def create_templates_2func_workflow(threshold=0.5,
name='templates_2func_workflow'):
templates_2func_workflow = Workflow(name=name)
# Input Node
inputspec = Node(utility.IdentityInterface(fields=[
'func_file',
'premat',
'warp',
'templates',
]),
name='inputspec')
# Get the overal EPI to MNI warp
func_2mni_warp = Node(fsl.ConvertWarp(), name='func_2mni_warp')
func_2mni_warp.inputs.reference = fsl.Info.standard_image(
'MNI152_T1_2mm.nii.gz')
# Calculate the inverse warp
mni_2func_warp = Node(fsl.InvWarp(), name='mni_2func_warp')
# Transform MNI templates to EPI space
templates_2func_apply = MapNode(fsl.ApplyWarp(),
iterfield=['in_file'],
name='templates_2func_apply')
# Threshold templates
templates_threshold = MapNode(
fsl.ImageMaths(op_string='-thr {0} -bin'.format(threshold)),
iterfield=['in_file'],
name='templates_threshold')
# Output Node
outputspec = Node(utility.IdentityInterface(
fields=['templates_2func_files', 'func_2mni_warp']),
name='outputspec')
# Connect the workflow nodes
templates_2func_workflow.connect(inputspec, 'premat', func_2mni_warp,
'premat')
templates_2func_workflow.connect(inputspec, 'warp', func_2mni_warp,
'warp1')
templates_2func_workflow.connect(inputspec, 'func_file', mni_2func_warp,
'reference')
templates_2func_workflow.connect(func_2mni_warp, 'out_file',
mni_2func_warp, 'warp')
templates_2func_workflow.connect(inputspec, 'templates',
templates_2func_apply, 'in_file')
templates_2func_workflow.connect(inputspec, 'func_file',
templates_2func_apply, 'ref_file')
templates_2func_workflow.connect(mni_2func_warp, 'inverse_warp',
templates_2func_apply, 'field_file')
templates_2func_workflow.connect(templates_2func_apply, 'out_file',
templates_threshold, 'in_file')
templates_2func_workflow.connect(func_2mni_warp, 'out_file', outputspec,
'func_2mni_warp')
templates_2func_workflow.connect(templates_threshold, 'out_file',
outputspec, 'templates_2func_files')
return templates_2func_workflow
def invert_warp(in_file: Path, ref: Path, out_warp: Path, in_warp: Path):
"""
Apply FSL's invwarp to invert a warp file
Arguments:
in_file {Path} -- [description]
out_file {Path} -- [description]
in_warp {Path} -- [description]
"""
invwarp = fsl.InvWarp()
invwarp.inputs.warp = in_warp
invwarp.inputs.reference = ref
invwarp.inputs.inverse_warp = out_warp
return invert_warp
def create_normalization_wf(transformations=["mni2func"]):
wf = pe.Workflow(name="normalization")
inputspec = pe.Node(util.IdentityInterface(fields=[
'T1', 'skullstripped_T1', 'preprocessed_epi', 'func2anat_transform'
]),
name="inputspec")
anat2mni = create_nonlinear_register("anat2mni")
linear_reg = anat2mni.get_node("linear_reg_0")
linear_reg.inputs.searchr_x = [-180, 180]
linear_reg.inputs.searchr_y = [-180, 180]
linear_reg.inputs.searchr_z = [-180, 180]
skull_mgz2nii = pe.Node(fs.MRIConvert(out_type="nii"),
name="skull_mgs2nii")
brain_mgz2nii = skull_mgz2nii.clone(name="brain_mgs2nii")
wf.connect(inputspec, "skullstripped_T1", brain_mgz2nii, "in_file")
wf.connect(inputspec, "T1", skull_mgz2nii, "in_file")
anat2mni.inputs.inputspec.reference_skull = fsl.Info.standard_image(
"MNI152_T1_2mm.nii.gz")
anat2mni.inputs.inputspec.reference_brain = fsl.Info.standard_image(
"MNI152_T1_2mm_brain.nii.gz")
anat2mni.inputs.inputspec.fnirt_config = "T1_2_MNI152_2mm"
wf.connect(skull_mgz2nii, "out_file", anat2mni, "inputspec.input_skull")
wf.connect(brain_mgz2nii, "out_file", anat2mni, "inputspec.input_brain")
if 'mni2func' in transformations:
invert_warp = pe.Node(fsl.InvWarp(), name="invert_warp")
wf.connect(anat2mni, "outputspec.nonlinear_xfm", invert_warp,
"warp_file")
wf.connect(skull_mgz2nii, "out_file", invert_warp, "ref_file")
if 'func2mni' in transformations:
mni_warp = pe.Node(interface=fsl.ApplyWarp(), name='mni_warp')
mni_warp.inputs.ref_file = fsl.Info.standard_image(
"MNI152_T1_2mm.nii.gz")
wf.connect(inputspec, 'preprocessed_epi', mni_warp, 'in_file')
wf.connect(anat2mni, 'outputspec.nonlinear_xfm', mni_warp,
'field_file')
wf.connect(inputspec, 'func2anat_transform', mni_warp, 'premat')
return wf
def process_vsm(name='VSM2DFM'):
"""
A workflow that computes the inverse and the determinant of jacobians
map
"""
inputnode = pe.Node(niu.IdentityInterface(
fields=['vsm', 'reference', 'scaling', 'enc_dir']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['dfm', 'inv_dfm', 'jacobian', 'inv_jacobian']),
name='outputnode')
vsm2dfm = nwu.vsm2warp()
invwarp = pe.Node(fsl.InvWarp(relative=True), name='InvWarp')
coeffs = pe.Node(fsl.WarpUtils(out_format='spline'), name='CoeffComp')
jacobian = pe.Node(fsl.WarpUtils(write_jacobian=True), name='JacobianComp')
invcoef = pe.Node(fsl.WarpUtils(out_format='spline'), name='InvCoeffComp')
invjac = pe.Node(fsl.WarpUtils(write_jacobian=True),
name='InvJacobianComp')
wf = pe.Workflow(name=name)
wf.connect([(inputnode, vsm2dfm, [('reference', 'inputnode.in_ref'),
('enc_dir', 'inputnode.enc_dir'),
('vsm', 'inputnode.in_vsm'),
('scaling', 'inputnode.scaling')]),
(vsm2dfm, invwarp, [('outputnode.out_warp', 'warp')]),
(inputnode, invwarp, [('reference', 'reference')]),
(vsm2dfm, coeffs, [('outputnode.out_warp', 'in_file')]),
(inputnode, coeffs, [('reference', 'reference')]),
(inputnode, jacobian, [('reference', 'reference')]),
(coeffs, jacobian, [('out_file', 'in_file')]),
(vsm2dfm, outputnode, [('outputnode.out_warp', 'dfm')]),
(invwarp, outputnode, [('inverse_warp', 'inv_dfm')]),
(jacobian, outputnode, [('out_jacobian', 'jacobian')]),
(invwarp, invcoef, [('inverse_warp', 'in_file')]),
(inputnode, invcoef, [('reference', 'reference')]),
(inputnode, invjac, [('reference', 'reference')]),
(invcoef, invjac, [('out_file', 'in_file')]),
(invjac, outputnode, [('out_jacobian', 'inv_jacobian')])])
return wf
def create_registration_pipeline(working_dir,
freesurfer_dir,
ds_dir,
name='registration'):
"""
find transformations between struct, funct, and MNI
"""
# initiate workflow
reg_wf = Workflow(name=name)
reg_wf.base_dir = os.path.join(working_dir, 'LeiCA_resting',
'rsfMRI_preprocessing')
# set fsl output
fsl.FSLCommand.set_default_output_type('NIFTI_GZ')
freesurfer.FSCommand.set_default_subjects_dir(freesurfer_dir)
# inputnode
inputnode = Node(util.IdentityInterface(fields=[
'initial_mean_epi_moco', 't1w', 't1w_brain', 'subject_id',
'wm_mask_4_bbr', 'struct_brain_mask'
]),
name='inputnode')
outputnode = Node(util.IdentityInterface(fields=[
'struct_2_MNI_warp', 'epi_2_struct_mat', 'struct_2_epi_mat',
'epi_2_MNI_warp', 'MNI_2_epi_warp', 'fs_2_struct_mat',
'mean_epi_structSpace', 'mean_epi_MNIspace', 'struct_MNIspace'
]),
name='outputnode')
ds = Node(nio.DataSink(base_directory=ds_dir), name='ds')
ds.inputs.substitutions = [('_TR_id_', 'TR_')]
##########################################
# TOC REGISTRATION MATS AND WARPS
##########################################
# I. STRUCT -> MNI
## 1. STRUCT -> MNI with FLIRT
## 2. CALC. WARP STRUCT -> MNI with FNIRT
# II.EPI -> STRUCT
## 3. calc EPI->STRUCT initial registration
## 4. run EPI->STRUCT via bbr
## 5. INVERT to get: STRUCT -> EPI
# III. COMBINE I. & II.: EPI -> MNI
## 6. COMBINE MATS: EPI -> MNI
## 7. MNI -> EPI
##########################################
# CREATE REGISTRATION MATS AND WARPS
##########################################
# I. STRUCT -> MNI
##########################################
# 1. REGISTER STRUCT -> MNI with FLIRT
struct_2_MNI_mat = Node(fsl.FLIRT(dof=12), name='struct_2_MNI_mat')
struct_2_MNI_mat.inputs.reference = fsl.Info.standard_image(
'MNI152_T1_2mm_brain.nii.gz')
reg_wf.connect(inputnode, 't1w_brain', struct_2_MNI_mat, 'in_file')
reg_wf.connect(struct_2_MNI_mat, 'out_matrix_file', outputnode,
'struct_2_MNI_mat_flirt')
# 2. CALC. WARP STRUCT -> MNI with FNIRT
# cf. wrt. 2mm
# https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=ind1311&L=FSL&P=R86108&1=FSL&9=A&J=on&d=No+Match%3BMatch%3BMatches&z=4
struct_2_MNI_warp = Node(fsl.FNIRT(), name='struct_2_MNI_warp')
struct_2_MNI_warp.inputs.config_file = 'T1_2_MNI152_2mm'
struct_2_MNI_warp.inputs.ref_file = fsl.Info.standard_image(
'MNI152_T1_2mm.nii.gz')
struct_2_MNI_warp.inputs.field_file = 'struct_2_MNI_warp.nii.gz'
struct_2_MNI_warp.plugin_args = {'submit_specs': 'request_memory = 4000'}
reg_wf.connect(inputnode, 't1w', struct_2_MNI_warp, 'in_file')
reg_wf.connect(struct_2_MNI_mat, 'out_matrix_file', struct_2_MNI_warp,
'affine_file')
reg_wf.connect(struct_2_MNI_warp, 'field_file', ds,
'registration.struct_2_MNI_warp')
reg_wf.connect(struct_2_MNI_warp, 'field_file', outputnode,
'struct_2_MNI_warp')
reg_wf.connect(struct_2_MNI_warp, 'warped_file', outputnode,
'struct_MNIspace')
reg_wf.connect(struct_2_MNI_warp, 'warped_file', ds,
'registration.struct_MNIspace')
# II.EPI -> STRUCT (via bbr)
##########################################
# 3. calc EPI->STRUCT initial registration with flirt dof=6 and corratio
epi_2_struct_flirt6_mat = Node(fsl.FLIRT(dof=6, cost='corratio'),
name='epi_2_struct_flirt6_mat')
epi_2_struct_flirt6_mat.inputs.out_file = 'epi_structSpace_flirt6.nii.gz'
reg_wf.connect(inputnode, 't1w_brain', epi_2_struct_flirt6_mat,
'reference')
reg_wf.connect(inputnode, 'initial_mean_epi_moco', epi_2_struct_flirt6_mat,
'in_file')
# 4. run EPI->STRUCT via bbr
bbr_shedule = os.path.join(os.getenv('FSLDIR'), 'etc/flirtsch/bbr.sch')
epi_2_struct_bbr_mat = Node(interface=fsl.FLIRT(dof=6, cost='bbr'),
name='epi_2_struct_bbr_mat')
epi_2_struct_bbr_mat.inputs.schedule = bbr_shedule
epi_2_struct_bbr_mat.inputs.out_file = 'epi_structSpace.nii.gz'
reg_wf.connect(inputnode, 'initial_mean_epi_moco', epi_2_struct_bbr_mat,
'in_file')
reg_wf.connect(inputnode, 't1w_brain', epi_2_struct_bbr_mat, 'reference')
reg_wf.connect(epi_2_struct_flirt6_mat, 'out_matrix_file',
epi_2_struct_bbr_mat, 'in_matrix_file')
reg_wf.connect(inputnode, 'wm_mask_4_bbr', epi_2_struct_bbr_mat, 'wm_seg')
reg_wf.connect(epi_2_struct_bbr_mat, 'out_matrix_file', ds,
'registration.epi_2_struct_mat')
reg_wf.connect(epi_2_struct_bbr_mat, 'out_file', outputnode,
'mean_epi_structSpace')
# 5. INVERT to get: STRUCT -> EPI
struct_2_epi_mat = Node(fsl.ConvertXFM(invert_xfm=True),
name='struct_2_epi_mat')
reg_wf.connect(epi_2_struct_bbr_mat, 'out_matrix_file', struct_2_epi_mat,
'in_file')
reg_wf.connect(struct_2_epi_mat, 'out_file', outputnode,
'struct_2_epi_mat')
# III. COMBINE I. & II.: EPI -> MNI
##########################################
# 6. COMBINE MATS: EPI -> MNI
epi_2_MNI_warp = Node(fsl.ConvertWarp(), name='epi_2_MNI_warp')
epi_2_MNI_warp.inputs.reference = fsl.Info.standard_image(
'MNI152_T1_2mm.nii.gz')
reg_wf.connect(epi_2_struct_bbr_mat, 'out_matrix_file', epi_2_MNI_warp,
'premat') # epi2struct
reg_wf.connect(struct_2_MNI_warp, 'field_file', epi_2_MNI_warp,
'warp1') # struct2mni
reg_wf.connect(epi_2_MNI_warp, 'out_file', outputnode, 'epi_2_MNI_warp')
reg_wf.connect(epi_2_MNI_warp, 'out_file', ds,
'registration.epi_2_MNI_warp')
# output: out_file
# 7. MNI -> EPI
MNI_2_epi_warp = Node(fsl.InvWarp(), name='MNI_2_epi_warp')
MNI_2_epi_warp.inputs.reference = fsl.Info.standard_image(
'MNI152_T1_2mm.nii.gz')
reg_wf.connect(epi_2_MNI_warp, 'out_file', MNI_2_epi_warp, 'warp')
reg_wf.connect(inputnode, 'initial_mean_epi_moco', MNI_2_epi_warp,
'reference')
reg_wf.connect(MNI_2_epi_warp, 'inverse_warp', outputnode,
'MNI_2_epi_warp')
# output: inverse_warp
##########################################
# TRANSFORM VOLUMES
##########################################
# CREATE STRUCT IN EPI SPACE FOR DEBUGGING
struct_epiSpace = Node(fsl.ApplyXfm(), name='struct_epiSpace')
struct_epiSpace.inputs.out_file = 'struct_brain_epiSpace.nii.gz'
reg_wf.connect(inputnode, 't1w_brain', struct_epiSpace, 'in_file')
reg_wf.connect(inputnode, 'initial_mean_epi_moco', struct_epiSpace,
'reference')
reg_wf.connect(struct_2_epi_mat, 'out_file', struct_epiSpace,
'in_matrix_file')
reg_wf.connect(struct_epiSpace, 'out_file', ds, 'QC.struct_brain_epiSpace')
# CREATE EPI IN MNI SPACE
mean_epi_MNIspace = Node(fsl.ApplyWarp(), name='mean_epi_MNIspace')
mean_epi_MNIspace.inputs.ref_file = fsl.Info.standard_image(
'MNI152_T1_2mm_brain.nii.gz')
mean_epi_MNIspace.inputs.out_file = 'mean_epi_MNIspace.nii.gz'
reg_wf.connect(inputnode, 'initial_mean_epi_moco', mean_epi_MNIspace,
'in_file')
reg_wf.connect(epi_2_MNI_warp, 'out_file', mean_epi_MNIspace, 'field_file')
reg_wf.connect(mean_epi_MNIspace, 'out_file', ds,
'registration.mean_epi_MNIspace')
reg_wf.connect(mean_epi_MNIspace, 'out_file', outputnode,
'mean_epi_MNIspace')
# CREATE MNI IN EPI SPACE FOR DEBUGGING
MNI_epiSpace = Node(fsl.ApplyWarp(), name='MNI_epiSpace')
MNI_epiSpace.inputs.in_file = fsl.Info.standard_image(
'MNI152_T1_2mm_brain.nii.gz')
MNI_epiSpace.inputs.out_file = 'MNI_epiSpace.nii.gz'
reg_wf.connect(inputnode, 'initial_mean_epi_moco', MNI_epiSpace,
'ref_file')
reg_wf.connect(MNI_2_epi_warp, 'inverse_warp', MNI_epiSpace, 'field_file')
reg_wf.connect(MNI_epiSpace, 'out_file', ds, 'registration.MNI_epiSpace')
reg_wf.write_graph(dotfilename=reg_wf.name, graph2use='flat', format='pdf')
return reg_wf
def prepro_func(i):
try:
subj = i
for s in (['session2']):
# Define input files: 2xfMRI + 1xMPRAGE
func1 = data_path + subj + '/Functional_scans/' + s[:-2] + s[
-1] + '_a/epi.nii.gz' #choose this for patients
func2 = data_path + subj + '/Functional_scans/' + s[:-2] + s[
-1] + '_b/epi.nii.gz' #choose this for patients
#anat = glob.glob(anat_path + subj +'/'+ s + '/anat/reorient/anat_*.nii.gz') #choose this for session 1
lesion_mask_file = anat_path + subj + '/session1/anat/reorient/lesion_seg.nii.gz'
old_lesion_mask_file = glob.glob(
anat_path + subj +
'/session1/anat/reorient/old_lesion_seg.nii.gz'
) #choose this for ones with no old lesion
#old_lesion_mask_file = anat_path + subj +'/session1/anat/reorient/old_lesion_seg.nii.gz' #choose this for ones with old lesion
anat = glob.glob(anat_path + subj + '/' + s +
'/anat/anat2hr/anat_*.nii.gz'
) #choose this for sessions 2 and 3
anat_CSF = glob.glob(
anat_path + subj +
'/session1/seg_anat/segmentation/anat_*_pve_0.nii.gz'
) # don't change, same for all sessions
anat_WM = glob.glob(
anat_path + subj +
'/session1/seg_anat/segmentation/anat_*_pve_2.nii.gz'
) # don't change, same for all sessions
anat_GM = glob.glob(
anat_path + subj +
'/session1/seg_anat/segmentation/anat_*_pve_1.nii.gz'
) # don't change, same for all sessions
anat2MNI_fieldwarp = glob.glob(
anat_path + subj +
'/session1/anat/nonlinear_reg/anat_*_fieldwarp.nii.gz'
) # don't change, same for all sessions
if not os.path.isdir(data_path + subj + '/' + s): # No data exists
continue
if not os.path.isfile(func1):
print '1. functional file ' + func1 + ' not found. Skipping!'
continue
if not os.path.isfile(func2):
print '2. functional file ' + func2 + ' not found. Skipping!'
continue
if not anat:
print 'Preprocessed anatomical file not found. Skipping!'
continue
if len(anat) > 1:
print 'WARNING: found multiple files of preprocessed anatomical image!'
continue
anat = anat[0]
if not anat2MNI_fieldwarp:
print 'Anatomical registration to MNI152-space field file not found. Skipping!'
continue
if len(anat2MNI_fieldwarp) > 1:
print 'WARNING: found multiple files of anat2MNI fieldwarp!'
continue
anat2MNI_fieldwarp = anat2MNI_fieldwarp[0]
if not anat_CSF:
anat_CSF = glob.glob(
anat_path + subj + '/' + s +
'/seg_anat/segmentation/anat_*_pve_0.nii.gz')
if not anat_CSF:
print 'Anatomical segmentation CSF file not found. Skipping!'
continue
if len(anat_CSF) > 1:
print 'WARNING: found multiple files of anatomical CSF file!'
continue
anat_CSF = anat_CSF[0]
if not anat_WM:
anat_WM = glob.glob(
anat_path + subj + '/' + s +
'/seg_anat/segmentation/anat_*_pve_2.nii.gz')
if not anat_WM:
print 'Anatomical segmentation WM file not found. Skipping!'
continue
if len(anat_WM) > 1:
print 'WARNING: found multiple files of anatomical WM file!'
continue
anat_WM = anat_WM[0]
if not anat_GM:
anat_GM = glob.glob(
anat_path + subj + '/' + s +
'/seg_anat/segmentation/anat_*_pve_1.nii.gz')
if not anat_GM:
print 'Anatomical segmentation GM file not found. Skipping!'
continue
if len(anat_GM) > 1:
print 'WARNING: found multiple files of anatomical GM file!'
continue
anat_GM = anat_GM[0]
if not os.path.isdir(results_path + subj):
os.mkdir(results_path + subj)
if not os.path.isdir(results_path + subj + '/' + s):
os.mkdir(results_path + subj + '/' + s)
for data in acquisitions:
os.chdir(results_path + subj + '/' + s)
print "Currently processing subject: " + subj + '/' + s + ' ' + data
#Initialize workflows
workflow = pe.Workflow(name=data)
workflow.base_dir = '.'
inputnode = pe.Node(
interface=util.IdentityInterface(fields=['source_file']),
name='inputspec')
outputnode = pe.Node(
interface=util.IdentityInterface(fields=['result_func']),
name='outputspec')
if data == 'func1':
inputnode.inputs.source_file = func1
else:
inputnode.inputs.source_file = func2
# Remove n_dummies first volumes
trim = pe.Node(interface=Trim(begin_index=n_dummies),
name='trim')
workflow.connect(inputnode, 'source_file', trim, 'in_file')
# Motion correction + slice timing correction
realign4d = pe.Node(interface=SpaceTimeRealigner(),
name='realign4d')
#realign4d.inputs.ignore_exception=True
realign4d.inputs.slice_times = 'asc_alt_siemens'
realign4d.inputs.slice_info = 2 # horizontal slices
realign4d.inputs.tr = mytr # TR in seconds
workflow.connect(trim, 'out_file', realign4d, 'in_file')
# Reorient
#deoblique = pe.Node(interface=afni.Warp(deoblique=True, outputtype='NIFTI_GZ'), name='deoblique') #leave out if you don't need this
#workflow.connect(realign4d, 'out_file', deoblique, 'in_file')
reorient = pe.Node(
interface=fsl.Reorient2Std(output_type='NIFTI_GZ'),
name='reorient')
workflow.connect(realign4d, 'out_file', reorient, 'in_file')
# AFNI skullstrip and mean image skullstrip
tstat1 = pe.Node(interface=afni.TStat(args='-mean',
outputtype="NIFTI_GZ"),
name='tstat1')
automask = pe.Node(interface=afni.Automask(
dilate=1, outputtype="NIFTI_GZ"),
name='automask')
skullstrip = pe.Node(interface=afni.Calc(
expr='a*b', outputtype="NIFTI_GZ"),
name='skullstrip')
tstat2 = pe.Node(interface=afni.TStat(args='-mean',
outputtype="NIFTI_GZ"),
name='tstat2')
workflow.connect(reorient, 'out_file', tstat1, 'in_file')
workflow.connect(tstat1, 'out_file', automask, 'in_file')
workflow.connect(automask, 'out_file', skullstrip, 'in_file_b')
workflow.connect(reorient, 'out_file', skullstrip, 'in_file_a')
workflow.connect(skullstrip, 'out_file', tstat2, 'in_file')
# Register to anatomical space #can be changed
#mean2anat = pe.Node(fsl.FLIRT(bins=40, cost='normmi', dof=7, interp='nearestneighbour', searchr_x=[-180,180], searchr_y=[-180,180], searchr_z=[-180,180]), name='mean2anat')
mean2anat = pe.Node(fsl.FLIRT(bins=40,
cost='normmi',
dof=7,
interp='nearestneighbour'),
name='mean2anat')
#mean2anat = pe.Node(fsl.FLIRT(no_search=True), name='mean2anat')
mean2anat.inputs.reference = anat
workflow.connect(tstat2, 'out_file', mean2anat, 'in_file')
# Transform mean functional image
warpmean = pe.Node(interface=fsl.ApplyWarp(), name='warpmean')
warpmean.inputs.ref_file = MNI_brain
warpmean.inputs.field_file = anat2MNI_fieldwarp
workflow.connect(mean2anat, 'out_matrix_file', warpmean,
'premat')
workflow.connect(tstat2, 'out_file', warpmean, 'in_file')
# ----- inversion matrix and eroded brain mask for regression -----
# create inverse matrix from mean2anat registration
invmat = pe.Node(fsl.ConvertXFM(), name='invmat')
invmat.inputs.invert_xfm = True
workflow.connect(mean2anat, 'out_matrix_file', invmat,
'in_file')
# erode functional brain mask
erode_brain = pe.Node(fsl.ImageMaths(), name='erode_brain')
erode_brain.inputs.args = '-kernel boxv 3 -ero'
workflow.connect(automask, 'out_file', erode_brain, 'in_file')
# register GM mask to functional image space, this is done for quality control
reg_GM = pe.Node(fsl.preprocess.ApplyXFM(), name='register_GM')
reg_GM.inputs.apply_xfm = True
reg_GM.inputs.in_file = anat_GM
workflow.connect(tstat2, 'out_file', reg_GM, 'reference')
workflow.connect(invmat, 'out_file', reg_GM, 'in_matrix_file')
# --------- motion regression and censor signals ------------------
# normalize motion parameters
norm_motion = pe.Node(interface=Function(
input_names=['in_file'],
output_names=['out_file'],
function=normalize_motion_data),
name='normalize_motion')
workflow.connect(realign4d, 'par_file', norm_motion, 'in_file')
# create censor file, for censoring motion
get_censor = pe.Node(afni.OneDToolPy(), name='motion_censors')
get_censor.inputs.set_nruns = 1
get_censor.inputs.censor_motion = (censor_thr, 'motion')
get_censor.inputs.show_censor_count = True
if overwrite: get_censor.inputs.args = '-overwrite'
workflow.connect(norm_motion, 'out_file', get_censor,
'in_file')
# compute motion parameter derivatives (for use in regression)
deriv_motion = pe.Node(afni.OneDToolPy(), name='deriv_motion')
deriv_motion.inputs.set_nruns = 1
deriv_motion.inputs.derivative = True
if overwrite: deriv_motion.inputs.args = '-overwrite'
deriv_motion.inputs.out_file = 'motion_derivatives.txt'
workflow.connect(norm_motion, 'out_file', deriv_motion,
'in_file')
# scale motion parameters and get quadratures
quadr_motion = pe.Node(interface=Function(
input_names=['in_file', 'multicol'],
output_names=['out_file', 'out_quadr_file'],
function=scale_and_quadrature),
name='quadr_motion')
quadr_motion.inputs.multicol = True
workflow.connect(norm_motion, 'out_file', quadr_motion,
'in_file')
# scale motion derivatives and get quadratures
quadr_motion_deriv = pe.Node(interface=Function(
input_names=['in_file', 'multicol'],
output_names=['out_file', 'out_quadr_file'],
function=scale_and_quadrature),
name='quadr_motion_deriv')
quadr_motion_deriv.inputs.multicol = True
workflow.connect(deriv_motion, 'out_file', quadr_motion_deriv,
'in_file')
# -------- CSF regression signals ---------------
# threshold and erode CSF mask
erode_CSF_mask = pe.Node(fsl.ImageMaths(),
name='erode_CSF_mask')
erode_CSF_mask.inputs.args = '-thr 0.5 -kernel boxv 3 -ero'
erode_CSF_mask.inputs.in_file = anat_CSF
# register CSF mask to functional image space
reg_CSF_mask = pe.Node(fsl.preprocess.ApplyXFM(),
name='register_CSF_mask')
reg_CSF_mask.inputs.apply_xfm = True
workflow.connect(tstat2, 'out_file', reg_CSF_mask, 'reference')
workflow.connect(invmat, 'out_file', reg_CSF_mask,
'in_matrix_file')
# inverse lesion mask and remove it from CSF mask #remove this if you don't have a lesion mask
inverse_lesion_mask = pe.Node(fsl.ImageMaths(),
name='inverse_lesion_mask')
inverse_lesion_mask.inputs.args = '-add 1 -rem 2'
inverse_lesion_mask.inputs.in_file = lesion_mask_file
rem_lesion = pe.Node(fsl.ImageMaths(), name='remove_lesion')
workflow.connect(erode_CSF_mask, 'out_file', rem_lesion,
'in_file')
workflow.connect(inverse_lesion_mask, 'out_file', rem_lesion,
'mask_file')
'''
# Transform lesion mask to MNI152 space #remove if lesion masks are already in MNI152 space
warp_lesion = pe.Node(interface=fsl.ApplyWarp(), name='warp_lesion')
warp_lesion.inputs.ref_file = MNI_brain
warp_lesion.inputs.field_file = anat2MNI_fieldwarp
warp_lesion.inputs.in_file = lesion_mask_file
warp_lesion.inputs.out_file = anat_path + subj +'/'+ s + '/anat/nonlinear_reg/lesion_seg_warp.nii.gz'
warp_lesion.run()
'''
# inverse old lesion mask and remove it from CSF mask #remove this if you don't have a lesion mask
if old_lesion_mask_file:
inverse_old_lesion_mask = pe.Node(
fsl.ImageMaths(), name='inverse_old_lesion_mask')
inverse_old_lesion_mask.inputs.args = '-add 1 -rem 3'
#inverse_old_lesion_mask.inputs.in_file = old_lesion_mask_file[0]
inverse_old_lesion_mask.inputs.in_file = old_lesion_mask_file
rem_old_lesion = pe.Node(fsl.ImageMaths(),
name='remove_old_lesion')
workflow.connect(rem_lesion, 'out_file', rem_old_lesion,
'in_file')
workflow.connect(inverse_old_lesion_mask, 'out_file',
rem_old_lesion, 'mask_file')
workflow.connect(rem_old_lesion, 'out_file', reg_CSF_mask,
'in_file')
'''
# Transform old lesion mask to MNI152 space #remove if lesion masks are already in MNI152 space
warp_old_lesion = pe.Node(interface=fsl.ApplyWarp(), name='warp_old_lesion')
warp_old_lesion.inputs.ref_file = MNI_brain
warp_old_lesion.inputs.field_file = anat2MNI_fieldwarp
warp_old_lesion.inputs.in_file = old_lesion_mask_file
warp_old_lesion.inputs.out_file = anat_path + subj +'/'+ s + '/anat/nonlinear_reg/old_lesion_seg_warp.nii.gz'
warp_old_lesion.run()
'''
else:
workflow.connect(rem_lesion, 'out_file', reg_CSF_mask,
'in_file')
# threshold CSF mask and intersect with functional brain mask
thr_CSF_mask = pe.Node(fsl.ImageMaths(),
name='threshold_CSF_mask')
thr_CSF_mask.inputs.args = '-thr 0.25'
workflow.connect(reg_CSF_mask, 'out_file', thr_CSF_mask,
'in_file')
workflow.connect(erode_brain, 'out_file', thr_CSF_mask,
'mask_file')
# extract CSF values
get_CSF_noise = pe.Node(fsl.ImageMeants(),
name='get_CSF_noise')
workflow.connect(skullstrip, 'out_file', get_CSF_noise,
'in_file')
workflow.connect(thr_CSF_mask, 'out_file', get_CSF_noise,
'mask')
# compute CSF noise derivatives
deriv_CSF = pe.Node(afni.OneDToolPy(), name='deriv_CSF')
deriv_CSF.inputs.set_nruns = 1
deriv_CSF.inputs.derivative = True
if overwrite: deriv_CSF.inputs.args = '-overwrite'
deriv_CSF.inputs.out_file = 'CSF_derivatives.txt'
workflow.connect(get_CSF_noise, 'out_file', deriv_CSF,
'in_file')
# scale SCF noise and get quadratures
quadr_CSF = pe.Node(interface=Function(
input_names=['in_file', 'multicol'],
output_names=['out_file', 'out_quadr_file'],
function=scale_and_quadrature),
name='quadr_CSF')
quadr_CSF.inputs.multicol = False
workflow.connect(get_CSF_noise, 'out_file', quadr_CSF,
'in_file')
# scale CSF noise derivatives and get quadratures
quadr_CSF_deriv = pe.Node(interface=Function(
input_names=['in_file', 'multicol'],
output_names=['out_file', 'out_quadr_file'],
function=scale_and_quadrature),
name='quadr_CSF_deriv')
quadr_CSF_deriv.inputs.multicol = False
workflow.connect(deriv_CSF, 'out_file', quadr_CSF_deriv,
'in_file')
# -------- WM regression signals -----------------
# threshold and erode WM mask
erode_WM_mask = pe.Node(fsl.ImageMaths(), name='erode_WM_mask')
erode_WM_mask.inputs.args = '-thr 0.5 -kernel boxv 7 -ero'
erode_WM_mask.inputs.in_file = anat_WM
# registrer WM mask to functional image space
reg_WM_mask = pe.Node(fsl.preprocess.ApplyXFM(),
name='register_WM_mask')
reg_WM_mask.inputs.apply_xfm = True
workflow.connect(tstat2, 'out_file', reg_WM_mask, 'reference')
workflow.connect(invmat, 'out_file', reg_WM_mask,
'in_matrix_file')
workflow.connect(erode_WM_mask, 'out_file', reg_WM_mask,
'in_file')
# create inverse nonlinear registration MNI2anat
invwarp = pe.Node(fsl.InvWarp(output_type='NIFTI_GZ'),
name='invwarp')
invwarp.inputs.warp = anat2MNI_fieldwarp
invwarp.inputs.reference = anat
# transform ventricle mask to functional space
reg_ventricles = pe.Node(fsl.ApplyWarp(),
name='register_ventricle_mask')
reg_ventricles.inputs.in_file = ventricle_mask
workflow.connect(tstat2, 'out_file', reg_ventricles,
'ref_file')
workflow.connect(invwarp, 'inverse_warp', reg_ventricles,
'field_file')
workflow.connect(invmat, 'out_file', reg_ventricles, 'postmat')
# threshold WM mask and intersect with functional brain mask
thr_WM_mask = pe.Node(fsl.ImageMaths(),
name='threshold_WM_mask')
thr_WM_mask.inputs.args = '-thr 0.25'
workflow.connect(reg_WM_mask, 'out_file', thr_WM_mask,
'in_file')
workflow.connect(erode_brain, 'out_file', thr_WM_mask,
'mask_file')
# remove ventricles from WM mask
exclude_ventricles = pe.Node(fsl.ImageMaths(),
name='exclude_ventricles')
workflow.connect(thr_WM_mask, 'out_file', exclude_ventricles,
'in_file')
workflow.connect(reg_ventricles, 'out_file',
exclude_ventricles, 'mask_file')
# check that WM is collected from both hemispheres
check_WM_bilat = pe.Node(interface=Function(
input_names=['in_file'],
output_names=['errors'],
function=check_bilateralism),
name='check_WM_bilateralism')
workflow.connect(exclude_ventricles, 'out_file',
check_WM_bilat, 'in_file')
# extract WM values
get_WM_noise = pe.Node(fsl.ImageMeants(), name='get_WM_noise')
workflow.connect(skullstrip, 'out_file', get_WM_noise,
'in_file')
workflow.connect(exclude_ventricles, 'out_file', get_WM_noise,
'mask')
# compute WM noise derivatives
deriv_WM = pe.Node(afni.OneDToolPy(), name='deriv_WM')
deriv_WM.inputs.set_nruns = 1
deriv_WM.inputs.derivative = True
if overwrite: deriv_WM.inputs.args = '-overwrite'
deriv_WM.inputs.out_file = 'WM_derivatives.txt'
workflow.connect(get_WM_noise, 'out_file', deriv_WM, 'in_file')
# scale WM noise and get quadratures
quadr_WM = pe.Node(interface=Function(
input_names=['in_file', 'multicol'],
output_names=['out_file', 'out_quadr_file'],
function=scale_and_quadrature),
name='quadr_WM')
quadr_WM.inputs.multicol = False
workflow.connect(get_WM_noise, 'out_file', quadr_WM, 'in_file')
# scale WM noise derivatives and get quadratures
quadr_WM_deriv = pe.Node(interface=Function(
input_names=['in_file', 'multicol'],
output_names=['out_file', 'out_quadr_file'],
function=scale_and_quadrature),
name='quadr_WM_deriv')
quadr_WM_deriv.inputs.multicol = False
workflow.connect(deriv_WM, 'out_file', quadr_WM_deriv,
'in_file')
# ---------- global regression signals ----------------
if global_reg:
# register anatomical whole brain mask to functional image space
reg_glob_mask = pe.Node(fsl.preprocess.ApplyXFM(),
name='register_global_mask')
reg_glob_mask.inputs.apply_xfm = True
reg_glob_mask.inputs.in_file = anat
workflow.connect(tstat2, 'out_file', reg_glob_mask,
'reference')
workflow.connect(invmat, 'out_file', reg_glob_mask,
'in_matrix_file')
# threshold anatomical brain mask and intersect with functional brain mask
thr_glob_mask = pe.Node(fsl.ImageMaths(),
name='threshold_global_mask')
thr_glob_mask.inputs.args = '-thr -0.1'
workflow.connect(reg_glob_mask, 'out_file', thr_glob_mask,
'in_file')
workflow.connect(erode_brain, 'out_file', thr_glob_mask,
'mask_file')
# extract global signal values
get_glob_noise = pe.Node(fsl.ImageMeants(),
name='get_global_noise')
workflow.connect(skullstrip, 'out_file', get_glob_noise,
'in_file')
workflow.connect(thr_glob_mask, 'out_file', get_glob_noise,
'mask')
# compute global noise derivative
deriv_glob = pe.Node(afni.OneDToolPy(),
name='deriv_global')
deriv_glob.inputs.set_nruns = 1
deriv_glob.inputs.derivative = True
if overwrite: deriv_glob.inputs.args = '-overwrite'
deriv_glob.inputs.out_file = 'global_derivatives.txt'
workflow.connect(get_glob_noise, 'out_file', deriv_glob,
'in_file')
# scale global noise and get quadratures
quadr_glob = pe.Node(interface=Function(
input_names=['in_file', 'multicol'],
output_names=['out_file', 'out_quadr_file'],
function=scale_and_quadrature),
name='quadr_glob')
quadr_glob.inputs.multicol = False
workflow.connect(get_glob_noise, 'out_file', quadr_glob,
'in_file')
# scale global noise derivatives and get quadratures
quadr_glob_deriv = pe.Node(interface=Function(
input_names=['in_file', 'multicol'],
output_names=['out_file', 'out_quadr_file'],
function=scale_and_quadrature),
name='quadr_glob_deriv')
quadr_glob_deriv.inputs.multicol = False
workflow.connect(deriv_glob, 'out_file', quadr_glob_deriv,
'in_file')
# ---------- regression matrix ----------
# create bandpass regressors, can not be easily implemented to workflow
get_bandpass = pe.Node(interface=Function(
input_names=['minf', 'maxf', 'example_file', 'tr'],
output_names=['out_tuple'],
function=bandpass),
name='bandpass_regressors')
get_bandpass.inputs.minf = myminf
get_bandpass.inputs.maxf = mymaxf
get_bandpass.inputs.tr = mytr
workflow.connect(norm_motion, 'out_file', get_bandpass,
'example_file')
# concatenate regressor time series
cat_reg_name = 'cat_regressors'
if global_reg: cat_reg_name = cat_reg_name + '_global'
cat_reg = pe.Node(interface=Function(
input_names=[
'mot', 'motd', 'motq', 'motdq', 'CSF', 'CSFd', 'CSFq',
'CSFdq', 'WM', 'WMd', 'WMq', 'WMdq', 'include_global',
'glob', 'globd', 'globq', 'globdq'
],
output_names=['reg_file_args'],
function=concatenate_regressors),
name=cat_reg_name)
cat_reg.inputs.include_global = global_reg
workflow.connect(quadr_motion, 'out_file', cat_reg, 'mot')
workflow.connect(quadr_motion_deriv, 'out_file', cat_reg,
'motd')
workflow.connect(quadr_motion, 'out_quadr_file', cat_reg,
'motq')
workflow.connect(quadr_motion_deriv, 'out_quadr_file', cat_reg,
'motdq')
workflow.connect(quadr_CSF, 'out_file', cat_reg, 'CSF')
workflow.connect(quadr_CSF_deriv, 'out_file', cat_reg, 'CSFd')
workflow.connect(quadr_CSF, 'out_quadr_file', cat_reg, 'CSFq')
workflow.connect(quadr_CSF_deriv, 'out_quadr_file', cat_reg,
'CSFdq')
workflow.connect(quadr_WM, 'out_file', cat_reg, 'WM')
workflow.connect(quadr_WM_deriv, 'out_file', cat_reg, 'WMd')
workflow.connect(quadr_WM, 'out_quadr_file', cat_reg, 'WMq')
workflow.connect(quadr_WM_deriv, 'out_quadr_file', cat_reg,
'WMdq')
if global_reg:
workflow.connect(quadr_glob, 'out_file', cat_reg, 'glob')
workflow.connect(quadr_glob_deriv, 'out_file', cat_reg,
'globd')
workflow.connect(quadr_glob, 'out_quadr_file', cat_reg,
'globq')
workflow.connect(quadr_glob_deriv, 'out_quadr_file',
cat_reg, 'globdq')
else:
cat_reg.inputs.glob = None
cat_reg.inputs.globd = None
cat_reg.inputs.globq = None
cat_reg.inputs.globdq = None
# create regression matrix
deconvolve_name = 'deconvolve'
if global_reg: deconvolve_name = deconvolve_name + '_global'
deconvolve = pe.Node(afni.Deconvolve(), name=deconvolve_name)
deconvolve.inputs.polort = 2 # contstant, linear and quadratic background signals removed
deconvolve.inputs.fout = True
deconvolve.inputs.tout = True
deconvolve.inputs.x1D_stop = True
deconvolve.inputs.force_TR = mytr
workflow.connect(cat_reg, 'reg_file_args', deconvolve, 'args')
workflow.connect(get_bandpass, 'out_tuple', deconvolve,
'ortvec')
workflow.connect([(skullstrip, deconvolve,
[(('out_file', str2list), 'in_files')])])
# regress out motion and other unwanted signals
tproject_name = 'tproject'
if global_reg: tproject_name = tproject_name + '_global'
tproject = pe.Node(afni.TProject(outputtype="NIFTI_GZ"),
name=tproject_name)
tproject.inputs.TR = mytr
tproject.inputs.polort = 0 # use matrix created with 3dDeconvolve, higher order polynomials not needed
tproject.inputs.cenmode = 'NTRP' # interpolate removed time points
workflow.connect(get_censor, 'out_file', tproject, 'censor')
workflow.connect(skullstrip, 'out_file', tproject, 'in_file')
workflow.connect(automask, 'out_file', tproject, 'mask')
workflow.connect(deconvolve, 'x1D', tproject, 'ort')
# Transform all images
warpall_name = 'warpall'
if global_reg: warpall_name = warpall_name + '_global'
warpall = pe.Node(interface=fsl.ApplyWarp(), name=warpall_name)
warpall.inputs.ref_file = MNI_brain
warpall.inputs.field_file = anat2MNI_fieldwarp
workflow.connect(mean2anat, 'out_matrix_file', warpall,
'premat')
workflow.connect(tproject, 'out_file', warpall, 'in_file')
workflow.connect(warpall, 'out_file', outputnode,
'result_func')
# Run workflow
workflow.write_graph()
workflow.run()
print "FUNCTIONAL PREPROCESSING DONE! Results in ", results_path + subj + '/' + s
except:
print "Error with patient: ", subj
traceback.print_exc()
def anat2mni_nonlinear():
config = '/scr/sambesi1/workspace/Projects/GluREST/registration/T1_2_MNI152_2mm.cnf'
mni_brain_2mm = '/usr/share/fsl/5.0/data/standard/MNI152_T1_2mm_brain.nii.gz'
mni_skull_2mm = '/usr/share/fsl/5.0/data/standard/MNI152_T1_2mm.nii.gz'
#mni_1mm = '/usr/share/fsl/5.0/data/standard/MNI152_T1_1mm_brain.nii.gz'
#define workflow
flow = Workflow('anat2mni')
inputnode = Node(util.IdentityInterface(fields=['anat_image',
'anat_wm',
'anat_gm',
'anat_cm',
'anat_first']),
name = 'inputnode')
outputnode = Node(util.IdentityInterface(fields=['anat2mni',
'nonlinear_warp',
'mni_mask_gm',
'mni_mask_wm',
'mni_mask_cm' ,
'mni_mask_first',
'mni2anat_warp']),
name = 'outputnode')
flirt = Node(interface= fsl.FLIRT(), name = 'anat2mni_linear_flirt')
flirt.inputs.cost = 'mutualinfo'
flirt.inputs.dof = 12
flirt.inputs.output_type = "NIFTI_GZ"
flirt.inputs.reference = mni_brain_2mm # without skull here.. see example on fsl website.
fnirt = Node(interface=fsl.FNIRT(), name = 'anat2mni_nonlinear_fnirt')
fnirt.inputs.config_file = config
fnirt.inputs.fieldcoeff_file = True
fnirt.inputs.jacobian_file = True
fnirt.inputs.ref_file = mni_brain_2mm # no skull
invwarp = Node(interface=fsl.InvWarp(), name = 'mni2anat_warp')
warp_gm = Node(interface=fsl.ApplyWarp(), name='warp_gm')
warp_gm.inputs.ref_file =mni_brain_2mm
warp_wm = Node(interface=fsl.ApplyWarp(), name='warp_wm')
warp_wm.inputs.ref_file =mni_brain_2mm
warp_cm = Node(interface=fsl.ApplyWarp(), name='warp_csf')
warp_cm.inputs.ref_file =mni_brain_2mm
warp_first = Node(interface=fsl.ApplyWarp(), name='warp_first')
warp_first.inputs.ref_file =mni_brain_2mm
thresh_gm = Node(fsl.Threshold(), name= 'mni_mask_gm')
thresh_gm.inputs.thresh = 0.5
thresh_gm.inputs.args = '-bin'
thresh_wm = Node(fsl.Threshold(), name= 'mni_mask_wm')
thresh_wm.inputs.thresh = 0.6
thresh_wm.inputs.args = '-bin'
thresh_csf = Node(fsl.Threshold(), name= 'mni_mask_csf')
thresh_csf.inputs.thresh = 0.6
thresh_csf.inputs.args = '-bin'
thresh_first = Node(fsl.Threshold(), name= 'mni_mask_first')
thresh_first.inputs.thresh = 0.5
thresh_first.inputs.args = '-bin'
flow.connect(inputnode, 'anat_image' , flirt, 'in_file' )
flow.connect(inputnode, 'anat_image' , fnirt, 'in_file' )
flow.connect(flirt, 'out_matrix_file' , fnirt, 'affine_file' )
flow.connect(inputnode, 'anat_gm' , warp_gm, 'in_file' )
flow.connect(inputnode, 'anat_wm' , warp_wm, 'in_file' )
flow.connect(inputnode, 'anat_cm' , warp_cm, 'in_file' )
flow.connect(inputnode, 'anat_first' , warp_first, 'in_file' )
flow.connect(fnirt, 'fieldcoeff_file' , invwarp, 'warp' )
flow.connect(inputnode, 'anat_image' , invwarp, 'reference' )
flow.connect(fnirt, 'fieldcoeff_file' , warp_gm, 'field_file' )
flow.connect(fnirt, 'fieldcoeff_file' , warp_wm, 'field_file' )
flow.connect(fnirt, 'fieldcoeff_file' , warp_cm, 'field_file' )
flow.connect(fnirt, 'fieldcoeff_file' , warp_first, 'field_file' )
flow.connect(warp_gm, 'out_file' , thresh_gm, 'in_file' )
flow.connect(warp_wm, 'out_file' , thresh_wm, 'in_file' )
flow.connect(warp_cm, 'out_file' , thresh_csf, 'in_file' )
flow.connect(warp_first,'out_file' , thresh_first,'in_file' )
flow.connect(fnirt, 'warped_file' , outputnode, 'anat2mni' )
flow.connect(invwarp, 'inverse_warp' , outputnode, 'mni2anat_warp' )
flow.connect(thresh_gm, 'out_file' , outputnode, 'mni_mask_gm' )
flow.connect(thresh_wm, 'out_file' , outputnode, 'mni_mask_wm' )
flow.connect(thresh_csf,'out_file' , outputnode, 'mni_mask_cm' )
flow.connect(thresh_first,'out_file' , outputnode, 'mni_mask_first' )
flow.connect(fnirt, 'fieldcoeff_file' , outputnode, 'nonlinear_warp' )
return flow
def prepare_template_wf(name="prepare_template"):
wf = Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(
fields=['tpl_t1w', 'tpl_t1w_brainmask', 'CSF_pve']),
name='inputnode')
tpl_t1w_brain = pe.Node(fsl.ApplyMask(), name='tpl_t1w_brain')
wf.connect(inputnode, "tpl_t1w", tpl_t1w_brain, "in_file")
wf.connect(inputnode, "tpl_t1w_brainmask", tpl_t1w_brain, "mask_file")
norm_wf = get_norm_wf()
wf.connect(inputnode, 'tpl_t1w', norm_wf, "inputnode.t1w")
wf.connect(tpl_t1w_brain, 'out_file', norm_wf, "inputnode.t1w_brain")
MNI_2_t1w_warp = pe.Node(fsl.InvWarp(), name='MNI_2_t1w_warp')
wf.connect(inputnode, 'tpl_t1w', MNI_2_t1w_warp, 'reference')
wf.connect(norm_wf, 'outputnode.t1w_2_MNI_warp', MNI_2_t1w_warp, 'warp')
bianca_mask = pe.Node(MakeBiancaMask(), name='bianca_mask')
bianca_mask.inputs.keep_intermediate_files = 0
wf.connect(inputnode, 'tpl_t1w', bianca_mask, 'structural_image')
wf.connect(inputnode, 'CSF_pve', bianca_mask, 'CSF_pve')
wf.connect(MNI_2_t1w_warp, 'inverse_warp', bianca_mask,
'warp_file_MNI2structural')
wf.connect(tpl_t1w_brain, 'out_file', bianca_mask,
'structural_image_brainextracted')
wf.connect(inputnode, 'tpl_t1w_brainmask', bianca_mask, 'brainmask')
# distancemap
# dilate for distancemap to avoid rim effects when resampling distancemap
brainmask_dil = pe.Node(fsl.DilateImage(operation="max"),
name="brainmask_dil")
wf.connect(inputnode, 'tpl_t1w_brainmask', brainmask_dil, "in_file")
distancemap = pe.Node(fsl.DistanceMap(), name="distancemap")
wf.connect(bianca_mask, 'vent_file', distancemap, "in_file")
wf.connect(brainmask_dil, 'out_file', distancemap, "mask_file")
perivent_mask_init = pe.Node(fsl.maths.MathsCommand(),
name="perivent_mask_init")
perivent_mask_init.inputs.args = "-uthr 10 -bin"
wf.connect(distancemap, "distance_map", perivent_mask_init, "in_file")
perivent_mask = pe.Node(fsl.ApplyMask(), name="perivent_mask")
wf.connect(perivent_mask_init, "out_file", perivent_mask, "in_file")
wf.connect(inputnode, "tpl_t1w_brainmask", perivent_mask, "mask_file")
deepWM_mask_init = pe.Node(fsl.maths.MathsCommand(),
name="deepWM_mask_init")
deepWM_mask_init.inputs.args = "-thr 10 -bin"
wf.connect(distancemap, "distance_map", deepWM_mask_init, "in_file")
deepWM_mask = pe.Node(fsl.ApplyMask(), name="deepWM_mask")
wf.connect(deepWM_mask_init, "out_file", deepWM_mask, "in_file")
wf.connect(inputnode, "tpl_t1w_brainmask", deepWM_mask, "mask_file")
outputnode = pe.Node(niu.IdentityInterface(fields=[
"t1w_2_MNI_mat", "t1w_MNIspace", "t1w_2_MNI_warp",
"bianca_wm_mask_file", "vent_file", "distance_map", "perivent_mask",
"deepWM_mask"
]),
name='outputnode')
wf.connect(norm_wf, 'outputnode.t1w_2_MNI_mat', outputnode,
"t1w_2_MNI_mat")
wf.connect(norm_wf, 'outputnode.t1w_MNIspace', outputnode, "t1w_MNIspace")
wf.connect(norm_wf, 'outputnode.t1w_2_MNI_warp', outputnode,
"t1w_2_MNI_warp")
wf.connect(bianca_mask, 'mask_file', outputnode, "bianca_wm_mask_file")
wf.connect(bianca_mask, 'vent_file', outputnode, "vent_file")
wf.connect(distancemap, "distance_map", outputnode, "distance_map")
wf.connect(perivent_mask, "out_file", outputnode, "perivent_mask")
wf.connect(deepWM_mask, "out_file", outputnode, "deepWM_mask")
return wf
