def anatomical_preprocessing():
'''
Inputs:
MP2RAGE Skull stripped image using Spectre-2010
Workflow:
1. reorient to RPI
2. create a brain mask
Returns:
brain
brain_mask
'''
# define workflow
flow = Workflow('anat_preprocess')
inputnode = Node(util.IdentityInterface(
fields=['anat', 'anat_gm', 'anat_wm', 'anat_csf', 'anat_first']),
name='inputnode')
outputnode = Node(util.IdentityInterface(fields=[
'brain',
'brain_gm',
'brain_wm',
'brain_csf',
'brain_first',
'brain_mask',
]),
name='outputnode')
reorient = Node(interface=preprocess.Resample(), name='anat_reorient')
reorient.inputs.orientation = 'RPI'
reorient.inputs.outputtype = 'NIFTI'
erode = Node(interface=fsl.ErodeImage(), name='anat_preproc')
reorient_gm = reorient.clone('anat_preproc_gm')
reorient_wm = reorient.clone('anat_preproc_wm')
reorient_cm = reorient.clone('anat_preproc_csf')
reorient_first = reorient.clone('anat_preproc_first')
make_mask = Node(interface=fsl.UnaryMaths(), name='anat_preproc_mask')
make_mask.inputs.operation = 'bin'
# connect workflow nodes
flow.connect(inputnode, 'anat', reorient, 'in_file')
flow.connect(inputnode, 'anat_gm', reorient_gm, 'in_file')
flow.connect(inputnode, 'anat_wm', reorient_wm, 'in_file')
flow.connect(inputnode, 'anat_csf', reorient_cm, 'in_file')
flow.connect(inputnode, 'anat_first', reorient_first, 'in_file')
flow.connect(reorient, 'out_file', erode, 'in_file')
flow.connect(erode, 'out_file', make_mask, 'in_file')
flow.connect(make_mask, 'out_file', outputnode, 'brain_mask')
flow.connect(erode, 'out_file', outputnode, 'brain')
flow.connect(reorient_gm, 'out_file', outputnode, 'brain_gm')
flow.connect(reorient_wm, 'out_file', outputnode, 'brain_wm')
flow.connect(reorient_cm, 'out_file', outputnode, 'brain_csf')
flow.connect(reorient_first, 'out_file', outputnode, 'brain_first')
return flow
def __init__(self, name, base_dir=None):
super(BrainExtractionWorkflow, self).__init__(name, base_dir)
# Segmentation
# ============
seg_node = npe.MapNode(name="Segmentation",
iterfield="data",
interface=spm.Segment())
seg_node.inputs.gm_output_type = [False, False, True]
seg_node.inputs.wm_output_type = [False, False, True]
seg_node.inputs.csf_output_type = [False, False, True]
add1_node = npe.MapNode(name="AddGMWM",
iterfield=["in_file", "operand_file"],
interface=fsl.BinaryMaths())
add1_node.inputs.operation = 'add'
add2_node = npe.MapNode(name="AddGMWMCSF",
iterfield=["in_file", "operand_file"],
interface=fsl.BinaryMaths())
add2_node.inputs.operation = 'add'
dil_node = npe.MapNode(name="Dilate",
iterfield="in_file",
interface=fsl.DilateImage())
dil_node.inputs.operation = 'mean'
ero_node = npe.MapNode(name="Erode",
iterfield="in_file",
interface=fsl.ErodeImage())
thre_node = npe.MapNode(name="Threshold",
iterfield="in_file",
interface=fsl.Threshold())
thre_node.inputs.thresh = 0.5
fill_node = npe.MapNode(name="Fill",
iterfield="in_file",
interface=fsl.UnaryMaths())
fill_node.inputs.operation = 'fillh'
mask_node = npe.MapNode(name="ApplyMask",
iterfield=["in_file", "mask_file"],
interface=fsl.ApplyMask())
mask_node.inputs.output_type = str("NIFTI")
self.connect([
(seg_node, add1_node, [('native_gm_image', 'in_file')]),
(seg_node, add1_node, [('native_wm_image', 'operand_file')]),
(seg_node, add2_node, [('native_csf_image', 'in_file')]),
(add1_node, add2_node, [('out_file', 'operand_file')]),
(add2_node, dil_node, [('out_file', 'in_file')]),
(dil_node, ero_node, [('out_file', 'in_file')]),
(ero_node, thre_node, [('out_file', 'in_file')]),
(thre_node, fill_node, [('out_file', 'in_file')]),
(fill_node, mask_node, [('out_file', 'mask_file')]),
])
def extractWhitematter(input_image, wmoutline_image, output_image):
thresHolder = fsl.MultiImageMaths(input_file=input_image,
out_file=output_image)
thresHolder.inputs.op_string = '-uthr 41 -thr 41'
thresHolder.run()
thresHolder.inputs.input_file = output_image
thresHolder.inputs.op_string = '-uthr 2 -thr 2'
thresHolder.run()
thresHolder.inputs.op_string = '-uthr 255 -thr 251'
thresHolder.run()
# Combine and binarize
combinizer = fsl.BinaryMaths(operation='add',
in_file=output_image,
operand_file=wmoutline_image,
out_file=output_image)
binarizer = fsl.UnaryMaths(operation='bin',
in_file=output_image,
out_file=output_image)
return output_image
def fieldmap_to_phasediff(name=WORKFLOW_NAME, settings=None):
"""Legacy workflow to create a phasediff map from a fieldmap, to be digested by FUGUE"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(
fields=['fieldmap', 'fmap_mask', 'unwarp_direction', 'dwell_time']),
name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(fields=['fmap_rads', 'fmap_unmasked']),
name='outputnode')
# Convert topup fieldmap to rad/s [ 1 Hz = 6.283 rad/s]
fmap_scale = pe.Node(fsl.BinaryMaths(operation='mul', operand_value=6.283),
name='fmap_scale')
# Compute a mask from the fieldmap (??)
fmap_abs = pe.Node(fsl.UnaryMaths(operation='abs', args='-bin'),
name='fmap_abs')
fmap_mul = pe.Node(fsl.BinaryMaths(operation='mul'), name='fmap_mul_mask')
# Compute an smoothed field without mask
fugue_unmask = pe.Node(fsl.FUGUE(save_unmasked_fmap=True),
name='fmap_unmask')
workflow.connect([
(inputnode, fmap_scale, [('fieldmap', 'in_file')]),
(inputnode, fmap_mul, [('fmap_mask', 'operand_file')]),
(inputnode, fugue_unmask, [('unwarp_direction', 'unwarp_direction'),
('dwell_time', 'dwell_time')]),
(fmap_scale, fmap_abs, [('out_file', 'in_file')]),
(fmap_abs, fmap_mul, [('out_file', 'in_file')]),
(fmap_scale, fugue_unmask, [('out_file', 'fmap_in_file')]),
(fmap_mul, fugue_unmask, [('out_file', 'mask_file')]),
(fmap_scale, outputnode, [('out_file', 'fmap_rads')]),
(fugue_unmask, outputnode, [('fmap_out_file', 'fmap_unmasked')])
])
return workflow
prepare = pe.Node(interface=fsl.epi.PrepareFieldmap(),name='prepare') prepare.inputs.output_type = "NIFTI_GZ" prepare.inputs.delta_TE = 2.46 # Co-Register EPI and Correct field inhomogeniety distortions #epireg = pe.Node(interface=fsl.epi.EpiReg(), name='epireg') #epireg.inputs.echospacing=0.00046 #epireg.inputs.pedir='-y' #epireg.inputs.output_type='NIFTI_GZ' # White Matter Segmentation (Make sure to generate edge map) segment=pe.Node(interface=fsl.FAST(),name='segment') #fast -o opname_fast anat_ss wmmapbin = pe.Node(interface=fsl.UnaryMaths(),name='wmmapbin') #fslmaths opname_fast_pve_2 -thr 0.5 -bin opname_fast_wmseg # in_file -> out_file wmmapbin.inputs.args='-thr 0.5 -bin' wmmapedge = pe.Node(interface=fsl.MultiImageMaths(),name='wmmapbin') #fslmaths opname_fast_wmseg -edge -bin -mas opname_fast_wmseg opname_fast_wmedge wmmapedge.inputs.op_string='-edge -bin -mas %s' # Flirt Pre-Alignment, using skullstripped T1 as reference #flirt -ref anat_ss -in epi -dof 6 -omat opname_init.mat
def create_extract_noT1_pipe(params_template, params={},
name="extract_noT1_pipe"):
"""
Description: Extract T1 brain using AtlasBrex
Inputs:
inputnode:
restore_T1: preprocessed (debiased/denoised) T1 file name
restore_T1: preprocessed (debiased/denoised)T2 file name
arguments:
params_template: dictionary of info about template
params: dictionary of node sub-parameters (from a json file)
name: pipeline name (default = "extract_pipe")
Outputs:
smooth_mask.out_file:
Computed mask (after some smoothing)
"""
# creating pipeline
extract_pipe = pe.Workflow(name=name)
# creating inputnode
inputnode = pe.Node(
niu.IdentityInterface(fields=['restore_T1',
"indiv_params"]),
name='inputnode')
# N4 intensity normalization with parameters from json
norm_intensity = NodeParams(ants.N4BiasFieldCorrection(),
params=parse_key(params, "norm_intensity"),
name='norm_intensity')
extract_pipe.connect(inputnode, 'restore_T1',
norm_intensity, "input_image")
# atlas_brex
atlas_brex = NodeParams(AtlasBREX(),
params=parse_key(params, "atlas_brex"),
name='atlas_brex')
extract_pipe.connect(norm_intensity, "output_image",
atlas_brex, 't1_restored_file')
atlas_brex.inputs.NMT_file = params_template["template_head"]
atlas_brex.inputs.NMT_SS_file = params_template["template_brain"]
extract_pipe.connect(
inputnode, ("indiv_params", parse_key, "atlas_brex"),
atlas_brex, 'indiv_params')
# mask_brex
mask_brex = pe.Node(fsl.UnaryMaths(), name='mask_brex')
mask_brex.inputs.operation = 'bin'
extract_pipe.connect(atlas_brex, 'brain_file', mask_brex, 'in_file')
# smooth_mask
smooth_mask = pe.Node(fsl.UnaryMaths(), name='smooth_mask')
smooth_mask.inputs.operation = "bin"
smooth_mask.inputs.args = "-s 1 -thr 0.5 -bin"
extract_pipe.connect(mask_brex, 'out_file', smooth_mask, 'in_file')
return extract_pipe
def register(warped_dir, atlas_image, atlas_image_brain, subject_T1ws_T2ws,
subject_T2ws, n_jobs):
input_spec = pe.Node(utility.IdentityInterface(fields=[
'subject_image_list', 'subject_image', 'atlas_image',
'atlas_image_brain'
]),
iterables=[('subject_image_list', subject_T1ws_T2ws),
('subject_image', subject_T2ws)],
synchronize=True,
name='input_spec')
# set input_spec
input_spec.inputs.subject_image_list = subject_T1ws_T2ws
input_spec.inputs.subject_image = subject_T2ws
input_spec.inputs.atlas_image = atlas_image
input_spec.inputs.atlas_image_brain = atlas_image_brain
'''
CC[x, x, 1, 8]: [fixed, moving, weight, radius]cd
-t SyN[0.25]: Syn transform with a gradient step of 0.25
-r Gauss[3, 0]: sigma 0
-I 30x50x20
use - Histogram - Matching
number - of - affine - iterations 10000x10000x10000x10000: 4 level image pyramid with 10000 iterations at each level
MI - option 32x16000: 32 bins, 16000 samples
'''
reg = pe.Node(
ants.Registration(
dimension=3,
output_transform_prefix="output_",
#interpolation='BSpline',
transforms=['Affine', 'SyN'],
transform_parameters=[(2.0, ), (0.25, )], #default values syn
shrink_factors=[[8, 4, 2, 1], [4, 2, 1]],
smoothing_sigmas=[[3, 2, 1, 0], [2, 1, 0]], #None for Syn?
sigma_units=['vox'] * 2,
sampling_percentage=[0.05, None], #just use default?
sampling_strategy=['Random', 'None'],
number_of_iterations=[[10000, 10000, 10000, 10000], [30, 50, 20]],
metric=['MI', 'CC'],
metric_weight=[1, 1],
radius_or_number_of_bins=[(32), (8)],
#winsorize_lower_quantile=0.05,
#winsorize_upper_quantile=0.95,
verbose=True,
use_histogram_matching=[True, True]),
name='calc_registration')
applytransforms = pe.Node(ants.ApplyTransforms(
dimension=3, interpolation='NearestNeighbor'),
name='apply_warpfield')
#Make warped atlas binary image
#https://nipype.readthedocs.io/en/latest/interfaces/generated/interfaces.fsl/preprocess.html#bet
binarize = pe.Node(fsl.UnaryMaths(operation='bin'), name='binarize')
#apply binary warped atlas as mask to T2w
#https://nipype.readthedocs.io/en/0.12.0/interfaces/generated/nipype.interfaces.fsl.maths.html#applymask
applymask = pe.Node(fsl.ApplyMask(), name='apply_mask')
wf = pe.Workflow(name='wf', base_dir=warped_dir)
wf.connect([
(input_spec, reg, [('atlas_image', 'moving_image'),
('subject_image_list', 'fixed_image')
]), #create warp field to register atlas to subject
(input_spec, applytransforms, [('atlas_image_brain', 'input_image'),
('subject_image', 'reference_image')]),
(reg, applytransforms, [('forward_transforms', 'transforms')]
) #apply warpfield to register atlas brain to subject
])
wf.connect(
applytransforms, 'output_image', binarize,
'in_file') #turn warped atlas brain into binary image to use as mask
wf.connect(binarize, 'out_file', applymask, 'mask_file')
wf.connect(input_spec, 'subject_image', applymask, 'in_file')
wf.config['execution']['parameterize_dirs'] = False
wf.write_graph()
output = wf.run(plugin='MultiProc', plugin_args={'n_procs': n_jobs})
def create_randomise(name='randomise', working_dir=None, crash_dir=None):
"""
Parameters
----------
Returns
-------
workflow : nipype.pipeline.engine.Workflow
Randomise workflow.
Notes
-----
Workflow Inputs::
Workflow Outputs::
References
----------
"""
if not working_dir:
working_dir = os.path.join(os.getcwd(), 'Randomise_work_dir')
if not crash_dir:
crash_dir = os.path.join(os.getcwd(), 'Randomise_crash_dir')
wf = pe.Workflow(name=name)
wf.base_dir = working_dir
wf.config['execution'] = {
'hash_method': 'timestamp',
'crashdump_dir': os.path.abspath(crash_dir)
}
inputspec = pe.Node(util.IdentityInterface(fields=[
'subjects_list', 'pipeline_output_folder', 'permutations',
'mask_boolean', 'demean', 'c_thresh'
]),
name='inputspec')
outputspec = pe.Node(util.IdentityInterface(fields=[
'tstat_files', 't_corrected_p_files', 'index_file', 'threshold_file',
'localmax_txt_file', 'localmax_vol_file', 'max_file', 'mean_file',
'pval_file', 'size_file'
]),
name='outputspec')
#merge = pe.Node(interface=fsl.Merge(), name='fsl_merge')
#merge.inputs.dimension = 't'
#merge.inputs.merged_file = "randomise_merged.nii.gz"
#wf.connect(inputspec, 'subjects', merge, 'in_files')
#mask = pe.Node(interface=fsl.maths.MathsCommand(), name='fsl_maths')
#mask.inputs.args = '-abs -Tmin -bin'
#mask.inputs.out_file = "randomise_mask.nii.gz"
#wf.connect(inputspec, 'subjects', mask, 'in_file')
randomise = pe.Node(interface=fsl.Randomise(), name='randomise')
randomise.inputs.base_name = "randomise"
randomise.inputs.demean = True
randomise.inputs.tfce = True
wf.connect([(inputspec, randomise, [
('subjects', 'in_file'),
('design_matrix_file', 'design_mat'),
('constrast_file', 'tcon'),
('permutations', 'num_perm'),
])])
wf.connect(randomise, 'tstat_files', outputspec, 'tstat_files')
wf.connect(randomise, 't_corrected_p_files', outputspec,
't_corrected_p_files')
#------------- issue here arises while using tfce. By not using tfce, you don't get t_corrected_p files. R V in a conundrum? --------------------#
select_tcorrp_files = pe.Node(Function(input_names=['input_list'],
output_names=['out_file'],
function=select),
name='select_t_corrp')
wf.connect(randomise, 't_corrected_p_files', select_tcorrp_files,
'input_list')
wf.connect(select_tcorrp_files, 'out_file', outputspec,
'out_tcorr_corrected')
select_tstat_files = pe.Node(Function(input_names=['input_list'],
output_names=['out_file'],
function=select),
name='select_t_stat')
wf.connect(randomise, 'tstat_files', select_tstat_files, 'input_list')
wf.connect(select_tstat_files, 'out_file', outputspec,
'out_tstat_corrected')
thresh = pe.Node(interface=fsl.Threshold(), name='fsl_threshold_contrast')
thresh.inputs.thresh = 0.95
thresh.inputs.out_file = 'rando_pipe_thresh_tstat.nii.gz'
wf.connect(select_tstat_files, 'out_file', thresh, 'in_file')
wf.connect(thresh, 'out_file', outputspec,
'rando_pipe_thresh_tstat.nii.gz')
thresh_bin = pe.Node(interface=fsl.UnaryMaths(),
name='fsl_threshold_bin_contrast')
thresh_bin.inputs.operation = 'bin'
wf.connect(thresh, 'out_file', thresh_bin, 'in_file')
wf.connect(thresh_bin, 'out_file', outputspec, 'thresh_bin_out')
apply_mask = pe.Node(interface=fsl.ApplyMask(),
name='fsl_applymask_contrast')
wf.connect(select_tstat_files, 'out_file', apply_mask, 'in_file')
wf.connect(thresh_bin, 'out_file', apply_mask, 'mask_file')
cluster = pe.Node(interface=fsl.Cluster(), name='cluster_contrast')
cluster.inputs.threshold = 0.0001
cluster.inputs.out_index_file = "index_file"
cluster.inputs.out_localmax_txt_file = "lmax_contrast.txt"
cluster.inputs.out_size_file = "cluster_size_contrast"
cluster.inputs.out_threshold_file = True
cluster.inputs.out_max_file = True
cluster.inputs.out_mean_file = True
cluster.inputs.out_pval_file = True
cluster.inputs.out_size_file = True
wf.connect(apply_mask, 'out_file', cluster, 'in_file')
wf.connect(cluster, 'index_file', outputspec, 'index_file')
wf.connect(cluster, 'threshold_file', outputspec, 'threshold_file')
wf.connect(cluster, 'localmax_txt_file', outputspec, 'localmax_txt_file')
wf.connect(cluster, 'localmax_vol_file', outputspec, 'localmax_vol_file')
wf.connect(cluster, 'max_file', outputspec, 'max_file')
wf.connect(cluster, 'mean_file', outputspec, 'meal_file')
wf.connect(cluster, 'pval_file', outputspec, 'pval_file')
wf.connect(cluster, 'size_file', outputspec, 'size_file')
return wf
def get_fmri2standard_wf(
tvols,
subject_id,
ACQ_PARAMS="/home/didac/LabScripts/fMRI_preprocess/acparams_hcp.txt"):
"""Estimates transformation from Gradiend Field Distortion-warped BOLD to T1
In general:
BOLD is field-inhomogeneity corrected and corregistered into standard space (T1).
To do so, the following steps are carried out:
1) Corregister SBref to SEgfmAP (fsl.FLIRT)
2) Realign BOLD to corrected SBref (fsl.MCFLIRT)
3) Field inhomogeneity correction estimation of SBref from SEfm_AP and SEfm_PA (fsl.TOPUP)
4) Apply field inhomogeneity correction to SBref (fsl.ApplyTOPUP)
5) Apply field inhomogeneity correction to BOLD (fsl.ApplyTOPUP)
6) Transform free-surfer brain mask (brain.mgz) to T1 space (freesurfer.ApplyVolTransform ;mri_vol2vol),
then binarized (fsl.UnaryMaths) and the mask is extracted from T1 (fsl.BinaryMaths)
7) Corregister BOLD (field-inhomogeneity corrected) to Standard T1 (fsl.Epi2Reg)
Parameters
----------
tvols: [t_initial, t_final] volumes included in the preprocess
ACQ_params: Path to txt file containing MRI acquisition parameters; needs to be specified for topup correction
Returns
-------
Workflow with the transformation
"""
from nipype import Workflow, Node, interfaces
from nipype.interfaces import fsl, utility, freesurfer
print("defining workflow...")
wf = Workflow(name=subject_id, base_dir='')
#Setting INPUT node...
print("defines input node...")
node_input = Node(utility.IdentityInterface(fields=[
'func_sbref_img', 'func_segfm_ap_img', 'func_segfm_pa_img',
'func_bold_ap_img', 'T1_img', 'T1_brain_freesurfer_mask'
]),
name='input_node')
print(
"Averages the three repeated Spin-Echo images with same Phase Encoding (AP or PA) for Susceptibility Correction (unwarping)..."
)
node_average_SEgfm = Node(fsl.maths.MeanImage(), name='Mean_SEgfm_AP')
print("Corregister SB-ref to average SEgfm-AP")
node_coregister_SBref2SEgfm = Node(
fsl.FLIRT(dof=6 #translation and rotation only
),
name='Corregister_SBref2SEgfm')
print("Eliminates first volumes.")
node_eliminate_first_scans = Node(
fsl.ExtractROI(
t_min=tvols[0], # first included volume
t_size=tvols[1] -
tvols[0], # number of volumes from the first to the last one
),
name="eliminate_first_scans")
print("Realigns fMRI BOLD volumes to SBref in SEgfm-AP space")
node_realign_bold = Node(
fsl.MCFLIRT(
save_plots=True, # save transformation matrices
),
name="realign_fmri2SBref")
print("Concatenates AP and PA SEgfm volumes...")
# AP AP AP PA PA PA
node_merge_ap_pa_inputs = Node(
utility.base.Merge(2 # number of inputs; it concatenates lists
),
name='Merge_ap_pa_inputs')
node_merge_SEgfm = Node(
fsl.Merge(dimension='t' # ¿?
),
name='Merge_SEgfm_AP_PA')
print(
"Estimates TopUp inhomogeneity correction from SEfm_AP and SEfm_PA...")
node_topup_SEgfm = Node(fsl.TOPUP(encoding_file=ACQ_PARAMS, ),
name='Topup_SEgfm_estimation')
print("Applies warp from TOPUP to correct SBref...")
node_apply_topup_to_SBref = Node(
fsl.ApplyTOPUP(
encoding_file=ACQ_PARAMS,
method='jac', # jacobian modulation
interp='spline', # interpolation method
),
name="apply_topup_to_SBref")
print("Applies warp from TOPUP to correct realigned BOLD...")
node_apply_topup = Node(
fsl.ApplyTOPUP(
encoding_file=ACQ_PARAMS,
method='jac', # jacobian modulation
interp='spline', # interpolation method
),
name="apply_topup")
## BRAIN MASK
#Registration to T1. Epireg without fieldmaps combined (see https://www.fmrib.ox.ac.uk/primers/intro_primer/ExBox20/IntroBox20.html)
# print ("Eliminates scalp from brain using T1 high res image");
# node_mask_T1=Node(fsl.BET(
# frac=0.7 # umbral
# ),
# name="mask_T1");
print('Transform brain mask T1 from freesurfer space to T1 space')
node_vol2vol_brain = Node(
freesurfer.ApplyVolTransform(
reg_header=True, # (--regheader)
transformed_file='brainmask_warped.nii.gz'
#source_file --mov (INPUT; freesurfer brain.mgz)
#transformed_file --o (OUTPUT; ...brain.nii.gz)
#target_file --targ (REFERENCE; ...T1w.nii.gz)
),
name="vol2vol")
print('Transform brain mask T1 to binary mask')
node_bin_mask_brain = Node(
fsl.UnaryMaths( # fslmaths T1_brain -bin T1_binarized mask
operation='bin', # (-bin)
#in_file (T1_brain)
#out_file (T1_binarized_mask)
),
name="binarize_mask")
print('Extract brain mask from T1 using binary mask')
node_extract_mask = Node(
fsl.
BinaryMaths( # fslmaths T1 -mul T1_binarized_mask T1_extracted_mask
operation='mul' # (-mul)
#in_file (T1)
#out_file (T1_extracted_mask)
#operand_file (T1_binarized_mask)
),
name="extract_mask")
##
print("Estimate and appply transformation from SBref to T1")
node_epireg = Node(
fsl.EpiReg(
#t1_head=SUBJECT_FSTRUCT_DIC['anat_T1'],
out_base='SEgfm2T1'),
name="epi2reg")
'''
EPI2REG ALREADY APPLIED
print ("Apply epi2reg to SBRef..");
node_apply_epi2reg_SBref= Node(fsl.ApplyXFM(
),
name="node_apply_epi2reg_SBref");
'''
print("Estimates inverse transform from epi2reg...")
# quality control
node_invert_epi2reg = Node(fsl.ConvertXFM(invert_xfm=True),
name="invert_epi2reg")
print("...")
node_mask_fMRI = Node(fsl.BET(mask=True, ), name='mask_fMRI')
#node_fmriMask.overwrite=True
print("Setting OUTPUT node...")
node_output = Node(interfaces.utility.IdentityInterface(fields=[
'SBref2SEgfm_mat',
'realign_movpar_txt',
'realign_fmri_img',
'topup_movpar_txt',
'topup_field_coef_img',
'epi2str_mat',
'epi2str_img',
'fmri_mask_img',
'rfmri_unwarped_imgs',
'sb_ref_unwarped_img',
]),
name='output_node')
print("All nodes created; Starts creating connections")
#Connects nodes
wf.connect([
#inputs
(node_input, node_average_SEgfm, [("func_segfm_ap_img", "in_file")]),
(node_input, node_coregister_SBref2SEgfm, [("func_sbref_img",
"in_file")]),
(node_input, node_eliminate_first_scans, [("func_bold_ap_img",
"in_file")]),
(node_input, node_merge_ap_pa_inputs, [("func_segfm_ap_img", "in1"),
("func_segfm_pa_img", "in2")]),
(node_merge_ap_pa_inputs, node_merge_SEgfm, [("out", "in_files")]),
(node_input, node_epireg, [("T1_img", "t1_head")]),
(node_input, node_vol2vol_brain, [("T1_brain_freesurfer_mask",
"source_file")]),
(node_input, node_vol2vol_brain, [("T1_img", "target_file")]),
(node_input, node_extract_mask, [("T1_img", "in_file")]),
#connections
(node_eliminate_first_scans, node_realign_bold, [("roi_file",
"in_file")]),
(node_average_SEgfm, node_coregister_SBref2SEgfm, [("out_file",
"reference")]),
(node_coregister_SBref2SEgfm, node_realign_bold, [("out_file",
"ref_file")]),
#T1 brain mask transformations (change space / vol2vol, binarize and extract)
(node_vol2vol_brain, node_bin_mask_brain, [("transformed_file",
"in_file")]),
(node_bin_mask_brain, node_extract_mask, [("out_file", "operand_file")
]),
#(node_realign_bold, node_tsnr, [("out_file", "in_file")]),
(node_merge_SEgfm, node_topup_SEgfm, [("merged_file", "in_file")]),
(node_realign_bold, node_apply_topup, [("out_file", "in_files")]),
(node_topup_SEgfm, node_apply_topup,
[("out_fieldcoef", "in_topup_fieldcoef"),
("out_movpar", "in_topup_movpar")]),
(node_topup_SEgfm, node_apply_topup_to_SBref,
[("out_fieldcoef", "in_topup_fieldcoef"),
("out_movpar", "in_topup_movpar")]),
(node_coregister_SBref2SEgfm, node_apply_topup_to_SBref,
[("out_file", "in_files")]),
#corregister to T1
(node_extract_mask, node_epireg, [("out_file", "t1_brain")]),
(node_apply_topup_to_SBref, node_epireg, [("out_corrected", "epi")]),
(node_epireg, node_invert_epi2reg, [("epi2str_mat", "in_file")]),
(node_coregister_SBref2SEgfm, node_mask_fMRI, [("out_file", "in_file")
]),
#yeld relevant data to output node
(node_coregister_SBref2SEgfm, node_output, [("out_matrix_file",
"SBref2SEgfm_mat")]),
(node_realign_bold, node_output, [("par_file", "realign_movpar_txt"),
("out_file", "realign_fmri_img")]),
(node_mask_fMRI, node_output, [("mask_file", "fmri_mask_img")]),
(node_epireg, node_output, [("epi2str_mat", "epi2str_mat")]),
(node_epireg, node_output, [("out_file", "epi2str_img")]),
(node_topup_SEgfm, node_output,
[("out_fieldcoef", "topup_field_coef_img"),
("out_corrected", "sb_ref_unwarped_img")]),
(node_apply_topup, node_output, [("out_corrected",
"rfmri_unwarped_imgs")])
])
print("All connections created")
return (wf)
def prep_randomise_workflow(c,
merged_file,
mask_file,
f_test,
mat_file,
con_file,
grp_file,
output_dir,
working_dir,
log_dir,
model_name,
fts_file=None):
import nipype.interfaces.utility as util
import nipype.interfaces.fsl as fsl
import nipype.interfaces.io as nio
wf = pe.Workflow(name='randomise_workflow')
wf.base_dir = c.work_dir
randomise = pe.Node(interface=fsl.Randomise(),
name='fsl-randomise_{0}'.format(model_name))
randomise.inputs.base_name = model_name
randomise.inputs.in_file = merged_file
randomise.inputs.mask = mask_file
randomise.inputs.num_perm = c.randomise_permutation
randomise.inputs.demean = c.randomise_demean
randomise.inputs.c_thresh = c.randomise_thresh
randomise.inputs.tfce = c.randomise_tfce
randomise.inputs.design_mat = mat_file
randomise.inputs.tcon = con_file
if fts_file:
randomise.inputs.fcon = fts_file
select_tcorrp_files = pe.Node(util.Function(input_names=['input_list'],
output_names=['out_file'],
function=select),
name='select_t_corrp')
wf.connect(randomise, 't_corrected_p_files', select_tcorrp_files,
'input_list')
select_tstat_files = pe.Node(util.Function(input_names=['input_list'],
output_names=['out_file'],
function=select),
name='select_t_stat')
wf.connect(randomise, 'tstat_files', select_tstat_files, 'input_list')
thresh = pe.Node(interface=fsl.Threshold(), name='fsl_threshold_contrast')
thresh.inputs.thresh = 0.95
thresh.inputs.out_file = 'randomise_pipe_thresh_tstat.nii.gz'
wf.connect(select_tstat_files, 'out_file', thresh, 'in_file')
thresh_bin = pe.Node(interface=fsl.UnaryMaths(),
name='fsl_threshold_bin_contrast')
thresh_bin.inputs.operation = 'bin'
wf.connect(thresh, 'out_file', thresh_bin, 'in_file')
apply_mask = pe.Node(interface=fsl.ApplyMask(),
name='fsl_applymask_contrast')
wf.connect(select_tstat_files, 'out_file', apply_mask, 'in_file')
wf.connect(thresh_bin, 'out_file', apply_mask, 'mask_file')
cluster = pe.Node(interface=fsl.Cluster(), name='cluster_contrast')
cluster.inputs.threshold = 0.0001
cluster.inputs.out_index_file = "index_file"
cluster.inputs.out_localmax_txt_file = "lmax_contrast.txt"
cluster.inputs.out_size_file = "cluster_size_contrast"
cluster.inputs.out_threshold_file = True
cluster.inputs.out_max_file = True
cluster.inputs.out_mean_file = True
cluster.inputs.out_pval_file = True
cluster.inputs.out_size_file = True
wf.connect(apply_mask, 'out_file', cluster, 'in_file')
ds = pe.Node(nio.DataSink(), name='fsl-randomise_sink')
ds.inputs.base_directory = str(output_dir)
ds.inputs.container = ''
wf.connect(randomise, 'tstat_files', ds, 'tstat_files')
wf.connect(randomise, 't_corrected_p_files', ds, 't_corrected_p_files')
wf.connect(select_tcorrp_files, 'out_file', ds, 'out_tcorr_corrected')
wf.connect(select_tstat_files, 'out_file', ds, 'out_tstat_corrected')
wf.connect(thresh, 'out_file', ds, 'randomise_pipe_thresh_tstat.nii.gz')
wf.connect(thresh_bin, 'out_file', ds, 'thresh_bin_out')
wf.connect(cluster, 'index_file', ds, 'index_file')
wf.connect(cluster, 'threshold_file', ds, 'threshold_file')
wf.connect(cluster, 'localmax_txt_file', ds, 'localmax_txt_file')
wf.connect(cluster, 'localmax_vol_file', ds, 'localmax_vol_file')
wf.connect(cluster, 'max_file', ds, 'max_file')
wf.connect(cluster, 'mean_file', ds, 'meal_file')
wf.connect(cluster, 'pval_file', ds, 'pval_file')
wf.connect(cluster, 'size_file', ds, 'size_file')
wf.run()
(TumorwarpHighRes, LowThreshTumor, [('out_file', 'in_file')]),
(NormalwarpHighRes, LowThreshNormal, [('out_file', 'in_file')])
])
datasink = pe.Node(interface=nio.DataSink(), name="datasink")
datasink.inputs.base_directory = parent_dir + '/FDM/Outputs'
datasink.inputs.substitutions = [
('_scan_id_', ''),
('_brain_flirt_volreg_masked_allineate_thresh', '_highres'),
('_brain_flirt_volreg_flirt_allineate_masked_allineate_thresh',
'_highres'),
('_flirt_allineate_flirt_allineate_masked_allineate_thresh',
'_highres')
]
TumorBin = pe.Node(interface=fsl.UnaryMaths(), name='TumorBin')
TumorBin.inputs.operation = 'bin'
FDM = pe.Workflow(name='FDM')
FDM.base_dir = parent_dir
FDM.connect([
(Reg, Warp, [('MaskADC.out_file', 'get_Ref.LinADC')]),
(Reg, Warp, [('MaskADC.out_file', 'ADCwarpHighRes.in_file')]),
(Reg, Warp, [('MaskREF.out_file', 'REFwarpHighRes.in_file')]),
(Reg, Warp, [('MaskTumor.out_file', 'TumorwarpHighRes.in_file')]),
(Reg, Warp, [('MaskNormal.out_file', 'NormalwarpHighRes.in_file')]),
(Warp, TumorBin, [('LowThreshTumor.out_file', 'in_file')]),
(Warp, datasink, [('LowThreshREF.out_file', 'Ref.@ref')]),
(Warp, datasink, [('LowThreshADC.out_file', 'ADC.@adc')]),
(TumorBin, datasink, [('out_file', 'Tumor.@tumor')]),
(Warp, datasink, [('LowThreshNormal.out_file', 'Normal.@normal')])
def create_correct_bias_pipe(params={}, name="correct_bias_pipe"):
"""
Description: Correct bias using T1 and T2 images
Same as bash_regis.T1xT2BiasFieldCorrection
Params:
- smooth (see `MathsCommand <https://nipype.readthedocs.io/en/0.12.1/\
interfaces/generated/nipype.interfaces.fsl.maths.html#mathscommand>`_)
- norm_smooth (see `MultiMathsCommand <https://nipype.readthedocs.io/\
en/0.12.1/interfaces/generated/nipype.interfaces.fsl.maths.html\
#multiimagemaths>`_)
- smooth_bias (see `IsotropicSmooth <https://nipype.readthedocs.io/en/\
0.12.1/interfaces/generated/nipype.interfaces.fsl.maths.html#\
isotropicsmooth>`_)
Inputs:
inputnode:
preproc_T1:
preprocessed T1 file name
preproc_T2:
preprocessed T2 file name
arguments:
params:
dictionary of node sub-parameters (from a json file)
name:
pipeline name (default = "correct_bias_pipe")
Outputs:
outputnode.debiased_T1:
T1 after bias correction
outputnode.debiased_T2:
T2 after bias correction
"""
# creating pipeline
correct_bias_pipe = pe.Workflow(name=name)
# creating inputnode
inputnode = pe.Node(
niu.IdentityInterface(fields=['preproc_T1', 'preproc_T2']),
name='inputnode')
# BinaryMaths
mult_T1_T2 = pe.Node(fsl.BinaryMaths(), name='mult_T1_T2')
mult_T1_T2.inputs.operation = "mul"
mult_T1_T2.inputs.args = "-abs -sqrt"
mult_T1_T2.inputs.output_datatype = "float"
correct_bias_pipe.connect(inputnode, 'preproc_T1', mult_T1_T2, 'in_file')
correct_bias_pipe.connect(inputnode, 'preproc_T2', mult_T1_T2,
'operand_file')
# Mean Brain Val
meanbrainval = pe.Node(fsl.ImageStats(), name='meanbrainval')
meanbrainval.inputs.op_string = "-M"
correct_bias_pipe.connect(mult_T1_T2, 'out_file', meanbrainval, 'in_file')
# norm_mult
norm_mult = pe.Node(fsl.BinaryMaths(), name='norm_mult')
norm_mult.inputs.operation = "div"
correct_bias_pipe.connect(mult_T1_T2, 'out_file', norm_mult, 'in_file')
correct_bias_pipe.connect(meanbrainval, 'out_stat', norm_mult,
'operand_value')
# smooth
smooth = NodeParams(fsl.maths.MathsCommand(),
params=parse_key(params, "smooth"),
name='smooth')
correct_bias_pipe.connect(norm_mult, 'out_file', smooth, 'in_file')
# norm_smooth
norm_smooth = NodeParams(fsl.MultiImageMaths(),
params=parse_key(params, "norm_smooth"),
name='norm_smooth')
correct_bias_pipe.connect(norm_mult, 'out_file', norm_smooth, 'in_file')
correct_bias_pipe.connect(smooth, 'out_file', norm_smooth, 'operand_files')
# modulate
modulate = pe.Node(fsl.BinaryMaths(), name='modulate')
modulate.inputs.operation = "div"
correct_bias_pipe.connect(norm_mult, 'out_file', modulate, 'in_file')
correct_bias_pipe.connect(norm_smooth, 'out_file', modulate,
'operand_file')
# std_modulate
std_modulate = pe.Node(fsl.ImageStats(), name='std_modulate')
std_modulate.inputs.op_string = "-S"
correct_bias_pipe.connect(modulate, 'out_file', std_modulate, 'in_file')
# mean_modulate
mean_modulate = pe.Node(fsl.ImageStats(), name='mean_modulate')
mean_modulate.inputs.op_string = "-M"
correct_bias_pipe.connect(modulate, 'out_file', mean_modulate, 'in_file')
# compute_lower_val
def compute_lower_val(mean_val, std_val):
return mean_val - (std_val * 0.5)
# compute_lower
lower = pe.Node(niu.Function(input_names=['mean_val', 'std_val'],
output_names=['lower_val'],
function=compute_lower_val),
name='lower')
correct_bias_pipe.connect(mean_modulate, 'out_stat', lower, 'mean_val')
correct_bias_pipe.connect(std_modulate, 'out_stat', lower, 'std_val')
# thresh_lower
thresh_lower = pe.Node(fsl.Threshold(), name='thresh_lower')
correct_bias_pipe.connect(lower, 'lower_val', thresh_lower, 'thresh')
correct_bias_pipe.connect(modulate, 'out_file', thresh_lower, 'in_file')
# mod_mask
mod_mask = pe.Node(fsl.UnaryMaths(), name='mod_mask')
mod_mask.inputs.operation = "bin"
mod_mask.inputs.args = "-ero -mul 255"
correct_bias_pipe.connect(thresh_lower, 'out_file', mod_mask, 'in_file')
# bias
bias = pe.Node(fsl.MultiImageMaths(), name='bias')
bias.inputs.op_string = "-mas %s -dilall"
bias.inputs.output_datatype = "float"
correct_bias_pipe.connect(norm_mult, 'out_file', bias, 'in_file')
correct_bias_pipe.connect(mod_mask, 'out_file', bias, 'operand_files')
# smooth_bias
smooth_bias = NodeParams(fsl.IsotropicSmooth(),
params=parse_key(params, "smooth_bias"),
name='smooth_bias')
correct_bias_pipe.connect(bias, 'out_file', smooth_bias, 'in_file')
# debiased_T1
debiased_T1 = pe.Node(fsl.BinaryMaths(), name='debiased_T1')
debiased_T1.inputs.operation = "div"
debiased_T1.inputs.output_datatype = "float"
correct_bias_pipe.connect(inputnode, 'preproc_T1', debiased_T1, 'in_file')
correct_bias_pipe.connect(smooth_bias, 'out_file', debiased_T1,
'operand_file')
# debiased_T2
debiased_T2 = pe.Node(fsl.BinaryMaths(), name='debiased_T2')
debiased_T2.inputs.operation = "div"
debiased_T2.inputs.output_datatype = "float"
correct_bias_pipe.connect(inputnode, 'preproc_T2', debiased_T2, 'in_file')
correct_bias_pipe.connect(smooth_bias, 'out_file', debiased_T2,
'operand_file')
# outputnode
outputnode = pe.Node(
niu.IdentityInterface(fields=["debiased_T1", "debiased_T2"]),
name='outputnode')
correct_bias_pipe.connect(debiased_T1, 'out_file', outputnode,
'debiased_T1')
correct_bias_pipe.connect(debiased_T2, 'out_file', outputnode,
'debiased_T2')
return correct_bias_pipe
def create_asl_processing_workflow(in_inversion_recovery_file,
in_asl_file,
output_dir,
in_t1_file=None,
name='asl_processing_workflow'):
workflow = pe.Workflow(name=name)
workflow.base_output_dir = name
subject_id = split_filename(os.path.basename(in_asl_file))[1]
ir_splitter = pe.Node(interface=fsl.Split(
dimension='t',
out_base_name='out_',
in_file=in_inversion_recovery_file),
name='ir_splitter')
ir_selector = pe.Node(interface=niu.Select(index=[0, 2, 4]),
name='ir_selector')
workflow.connect(ir_splitter, 'out_files', ir_selector, 'inlist')
ir_merger = pe.Node(interface=fsl.Merge(dimension='t'), name='ir_merger')
workflow.connect(ir_selector, 'out', ir_merger, 'in_files')
fitqt1 = pe.Node(interface=niftyfit.FitQt1(TIs=[4, 2, 1], SR=True),
name='fitqt1')
workflow.connect(ir_merger, 'merged_file', fitqt1, 'source_file')
extract_ir_0 = pe.Node(interface=niftyseg.BinaryMathsInteger(
operation='tp', operand_value=0, in_file=in_inversion_recovery_file),
name='extract_ir_0')
ir_thresolder = pe.Node(interface=fsl.Threshold(thresh=250),
name='ir_thresolder')
workflow.connect(extract_ir_0, 'out_file', ir_thresolder, 'in_file')
create_mask = pe.Node(interface=fsl.UnaryMaths(operation='bin'),
name='create_mask')
workflow.connect(ir_thresolder, 'out_file', create_mask, 'in_file')
model_fitting = pe.Node(niftyfit.FitAsl(source_file=in_asl_file,
pcasl=True,
PLD=1800,
LDD=1800,
eff=0.614,
mul=0.1),
name='model_fitting')
workflow.connect(fitqt1, 'm0map', model_fitting, 'm0map')
workflow.connect(create_mask, 'out_file', model_fitting, 'mask')
t1_to_asl_registration = pe.Node(niftyreg.RegAladin(rig_only_flag=True),
name='t1_to_asl_registration')
m0_resampling = pe.Node(niftyreg.RegResample(inter_val='LIN'),
name='m0_resampling')
mc_resampling = pe.Node(niftyreg.RegResample(inter_val='LIN'),
name='mc_resampling')
t1_resampling = pe.Node(niftyreg.RegResample(inter_val='LIN'),
name='t1_resampling')
cbf_resampling = pe.Node(niftyreg.RegResample(inter_val='LIN'),
name='cbf_resampling')
if in_t1_file:
t1_to_asl_registration.inputs.flo_file = in_asl_file
t1_to_asl_registration.inputs.ref_file = in_t1_file
m0_resampling.inputs.ref_file = in_t1_file
mc_resampling.inputs.ref_file = in_t1_file
t1_resampling.inputs.ref_file = in_t1_file
cbf_resampling.inputs.ref_file = in_t1_file
workflow.connect(fitqt1, 'm0map', m0_resampling, 'flo_file')
workflow.connect(fitqt1, 'mcmap', mc_resampling, 'flo_file')
workflow.connect(fitqt1, 't1map', t1_resampling, 'flo_file')
workflow.connect(model_fitting, 'cbf_file', cbf_resampling, 'flo_file')
workflow.connect(t1_to_asl_registration, 'aff_file', m0_resampling,
'trans_file')
workflow.connect(t1_to_asl_registration, 'aff_file', mc_resampling,
'trans_file')
workflow.connect(t1_to_asl_registration, 'aff_file', t1_resampling,
'trans_file')
workflow.connect(t1_to_asl_registration, 'aff_file', cbf_resampling,
'trans_file')
maskrenamer = pe.Node(interface=niu.Rename(format_string=subject_id +
'_mask',
keep_ext=True),
name='maskrenamer')
m0renamer = pe.Node(interface=niu.Rename(format_string=subject_id +
'_m0map',
keep_ext=True),
name='m0renamer')
mcrenamer = pe.Node(interface=niu.Rename(format_string=subject_id +
'_mcmap',
keep_ext=True),
name='mcrenamer')
t1renamer = pe.Node(interface=niu.Rename(format_string=subject_id +
'_t1map',
keep_ext=True),
name='t1renamer')
workflow.connect(create_mask, 'out_file', maskrenamer, 'in_file')
if in_t1_file:
workflow.connect(m0_resampling, 'out_file', m0renamer, 'in_file')
workflow.connect(mc_resampling, 'out_file', mcrenamer, 'in_file')
workflow.connect(t1_resampling, 'out_file', t1renamer, 'in_file')
else:
workflow.connect(fitqt1, 'm0map', m0renamer, 'in_file')
workflow.connect(fitqt1, 'mcmap', mcrenamer, 'in_file')
workflow.connect(fitqt1, 't1map', t1renamer, 'in_file')
ds = pe.Node(nio.DataSink(parameterization=False,
base_directory=output_dir),
name='ds')
workflow.connect(maskrenamer, 'out_file', ds, '@mask_file')
workflow.connect(m0renamer, 'out_file', ds, '@m0_file')
workflow.connect(mcrenamer, 'out_file', ds, '@mc_file')
workflow.connect(t1renamer, 'out_file', ds, '@t1_file')
if in_t1_file:
workflow.connect(cbf_resampling, 'out_file', ds, '@cbf_file')
else:
workflow.connect(model_fitting, 'cbf_file', ds, '@cbf_file')
workflow.connect(model_fitting, 'error_file', ds, '@err_file')
return workflow
def create_segment_atropos_pipe(params={}, name="segment_atropos_pipe"):
"""
Description: Segmentation with ANTS atropos script
Inputs:
inputnode:
brain_file: T1 image, after extraction and norm bin_norm_intensity
gm_prior_file: grey matter tissue intensity file
wm_prior_file: white matter tissue intensity file
csf_prior_file: csf tissue intensity file
arguments:
params: dictionary of node sub-parameters (from a json file)
name: pipeline name (default = "segment_atropos_pipe")
Outputs:
threshold_csf.out_file:
csf tissue intensity mask in subject space
threshold_gm.out_file:
grey matter tissue intensity mask in subject space
threshold_wm.out_file:
white matter tissue intensity mask in subject space
"""
# creating pipeline
segment_pipe = pe.Workflow(name=name)
# creating inputnode
inputnode = pe.Node(
niu.IdentityInterface(
fields=["brain_file", "gm_prior_file", "wm_prior_file",
"csf_prior_file"]),
name='inputnode')
# bin_norm_intensity (a cheat from Kepkee if I understood well!)
bin_norm_intensity = pe.Node(fsl.UnaryMaths(), name="bin_norm_intensity")
bin_norm_intensity.inputs.operation = "bin"
segment_pipe.connect(inputnode, "brain_file",
bin_norm_intensity, "in_file")
# merging priors as a list
merge_3_elem = pe.Node(niu.Function(
input_names=['elem1', 'elem2', 'elem3'],
output_names=['merged_list'],
function=merge_3_elem_to_list), name='merge_3_elem')
# was like this before (1 -> csf, 2 -> gm, 3 -> wm, to check)
segment_pipe.connect(inputnode, 'csf_prior_file', merge_3_elem, "elem1")
segment_pipe.connect(inputnode, 'gm_prior_file', merge_3_elem, "elem2")
segment_pipe.connect(inputnode, 'wm_prior_file', merge_3_elem, "elem3")
# Atropos
seg_at = NodeParams(AtroposN4(),
params=parse_key(params, "Atropos"),
name='seg_at')
segment_pipe.connect(inputnode, "brain_file", seg_at, "brain_file")
segment_pipe.connect(bin_norm_intensity, 'out_file',
seg_at, "brainmask_file")
segment_pipe.connect(merge_3_elem, 'merged_list',
seg_at, "priors")
# Threshold GM, WM and CSF
thd_nodes = {}
for i, tissue in enumerate(['csf', 'gm', 'wm']):
tmp_node = NodeParams(fsl.Threshold(),
params=parse_key(params, "threshold_" + tissue),
name="threshold_" + tissue)
segment_pipe.connect(seg_at, ('segmented_files', get_elem, i),
tmp_node, 'in_file')
thd_nodes[tissue] = tmp_node
return segment_pipe
def create_extract_pipe(params_template, params={}, name="extract_pipe"):
"""
Description: Extract T1 brain using AtlasBrex
Params:
- norm_intensity (see `N4BiasFieldCorrection <https://nipype.readthedocs\
.io/en/0.12.1/interfaces/generated/nipype.interfaces.ants.segmentation.\
html#n4biasfieldcorrection>`_ for arguments)
- atlas_brex (see :class:`AtlasBREX \
<macapype.nodes.extract_brain.AtlasBREX>` for arguments) - also \
available as :ref:`indiv_params <indiv_params>`
Inputs:
inputnode:
restore_T1:
preprocessed (debiased/denoised) T1 file name
restore_T2:
preprocessed (debiased/denoised)T2 file name
arguments:
params_template:
dictionary of info about template
params:
dictionary of node sub-parameters (from a json file)
name:
pipeline name (default = "extract_pipe")
Outputs:
smooth_mask.out_file:
Computed mask (after some smoothing)
"""
# creating pipeline
extract_pipe = pe.Workflow(name=name)
# creating inputnode
inputnode = pe.Node(niu.IdentityInterface(
fields=['restore_T1', 'restore_T2', "indiv_params"]),
name='inputnode')
# atlas_brex
atlas_brex = NodeParams(AtlasBREX(),
params=parse_key(params, "atlas_brex"),
name='atlas_brex')
extract_pipe.connect(inputnode, "restore_T1", atlas_brex,
't1_restored_file')
atlas_brex.inputs.NMT_file = params_template["template_head"]
atlas_brex.inputs.NMT_SS_file = params_template["template_brain"]
extract_pipe.connect(inputnode, ("indiv_params", parse_key, "atlas_brex"),
atlas_brex, 'indiv_params')
# mask_brex
mask_brex = pe.Node(fsl.UnaryMaths(), name='mask_brex')
mask_brex.inputs.operation = 'bin'
extract_pipe.connect(atlas_brex, 'brain_file', mask_brex, 'in_file')
# smooth_mask
smooth_mask = pe.Node(fsl.UnaryMaths(), name='smooth_mask')
smooth_mask.inputs.operation = "bin"
smooth_mask.inputs.args = "-s 1 -thr 0.5 -bin"
extract_pipe.connect(mask_brex, 'out_file', smooth_mask, 'in_file')
# mult_T1
mult_T1 = pe.Node(afni.Calc(), name='mult_T1')
mult_T1.inputs.expr = "a*b"
mult_T1.inputs.outputtype = 'NIFTI_GZ'
extract_pipe.connect(inputnode, "restore_T1", mult_T1, 'in_file_a')
extract_pipe.connect(smooth_mask, 'out_file', mult_T1, 'in_file_b')
# mult_T2
mult_T2 = pe.Node(afni.Calc(), name='mult_T2')
mult_T2.inputs.expr = "a*b"
mult_T2.inputs.outputtype = 'NIFTI_GZ'
extract_pipe.connect(inputnode, 'restore_T2', mult_T2, 'in_file_a')
extract_pipe.connect(smooth_mask, 'out_file', mult_T2, 'in_file_b')
return extract_pipe
def create_segment_atropos_seg_pipe(params={}, name="segment_atropos_pipe"):
"""
Description: Segmentation with ANTS atropos script using seg file
Params:
- Atropos (see :class:`AtroposN4 <macapype.nodes.segment.AtroposN4>`)
- threshold_gm, threshold_wm, threshold_csf (see `Threshold \
<https://nipype.readthedocs.io/en/0.12.1/interfaces/generated/nipype.\
interfaces.fsl.maths.html#threshold>`_ for arguments)
Inputs:
inputnode:
brain_file: T1 image, after extraction and norm bin_norm_intensity
seg_file: indexed with all tissues as index file
arguments:
params: dictionary of node sub-parameters (from a json file)
name: pipeline name (default = "segment_atropos_pipe")
Outputs:
threshold_csf.out_file:
csf tissue intensity mask in subject space
threshold_gm.out_file:
grey matter tissue intensity mask in subject space
threshold_wm.out_file:
white matter tissue intensity mask in subject space
"""
# creating pipeline
segment_pipe = pe.Workflow(name=name)
# creating inputnode
inputnode = pe.Node(
niu.IdentityInterface(fields=["brain_file", "seg_file"]),
name='inputnode')
# bin_norm_intensity (a cheat from Kepkee if I understood well!)
bin_norm_intensity = pe.Node(fsl.UnaryMaths(), name="bin_norm_intensity")
bin_norm_intensity.inputs.operation = "bin"
segment_pipe.connect(inputnode, "brain_file", bin_norm_intensity,
"in_file")
# merging priors as a list
split_seg = pe.Node(niu.Function(input_names=['nii_file'],
output_names=['list_split_files'],
function=split_indexed_mask),
name='split_seg')
segment_pipe.connect(inputnode, 'seg_file', split_seg, "nii_file")
# Atropos
seg_at = NodeParams(AtroposN4(),
params=parse_key(params, "Atropos"),
name='seg_at')
segment_pipe.connect(inputnode, "brain_file", seg_at, "brain_file")
segment_pipe.connect(bin_norm_intensity, 'out_file', seg_at,
"brainmask_file")
segment_pipe.connect(split_seg, 'list_split_files', seg_at, "priors")
# on segmentation indexed mask (with labels)
# 1 -> CSF
# 2 -> GM
# 3 -> sub cortical?
# 4 -> WM
# 5 -> ?
thd_nodes = {}
for key, tissue in {0: 'csf', 1: 'gm', 3: 'wm'}.items():
tmp_node = NodeParams(fsl.Threshold(),
params=parse_key(params, "threshold_" + tissue),
name="threshold_" + tissue)
segment_pipe.connect(seg_at, ('segmented_files', get_elem, key),
tmp_node, 'in_file')
thd_nodes[tissue] = tmp_node
outputnode = pe.Node(niu.IdentityInterface(fields=[
"segmented_file", "threshold_gm", "threshold_wm", "threshold_csf"
]),
name='outputnode')
segment_pipe.connect(seg_at, 'segmented_file', outputnode,
'segmented_file')
segment_pipe.connect(thd_nodes["gm"], 'out_file', outputnode,
'threshold_gm')
segment_pipe.connect(thd_nodes["wm"], 'out_file', outputnode,
'threshold_wm')
segment_pipe.connect(thd_nodes["csf"], 'out_file', outputnode,
'threshold_csf')
return segment_pipe
def create_mask_from_seg_pipe(params={}, name="mask_from_seg_pipe"):
"""
Description: mask from segmentation tissues
#TODO To be added if required (was in old_segment before)
Function:
- Compute union of those 3 tissues;
- Apply morphological opening on the union mask
- Fill holes
Inputs:
mask_gm, mask_wm:
binary mask for grey matter and white matter
Outputs:
fill_holes.out_file:
filled mask after erode
fill_holes_dil.out_file
filled mask after dilate
"""
# creating pipeline
seg_pipe = pe.Workflow(name=name)
# Creating inputnode
inputnode = pe.Node(niu.IdentityInterface(
fields=['mask_gm', 'mask_wm', 'mask_csf', 'indiv_params']),
name='inputnode')
# bin_gm
bin_gm = pe.Node(interface=fsl.UnaryMaths(), name="bin_gm")
bin_gm.inputs.operation = "fillh"
seg_pipe.connect(inputnode, 'mask_gm', bin_gm, 'in_file')
# bin_csf
bin_csf = pe.Node(interface=fsl.UnaryMaths(), name="bin_csf")
bin_csf.inputs.operation = "fillh"
seg_pipe.connect(inputnode, 'mask_csf', bin_csf, 'in_file')
# bin_wm
bin_wm = pe.Node(interface=fsl.UnaryMaths(), name="bin_wm")
bin_wm.inputs.operation = "fillh"
seg_pipe.connect(inputnode, 'mask_wm', bin_wm, 'in_file')
# Compute union of the 3 tissues
# Done with 2 fslmaths as it seems to hard to do it
wmgm_union = pe.Node(fsl.BinaryMaths(), name="wmgm_union")
wmgm_union.inputs.operation = "add"
seg_pipe.connect(bin_gm, 'out_file', wmgm_union, 'in_file')
seg_pipe.connect(bin_wm, 'out_file', wmgm_union, 'operand_file')
tissues_union = pe.Node(fsl.BinaryMaths(), name="tissues_union")
tissues_union.inputs.operation = "add"
seg_pipe.connect(wmgm_union, 'out_file', tissues_union, 'in_file')
seg_pipe.connect(bin_csf, 'out_file', tissues_union, 'operand_file')
# Opening (dilating) mask
dilate_mask = NodeParams(fsl.DilateImage(),
params=parse_key(params, "dilate_mask"),
name="dilate_mask")
dilate_mask.inputs.operation = "mean" # Arbitrary operation
seg_pipe.connect(tissues_union, 'out_file', dilate_mask, 'in_file')
# fill holes of dilate_mask
fill_holes_dil = pe.Node(BinaryFillHoles(), name="fill_holes_dil")
seg_pipe.connect(dilate_mask, 'out_file', fill_holes_dil, 'in_file')
# Eroding mask
erode_mask = NodeParams(fsl.ErodeImage(),
params=parse_key(params, "erode_mask"),
name="erode_mask")
seg_pipe.connect(tissues_union, 'out_file', erode_mask, 'in_file')
# fill holes of erode_mask
fill_holes = pe.Node(BinaryFillHoles(), name="fill_holes")
seg_pipe.connect(erode_mask, 'out_file', fill_holes, 'in_file')
# merge to index
merge_indexed_mask = NodeParams(
interface=niu.Function(input_names=[
"mask_csf_file", "mask_wm_file", "mask_gm_file", "index_csf",
"index_gm", "index_wm"
],
output_names=['indexed_mask'],
function=merge_masks),
params=parse_key(params, "merge_indexed_mask"),
name="merge_indexed_mask")
seg_pipe.connect(bin_gm, 'out_file', merge_indexed_mask, "mask_gm_file")
seg_pipe.connect(bin_wm, 'out_file', merge_indexed_mask, "mask_wm_file")
seg_pipe.connect(bin_csf, 'out_file', merge_indexed_mask, "mask_csf_file")
return seg_pipe
import nibabel as nb
input = posteriors
GM = posteriors[1] #posterior_01
print(GM)
return GM
get_gm = Node(name='Get_GM',
interface=Function(input_names=['posteriors'],
output_names=['GM'],
function=Get_GM))
#-----------------------------------------------------------------------------------------------------
# In[1]:
#Make a mask of the warped image, to use it with atropos
binarize_warped_image = Node(fsl.UnaryMaths(), name='Binarize_Warped_Image')
binarize_warped_image.inputs.operation = 'bin'
binarize_warped_image.output_datatype = 'char'
#-----------------------------------------------------------------------------------------------------
# In[1]:
#Multiply by Jacobian determinant to ge the modulate image
modulate_GM = Node(ants.MultiplyImages(), name='Modulate_GM')
modulate_GM.inputs.dimension = 3
modulate_GM.inputs.output_product_image = 'Modulated_GM.nii.gz'
#-----------------------------------------------------------------------------------------------------
# In[1]:
#Smooth the modulated images
smoothing = Node(fsl.Smooth(), name='Smoothing')
smoothing.iterables = ('fwhm', [1.5, 2, 2.3, 2.7, 3])
root_dir + nipype_dir + nlin_displacement_field_4d_only_split1,
root_dir + nipype_dir + nlin_displacement_field_4d_only_split2,
root_dir + nipype_dir + nlin_displacement_field_4d_only_split3
]
# Node 13
node_select3 = pe.Node(interface=util.Select(), name='node_select3')
node_select3.inputs.index = [2]
node_select3.inputs.inlist = [
root_dir + nipype_dir + nlin_displacement_field_4d_only_split1,
root_dir + nipype_dir + nlin_displacement_field_4d_only_split2,
root_dir + nipype_dir + nlin_displacement_field_4d_only_split3
]
# Node 14
node_fsl_sqr1 = pe.Node(interface=fsl.UnaryMaths(), name='node_fsl_sqr1')
node_fsl_sqr1.inputs.operation = 'sqr'
#node_fsl_sqr1.inputs.in_file = root_dir + nipype_dir + nlin_displacement_field_4d_only_split1
node_fsl_sqr1.inputs.out_file = root_dir + nipype_dir + nlin_displacement_field_4d_only_split1
# Node 15
node_fsl_sqr2 = pe.Node(interface=fsl.UnaryMaths(), name='node_fsl_sqr2')
node_fsl_sqr2.inputs.operation = 'sqr'
#node_fsl_sqr2.inputs.in_file = root_dir + nipype_dir + nlin_displacement_field_4d_only_split2
node_fsl_sqr2.inputs.out_file = root_dir + nipype_dir + nlin_displacement_field_4d_only_split2
# Node 16
node_fsl_sqr3 = pe.Node(interface=fsl.UnaryMaths(), name='node_fsl_sqr3')
node_fsl_sqr3.inputs.operation = 'sqr'
#node_fsl_sqr3.inputs.in_file = root_dir + nipype_dir + nlin_displacement_field_4d_only_split3
node_fsl_sqr3.inputs.out_file = root_dir + nipype_dir + nlin_displacement_field_4d_only_split3
def _create_split_hemi_pipe(params, params_template, name="split_hemi_pipe"):
"""Description: Split segmentated tissus according hemisheres after \
removal of cortical structure
Processing steps:
- TODO
Params:
- None so far
Inputs:
inputnode:
warpinv_file:
non-linear transformation (from NMT_subject_align)
inv_transfo_file:
inverse transformation
aff_file:
affine transformation file
t1_ref_file:
preprocessd T1
segmented_file:
from atropos segmentation, with all the tissues segmented
arguments:
params:
dictionary of node sub-parameters (from a json file)
name:
pipeline name (default = "split_hemi_pipe")
Outputs:
"""
split_hemi_pipe = pe.Workflow(name=name)
# creating inputnode
inputnode = pe.Node(niu.IdentityInterface(fields=[
'warpinv_file', 'inv_transfo_file', 'aff_file', 't1_ref_file',
'segmented_file'
]),
name='inputnode')
# get values
if "cereb_template" in params_template.keys():
cereb_template_file = params_template["cereb_template"]
# ### cereb
# Binarize cerebellum
bin_cereb = pe.Node(interface=fsl.UnaryMaths(), name='bin_cereb')
bin_cereb.inputs.operation = "bin"
bin_cereb.inputs.in_file = cereb_template_file
# Warp cereb brainmask to subject space
warp_cereb = pe.Node(interface=reg.NwarpApplyPriors(),
name='warp_cereb')
warp_cereb.inputs.in_file = cereb_template_file
warp_cereb.inputs.out_file = cereb_template_file
warp_cereb.inputs.interp = "NN"
warp_cereb.inputs.args = "-overwrite"
split_hemi_pipe.connect(bin_cereb, 'out_file', warp_cereb, 'in_file')
split_hemi_pipe.connect(inputnode, 'aff_file', warp_cereb, 'master')
split_hemi_pipe.connect(inputnode, 'warpinv_file', warp_cereb, "warp")
# Align cereb template
align_cereb = pe.Node(interface=afni.Allineate(), name='align_cereb')
align_cereb.inputs.final_interpolation = "nearestneighbour"
align_cereb.inputs.overwrite = True
align_cereb.inputs.outputtype = "NIFTI_GZ"
split_hemi_pipe.connect(warp_cereb, 'out_file', align_cereb,
"in_file") # -source
split_hemi_pipe.connect(inputnode, 't1_ref_file', align_cereb,
"reference") # -base
split_hemi_pipe.connect(inputnode, 'inv_transfo_file', align_cereb,
"in_matrix") # -1Dmatrix_apply
if "L_hemi_template" in params_template.keys() and \
"R_hemi_template" in params_template.keys():
L_hemi_template_file = params_template["L_hemi_template"]
R_hemi_template_file = params_template["R_hemi_template"]
# Warp L hemi template brainmask to subject space
warp_L_hemi = pe.Node(interface=reg.NwarpApplyPriors(),
name='warp_L_hemi')
warp_L_hemi.inputs.in_file = L_hemi_template_file
warp_L_hemi.inputs.out_file = L_hemi_template_file
warp_L_hemi.inputs.interp = "NN"
warp_L_hemi.inputs.args = "-overwrite"
split_hemi_pipe.connect(inputnode, 'aff_file', warp_L_hemi, 'master')
split_hemi_pipe.connect(inputnode, 'warpinv_file', warp_L_hemi, "warp")
# Align L hemi template
align_L_hemi = pe.Node(interface=afni.Allineate(), name='align_L_hemi')
align_L_hemi.inputs.final_interpolation = "nearestneighbour"
align_L_hemi.inputs.overwrite = True
align_L_hemi.inputs.outputtype = "NIFTI_GZ"
split_hemi_pipe.connect(warp_L_hemi, 'out_file', align_L_hemi,
"in_file") # -source
split_hemi_pipe.connect(inputnode, 't1_ref_file', align_L_hemi,
"reference") # -base
split_hemi_pipe.connect(inputnode, 'inv_transfo_file', align_L_hemi,
"in_matrix") # -1Dmatrix_apply
# Warp R hemi template brainmask to subject space
warp_R_hemi = pe.Node(interface=reg.NwarpApplyPriors(),
name='warp_R_hemi')
warp_R_hemi.inputs.in_file = R_hemi_template_file
warp_R_hemi.inputs.out_file = R_hemi_template_file
warp_R_hemi.inputs.interp = "NN"
warp_R_hemi.inputs.args = "-overwrite"
split_hemi_pipe.connect(inputnode, 'aff_file', warp_R_hemi, 'master')
split_hemi_pipe.connect(inputnode, 'warpinv_file', warp_R_hemi, "warp")
# Align R hemi template
align_R_hemi = pe.Node(interface=afni.Allineate(), name='align_R_hemi')
align_R_hemi.inputs.final_interpolation = "nearestneighbour"
align_R_hemi.inputs.overwrite = True
align_R_hemi.inputs.outputtype = "NIFTI_GZ"
split_hemi_pipe.connect(warp_R_hemi, 'out_file', align_R_hemi,
"in_file") # -source
split_hemi_pipe.connect(inputnode, 't1_ref_file', align_R_hemi,
"reference") # -base
split_hemi_pipe.connect(inputnode, 'inv_transfo_file', align_R_hemi,
"in_matrix") # -1Dmatrix_apply
elif "LR_hemi_template" in params_template.keys():
LR_hemi_template_file = params_template["LR_hemi_template"]
# Warp LR hemi template brainmask to subject space
warp_LR_hemi = pe.Node(interface=reg.NwarpApplyPriors(),
name='warp_LR_hemi')
warp_LR_hemi.inputs.in_file = LR_hemi_template_file
warp_LR_hemi.inputs.out_file = LR_hemi_template_file
warp_LR_hemi.inputs.interp = "NN"
warp_LR_hemi.inputs.args = "-overwrite"
split_hemi_pipe.connect(inputnode, 'aff_file', warp_LR_hemi, 'master')
split_hemi_pipe.connect(inputnode, 'warpinv_file', warp_LR_hemi,
"warp")
# Align LR hemi template
align_LR_hemi = pe.Node(interface=afni.Allineate(),
name='align_LR_hemi')
align_LR_hemi.inputs.final_interpolation = "nearestneighbour"
align_LR_hemi.inputs.overwrite = True
align_LR_hemi.inputs.outputtype = "NIFTI_GZ"
split_hemi_pipe.connect(warp_LR_hemi, 'out_file', align_LR_hemi,
"in_file") # -source
split_hemi_pipe.connect(inputnode, 't1_ref_file', align_LR_hemi,
"reference") # -base
split_hemi_pipe.connect(inputnode, 'inv_transfo_file', align_LR_hemi,
"in_matrix") # -1Dmatrix_apply
split_LR = pe.Node(interface=niu.Function(
input_names=["LR_mask_file"],
output_names=["L_mask_file", "R_mask_file"],
function=split_LR_mask),
name="split_LR")
split_hemi_pipe.connect(align_LR_hemi, "out_file", split_LR,
'LR_mask_file')
else:
print("Error, could not find LR_hemi_template or L_hemi_template and \
R_hemi_template, skipping")
print(params_template.keys())
exit()
# Using LH and RH masks to obtain hemisphere segmentation masks
calc_L_hemi = pe.Node(interface=afni.Calc(), name='calc_L_hemi')
calc_L_hemi.inputs.expr = 'a*b/b'
calc_L_hemi.inputs.outputtype = 'NIFTI_GZ'
split_hemi_pipe.connect(inputnode, 'segmented_file', calc_L_hemi,
"in_file_a")
if "LR_hemi_template" in params_template.keys():
split_hemi_pipe.connect(split_LR, 'L_mask_file', calc_L_hemi,
"in_file_b")
else:
split_hemi_pipe.connect(align_L_hemi, 'out_file', calc_L_hemi,
"in_file_b")
# R_hemi
calc_R_hemi = pe.Node(interface=afni.Calc(), name='calc_R_hemi')
calc_R_hemi.inputs.expr = 'a*b/b'
calc_R_hemi.inputs.outputtype = 'NIFTI_GZ'
split_hemi_pipe.connect(inputnode, 'segmented_file', calc_R_hemi,
"in_file_a")
if "LR_hemi_template" in params_template.keys():
split_hemi_pipe.connect(split_LR, 'R_mask_file', calc_R_hemi,
"in_file_b")
else:
split_hemi_pipe.connect(align_R_hemi, 'out_file', calc_R_hemi,
"in_file_b")
# remove cerebellum from left and right brain segmentations
calc_nocb_L_hemi = pe.Node(interface=afni.Calc(), name='calc_nocb_L_hemi')
calc_nocb_L_hemi.inputs.expr = '(a*(not (b)))'
calc_nocb_L_hemi.inputs.outputtype = 'NIFTI_GZ'
split_hemi_pipe.connect(calc_L_hemi, 'out_file', calc_nocb_L_hemi,
"in_file_a")
split_hemi_pipe.connect(align_cereb, 'out_file', calc_nocb_L_hemi,
"in_file_b")
calc_nocb_R_hemi = pe.Node(interface=afni.Calc(), name='calc_nocb_R_hemi')
calc_nocb_R_hemi.inputs.expr = '(a*(not (b)))'
calc_nocb_R_hemi.inputs.outputtype = 'NIFTI_GZ'
split_hemi_pipe.connect(calc_R_hemi, 'out_file', calc_nocb_R_hemi,
"in_file_a")
split_hemi_pipe.connect(align_cereb, 'out_file', calc_nocb_R_hemi,
"in_file_b")
# create L/R GM and WM no-cerebellum masks from subject brain segmentation
calc_GM_nocb_L_hemi = pe.Node(interface=afni.Calc(),
name='calc_GM_nocb_L_hemi')
calc_GM_nocb_L_hemi.inputs.expr = 'iszero(a-2)'
calc_GM_nocb_L_hemi.inputs.outputtype = 'NIFTI_GZ'
split_hemi_pipe.connect(calc_nocb_L_hemi, 'out_file', calc_GM_nocb_L_hemi,
"in_file_a")
calc_WM_nocb_L_hemi = pe.Node(interface=afni.Calc(),
name='calc_WM_nocb_L_hemi')
calc_WM_nocb_L_hemi.inputs.expr = 'iszero(a-3)'
calc_WM_nocb_L_hemi.inputs.outputtype = 'NIFTI_GZ'
split_hemi_pipe.connect(calc_nocb_L_hemi, 'out_file', calc_WM_nocb_L_hemi,
"in_file_a")
calc_GM_nocb_R_hemi = pe.Node(interface=afni.Calc(),
name='calc_GM_nocb_R_hemi')
calc_GM_nocb_R_hemi.inputs.expr = 'iszero(a-2)'
calc_GM_nocb_R_hemi.inputs.outputtype = 'NIFTI_GZ'
split_hemi_pipe.connect(calc_nocb_R_hemi, 'out_file', calc_GM_nocb_R_hemi,
"in_file_a")
calc_WM_nocb_R_hemi = pe.Node(interface=afni.Calc(),
name='calc_WM_nocb_R_hemi')
calc_WM_nocb_R_hemi.inputs.expr = 'iszero(a-3)'
calc_WM_nocb_R_hemi.inputs.outputtype = 'NIFTI_GZ'
split_hemi_pipe.connect(calc_nocb_R_hemi, 'out_file', calc_WM_nocb_R_hemi,
"in_file_a")
# Extract Cerebellum using template mask transformed to subject space
extract_cereb = pe.Node(interface=afni.Calc(), name='extract_cereb')
extract_cereb.inputs.expr = 'a*b/b'
extract_cereb.inputs.outputtype = 'NIFTI_GZ'
split_hemi_pipe.connect(inputnode, 't1_ref_file', extract_cereb,
"in_file_a")
split_hemi_pipe.connect(align_cereb, 'out_file', extract_cereb,
"in_file_b")
# Extract L.GM using template mask transformed to subject space
extract_L_GM = pe.Node(interface=afni.Calc(), name='extract_L_GM')
extract_L_GM.inputs.expr = 'a*b/b'
extract_L_GM.inputs.outputtype = 'NIFTI_GZ'
split_hemi_pipe.connect(inputnode, 't1_ref_file', extract_L_GM,
"in_file_a")
split_hemi_pipe.connect(calc_GM_nocb_L_hemi, 'out_file', extract_L_GM,
"in_file_b")
# Extract L.WM using template mask transformed to subject space
extract_L_WM = pe.Node(interface=afni.Calc(), name='extract_L_WM')
extract_L_WM.inputs.expr = 'a*b/b'
extract_L_WM.inputs.outputtype = 'NIFTI_GZ'
split_hemi_pipe.connect(inputnode, 't1_ref_file', extract_L_WM,
"in_file_a")
split_hemi_pipe.connect(calc_WM_nocb_L_hemi, 'out_file', extract_L_WM,
"in_file_b")
# Extract L.GM using template mask transformed to subject space
extract_R_GM = pe.Node(interface=afni.Calc(), name='extract_R_GM')
extract_R_GM.inputs.expr = 'a*b/b'
extract_R_GM.inputs.outputtype = 'NIFTI_GZ'
split_hemi_pipe.connect(inputnode, 't1_ref_file', extract_R_GM,
"in_file_a")
split_hemi_pipe.connect(calc_GM_nocb_R_hemi, 'out_file', extract_R_GM,
"in_file_b")
# Extract L.WM using template mask transformed to subject space
extract_R_WM = pe.Node(interface=afni.Calc(), name='extract_R_WM')
extract_R_WM.inputs.expr = 'a*b/b'
extract_R_WM.inputs.outputtype = 'NIFTI_GZ'
split_hemi_pipe.connect(inputnode, 't1_ref_file', extract_R_WM,
"in_file_a")
split_hemi_pipe.connect(calc_WM_nocb_R_hemi, 'out_file', extract_R_WM,
"in_file_b")
return split_hemi_pipe
palm_corr = Node(name='palm_corr',
interface=Function(
input_names=['in_file', 'mask', 'design', 'contrast'],
output_names=['tstat1', 'tstat2', 'P_value1', 'P_value2'],
function=palm_corr))
palm_corr.iterables = [("design", designs), ("contrast", contrasts)]
palm_corr.synchronize = True # synchronize here serves to make sure design and contrast are used in pairs
# Not using all the possible permuatations
#-----------------------------------------------------------------------------------------------------
# use the tstat maps to calculate r-pearson correlation coeeficient
# >>> fslmaths tstat.nii.gz -sqr tstat2.nii.gz
# >>> fslmaths tstat.nii.gz -abs -div tstat.nii.gz sign.nii.gz
# >>> fslmaths tstat2.nii.gz -add DF denominator.nii.gz
# >>> fslmaths tstat2.nii.gz -div denominator.nii.gz -sqrt -mul sign.nii.gz correlation.nii.gz
square1 = Node(fsl.UnaryMaths(), name='square1')
square1.inputs.operation = 'sqr'
square1.inputs.out_file = 'tstat1_squared.nii.gz'
sign_t1 = Node(fsl.ImageMaths(), name='sign_t1')
sign_t1.inputs.op_string = '-abs -div'
sign_t1.inputs.out_file = 'sign_tstat1.nii.gz'
add_df1 = Node(fsl.BinaryMaths(), name='add_df1')
add_df1.inputs.operation = 'add'
add_df1.inputs.operand_value = 27 #29 animals-2contrast = 27 dof
add_df1.inputs.out_file = 'denominator_tstat1.nii.gz'
div_by_denom1 = Node(fsl.BinaryMaths(), name='div_by_denom1')
div_by_denom1.inputs.operation = 'div'
div_by_denom1.inputs.out_file = 'divided_by_denominator.nii.gz'
def create_nii_to_mesh_fs_pipe(params, name="nii_to_mesh_fs_pipe"):
"""
Description: surface generation using freesurfer tools
Params:
- fill_wm (see `MRIFill <https://nipype.readthedocs.io/en/0.12.1/\
interfaces/generated/nipype.interfaces.freesurfer.utils.html#mrifill>`_) \
- also available as :ref:`indiv_params <indiv_params>`
Inputs:
inputnode:
wm_mask_file:
segmented white matter mask (binary) in template space
reg_brain_file:
preprocessd T1, registered to template
indiv_params (opt):
dict with individuals parameters for some nodes
arguments:
params:
dictionary of node sub-parameters (from a json file)
name:
pipeline name (default = "nii_to_mesh_fs_pipe")
Outputs:
"""
# creating pipeline
nii_to_mesh_fs_pipe = pe.Workflow(name=name)
# creating inputnode
inputnode = pe.Node(niu.IdentityInterface(
fields=['wm_mask_file', 'reg_brain_file', 'indiv_params']),
name='inputnode')
# bin_wm
bin_wm = pe.Node(interface=fsl.UnaryMaths(), name="bin_wm")
bin_wm.inputs.operation = "fillh"
nii_to_mesh_fs_pipe.connect(inputnode, 'wm_mask_file', bin_wm, 'in_file')
# resample everything
refit_wm = pe.Node(interface=afni.Refit(), name="refit_wm")
refit_wm.inputs.args = "-xdel 1.0 -ydel 1.0 -zdel 1.0 -keepcen"
nii_to_mesh_fs_pipe.connect(bin_wm, 'out_file', refit_wm, 'in_file')
# resample everything
refit_reg = pe.Node(interface=afni.Refit(), name="refit_reg")
refit_reg.inputs.args = "-xdel 1.0 -ydel 1.0 -zdel 1.0 -keepcen"
nii_to_mesh_fs_pipe.connect(inputnode, 'reg_brain_file', refit_reg,
'in_file')
# mri_convert wm to freesurfer mgz
convert_wm = pe.Node(interface=fs.MRIConvert(), name="convert_wm")
convert_wm.inputs.out_type = "mgz"
convert_wm.inputs.conform = True
nii_to_mesh_fs_pipe.connect(refit_wm, 'out_file', convert_wm, 'in_file')
# mri_convert reg to freesurfer mgz
convert_reg = pe.Node(interface=fs.MRIConvert(), name="convert_reg")
convert_reg.inputs.out_type = "mgz"
convert_reg.inputs.conform = True
nii_to_mesh_fs_pipe.connect(refit_reg, 'out_file', convert_reg, 'in_file')
# mri_fill
fill_wm = NodeParams(interface=fs.MRIFill(),
params=parse_key(params, "fill_wm"),
name="fill_wm")
fill_wm.inputs.out_file = "filled.mgz"
nii_to_mesh_fs_pipe.connect(convert_wm, 'out_file', fill_wm, 'in_file')
nii_to_mesh_fs_pipe.connect(inputnode,
("indiv_params", parse_key, "fill_wm"),
fill_wm, 'indiv_params')
# pretesselate wm
pretess_wm = pe.Node(interface=fs.MRIPretess(), name="pretess_wm")
pretess_wm.inputs.label = 255
nii_to_mesh_fs_pipe.connect(fill_wm, 'out_file', pretess_wm, 'in_filled')
nii_to_mesh_fs_pipe.connect(convert_reg, 'out_file', pretess_wm, 'in_norm')
# tesselate wm lh
tess_wm_lh = pe.Node(interface=fs.MRITessellate(), name="tess_wm_lh")
tess_wm_lh.inputs.label_value = 255
tess_wm_lh.inputs.out_file = "lh_tess"
nii_to_mesh_fs_pipe.connect(pretess_wm, 'out_file', tess_wm_lh, 'in_file')
# tesselate wm rh
tess_wm_rh = pe.Node(interface=fs.MRITessellate(), name="tess_wm_rh")
tess_wm_rh.inputs.label_value = 127
tess_wm_rh.inputs.out_file = "rh_tess"
nii_to_mesh_fs_pipe.connect(pretess_wm, 'out_file', tess_wm_rh, 'in_file')
# ExtractMainComponent lh
extract_mc_lh = pe.Node(interface=fs.ExtractMainComponent(),
name="extract_mc_lh")
nii_to_mesh_fs_pipe.connect(tess_wm_lh, 'surface', extract_mc_lh,
'in_file')
extract_mc_lh.inputs.out_file = "lh.lh_tess.maincmp"
# ExtractMainComponent rh
extract_mc_rh = pe.Node(interface=fs.ExtractMainComponent(),
name="extract_mc_rh")
nii_to_mesh_fs_pipe.connect(tess_wm_rh, 'surface', extract_mc_rh,
'in_file')
extract_mc_rh.inputs.out_file = "rh.rh_tess.maincmp"
# SmoothTessellation lh
smooth_tess_lh = pe.Node(interface=fs.SmoothTessellation(),
name="smooth_tess_lh")
smooth_tess_lh.inputs.disable_estimates = True
nii_to_mesh_fs_pipe.connect(extract_mc_lh, 'out_file', smooth_tess_lh,
'in_file')
# SmoothTessellation rh
smooth_tess_rh = pe.Node(interface=fs.SmoothTessellation(),
name="smooth_tess_rh")
smooth_tess_rh.inputs.disable_estimates = True
nii_to_mesh_fs_pipe.connect(extract_mc_rh, 'out_file', smooth_tess_rh,
'in_file')
return nii_to_mesh_fs_pipe
motionMask = pe.Node(fsl.maths.ApplyMask(output_type='NIFTI_GZ'),
name='motionMask')
# Pick the middle volume from the given run
def middleVol(func):
from nibabel import load
funcfile = func
_, _, _, timepoints = load(funcfile).shape
return int((timepoints / 2) - 1)
extractEPIref = pe.Node(fsl.ExtractROI(t_size=1, output_type='NIFTI_GZ'),
name='extractEPIref')
maskEPIref = pe.Node(fsl.UnaryMaths(operation='bin', output_type='NIFTI_GZ'),
name='maskEPIref')
# Despike raw data
despike = pe.Node(afni.preprocess.Despike(outputtype='NIFTI_GZ'),
name="despike")
# Remove negative values (from despike procedure)
posLimit = pe.Node(fsl.maths.Threshold(nan2zeros=True,
thresh=0,
output_type='NIFTI_GZ'),
name='posLimit')
## Fieldmap workflow
# Select magnitude image
get_mag = pe.Node(fsl.ExtractROI(t_min=1, t_size=1, output_type='NIFTI_GZ'),
def ct_brain_extraction(data,
working_directory=None,
fractional_intensity_threshold=0.01,
save_output=False,
fsl_path=None):
"""
Automatic brain extraction of non contrast head CT images using bet2 by fsl.
Ref.: Muschelli J, Ullman NL, Mould WA, Vespa P, Hanley DF, Crainiceanu CM. Validated automatic brain extraction of head CT images. NeuroImage. 2015 Jul 1;114:379–85.
:param data: [str; np.ndarray] path to input data or input data in form of np.ndarray (x, y, z)
:param working_directory: [str] path to directory to use to save temporary files and final output files
:param fractional_intensity_threshold: fractional intensity threshold (0->1); default=0.01; smaller values give larger brain outline estimates
:param save_output: [boolean] save or discard output
:param fsl_path: [str], Optional path to fsl executable to help nipype find it
:return: brain_mask, masked_image: np.ndarray
"""
if fsl_path is not None:
os.environ["PATH"] += os.pathsep + fsl_path
os.environ["FSLOUTPUTTYPE"] = 'NIFTI'
temp_files = []
if working_directory is None:
working_directory = tempfile.mkdtemp()
if isinstance(data, np.ndarray):
data_path = os.path.join(working_directory, 'temp_bet_input.nii')
data_img = nib.Nifti1Image(data.astype('float64'), affine=None)
nib.save(data_img, data_path)
temp_files.append(data_path)
elif os.path.exists(data):
data_path = data
else:
raise NotImplementedError('Data has to be a path or an np.ndarray')
output_file = os.path.join(working_directory, 'bet_output.nii')
output_mask_file = os.path.join(working_directory, 'bet_output_mask.nii')
if not save_output:
temp_files.append(output_file)
temp_files.append(output_mask_file)
temp_intermediate_file = os.path.join(working_directory,
'temp_intermediate_file.nii')
temp_files.append(temp_intermediate_file)
# Thresholding Image to 0-100
# cli: fslmaths "${img}" -thr 0.000000 -uthr 100.000000 "${outfile}"
thresholder1 = fsl.Threshold()
thresholder1.inputs.in_file = data_path
thresholder1.inputs.out_file = output_file
thresholder1.inputs.thresh = 0
thresholder1.inputs.direction = 'below'
thresholder1.inputs.output_type = 'NIFTI'
thresholder1.run()
thresholder2 = fsl.Threshold()
thresholder2.inputs.in_file = output_file
thresholder2.inputs.out_file = output_file
thresholder2.inputs.thresh = 100
thresholder2.inputs.direction = 'above'
thresholder2.inputs.output_type = 'NIFTI'
thresholder2.run()
# Creating 0 - 100 mask to remask after filling
# cli: fslmaths "${outfile}" -bin "${tmpfile}";
# cli: fslmaths "${tmpfile}.nii.gz" -bin -fillh "${tmpfile}"
binarizer1 = fsl.UnaryMaths()
binarizer1.inputs.in_file = output_file
binarizer1.inputs.out_file = temp_intermediate_file
binarizer1.inputs.operation = 'bin'
binarizer1.inputs.output_type = 'NIFTI'
binarizer1.run()
binarizer2 = fsl.UnaryMaths()
binarizer2.inputs.in_file = temp_intermediate_file
binarizer2.inputs.out_file = temp_intermediate_file
binarizer2.inputs.operation = 'bin'
binarizer2.inputs.output_type = 'NIFTI'
binarizer2.run()
fill_holes1 = fsl.UnaryMaths()
fill_holes1.inputs.in_file = temp_intermediate_file
fill_holes1.inputs.out_file = temp_intermediate_file
fill_holes1.inputs.operation = 'fillh'
fill_holes1.inputs.output_type = 'NIFTI'
fill_holes1.run()
# Presmoothing image
# cli: fslmaths "${outfile}" - s 1 "${outfile}"
smoothing = fsl.IsotropicSmooth()
smoothing.inputs.in_file = output_file
smoothing.inputs.out_file = output_file
smoothing.inputs.sigma = 1
smoothing.inputs.output_type = 'NIFTI'
smoothing.run()
# Remasking Smoothed Image
# cli: fslmaths "${outfile}" - mas "${tmpfile}" "${outfile}"
masking1 = fsl.ApplyMask()
masking1.inputs.in_file = output_file
masking1.inputs.out_file = output_file
masking1.inputs.mask_file = temp_intermediate_file
masking1.inputs.output_type = 'NIFTI'
masking1.run()
# Running bet2
# cli: bet2 "${outfile}" "${outfile}" - f ${intensity} - v
try:
btr = fsl.BET()
btr.inputs.in_file = output_file
btr.inputs.out_file = output_file
btr.inputs.frac = fractional_intensity_threshold
btr.inputs.output_type = 'NIFTI'
btr.run()
except Exception as e: # sometimes nipype fails to find bet
if fsl_path is not None:
bet_path = os.path.join(fsl_path, 'bet2')
else:
bet_path = 'bet2'
subprocess.run([
bet_path, output_file, output_file, '-f',
str(fractional_intensity_threshold)
])
# Using fslfill to fill in any holes in mask
# cli: fslmaths "${outfile}" - bin - fillh "${outfile}_Mask"
binarizer3 = fsl.UnaryMaths()
binarizer3.inputs.in_file = output_file
binarizer3.inputs.out_file = output_mask_file
binarizer3.inputs.operation = 'bin'
binarizer3.inputs.output_type = 'NIFTI'
binarizer3.run()
fill_holes2 = fsl.UnaryMaths()
fill_holes2.inputs.in_file = output_mask_file
fill_holes2.inputs.out_file = output_mask_file
fill_holes2.inputs.operation = 'fillh'
fill_holes2.inputs.output_type = 'NIFTI'
fill_holes2.run()
# Using the filled mask to mask original image
# cli: fslmaths "${img}" -mas "${outfile}_Mask" "${outfile}"
masking2 = fsl.ApplyMask()
masking2.inputs.in_file = data_path
masking2.inputs.out_file = output_file
masking2.inputs.mask_file = output_mask_file
masking2.inputs.output_type = 'NIFTI'
masking2.run()
brain_mask = nib.load(output_mask_file).get_fdata()
masked_image = nib.load(output_file).get_fdata()
# delete temporary files
for file in temp_files:
os.remove(file)
if not save_output:
shutil.rmtree(working_directory)
return brain_mask, masked_image
nilearn_smoothing = Node(name='nilearn_smoothing',
interface=Function(input_names=['image'],
output_names=['smoothed_output'],
function=nilearn_smoothing))
#-----------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------
#mask only FA values > 0.2 to gurantee it is WM
thresh_FA = Node(fsl.Threshold(), name='thresh_FA')
thresh_FA.inputs.thresh = 0.2
#-----------------------------------------------------------------------------------------------------
#binarize this mask
binarize_FA = Node(fsl.UnaryMaths(), name='binarize_FA')
binarize_FA.inputs.operation = 'bin'
binarize_FA.inputs.output_datatype = 'char'
#-----------------------------------------------------------------------------------------------------
#randomise on the smoothed all images
randomise_VBA = Node(fsl.Randomise(), name='randomise_vba')
randomise_VBA.inputs.design_mat = design
randomise_VBA.inputs.tcon = contrast
randomise_VBA.inputs.num_perm = 10000
randomise_VBA.inputs.tfce = True
randomise_VBA.inputs.vox_p_values = True
randomise_VBA.inputs.base_name = 'VBA_'
#-----------------------------------------------------------------------------------------------------
DTI_TBSS_Wax.connect([
# wraps command fslmaths with -mul flag #
node_fslmath_multiply = pe.Node(interface=fsl.BinaryMaths(),
name='node_fslmath_multiply')
# uncomment for individual node testing #
#node_fslmath_multiply.inputs.in_file = root_dir + despot1_nuc
#node_fslmath_multiply.inputs.in_file = node_N4BiasFieldCorrection.inputs.output_image
node_fslmath_multiply.inputs.operand_file = root_dir + despot1_mask
node_fslmath_multiply.inputs.operation = "mul"
node_fslmath_multiply.inputs.out_file = root_dir + despot1_nuc_masked
#print node_fslmath_multiply.interface.cmdline
# wraps command fslmaths with -bin flag #
node_fslmath_binarize = pe.Node(interface=fsl.UnaryMaths(),
name='node_fslmath_binarize')
node_fslmath_binarize.inputs.operation = 'bin'
node_fslmath_binarize.inputs.in_file = root_dir + highres_bet
node_fslmath_binarize.inputs.out_file = root_dir + highres_mask
#print node_fslmath_binarize.interface.cmdline
# wraps command mri_convert #
# mRs = mri_Resample
#highres
node_mRs = pe.Node(interface=fsurfer.Resample(), name='node_mRs')
node_mRs.inputs.voxel_size = resampleVS
node_mRs.inputs.in_file = root_dir + highres
node_mRs.inputs.resampled_file = root_dir + resampled_highres
#print node_mRs.interface.cmdline
def create_extract_pipe(params_template, params={}, name="extract_pipe"):
"""
Description: Extract T1 brain using AtlasBrex
Inputs:
inputnode:
restore_T1: preprocessed (debiased/denoised) T1 file name
restore_T1: preprocessed (debiased/denoised)T2 file name
arguments:
params_template: dictionary of info about template
params: dictionary of node sub-parameters (from a json file)
name: pipeline name (default = "extract_pipe")
Outputs:
smooth_mask.out_file:
Computed mask (after some smoothing)
"""
# creating pipeline
extract_pipe = pe.Workflow(name=name)
# creating inputnode
inputnode = pe.Node(
niu.IdentityInterface(fields=['restore_T1', 'restore_T2',
"indiv_params"]),
name='inputnode')
# atlas_brex
atlas_brex = NodeParams(AtlasBREX(),
params=parse_key(params, "atlas_brex"),
name='atlas_brex')
extract_pipe.connect(inputnode, "restore_T1",
atlas_brex, 't1_restored_file')
atlas_brex.inputs.NMT_file = params_template["template_head"]
atlas_brex.inputs.NMT_SS_file = params_template["template_brain"]
extract_pipe.connect(
inputnode, ("indiv_params", parse_key, "atlas_brex"),
atlas_brex, 'indiv_params')
# mask_brex
mask_brex = pe.Node(fsl.UnaryMaths(), name='mask_brex')
mask_brex.inputs.operation = 'bin'
extract_pipe.connect(atlas_brex, 'brain_file', mask_brex, 'in_file')
# smooth_mask
smooth_mask = pe.Node(fsl.UnaryMaths(), name='smooth_mask')
smooth_mask.inputs.operation = "bin"
smooth_mask.inputs.args = "-s 1 -thr 0.5 -bin"
extract_pipe.connect(mask_brex, 'out_file', smooth_mask, 'in_file')
# mult_T1
mult_T1 = pe.Node(afni.Calc(), name='mult_T1')
mult_T1.inputs.expr = "a*b"
mult_T1.inputs.outputtype = 'NIFTI_GZ'
extract_pipe.connect(inputnode, "restore_T1", mult_T1, 'in_file_a')
extract_pipe.connect(smooth_mask, 'out_file', mult_T1, 'in_file_b')
# mult_T2
mult_T2 = pe.Node(afni.Calc(), name='mult_T2')
mult_T2.inputs.expr = "a*b"
mult_T2.inputs.outputtype = 'NIFTI_GZ'
extract_pipe.connect(inputnode, 'restore_T2', mult_T2, 'in_file_a')
extract_pipe.connect(smooth_mask, 'out_file', mult_T2, 'in_file_b')
return extract_pipe
def create_segment_atropos_pipe(params={},
name="segment_atropos_pipe",
space="native"):
"""
Description: Segmentation with ANTS atropos script
Params:
- Atropos (see :class:`AtroposN4 <macapype.nodes.segment.AtroposN4>`)
- threshold_gm, threshold_wm, threshold_csf (see `Threshold \
<https://nipype.readthedocs.io/en/0.12.1/interfaces/generated/nipype.\
interfaces.fsl.maths.html#threshold>`_ for arguments)
Inputs:
inputnode:
brain_file: T1 image, after extraction and norm bin_norm_intensity
gm_prior_file: grey matter tissue intensity file
wm_prior_file: white matter tissue intensity file
csf_prior_file: csf tissue intensity file
arguments:
params: dictionary of node sub-parameters (from a json file)
name: pipeline name (default = "segment_atropos_pipe")
Outputs:
threshold_csf.out_file:
csf tissue intensity mask in subject space
threshold_gm.out_file:
grey matter tissue intensity mask in subject space
threshold_wm.out_file:
white matter tissue intensity mask in subject space
"""
# creating pipeline
segment_pipe = pe.Workflow(name=name)
# creating inputnode
inputnode = pe.Node(niu.IdentityInterface(fields=[
"brain_file", "gm_prior_file", "wm_prior_file", "csf_prior_file"
]),
name='inputnode')
# bin_norm_intensity (a cheat from Kepkee if I understood well!)
bin_norm_intensity = pe.Node(fsl.UnaryMaths(), name="bin_norm_intensity")
bin_norm_intensity.inputs.operation = "bin"
segment_pipe.connect(inputnode, "brain_file", bin_norm_intensity,
"in_file")
if "use_priors" in params.keys():
# copying header from img to csf_prior_file
copy_header_to_csf = pe.Node(niu.Function(
input_names=['ref_img', 'img_to_modify'],
output_names=['modified_img'],
function=copy_header),
name='copy_header_to_csf')
segment_pipe.connect(inputnode, "brain_file", copy_header_to_csf,
"ref_img")
segment_pipe.connect(inputnode, 'csf_prior_file', copy_header_to_csf,
"img_to_modify")
# copying header from img to gm_prior_file
copy_header_to_gm = pe.Node(niu.Function(
input_names=['ref_img', 'img_to_modify'],
output_names=['modified_img'],
function=copy_header),
name='copy_header_to_gm')
segment_pipe.connect(inputnode, "brain_file", copy_header_to_gm,
"ref_img")
segment_pipe.connect(inputnode, 'gm_prior_file', copy_header_to_gm,
"img_to_modify")
# copying header from img to wm_prior_file
copy_header_to_wm = pe.Node(niu.Function(
input_names=['ref_img', 'img_to_modify'],
output_names=['modified_img'],
function=copy_header),
name='copy_header_to_wm')
segment_pipe.connect(inputnode, "brain_file", copy_header_to_wm,
"ref_img")
segment_pipe.connect(inputnode, 'wm_prior_file', copy_header_to_wm,
"img_to_modify")
# merging priors as a list
merge_3_elem = pe.Node(niu.Function(
input_names=['elem1', 'elem2', 'elem3'],
output_names=['merged_list'],
function=merge_3_elem_to_list),
name='merge_3_elem')
# was like this before (1 -> csf, 2 -> gm, 3 -> wm, to check)
segment_pipe.connect(copy_header_to_csf, 'modified_img', merge_3_elem,
"elem1")
segment_pipe.connect(copy_header_to_gm, 'modified_img', merge_3_elem,
"elem2")
segment_pipe.connect(copy_header_to_wm, 'modified_img', merge_3_elem,
"elem3")
# Atropos
seg_at = NodeParams(AtroposN4(),
params=parse_key(params, "Atropos"),
name='seg_at')
segment_pipe.connect(inputnode, "brain_file", seg_at, "brain_file")
segment_pipe.connect(bin_norm_intensity, 'out_file', seg_at,
"brainmask_file")
if "use_priors" in params.keys():
segment_pipe.connect(merge_3_elem, 'merged_list', seg_at, "priors")
seg_at.inputs.prior_weight = params["use_priors"]
# Threshold GM, WM and CSF
thd_nodes = {}
for i, tissue in enumerate(['csf', 'gm', 'wm']):
tmp_node = NodeParams(fsl.Threshold(),
params=parse_key(params, "threshold_" + tissue),
name="threshold_" + tissue)
segment_pipe.connect(seg_at, ('segmented_files', get_elem, i),
tmp_node, 'in_file')
thd_nodes[tissue] = tmp_node
outputnode = pe.Node(niu.IdentityInterface(fields=[
"segmented_file", "threshold_gm", "threshold_wm", "threshold_csf"
]),
name='outputnode')
segment_pipe.connect(seg_at, 'segmented_file', outputnode,
'segmented_file')
segment_pipe.connect(thd_nodes["gm"], 'out_file', outputnode,
'threshold_gm')
segment_pipe.connect(thd_nodes["wm"], 'out_file', outputnode,
'threshold_wm')
segment_pipe.connect(thd_nodes["csf"], 'out_file', outputnode,
'threshold_csf')
return segment_pipe
def create_EPI_DistCorr(use_BET, wf_name='epi_distcorr'):
"""
Fieldmap correction takes in an input magnitude image which is Skull Stripped (Tight).
The magnitude images are obtained from each echo series. It also requires a phase image
as an input, the phase image is a subtraction of the two phase images from each echo.
Created on Thu Nov 9 10:44:47 2017
@author: nrajamani
Order of commands and inputs:
-- SkullStrip: 3d-SkullStrip (or FSL-BET) is used to strip the non-brain (tissue) regions
from the fMRI
Parameters: -f, default: 0.5
in_file: fmap_mag
-- fslmath_mag: Magnitude image is eroded using the -ero option in fslmath, in order to remove
the non-zero voxels
Parameters: -ero
in_file:fmap_mag
-- bet_anat : Brain extraction of the anat file to provide as an input for the epi-registration interface
Parameters: -f, default: 0.5
in_file: anat_file
-- fast_anat : Fast segmentation to provide partial volume files of the anat file, which is further processed
to provide the white mater segmentation input for the epi-registration interface.
The most important output required from this is the second segment, (e.g.,'T1_brain_pve_2.nii.gz')
Parameters: -img_type = 1
-bias_iters = 10 (-I)
-bias_lowpass = 10 (-l)
in_file: brain_extracted anat_file
-- fslmath_anat: The output of the FAST interface is then analyzed to select all the voxels with more than 50%
partial volume into the binary mask
Parameters: -thr = 0.5
in_file : T1_brain_pve_2
-- fslmath_wmseg:The selected voxels are now used to create a binary mask which would can then be sent as the
white matter segmentation (wm_seg)
Parameters: -bin
in_file: T1_brain_pve_2
-- Prepare :Preparing the fieldmap.
Parameters: -deltaTE = default, 2.46 ms
-Scanner = SIEMENS
in_files: fmap_phase
fmap_magnitude
For more details, check:https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FUGUE/Guide
-- FUGUE :One of the steps in EPI-DistCorrection toolbox, it unwarps the fieldmaps
Parameters: dwell_to_asymm ratio = (0.77e-3 * 3)/(2.46e-3)
dwell time = 0.0005 ms
in_file = field map which is a 4D image (containing 2 unwarpped image)
"""
preproc = pe.Workflow(name=wf_name)
inputNode = pe.Node(
util.IdentityInterface(fields=['anat_file', 'fmap_pha', 'fmap_mag']),
name='inputspec')
inputNode_delTE = pe.Node(util.IdentityInterface(fields=['deltaTE']),
name='deltaTE_input')
inputNode_dwellT = pe.Node(util.IdentityInterface(fields=['dwellT']),
name='dwellT_input')
inputNode_dwell_asym_ratio = pe.Node(
util.IdentityInterface(fields=['dwell_asym_ratio']),
name='dwell_asym_ratio_input')
inputNode_bet_frac = pe.Node(util.IdentityInterface(fields=['bet_frac']),
name='bet_frac_input')
inputNode_afni_threshold = pe.Node(
util.IdentityInterface(fields=['afni_threshold']),
name='afni_threshold_input')
outputNode = pe.Node(util.IdentityInterface(
fields=['fieldmap', 'fmap_despiked', 'fmapmagbrain', 'fieldmapmask']),
name='outputspec')
# Skull-strip, outputs a masked image file
if use_BET == False:
skullstrip_args = pe.Node(util.Function(input_names=['shrink_fac'],
output_names=['expr'],
function=createAFNIiterable),
name='distcorr_skullstrip_arg')
preproc.connect(inputNode_afni_threshold, 'afni_threshold',
skullstrip_args, 'shrink_fac')
bet = pe.Node(interface=afni.SkullStrip(), name='bet')
bet.inputs.outputtype = 'NIFTI_GZ'
preproc.connect(skullstrip_args, 'expr', bet, 'args')
preproc.connect(inputNode, 'fmap_mag', bet, 'in_file')
preproc.connect(bet, 'out_file', outputNode, 'magnitude_image')
else:
bet = pe.Node(interface=fsl.BET(), name='bet')
bet.inputs.output_type = 'NIFTI_GZ'
preproc.connect(inputNode_bet_frac, 'bet_frac', bet, 'frac')
preproc.connect(inputNode, 'fmap_mag', bet, 'in_file')
preproc.connect(bet, 'out_file', outputNode, 'magnitude_image')
# Prepare Fieldmap
# prepare the field map
prepare = pe.Node(interface=fsl.epi.PrepareFieldmap(), name='prepare')
prepare.inputs.output_type = "NIFTI_GZ"
preproc.connect(inputNode_delTE, 'deltaTE', prepare, 'delta_TE')
preproc.connect(inputNode, 'fmap_pha', prepare, 'in_phase')
preproc.connect(bet, 'out_file', prepare, 'in_magnitude')
preproc.connect(prepare, 'out_fieldmap', outputNode, 'fieldmap')
# erode the masked magnitude image
fslmath_mag = pe.Node(interface=fsl.ErodeImage(), name='fslmath_mag')
preproc.connect(bet, 'out_file', fslmath_mag, 'in_file')
preproc.connect(fslmath_mag, 'out_file', outputNode, 'fmapmagbrain')
# calculate the absolute value of the eroded and masked magnitude
# image
fslmath_abs = pe.Node(interface=fsl.UnaryMaths(), name='fslmath_abs')
fslmath_abs.inputs.operation = 'abs'
preproc.connect(fslmath_mag, 'out_file', fslmath_abs, 'in_file')
preproc.connect(fslmath_abs, 'out_file', outputNode, 'fmapmag_abs')
# binarize the absolute value of the eroded and masked magnitude
# image
fslmath_bin = pe.Node(interface=fsl.UnaryMaths(), name='fslmath_bin')
fslmath_bin.inputs.operation = 'bin'
preproc.connect(fslmath_abs, 'out_file', fslmath_bin, 'in_file')
preproc.connect(fslmath_bin, 'out_file', outputNode, 'fmapmag_bin')
# take the absolute value of the fieldmap calculated in the prepare step
fslmath_mask_1 = pe.Node(interface=fsl.UnaryMaths(), name='fslmath_mask_1')
fslmath_mask_1.inputs.operation = 'abs'
preproc.connect(prepare, 'out_fieldmap', fslmath_mask_1, 'in_file')
preproc.connect(fslmath_mask_1, 'out_file', outputNode, 'fieldmapmask_abs')
# binarize the absolute value of the fieldmap calculated in the prepare step
fslmath_mask_2 = pe.Node(interface=fsl.UnaryMaths(), name='fslmath_mask_2')
fslmath_mask_2.inputs.operation = 'bin'
preproc.connect(fslmath_mask_1, 'out_file', fslmath_mask_2, 'in_file')
preproc.connect(fslmath_mask_2, 'out_file', outputNode, 'fieldmapmask_bin')
# multiply together the binarized magnitude and fieldmap images
fslmath_mask = pe.Node(interface=fsl.BinaryMaths(), name='fslmath_mask')
fslmath_mask.inputs.operation = 'mul'
preproc.connect(fslmath_mask_2, 'out_file', fslmath_mask, 'in_file')
preproc.connect(fslmath_bin, 'out_file', fslmath_mask, 'operand_file')
preproc.connect(fslmath_mask, 'out_file', outputNode, 'fieldmapmask')
# Note for the user. Ensure the phase image is within 0-4096 (upper
# threshold is 90% of 4096), fsl_prepare_fieldmap will only work in the
# case of the SIEMENS format. #Maybe we could use deltaTE also as an
# option in the GUI.
# fugue
fugue1 = pe.Node(interface=fsl.FUGUE(), name='fugue1')
fugue1.inputs.save_fmap = True
fugue1.outputs.fmap_out_file = 'fmap_rads'
preproc.connect(fslmath_mask, 'out_file', fugue1, 'mask_file')
preproc.connect(inputNode_dwellT, 'dwellT', fugue1, 'dwell_time')
preproc.connect(inputNode_dwell_asym_ratio, 'dwell_asym_ratio', fugue1,
'dwell_to_asym_ratio')
preproc.connect(prepare, 'out_fieldmap', fugue1, 'fmap_in_file')
preproc.connect(fugue1, 'fmap_out_file', outputNode, 'fmap_despiked')
return preproc
def create_restingstatefmri_preprocessing_pipeline(in_fmri,
in_t1,
in_segmentation,
in_parcellation,
output_dir,
in_mag=None,
in_phase=None,
in_susceptibility_parameters=None,
name='restingstatefmri'):
"""Perform pre-processing steps for the resting state fMRI using AFNI
Parameters
----------
::
name : name of workflow (default: restingstatefmri)
Inputs::
in_fmri : functional runs into a single 4D image in NIFTI format
in_t1 : The structural T1 image
in_segmentation : The segmentation image containing 6 volumes (background, CSF, GM, WM, deep GM, brainstem),
in the space of the fMRI image
in_parcellation : The parcellation image coming out of the GIF parcellation algorithm
output_dir : The output directory for the workflow
in_mag : *OPT*, magnitude image to use for susceptibility correction (default: None)
in_phase : *OPT*, phase image to use for susceptibility correction (default: None)
in_susceptibility_parameters : *OPT*, susceptibility parameters
(in a vector: read-out-time, echo time difference, phase encoding direction], default : None)
name : *OPT*, name of the workflow (default : restingstatefmri)
Outputs::
Example
-------
>>> preproc = create_restingstatefmri_preprocessing_pipeline(in_fmri, in_t1, in_segmentation, in_parcellation, output_dir) # doctest: +SKIP
>>> preproc.base_dir = '/tmp' # doctest: +SKIP
>>> preproc.run() # doctest: +SKIP
"""
workflow = pe.Workflow(name=name)
workflow.base_output_dir = name
# We need to create an input node for the workflow
input_node = pe.Node(
niu.IdentityInterface(
fields=['in_fmri',
'in_t1',
'in_segmentation',
'in_parcellation']),
name='input_node')
input_node.inputs.in_fmri = in_fmri
input_node.inputs.in_t1 = in_t1
input_node.inputs.in_segmentation = in_segmentation
input_node.inputs.in_parcellation = in_parcellation
resting_state_preproc = pe.Node(interface=RestingStatefMRIPreprocess(),
name='resting_state_preproc')
workflow.connect(input_node, 'in_fmri', resting_state_preproc, 'in_fmri')
workflow.connect(input_node, 'in_t1', resting_state_preproc, 'in_t1')
workflow.connect(input_node, 'in_segmentation', resting_state_preproc, 'in_tissue_segmentation')
workflow.connect(input_node, 'in_parcellation', resting_state_preproc, 'in_parcellation')
# fMRI QC plot
plotter = pe.Node(interface=FmriQcPlot(),
name='plotter')
workflow.connect(input_node, 'in_fmri', plotter, 'in_raw_fmri')
workflow.connect(resting_state_preproc, 'out_raw_fmri_gm', plotter, 'in_raw_fmri_gm')
workflow.connect(resting_state_preproc, 'out_raw_fmri_wm', plotter, 'in_raw_fmri_wm')
workflow.connect(resting_state_preproc, 'out_raw_fmri_csf', plotter, 'in_raw_fmri_csf')
workflow.connect(resting_state_preproc, 'out_mrp_file', plotter, 'in_mrp_file')
workflow.connect(resting_state_preproc, 'out_spike_file', plotter, 'in_spike_file')
workflow.connect(resting_state_preproc, 'out_rms_file', plotter, 'in_rms_file')
# Output node
output_node = pe.Node(interface=niu.IdentityInterface(fields=['out_corrected_fmri',
'out_atlas_fmri',
'out_fmri_to_t1_transformation',
'out_raw_fmri_gm',
'out_raw_fmri_wm',
'out_raw_fmri_csf',
'out_fmri_qc',
'out_motioncorrected_file']),
name="output_node")
workflow.connect(resting_state_preproc, 'out_corrected_fmri', output_node, 'out_corrected_fmri')
workflow.connect(resting_state_preproc, 'out_atlas_fmri', output_node, 'out_atlas_fmri')
workflow.connect(resting_state_preproc, 'out_fmri_to_t1_transformation', output_node,
'out_fmri_to_t1_transformation')
workflow.connect(resting_state_preproc, 'out_raw_fmri_gm', output_node, 'out_raw_fmri_gm')
workflow.connect(resting_state_preproc, 'out_raw_fmri_wm', output_node, 'out_raw_fmri_wm')
workflow.connect(resting_state_preproc, 'out_raw_fmri_csf', output_node, 'out_raw_fmri_csf')
workflow.connect(resting_state_preproc, 'out_motioncorrected_file', output_node, 'out_motioncorrected_file')
workflow.connect(plotter, 'out_file', output_node, 'out_fmri_qc')
ds = pe.Node(nio.DataSink(), name='ds')
ds.inputs.base_directory = os.path.abspath(output_dir)
ds.inputs.parameterization = False
workflow.connect(output_node, 'out_fmri_to_t1_transformation', ds, '@fmri_to_t1_transformation')
workflow.connect(output_node, 'out_atlas_fmri', ds, '@atlas_in_fmri')
workflow.connect(output_node, 'out_raw_fmri_gm', ds, '@gm_in_fmri')
workflow.connect(output_node, 'out_raw_fmri_wm', ds, '@wm_in_fmri')
workflow.connect(output_node, 'out_raw_fmri_csf', ds, '@csf_in_fmri')
workflow.connect(output_node, 'out_fmri_qc', ds, '@fmri_qc')
if in_mag is None or in_phase is None or in_susceptibility_parameters is None:
workflow.connect(output_node, 'out_motioncorrected_file', ds, '@motioncorrected_file')
workflow.connect(output_node, 'out_corrected_fmri', ds, '@corrected_fmri')
else:
split_fmri = pe.Node(interface=fsl.Split(dimension='t'),
name='split_fmri')
workflow.connect(output_node, 'out_motioncorrected_file', split_fmri, 'in_file')
select_1st_fmri = pe.Node(interface=niu.Select(index=0),
name='select_1st_fmri')
workflow.connect(split_fmri, 'out_files', select_1st_fmri, 'inlist')
binarise_parcellation = pe.Node(interface=fsl.UnaryMaths(operation='bin'),
name='binarise_parcellation')
workflow.connect(input_node, 'in_parcellation', binarise_parcellation, 'in_file')
# Perform susceptibility correction, where we already have a mask in the b0 space
susceptibility_correction = create_fieldmap_susceptibility_workflow('susceptibility_correction',
reg_to_t1=True)
susceptibility_correction.inputs.input_node.mag_image = os.path.abspath(in_mag)
susceptibility_correction.inputs.input_node.phase_image = os.path.abspath(in_phase)
susceptibility_correction.inputs.input_node.t1 = os.path.abspath(in_t1)
susceptibility_correction.inputs.input_node.rot = in_susceptibility_parameters[0]
susceptibility_correction.inputs.input_node.etd = in_susceptibility_parameters[1]
susceptibility_correction.inputs.input_node.ped = in_susceptibility_parameters[2]
workflow.connect(select_1st_fmri, 'out', susceptibility_correction, 'input_node.epi_image')
workflow.connect(binarise_parcellation, 'out_file', susceptibility_correction, 'input_node.t1_mask')
fmri_corrected_resample = pe.Node(interface=niftyreg.RegResample(inter_val='LIN'),
name='fmri_corrected_resample')
workflow.connect(output_node, 'out_corrected_fmri', fmri_corrected_resample, 'ref_file')
workflow.connect(output_node, 'out_corrected_fmri', fmri_corrected_resample, 'flo_file')
workflow.connect(susceptibility_correction.get_node('output_node'), 'out_field',
fmri_corrected_resample, 'trans_file')
workflow.connect(fmri_corrected_resample, 'out_file', ds, '@corrected_fmri')
fmri_motion_corrected_resample = pe.Node(interface=niftyreg.RegResample(inter_val='LIN'),
name='fmri_motion_corrected_resample')
workflow.connect(output_node, 'out_motioncorrected_file', fmri_motion_corrected_resample, 'ref_file')
workflow.connect(output_node, 'out_motioncorrected_file', fmri_motion_corrected_resample, 'flo_file')
workflow.connect(susceptibility_correction.get_node('output_node'), 'out_field',
fmri_motion_corrected_resample, 'trans_file')
workflow.connect(fmri_motion_corrected_resample, 'out_file', ds, '@motioncorrected_file')
return workflow
