name='rename_zstats',
iterfield=['in_file', 'format_string'])
rename_zstats.inputs.keep_ext = True
# create analysis specific mask
# fslmaths merged.nii.gz -abs -bin -Tmean -mul volume out.nii.gz
# -Tmean: mean across time
# create group_reg file
# this file can provide an idea of how well the subjects
# in our analysis overlay with each other and the MNI brain.
# e.g., maybe there is one subject with limited coverage.
# not attached to sink currently
merge_mean_mask = pe.Node(interface=fsl.ImageMaths(),
name='merge_mean_mask')
# function node to get the operation string for fslmaths command
get_opstring = pe.Node(util.Function(input_names=['in_file'],
output_names=['out_file'],
function=get_operation),
name='get_opstring')
# connections
'''
grp_analysis.connect(inputnode, 'zmap_files',
merge_to_4d, 'in_files')
grp_analysis.connect(merge_to_4d, 'merged_file',
merge_mask, 'in_file')
'''
grp_analysis.connect(inputnode, 'merged_file', fsl_flameo, 'cope_file')
grp_analysis.connect(inputnode, 'merge_mask', fsl_flameo, 'mask_file')
grp_analysis.connect(inputnode, 'mat_file', fsl_flameo, 'design_file')
grp_analysis.connect(inputnode, 'con_file', fsl_flameo, 't_con_file')
grp_analysis.connect(inputnode, 'grp_file', fsl_flameo, 'cov_split_file')
grp_analysis.connect(fsl_flameo, 'zstats', label_zstat, 'zstat_list')
grp_analysis.connect(inputnode, 'con_file', label_zstat, 'con_file')
grp_analysis.connect(fsl_flameo, 'zstats', rename_zstats, 'in_file')
grp_analysis.connect(label_zstat, 'new_zstat_list', rename_zstats,
'format_string')
if ftest:
grp_analysis.connect(inputnode, 'fts_file', fsl_flameo, 'f_con_file')
easy_thresh_zf = easy_thresh('easy_thresh_zf')
grp_analysis.connect(fsl_flameo, 'zfstats', easy_thresh_zf,
'inputspec.z_stats')
grp_analysis.connect(inputnode, 'merge_mask', easy_thresh_zf,
'inputspec.merge_mask')
grp_analysis.connect(inputnode, 'z_threshold', easy_thresh_zf,
'inputspec.z_threshold')
grp_analysis.connect(inputnode, 'p_threshold', easy_thresh_zf,
'inputspec.p_threshold')
grp_analysis.connect(inputnode, 'parameters', easy_thresh_zf,
'inputspec.parameters')
grp_analysis.connect(easy_thresh_zf, 'outputspec.cluster_threshold',
outputnode, 'cluster_threshold_zf')
grp_analysis.connect(easy_thresh_zf, 'outputspec.cluster_index',
outputnode, 'cluster_index_zf')
grp_analysis.connect(easy_thresh_zf, 'outputspec.cluster_localmax_txt',
outputnode, 'cluster_localmax_txt_zf')
grp_analysis.connect(easy_thresh_zf, 'outputspec.overlay_threshold',
outputnode, 'overlay_threshold_zf')
grp_analysis.connect(easy_thresh_zf, 'outputspec.rendered_image',
outputnode, 'rendered_image_zf')
# calling easythresh for zstats files
easy_thresh_z = easy_thresh('easy_thresh_z')
grp_analysis.connect(rename_zstats, 'out_file', easy_thresh_z,
'inputspec.z_stats')
grp_analysis.connect(inputnode, 'merge_mask', easy_thresh_z,
'inputspec.merge_mask')
grp_analysis.connect(inputnode, 'z_threshold', easy_thresh_z,
'inputspec.z_threshold')
grp_analysis.connect(inputnode, 'p_threshold', easy_thresh_z,
'inputspec.p_threshold')
grp_analysis.connect(inputnode, 'parameters', easy_thresh_z,
'inputspec.parameters')
grp_analysis.connect(inputnode, 'merged_file', get_opstring, 'in_file')
grp_analysis.connect(inputnode, 'merged_file', merge_mean_mask, 'in_file')
grp_analysis.connect(get_opstring, 'out_file', merge_mean_mask,
'op_string')
grp_analysis.connect(fsl_flameo, 'zfstats', outputnode, 'zfstats')
grp_analysis.connect(fsl_flameo, 'fstats', outputnode, 'fstats')
grp_analysis.connect(inputnode, 'merged_file', outputnode, 'merged')
grp_analysis.connect(rename_zstats, 'out_file', outputnode, 'zstats')
grp_analysis.connect(easy_thresh_z, 'outputspec.cluster_threshold',
outputnode, 'cluster_threshold')
grp_analysis.connect(easy_thresh_z, 'outputspec.cluster_index', outputnode,
'cluster_index')
grp_analysis.connect(easy_thresh_z, 'outputspec.cluster_localmax_txt',
outputnode, 'cluster_localmax_txt')
grp_analysis.connect(easy_thresh_z, 'outputspec.overlay_threshold',
outputnode, 'overlay_threshold')
grp_analysis.connect(easy_thresh_z, 'outputspec.rendered_image',
outputnode, 'rendered_image')
return grp_analysis
def run_feat_pipeline(group_config,
merge_file,
merge_mask,
f_test,
mat_file,
con_file,
grp_file,
out_dir,
work_dir,
log_dir,
model_name,
fts_file=None):
'''
needed:
- z thresh, p thresh
- out dir
- work, crash, log etc.
-
'''
import nipype.interfaces.io as nio
# get thresholds
z_threshold = float(group_config.z_threshold[0])
p_threshold = float(group_config.p_threshold[0])
# workflow time
wf_name = "fsl-feat_".format(model_name)
wf = pe.Workflow(name=wf_name)
wf.base_dir = work_dir
wf.config['execution'] = {
'hash_method': 'timestamp',
'crashdump_dir': log_dir
}
gpa_wf = create_fsl_flame_wf(f_test, "fsl-flame")
gpa_wf.inputs.inputspec.merged_file = merge_file
gpa_wf.inputs.inputspec.merge_mask = merge_mask
gpa_wf.inputs.inputspec.z_threshold = z_threshold
gpa_wf.inputs.inputspec.p_threshold = p_threshold
gpa_wf.inputs.inputspec.parameters = (group_config.FSLDIR, 'MNI152')
gpa_wf.inputs.inputspec.mat_file = mat_file
gpa_wf.inputs.inputspec.con_file = con_file
gpa_wf.inputs.inputspec.grp_file = grp_file
if f_test:
gpa_wf.inputs.inputspec.fts_file = fts_file
ds = pe.Node(nio.DataSink(), name='gpa_sink')
ds.inputs.base_directory = str(out_dir)
ds.inputs.container = ''
ds.inputs.regexp_substitutions = [(r'(?<=rendered)(.)*[/]', '/'),
(r'(?<=model_files)(.)*[/]', '/'),
(r'(?<=merged)(.)*[/]', '/'),
(r'(?<=stats/clusterMap)(.)*[/]', '/'),
(r'(?<=stats/unthreshold)(.)*[/]', '/'),
(r'(?<=stats/threshold)(.)*[/]', '/'),
(r'_cluster(.)*[/]', ''),
(r'_slicer(.)*[/]', ''),
(r'_overlay(.)*[/]', '')]
wf.connect(gpa_wf, 'outputspec.merged', ds, 'merged')
wf.connect(gpa_wf, 'outputspec.zstats', ds, 'stats.unthreshold')
wf.connect(gpa_wf, 'outputspec.zfstats', ds, 'stats.unthreshold.@01')
wf.connect(gpa_wf, 'outputspec.fstats', ds, 'stats.unthreshold.@02')
wf.connect(gpa_wf, 'outputspec.cluster_threshold_zf', ds,
'stats.threshold')
wf.connect(gpa_wf, 'outputspec.cluster_index_zf', ds, 'stats.clusterMap')
wf.connect(gpa_wf, 'outputspec.cluster_localmax_txt_zf', ds,
'stats.clusterMap.@01')
wf.connect(gpa_wf, 'outputspec.overlay_threshold_zf', ds, 'rendered')
wf.connect(gpa_wf, 'outputspec.rendered_image_zf', ds, 'rendered.@01')
wf.connect(gpa_wf, 'outputspec.cluster_threshold', ds,
'stats.threshold.@01')
wf.connect(gpa_wf, 'outputspec.cluster_index', ds, 'stats.clusterMap.@02')
wf.connect(gpa_wf, 'outputspec.cluster_localmax_txt', ds,
'stats.clusterMap.@03')
wf.connect(gpa_wf, 'outputspec.overlay_threshold', ds, 'rendered.@02')
wf.connect(gpa_wf, 'outputspec.rendered_image', ds, 'rendered.@03')
# Run the actual group analysis workflow
wf.run()
def init_brain_extraction_wf(tpl_target_path,
tpl_mask_path,
tpl_regmask_path,
name='brain_extraction_wf',
template_spec=None,
use_float=True,
normalization_quality='precise',
omp_nthreads=None,
mem_gb=3.0,
bids_suffix='T1w',
atropos_refine=True,
atropos_use_random_seed=True,
atropos_model=None,
use_laplacian=True,
bspline_fitting_distance=200):
"""
A Nipype implementation of the official ANTs' ``antsBrainExtraction.sh``
workflow (only for 3D images).
The official workflow is built as follows (and this implementation
follows the same organization):
1. Step 1 performs several clerical tasks (adding padding, calculating
the Laplacian of inputs, affine initialization) and the core
spatial normalization.
2. Maps the brain mask into target space using the normalization
calculated in 1.
3. Superstep 1b: smart binarization of the brain mask
4. Superstep 6: apply ATROPOS and massage its outputs
5. Superstep 7: use results from 4 to refine the brain mask
.. workflow::
:graph2use: orig
:simple_form: yes
from niworkflows.anat import init_brain_extraction_wf
wf = init_brain_extraction_wf()
**Parameters**
in_template : str
Name of the skull-stripping template ('OASIS30ANTs', 'NKI', or
path).
The brain template from which regions will be projected
Anatomical template created using e.g. LPBA40 data set with
``buildtemplateparallel.sh`` in ANTs.
The workflow will automatically search for a brain probability
mask created using e.g. LPBA40 data set which have brain masks
defined, and warped to anatomical template and
averaged resulting in a probability image.
use_float : bool
Whether single precision should be used
normalization_quality : str
Use more precise or faster registration parameters
(default: ``precise``, other possible values: ``testing``)
omp_nthreads : int
Maximum number of threads an individual process may use
mem_gb : float
Estimated peak memory consumption of the most hungry nodes
in the workflow
bids_suffix : str
Sequence type of the first input image. For a list of acceptable values
see https://bids-specification.readthedocs.io/en/latest/\
04-modality-specific-files/01-magnetic-resonance-imaging-data.html#anatomy-imaging-data
atropos_refine : bool
Enables or disables the whole ATROPOS sub-workflow
atropos_use_random_seed : bool
Whether ATROPOS should generate a random seed based on the
system's clock
atropos_model : tuple or None
Allows to specify a particular segmentation model, overwriting
the defaults based on ``bids_suffix``
use_laplacian : bool
Enables or disables alignment of the Laplacian as an additional
criterion for image registration quality (default: True)
bspline_fitting_distance : float
The size of the b-spline mesh grid elements, in mm (default: 200)
name : str, optional
Workflow name (default: antsBrainExtraction)
**Inputs**
in_files
List of input anatomical images to be brain-extracted,
typically T1-weighted.
If a list of anatomical images is provided, subsequently
specified images are used during the segmentation process.
However, only the first image is used in the registration
of priors.
Our suggestion would be to specify the T1w as the first image.
in_mask
(optional) Mask used for registration to limit the metric
computation to a specific region.
**Outputs**
out_file
Skull-stripped and :abbr:`INU (intensity non-uniformity)`-corrected ``in_files``
out_mask
Calculated brain mask
bias_corrected
The ``in_files`` input images, after :abbr:`INU (intensity non-uniformity)`
correction, before skull-stripping.
bias_image
The :abbr:`INU (intensity non-uniformity)` field estimated for each
input in ``in_files``
out_segm
Output segmentation by ATROPOS
out_tpms
Output :abbr:`TPMs (tissue probability maps)` by ATROPOS
"""
# from templateflow.api import get as get_template
wf = pe.Workflow(name)
template_spec = template_spec or {}
# suffix passed via spec takes precedence
template_spec['suffix'] = template_spec.get('suffix', bids_suffix)
# # Get probabilistic brain mask if available
inputnode = pe.Node(niu.IdentityInterface(fields=['in_files', 'in_mask']),
name='inputnode')
# # Try to find a registration mask, set if available
if tpl_regmask_path:
inputnode.inputs.in_mask = str(tpl_regmask_path)
outputnode = pe.Node(niu.IdentityInterface(fields=[
'out_file', 'out_mask', 'bias_corrected', 'bias_image', 'out_segm',
'out_tpms'
]),
name='outputnode')
copy_xform = pe.Node(CopyXForm(
fields=['out_file', 'out_mask', 'bias_corrected', 'bias_image']),
name='copy_xform',
run_without_submitting=True,
mem_gb=2.5)
trunc = pe.MapNode(ImageMath(operation='TruncateImageIntensity',
op2='0.01 0.999 256'),
name='truncate_images',
iterfield=['op1'])
inu_n4 = pe.MapNode(N4BiasFieldCorrection(
dimension=3,
save_bias=False,
copy_header=True,
n_iterations=[50] * 4,
convergence_threshold=1e-7,
shrink_factor=4,
bspline_fitting_distance=bspline_fitting_distance),
n_procs=omp_nthreads,
name='inu_n4',
iterfield=['input_image'])
res_tmpl = pe.Node(ResampleImageBySpacing(out_spacing=(4, 4, 4),
apply_smoothing=True),
name='res_tmpl')
res_tmpl.inputs.input_image = tpl_target_path
res_target = pe.Node(ResampleImageBySpacing(out_spacing=(4, 4, 4),
apply_smoothing=True),
name='res_target')
lap_tmpl = pe.Node(ImageMath(operation='Laplacian', op2='1.5 1'),
name='lap_tmpl')
lap_tmpl.inputs.op1 = tpl_target_path
lap_target = pe.Node(ImageMath(operation='Laplacian', op2='1.5 1'),
name='lap_target')
mrg_tmpl = pe.Node(niu.Merge(2), name='mrg_tmpl')
mrg_tmpl.inputs.in1 = tpl_target_path
mrg_target = pe.Node(niu.Merge(2), name='mrg_target')
# Initialize transforms with antsAI
init_aff = pe.Node(AI(metric=('Mattes', 32, 'Regular', 0.25),
transform=('Affine', 0.1),
search_factor=(15, 0.1),
principal_axes=False,
convergence=(10, 1e-6, 10),
verbose=True),
name='init_aff',
n_procs=omp_nthreads)
# Tolerate missing ANTs at construction time
_ants_version = Registration().version
if _ants_version and parseversion(_ants_version) >= Version('2.3.0'):
init_aff.inputs.search_grid = (40, (0, 40, 40))
# Set up spatial normalization
settings_file = 'antsBrainExtraction_%s.json' if use_laplacian \
else 'antsBrainExtractionNoLaplacian_%s.json'
norm = pe.Node(Registration(
from_file=pkgr_fn('CPAC.anat_preproc', 'data/' +
settings_file % normalization_quality)),
name='norm',
n_procs=omp_nthreads,
mem_gb=mem_gb)
norm.inputs.float = use_float
fixed_mask_trait = 'fixed_image_mask'
if _ants_version and parseversion(_ants_version) >= Version('2.2.0'):
fixed_mask_trait += 's'
map_brainmask = pe.Node(ApplyTransforms(interpolation='Gaussian',
float=True),
name='map_brainmask',
mem_gb=1)
map_brainmask.inputs.input_image = str(tpl_mask_path)
thr_brainmask = pe.Node(ThresholdImage(dimension=3,
th_low=0.5,
th_high=1.0,
inside_value=1,
outside_value=0),
name='thr_brainmask')
# Morphological dilation, radius=2
dil_brainmask = pe.Node(ImageMath(operation='MD', op2='2'),
name='dil_brainmask')
# Get largest connected component
get_brainmask = pe.Node(ImageMath(operation='GetLargestComponent'),
name='get_brainmask')
# Refine INU correction
inu_n4_final = pe.MapNode(N4BiasFieldCorrection(
dimension=3,
save_bias=True,
copy_header=True,
n_iterations=[50] * 5,
convergence_threshold=1e-7,
shrink_factor=4,
bspline_fitting_distance=bspline_fitting_distance),
n_procs=omp_nthreads,
name='inu_n4_final',
iterfield=['input_image'])
# Apply mask
apply_mask = pe.MapNode(ApplyMask(),
iterfield=['in_file'],
name='apply_mask')
wf.connect([
(inputnode, trunc, [('in_files', 'op1')]),
(inputnode, copy_xform, [(('in_files', _pop), 'hdr_file')]),
(inputnode, inu_n4_final, [('in_files', 'input_image')]),
(inputnode, init_aff, [('in_mask', 'fixed_image_mask')]),
(inputnode, norm, [('in_mask', fixed_mask_trait)]),
(inputnode, map_brainmask, [(('in_files', _pop), 'reference_image')]),
(trunc, inu_n4, [('output_image', 'input_image')]),
(inu_n4, res_target, [(('output_image', _pop), 'input_image')]),
(res_tmpl, init_aff, [('output_image', 'fixed_image')]),
(res_target, init_aff, [('output_image', 'moving_image')]),
(init_aff, norm, [('output_transform', 'initial_moving_transform')]),
(norm, map_brainmask, [('reverse_transforms', 'transforms'),
('reverse_invert_flags',
'invert_transform_flags')]),
(map_brainmask, thr_brainmask, [('output_image', 'input_image')]),
(thr_brainmask, dil_brainmask, [('output_image', 'op1')]),
(dil_brainmask, get_brainmask, [('output_image', 'op1')]),
(inu_n4_final, apply_mask, [('output_image', 'in_file')]),
(get_brainmask, apply_mask, [('output_image', 'mask_file')]),
(get_brainmask, copy_xform, [('output_image', 'out_mask')]),
(apply_mask, copy_xform, [('out_file', 'out_file')]),
(inu_n4_final, copy_xform, [('output_image', 'bias_corrected'),
('bias_image', 'bias_image')]),
(copy_xform, outputnode, [('out_file', 'out_file'),
('out_mask', 'out_mask'),
('bias_corrected', 'bias_corrected'),
('bias_image', 'bias_image')]),
])
if use_laplacian:
lap_tmpl = pe.Node(ImageMath(operation='Laplacian', op2='1.5 1'),
name='lap_tmpl')
lap_tmpl.inputs.op1 = tpl_target_path
lap_target = pe.Node(ImageMath(operation='Laplacian', op2='1.5 1'),
name='lap_target')
mrg_tmpl = pe.Node(niu.Merge(2), name='mrg_tmpl')
mrg_tmpl.inputs.in1 = tpl_target_path
mrg_target = pe.Node(niu.Merge(2), name='mrg_target')
wf.connect([
(inu_n4, lap_target, [(('output_image', _pop), 'op1')]),
(lap_tmpl, mrg_tmpl, [('output_image', 'in2')]),
(inu_n4, mrg_target, [('output_image', 'in1')]),
(lap_target, mrg_target, [('output_image', 'in2')]),
(mrg_tmpl, norm, [('out', 'fixed_image')]),
(mrg_target, norm, [('out', 'moving_image')]),
])
else:
norm.inputs.fixed_image = tpl_target_path
wf.connect([
(inu_n4, norm, [(('output_image', _pop), 'moving_image')]),
])
if atropos_refine:
atropos_model = atropos_model or list(
ATROPOS_MODELS[bids_suffix].values())
atropos_wf = init_atropos_wf(
use_random_seed=atropos_use_random_seed,
omp_nthreads=omp_nthreads,
mem_gb=mem_gb,
in_segmentation_model=atropos_model,
)
sel_wm = pe.Node(niu.Select(index=atropos_model[-1] - 1),
name='sel_wm',
run_without_submitting=True)
wf.disconnect([
(get_brainmask, apply_mask, [('output_image', 'mask_file')]),
(copy_xform, outputnode, [('out_mask', 'out_mask')]),
])
wf.connect([
(inu_n4, atropos_wf, [('output_image', 'inputnode.in_files')]),
(thr_brainmask, atropos_wf, [('output_image', 'inputnode.in_mask')
]),
(get_brainmask, atropos_wf, [('output_image',
'inputnode.in_mask_dilated')]),
(atropos_wf, sel_wm, [('outputnode.out_tpms', 'inlist')]),
(sel_wm, inu_n4_final, [('out', 'weight_image')]),
(atropos_wf, apply_mask, [('outputnode.out_mask', 'mask_file')]),
(atropos_wf, outputnode, [('outputnode.out_mask', 'out_mask'),
('outputnode.out_segm', 'out_segm'),
('outputnode.out_tpms', 'out_tpms')]),
])
return wf
def init_atropos_wf(name='atropos_wf',
use_random_seed=True,
omp_nthreads=None,
mem_gb=3.0,
padding=10,
in_segmentation_model=list(
ATROPOS_MODELS['T1w'].values())):
"""
Implements supersteps 6 and 7 of ``antsBrainExtraction.sh``,
which refine the mask previously computed with the spatial
normalization to the template.
**Parameters**
use_random_seed : bool
Whether ATROPOS should generate a random seed based on the
system's clock
omp_nthreads : int
Maximum number of threads an individual process may use
mem_gb : float
Estimated peak memory consumption of the most hungry nodes
in the workflow
padding : int
Pad images with zeros before processing
in_segmentation_model : tuple
A k-means segmentation is run to find gray or white matter
around the edge of the initial brain mask warped from the
template.
This produces a segmentation image with :math:`$K$` classes,
ordered by mean intensity in increasing order.
With this option, you can control :math:`$K$` and tell
the script which classes represent CSF, gray and white matter.
Format (K, csfLabel, gmLabel, wmLabel).
Examples:
- ``(3,1,2,3)`` for T1 with K=3, CSF=1, GM=2, WM=3 (default)
- ``(3,3,2,1)`` for T2 with K=3, CSF=3, GM=2, WM=1
- ``(3,1,3,2)`` for FLAIR with K=3, CSF=1 GM=3, WM=2
- ``(4,4,2,3)`` uses K=4, CSF=4, GM=2, WM=3
name : str, optional
Workflow name (default: atropos_wf)
**Inputs**
in_files
:abbr:`INU (intensity non-uniformity)`-corrected files.
in_mask
Brain mask calculated previously
**Outputs**
out_mask
Refined brain mask
out_segm
Output segmentation
out_tpms
Output :abbr:`TPMs (tissue probability maps)`
"""
wf = pe.Workflow(name)
inputnode = pe.Node(niu.IdentityInterface(
fields=['in_files', 'in_mask', 'in_mask_dilated']),
name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(fields=['out_mask', 'out_segm', 'out_tpms']),
name='outputnode')
copy_xform = pe.Node(
CopyXForm(fields=['out_mask', 'out_segm', 'out_tpms']),
name='copy_xform',
run_without_submitting=True,
mem_gb=2.5)
# Run atropos (core node)
atropos = pe.Node(Atropos(
dimension=3,
initialization='KMeans',
number_of_tissue_classes=in_segmentation_model[0],
n_iterations=3,
convergence_threshold=0.0,
mrf_radius=[1, 1, 1],
mrf_smoothing_factor=0.1,
likelihood_model='Gaussian',
use_random_seed=use_random_seed),
name='01_atropos',
n_procs=omp_nthreads,
mem_gb=mem_gb)
# massage outputs
pad_segm = pe.Node(ImageMath(operation='PadImage', op2='%d' % padding),
name='02_pad_segm')
pad_mask = pe.Node(ImageMath(operation='PadImage', op2='%d' % padding),
name='03_pad_mask')
# Split segmentation in binary masks
sel_labels = pe.Node(niu.Function(
function=_select_labels, output_names=['out_wm', 'out_gm', 'out_csf']),
name='04_sel_labels')
sel_labels.inputs.labels = list(reversed(in_segmentation_model[1:]))
# Select largest components (GM, WM)
# ImageMath ${DIMENSION} ${EXTRACTION_WM} GetLargestComponent ${EXTRACTION_WM}
get_wm = pe.Node(ImageMath(operation='GetLargestComponent'),
name='05_get_wm')
get_gm = pe.Node(ImageMath(operation='GetLargestComponent'),
name='06_get_gm')
# Fill holes and calculate intersection
# ImageMath ${DIMENSION} ${EXTRACTION_TMP} FillHoles ${EXTRACTION_GM} 2
# MultiplyImages ${DIMENSION} ${EXTRACTION_GM} ${EXTRACTION_TMP} ${EXTRACTION_GM}
fill_gm = pe.Node(ImageMath(operation='FillHoles', op2='2'),
name='07_fill_gm')
mult_gm = pe.Node(MultiplyImages(dimension=3,
output_product_image='08_mult_gm.nii.gz'),
name='08_mult_gm')
# MultiplyImages ${DIMENSION} ${EXTRACTION_WM} ${ATROPOS_WM_CLASS_LABEL} ${EXTRACTION_WM}
# ImageMath ${DIMENSION} ${EXTRACTION_TMP} ME ${EXTRACTION_CSF} 10
relabel_wm = pe.Node(MultiplyImages(
dimension=3,
second_input=in_segmentation_model[-1],
output_product_image='09_relabel_wm.nii.gz'),
name='09_relabel_wm')
me_csf = pe.Node(ImageMath(operation='ME', op2='10'), name='10_me_csf')
# ImageMath ${DIMENSION} ${EXTRACTION_GM} addtozero ${EXTRACTION_GM} ${EXTRACTION_TMP}
# MultiplyImages ${DIMENSION} ${EXTRACTION_GM} ${ATROPOS_GM_CLASS_LABEL} ${EXTRACTION_GM}
# ImageMath ${DIMENSION} ${EXTRACTION_SEGMENTATION} addtozero ${EXTRACTION_WM} ${EXTRACTION_GM}
add_gm = pe.Node(ImageMath(operation='addtozero'), name='11_add_gm')
relabel_gm = pe.Node(MultiplyImages(
dimension=3,
second_input=in_segmentation_model[-2],
output_product_image='12_relabel_gm.nii.gz'),
name='12_relabel_gm')
add_gm_wm = pe.Node(ImageMath(operation='addtozero'), name='13_add_gm_wm')
# Superstep 7
# Split segmentation in binary masks
sel_labels2 = pe.Node(niu.Function(function=_select_labels,
output_names=['out_gm', 'out_wm']),
name='14_sel_labels2')
sel_labels2.inputs.labels = in_segmentation_model[2:]
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} addtozero ${EXTRACTION_MASK} ${EXTRACTION_TMP}
add_7 = pe.Node(ImageMath(operation='addtozero'), name='15_add_7')
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} ME ${EXTRACTION_MASK} 2
me_7 = pe.Node(ImageMath(operation='ME', op2='2'), name='16_me_7')
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} GetLargestComponent ${EXTRACTION_MASK}
comp_7 = pe.Node(ImageMath(operation='GetLargestComponent'),
name='17_comp_7')
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} MD ${EXTRACTION_MASK} 4
md_7 = pe.Node(ImageMath(operation='MD', op2='4'), name='18_md_7')
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} FillHoles ${EXTRACTION_MASK} 2
fill_7 = pe.Node(ImageMath(operation='FillHoles', op2='2'),
name='19_fill_7')
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} addtozero ${EXTRACTION_MASK} \
# ${EXTRACTION_MASK_PRIOR_WARPED}
add_7_2 = pe.Node(ImageMath(operation='addtozero'), name='20_add_7_2')
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} MD ${EXTRACTION_MASK} 5
md_7_2 = pe.Node(ImageMath(operation='MD', op2='5'), name='21_md_7_2')
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} ME ${EXTRACTION_MASK} 5
me_7_2 = pe.Node(ImageMath(operation='ME', op2='5'), name='22_me_7_2')
# De-pad
depad_mask = pe.Node(ImageMath(operation='PadImage', op2='-%d' % padding),
name='23_depad_mask')
depad_segm = pe.Node(ImageMath(operation='PadImage', op2='-%d' % padding),
name='24_depad_segm')
depad_gm = pe.Node(ImageMath(operation='PadImage', op2='-%d' % padding),
name='25_depad_gm')
depad_wm = pe.Node(ImageMath(operation='PadImage', op2='-%d' % padding),
name='26_depad_wm')
depad_csf = pe.Node(ImageMath(operation='PadImage', op2='-%d' % padding),
name='27_depad_csf')
msk_conform = pe.Node(niu.Function(function=_conform_mask),
name='msk_conform')
merge_tpms = pe.Node(niu.Merge(in_segmentation_model[0]),
name='merge_tpms')
wf.connect([
(inputnode, copy_xform, [(('in_files', _pop), 'hdr_file')]),
(inputnode, pad_mask, [('in_mask', 'op1')]),
(inputnode, atropos, [('in_files', 'intensity_images'),
('in_mask_dilated', 'mask_image')]),
(inputnode, msk_conform, [(('in_files', _pop), 'in_reference')]),
(atropos, pad_segm, [('classified_image', 'op1')]),
(pad_segm, sel_labels, [('output_image', 'in_segm')]),
(sel_labels, get_wm, [('out_wm', 'op1')]),
(sel_labels, get_gm, [('out_gm', 'op1')]),
(get_gm, fill_gm, [('output_image', 'op1')]),
(get_gm, mult_gm, [('output_image', 'first_input')]),
(fill_gm, mult_gm, [('output_image', 'second_input')]),
(get_wm, relabel_wm, [('output_image', 'first_input')]),
(sel_labels, me_csf, [('out_csf', 'op1')]),
(mult_gm, add_gm, [('output_product_image', 'op1')]),
(me_csf, add_gm, [('output_image', 'op2')]),
(add_gm, relabel_gm, [('output_image', 'first_input')]),
(relabel_wm, add_gm_wm, [('output_product_image', 'op1')]),
(relabel_gm, add_gm_wm, [('output_product_image', 'op2')]),
(add_gm_wm, sel_labels2, [('output_image', 'in_segm')]),
(sel_labels2, add_7, [('out_wm', 'op1'), ('out_gm', 'op2')]),
(add_7, me_7, [('output_image', 'op1')]),
(me_7, comp_7, [('output_image', 'op1')]),
(comp_7, md_7, [('output_image', 'op1')]),
(md_7, fill_7, [('output_image', 'op1')]),
(fill_7, add_7_2, [('output_image', 'op1')]),
(pad_mask, add_7_2, [('output_image', 'op2')]),
(add_7_2, md_7_2, [('output_image', 'op1')]),
(md_7_2, me_7_2, [('output_image', 'op1')]),
(me_7_2, depad_mask, [('output_image', 'op1')]),
(add_gm_wm, depad_segm, [('output_image', 'op1')]),
(relabel_wm, depad_wm, [('output_product_image', 'op1')]),
(relabel_gm, depad_gm, [('output_product_image', 'op1')]),
(sel_labels, depad_csf, [('out_csf', 'op1')]),
(depad_csf, merge_tpms, [('output_image', 'in1')]),
(depad_gm, merge_tpms, [('output_image', 'in2')]),
(depad_wm, merge_tpms, [('output_image', 'in3')]),
(depad_mask, msk_conform, [('output_image', 'in_mask')]),
(msk_conform, copy_xform, [('out', 'out_mask')]),
(depad_segm, copy_xform, [('output_image', 'out_segm')]),
(merge_tpms, copy_xform, [('out', 'out_tpms')]),
(copy_xform, outputnode, [('out_mask', 'out_mask'),
('out_segm', 'out_segm'),
('out_tpms', 'out_tpms')]),
])
return wf
def test_registration_lesion():
import os
from CPAC.pipeline import nipype_pipeline_engine as pe
from ..registration import create_wf_calculate_ants_warp
from CPAC.anat_preproc.anat_preproc import create_anat_preproc
from CPAC.anat_preproc.lesion_preproc import create_lesion_preproc
# Skull stripped anat image
anat_file = '/bids_dataset/sub-0027228/ses-1/anat/sub-0027228_ses-1_run-1_T1w.nii.gz'
lesion_file = '/bids_dataset/sub-0027228/ses-1/anat/sub-0027228_ses-1_run-1_T1w_lesion-mask.nii.gz'
mni_brain_file = '/usr/share/fsl/5.0/data/standard/MNI152_T1_3mm_brain.nii.gz'
if not os.path.exists(anat_file):
raise IOError(anat_file + ' not found')
if not os.path.exists(lesion_file):
raise IOError(lesion_file + ' not found')
if not os.path.exists(mni_brain_file):
raise IOError(mni_brain_file + ' not found')
wf = pe.Workflow(name='test_reg_lesion')
anat_preproc = create_anat_preproc(method='mask',
already_skullstripped=True,
wf_name='anat_preproc')
anat_preproc.inputs.inputspec.anat = anat_file
lesion_preproc = create_lesion_preproc(wf_name='lesion_preproc')
lesion_preproc.inputs.inputspec.lesion = lesion_file
ants_reg_anat_mni = \
create_wf_calculate_ants_warp(
'anat_mni_ants_register',
0,
num_threads=4
)
# pass the reference file
ants_reg_anat_mni.inputs.inputspec.reference_brain = mni_brain_file
wf.connect(anat_preproc, 'outputspec.reorient', ants_reg_anat_mni,
'inputspec.moving_brain')
wf.connect(lesion_preproc, 'outputspec.reorient', ants_reg_anat_mni,
'inputspec.fixed_image_mask')
ants_reg_anat_mni.inputs.inputspec.set(
dimension=3,
use_histogram_matching=True,
winsorize_lower_quantile=0.01,
winsorize_upper_quantile=0.99,
metric=['MI', 'MI', 'CC'],
metric_weight=[1, 1, 1],
radius_or_number_of_bins=[32, 32, 4],
sampling_strategy=['Regular', 'Regular', None],
sampling_percentage=[0.25, 0.25, None],
number_of_iterations=[[1000, 500, 250, 100], [1000, 500, 250, 100],
[100, 100, 70, 20]],
convergence_threshold=[1e-8, 1e-8, 1e-9],
convergence_window_size=[10, 10, 15],
transforms=['Rigid', 'Affine', 'SyN'],
transform_parameters=[[0.1], [0.1], [0.1, 3, 0]],
shrink_factors=[[8, 4, 2, 1], [8, 4, 2, 1], [6, 4, 2, 1]],
smoothing_sigmas=[[3, 2, 1, 0], [3, 2, 1, 0], [3, 2, 1, 0]])
wf.run()
def spatial_smoothing(wf_name,
fwhm,
input_image_type='func_derivative',
opt=None):
wf = pe.Workflow(name=wf_name)
inputnode = pe.Node(util.IdentityInterface(fields=['in_file', 'mask']),
name='inputspec')
inputnode_fwhm = pe.Node(util.IdentityInterface(fields=['fwhm']),
name='fwhm_input')
inputnode_fwhm.iterables = ("fwhm", fwhm)
image_types = [
'func_derivative', 'func_derivative_multi', 'func_4d', 'func_mask'
]
if input_image_type not in image_types:
raise ValueError('Input image type {0} should be one of '
'{1}'.format(input_image_type,
', '.join(image_types)))
if opt == 'FSL':
output_smooth_mem_gb = 4.0
if input_image_type == 'func_derivative_multi':
output_smooth = pe.MapNode(interface=fsl.MultiImageMaths(),
name='smooth_multi',
iterfield=['in_file'],
mem_gb=output_smooth_mem_gb)
else:
output_smooth = pe.Node(interface=fsl.MultiImageMaths(),
name='smooth',
mem_gb=output_smooth_mem_gb)
elif opt == 'AFNI':
if input_image_type == 'func_derivative_multi':
output_smooth = pe.MapNode(interface=afni.BlurToFWHM(),
name='smooth_multi',
iterfield=['in_file'])
else:
output_smooth = pe.Node(interface=afni.BlurToFWHM(),
name='smooth',
iterfield=['in_file'])
output_smooth.inputs.outputtype = 'NIFTI_GZ'
if opt == 'FSL':
# wire in the resource to be smoothed
wf.connect(inputnode, 'in_file', output_smooth, 'in_file')
# get the parameters for fwhm
wf.connect(inputnode_fwhm, ('fwhm', set_gauss), output_smooth,
'op_string')
wf.connect(inputnode, 'mask', output_smooth, 'operand_files')
elif opt == 'AFNI':
wf.connect(inputnode, 'in_file', output_smooth, 'in_file')
wf.connect(inputnode_fwhm, 'fwhm', output_smooth, 'fwhm')
wf.connect(inputnode, 'mask', output_smooth, 'mask')
outputnode = pe.Node(util.IdentityInterface(fields=['out_file', 'fwhm']),
name='outputspec')
wf.connect(output_smooth, 'out_file', outputnode, 'out_file')
wf.connect(inputnode_fwhm, 'fwhm', outputnode, 'fwhm')
return wf