def create_tbss_4_prestats(name='tbss_4_prestats'):
"""Post-registration processing:Creating skeleton mask using a threshold
projecting all FA data onto skeleton.
A pipeline that does the same as tbss_4_prestats script from FSL
Example
-------
>>> from nipype.workflows.dmri.fsl import tbss
>>> tbss4 = tbss.create_tbss_4_prestats(name='tbss4')
>>> tbss4.inputs.inputnode.skeleton_thresh = 0.2
Inputs::
inputnode.skeleton_thresh
inputnode.groupmask
inputnode.skeleton_file
inputnode.meanfa_file
inputnode.mergefa_file
Outputs::
outputnode.all_FA_skeletonised
outputnode.mean_FA_skeleton_mask
outputnode.distance_map
outputnode.skeleton_file
"""
# Create inputnode
inputnode = pe.Node(interface=util.IdentityInterface(fields=[
'groupmask', 'skeleton_file', 'meanfa_file', 'mergefa_file',
'skeleton_thresh'
]),
name='inputnode')
# Mask the skeleton at the threshold
skeletonmask = pe.Node(fsl.ImageMaths(suffix="_mask"), name="skeletonmask")
# Invert the brainmask then add in the tract skeleton
invertmask = pe.Node(fsl.ImageMaths(suffix="_inv",
op_string="-mul -1 -add 1 -add"),
name="invertmask")
# Generate a distance map with the tract skeleton
distancemap = pe.Node(fsl.DistanceMap(), name="distancemap")
# Project the FA values onto the skeleton
projectfa = pe.Node(fsl.TractSkeleton(project_data=True,
skeleton_file=True,
use_cingulum_mask=True),
name="projectfa")
# Create tbss4 workflow
tbss4 = pe.Workflow(name=name)
tbss4.connect([
(inputnode, invertmask, [("groupmask", "in_file")]),
(inputnode, skeletonmask, [("skeleton_file", "in_file"),
(('skeleton_thresh', tbss4_op_string),
'op_string')]),
(inputnode, projectfa, [('skeleton_thresh', 'threshold'),
("meanfa_file", "in_file"),
("mergefa_file", "data_file")]),
(skeletonmask, invertmask, [("out_file", "in_file2")]),
(invertmask, distancemap, [("out_file", "in_file")]),
(distancemap, projectfa, [("distance_map", "distance_map")]),
])
# Create the outputnode
outputnode = pe.Node(interface=util.IdentityInterface(fields=[
'projectedfa_file', 'skeleton_mask', 'distance_map', 'skeleton_file'
]),
name='outputnode')
tbss4.connect([
(projectfa, outputnode, [('projected_data', 'projectedfa_file'),
('skeleton_file', 'skeleton_file')]),
(distancemap, outputnode, [('distance_map', 'distance_map')]),
(skeletonmask, outputnode, [('out_file', 'skeleton_mask')])
])
return tbss4
def create_cross_sectional_tbss_pipeline(in_files,
output_dir,
name='cross_sectional_tbss',
skeleton_threshold=0.2,
design_mat=None,
design_con=None):
workflow = pe.Workflow(name=name)
workflow.base_dir = output_dir
workflow.base_output_dir = name
# Create the dtitk groupwise registration workflow
groupwise_dtitk = create_dtitk_groupwise_workflow(in_files=in_files,
name="dtitk_groupwise",
rig_iteration=3,
aff_iteration=3,
nrr_iteration=6)
# Create the average FA map
mean_fa = pe.Node(interface=dtitk.TVtool(), name="mean_fa")
workflow.connect(groupwise_dtitk, 'output_node.out_template', mean_fa,
'in_file')
mean_fa.inputs.operation = 'fa'
# Register the FMRIB58_FA_1mm.nii.gz atlas to the mean FA map
reg_atlas = pe.Node(interface=niftyreg.RegAladin(), name='reg_atlas')
workflow.connect(mean_fa, 'out_file', reg_atlas, 'ref_file')
reg_atlas.inputs.flo_file = os.path.join(os.environ['FSLDIR'], 'data',
'standard',
'FMRIB58_FA_1mm.nii.gz')
# Apply the transformation to the lower cingulum image
war_atlas = pe.Node(interface=niftyreg.RegResample(), name='war_atlas')
workflow.connect(mean_fa, 'out_file', war_atlas, 'ref_file')
war_atlas.inputs.flo_file = os.path.join(os.environ['FSLDIR'], 'data',
'standard',
'LowerCingulum_1mm.nii.gz')
workflow.connect(reg_atlas, 'aff_file', war_atlas, 'trans_file')
war_atlas.inputs.inter_val = 'LIN'
# Threshold the propagated lower cingulum
thr_atlas = pe.Node(interface=niftyseg.BinaryMaths(), name='thr_atlas')
workflow.connect(war_atlas, 'out_file', thr_atlas, 'in_file')
thr_atlas.inputs.operation = 'thr'
thr_atlas.inputs.operand_value = 0.5
# Binarise the propagated lower cingulum
bin_atlas = pe.Node(interface=niftyseg.UnaryMaths(), name='bin_atlas')
workflow.connect(thr_atlas, 'out_file', bin_atlas, 'in_file')
bin_atlas.inputs.operation = 'bin'
# Create all the individual FA maps
individual_fa = pe.MapNode(interface=dtitk.TVtool(),
name="individual_fa",
iterfield=['in_file'])
workflow.connect(groupwise_dtitk, 'output_node.out_res', individual_fa,
'in_file')
individual_fa.inputs.operation = 'fa'
# Create all the individual MD maps
individual_md = pe.MapNode(interface=dtitk.TVtool(),
name="individual_md",
iterfield=['in_file'])
workflow.connect(groupwise_dtitk, 'output_node.out_res', individual_md,
'in_file')
individual_md.inputs.operation = 'tr'
# Create all the individual RD maps
individual_rd = pe.MapNode(interface=dtitk.TVtool(),
name="individual_rd",
iterfield=['in_file'])
workflow.connect(groupwise_dtitk, 'output_node.out_res', individual_rd,
'in_file')
individual_rd.inputs.operation = 'rd'
# Create all the individual RD maps
individual_ad = pe.MapNode(interface=dtitk.TVtool(),
name="individual_ad",
iterfield=['in_file'])
workflow.connect(groupwise_dtitk, 'output_node.out_res', individual_ad,
'in_file')
individual_ad.inputs.operation = 'ad'
# Combine all the warped FA images into a 4D image
merged_4d_fa = pe.Node(interface=fsl.Merge(), name='merged_4d_fa')
merged_4d_fa.inputs.dimension = 't'
workflow.connect(individual_fa, 'out_file', merged_4d_fa, 'in_files')
# Combine all the warped MD images into a 4D image
merged_4d_md = pe.Node(interface=fsl.Merge(), name='merged_4d_md')
merged_4d_md.inputs.dimension = 't'
workflow.connect(individual_md, 'out_file', merged_4d_md, 'in_files')
# Combine all the warped RD images into a 4D image
merged_4d_rd = pe.Node(interface=fsl.Merge(), name='merged_4d_rd')
merged_4d_rd.inputs.dimension = 't'
workflow.connect(individual_rd, 'out_file', merged_4d_rd, 'in_files')
# Combine all the warped AD images into a 4D image
merged_4d_ad = pe.Node(interface=fsl.Merge(), name='merged_4d_ad')
merged_4d_ad.inputs.dimension = 't'
workflow.connect(individual_ad, 'out_file', merged_4d_ad, 'in_files')
# Threshold the 4D FA image to 0
merged_4d_fa_thresholded = pe.Node(interface=niftyseg.BinaryMaths(),
name='merged_4d_fa_thresholded')
merged_4d_fa_thresholded.inputs.operation = 'thr'
merged_4d_fa_thresholded.inputs.operand_value = 0
workflow.connect(merged_4d_fa, 'merged_file', merged_4d_fa_thresholded,
'in_file')
# Extract the min value from the 4D FA image
minimal_value_across_all_fa = pe.Node(interface=niftyseg.UnaryMaths(),
name='minimal_value_across_all_fa')
minimal_value_across_all_fa.inputs.operation = 'tmin'
workflow.connect(merged_4d_fa_thresholded, 'out_file',
minimal_value_across_all_fa, 'in_file')
# Create the mask image
fa_mask = pe.Node(interface=niftyseg.UnaryMaths(), name='fa_mask')
fa_mask.inputs.operation = 'bin'
fa_mask.inputs.output_datatype = 'char'
workflow.connect(minimal_value_across_all_fa, 'out_file', fa_mask,
'in_file')
# Mask the mean FA image
masked_mean_fa = pe.Node(interface=fsl.ApplyMask(), name='masked_mean_fa')
workflow.connect(mean_fa, 'out_file', masked_mean_fa, 'in_file')
workflow.connect(fa_mask, 'out_file', masked_mean_fa, 'mask_file')
# Create the skeleton image
skeleton = pe.Node(interface=fsl.TractSkeleton(), name='skeleton')
skeleton.inputs.skeleton_file = True
workflow.connect(masked_mean_fa, 'out_file', skeleton, 'in_file')
# Threshold the skeleton image
thresholded_skeleton = pe.Node(interface=niftyseg.BinaryMaths(),
name='thresholded_skeleton')
thresholded_skeleton.inputs.operation = 'thr'
thresholded_skeleton.inputs.operand_value = skeleton_threshold
workflow.connect(skeleton, 'skeleton_file', thresholded_skeleton,
'in_file')
# Binarise the skeleton image
binarised_skeleton = pe.Node(interface=niftyseg.UnaryMaths(),
name='binarised_skeleton')
binarised_skeleton.inputs.operation = 'bin'
workflow.connect(thresholded_skeleton, 'out_file', binarised_skeleton,
'in_file')
# Create skeleton distance map
invert_mask1 = pe.Node(interface=niftyseg.BinaryMaths(),
name='invert_mask1')
invert_mask1.inputs.operation = 'mul'
invert_mask1.inputs.operand_value = -1
workflow.connect(fa_mask, 'out_file', invert_mask1, 'in_file')
invert_mask2 = pe.Node(interface=niftyseg.BinaryMaths(),
name='invert_mask2')
invert_mask2.inputs.operation = 'add'
invert_mask2.inputs.operand_value = 1
workflow.connect(invert_mask1, 'out_file', invert_mask2, 'in_file')
invert_mask3 = pe.Node(interface=niftyseg.BinaryMaths(),
name='invert_mask3')
invert_mask3.inputs.operation = 'add'
workflow.connect(invert_mask2, 'out_file', invert_mask3, 'in_file')
workflow.connect(binarised_skeleton, 'out_file', invert_mask3,
'operand_file')
distance_map = pe.Node(interface=fsl.DistanceMap(), name='distance_map')
workflow.connect(invert_mask3, 'out_file', distance_map, 'in_file')
# Project the FA values onto the skeleton
all_fa_projected = pe.Node(interface=fsl.TractSkeleton(),
name='all_fa_projected')
all_fa_projected.inputs.threshold = skeleton_threshold
all_fa_projected.inputs.project_data = True
workflow.connect(masked_mean_fa, 'out_file', all_fa_projected, 'in_file')
workflow.connect(distance_map, 'distance_map', all_fa_projected,
'distance_map')
workflow.connect(merged_4d_fa, 'merged_file', all_fa_projected,
'data_file')
workflow.connect(bin_atlas, 'out_file', all_fa_projected,
'search_mask_file')
# Project the MD values onto the skeleton
all_md_projected = pe.Node(interface=fsl.TractSkeleton(),
name='all_md_projected')
all_md_projected.inputs.threshold = skeleton_threshold
all_md_projected.inputs.project_data = True
workflow.connect(masked_mean_fa, 'out_file', all_md_projected, 'in_file')
workflow.connect(distance_map, 'distance_map', all_md_projected,
'distance_map')
workflow.connect(merged_4d_fa, 'merged_file', all_md_projected,
'data_file')
workflow.connect(merged_4d_md, 'merged_file', all_md_projected,
'alt_data_file')
workflow.connect(bin_atlas, 'out_file', all_md_projected,
'search_mask_file')
# Project the RD values onto the skeleton
all_rd_projected = pe.Node(interface=fsl.TractSkeleton(),
name='all_rd_projected')
all_rd_projected.inputs.threshold = skeleton_threshold
all_rd_projected.inputs.project_data = True
workflow.connect(masked_mean_fa, 'out_file', all_rd_projected, 'in_file')
workflow.connect(distance_map, 'distance_map', all_rd_projected,
'distance_map')
workflow.connect(merged_4d_fa, 'merged_file', all_rd_projected,
'data_file')
workflow.connect(merged_4d_rd, 'merged_file', all_rd_projected,
'alt_data_file')
workflow.connect(bin_atlas, 'out_file', all_rd_projected,
'search_mask_file')
# Project the RD values onto the skeleton
all_ad_projected = pe.Node(interface=fsl.TractSkeleton(),
name='all_ad_projected')
all_ad_projected.inputs.threshold = skeleton_threshold
all_ad_projected.inputs.project_data = True
workflow.connect(masked_mean_fa, 'out_file', all_ad_projected, 'in_file')
workflow.connect(distance_map, 'distance_map', all_ad_projected,
'distance_map')
workflow.connect(merged_4d_fa, 'merged_file', all_ad_projected,
'data_file')
workflow.connect(merged_4d_ad, 'merged_file', all_ad_projected,
'alt_data_file')
workflow.connect(bin_atlas, 'out_file', all_ad_projected,
'search_mask_file')
# Create an output node
output_node = pe.Node(interface=niu.IdentityInterface(fields=[
'mean_fa', 'all_fa_skeletonised', 'all_md_skeletonised',
'all_rd_skeletonised', 'all_ad_skeletonised', 'skeleton',
'skeleton_bin', 't_contrast_raw_stat', 't_contrast_uncorrected_pvalue',
't_contrast_corrected_pvalue'
]),
name='output_node')
# Connect the workflow to the output node
workflow.connect(masked_mean_fa, 'out_file', output_node, 'mean_fa')
workflow.connect(all_fa_projected, 'projected_data', output_node,
'all_fa_skeletonised')
workflow.connect(all_md_projected, 'projected_data', output_node,
'all_md_skeletonised')
workflow.connect(all_rd_projected, 'projected_data', output_node,
'all_rd_skeletonised')
workflow.connect(all_ad_projected, 'projected_data', output_node,
'all_ad_skeletonised')
workflow.connect(skeleton, 'skeleton_file', output_node, 'skeleton')
workflow.connect(binarised_skeleton, 'out_file', output_node,
'skeleton_bin')
# Run randomise if required and connect its output to the output node
if design_mat is not None and design_con is not None:
randomise = pe.Node(interface=fsl.Randomise(), name='randomise')
randomise.inputs.base_name = 'stats_tbss'
randomise.inputs.tfce2D = True
randomise.inputs.num_perm = 5000
workflow.connect(all_fa_projected, 'projected_data', randomise,
'in_file')
randomise.inputs.design_mat = design_mat
randomise.inputs.design_con = design_con
workflow.connect(binarised_skeleton, 'out_file', randomise, 'mask')
workflow.connect(randomise, 'tstat_files', output_node,
't_contrast_raw_stat')
workflow.connect(randomise, 't_p_files', output_node,
't_contrast_uncorrected_pvalue')
workflow.connect(randomise, 't_corrected_p_files', output_node,
't_contrast_corrected_pvalue')
# Create nodes to rename the outputs
mean_fa_renamer = pe.Node(interface=niu.Rename(
format_string='tbss_mean_fa', keep_ext=True),
name='mean_fa_renamer')
workflow.connect(output_node, 'mean_fa', mean_fa_renamer, 'in_file')
mean_sk_renamer = pe.Node(interface=niu.Rename(
format_string='tbss_mean_fa_skeleton', keep_ext=True),
name='mean_sk_renamer')
workflow.connect(output_node, 'skeleton', mean_sk_renamer, 'in_file')
bin_ske_renamer = pe.Node(interface=niu.Rename(
format_string='tbss_mean_fa_skeleton_mask', keep_ext=True),
name='bin_ske_renamer')
workflow.connect(output_node, 'skeleton_bin', bin_ske_renamer, 'in_file')
fa_skel_renamer = pe.Node(interface=niu.Rename(
format_string='tbss_all_fa_skeletonised', keep_ext=True),
name='fa_skel_renamer')
workflow.connect(output_node, 'all_fa_skeletonised', fa_skel_renamer,
'in_file')
md_skel_renamer = pe.Node(interface=niu.Rename(
format_string='tbss_all_md_skeletonised', keep_ext=True),
name='md_skel_renamer')
workflow.connect(output_node, 'all_md_skeletonised', md_skel_renamer,
'in_file')
rd_skel_renamer = pe.Node(interface=niu.Rename(
format_string='tbss_all_rd_skeletonised', keep_ext=True),
name='rd_skel_renamer')
workflow.connect(output_node, 'all_rd_skeletonised', rd_skel_renamer,
'in_file')
ad_skel_renamer = pe.Node(interface=niu.Rename(
format_string='tbss_all_ad_skeletonised', keep_ext=True),
name='ad_skel_renamer')
workflow.connect(output_node, 'all_ad_skeletonised', ad_skel_renamer,
'in_file')
# Create a data sink
ds = pe.Node(nio.DataSink(parameterization=False), name='data_sink')
ds.inputs.base_directory = os.path.abspath(output_dir)
# Connect the data sink
workflow.connect(mean_fa_renamer, 'out_file', ds, '@mean_fa')
workflow.connect(mean_sk_renamer, 'out_file', ds, '@skel_fa')
workflow.connect(bin_ske_renamer, 'out_file', ds, '@bkel_fa')
workflow.connect(fa_skel_renamer, 'out_file', ds, '@all_fa')
workflow.connect(md_skel_renamer, 'out_file', ds, '@all_md')
workflow.connect(rd_skel_renamer, 'out_file', ds, '@all_rd')
workflow.connect(ad_skel_renamer, 'out_file', ds, '@all_ad')
if design_mat is not None and design_con is not None:
workflow.connect(output_node, 't_contrast_raw_stat', ds,
'@t_contrast_raw_stat')
workflow.connect(output_node, 't_contrast_uncorrected_pvalue', ds,
'@t_contrast_uncorrected_pvalue')
workflow.connect(output_node, 't_contrast_corrected_pvalue', ds,
'@t_contrast_corrected_pvalue')
return workflow
def tbss4d(self, tbss_dir, oDir, template, **kwargs):
"""
python function to generate 4d nii files for randomise from TBSS.
Required arguments:
tbss_dir --- where the 'FA' and 'stats' folder exists. This by default on knicr servers is here:
/Volumes/rbraid/mr_data_idc/aug2013_final/dti/tbss
oDir --- this is where you want the 4d files to go. This should be specific to the analysis you are doing
e.g., /home/tonya/my_data/tbss/dysregulation_analysis/
Default is to assume scalars FA, MD, RD, AD are all processed.
Also assumes the subject files are named as such:
idc_#_SCALAR_to_FMRIB58_FA_2mm_nonlinear.nii.gz
Optional Arguments:
tbss_pfix --- default is "idc_"
This is what goes before the subject id
String type
tbss_sfix --- default is "_to_FMRIB58_FA_2mm_nonlin.nii.gz"
This is what goes after the subject id and scalar name (eg FA, MD)
String type
Scalars --- List of Scalars
default is: [FA, MD, RD, AD]
List type
opfix --- prefix for the 4d output file. suffix will be the scalar type.
default is tbss_all_
string type.
"""
tbss_pfix = 'idc_'
tbss_sfix = '_to_FMRIB58_FA_2mm_nonlin.nii.gz'
scalars = ['FA', 'MD', 'RD', 'AD']
opfix = 'tbss_all_'
threshold = 0.2
for i in kwargs.keys():
if i == 'tbss_pfix':
tbss_pfix = kwargs[i]
elif i == 'tbss_sfix':
tbss_sfix = kwargs[i]
elif i == 'opfix':
opfix = kwargs[i]
elif i == 'scalars':
scalars = kwargs[i]
if not type(scalars) is list:
print 'ERROR....scalar option must be a LIST!'
print 'Resetting scalars to be FA only...'
scalars = ['FA']
elif i == 'threshold':
threshold = kwarg[i]
for scalar in scalars:
print 'writing out 4d file for component: ', scalar
fourDlist = []
for s in range(0, self.subj_list.shape[0]):
subj = str(int(self.subj_list[s][0]))
tbss_file = os.path.join(
tbss_dir, 'FA',
tbss_pfix + subj + '_' + scalar + tbss_sfix)
if not os.path.exists(tbss_file):
print 'CANNOT FIND tbss data for :', subj, scalar
print 'Looked here: ', tbss_file
print 'Cannot continue...must exit...'
sys.exit(0)
fourDlist.append(tbss_file)
#merge the file
logFile = os.path.join(oDir, 'tbss_nonFA.log')
merged4d = os.path.join(oDir, opfix + scalar + '.nii.gz')
if not os.path.exists(merged4d):
fslmerge = fsl.Merge(dimension='t',
terminal_output='stream',
in_files=fourDlist,
merged_file=merged4d,
output_type='NIFTI_GZ')
fslmerge.run()
write_log(fslmerge.cmdline, logFile)
subjinfo = merged4d.replace('.nii.gz', '_mergeInfo.csv')
write_txt(fourDlist, subjinfo)
if scalar.endswith('_FA'):
#next we make the mean FA image
op_str = '-max 0 -Tmin -bin'
fslmaths = fsl.ImageMaths(in_file=merged4d,
op_string=op_str,
out_file=os.path.join(
oDir, 'mean_FA_mask.nii.gz'),
output_type='NIFTI_GZ',
out_data_type='char')
fslmaths.run()
write_log(fslmaths.cmdline, logFile)
op_str = '-mas ' + os.path.join(oDir, 'mean_FA_mask.nii.gz')
fslmaths = fsl.ImageMaths(in_file=merged4d,
op_string=op_str,
out_file=merged4d.replace(
'.nii.gz', '_masked.nii.gz'),
output_type='NIFTI_GZ')
fslmaths.run()
write_log(fslmaths.cmdline, logFile)
op_str = '-Tmean'
fslmaths = fsl.ImageMaths(
in_file=merged4d.replace('.nii.gz', '_masked.nii.gz'),
op_string=op_str,
out_file=os.path.join(oDir, 'mean_FA.nii.gz'),
output_type='NIFTI_GZ')
fslmaths.run()
write_log(fslmaths.cmdline, logFile)
#now make the initial skeleton
tbss_skeleton = fsl.TractSkeleton(
in_file=os.path.join(oDir, 'mean_FA.nii.gz'),
skeleton_file=os.path.join(oDir,
'mean_FA_skeleton.nii.gz'))
tbss_skeleton.run()
write_log(tbss_skeleton.cmdline, logFile)
print 'Creating Skeleton Mask using threshold', str(threshold)
op_str = '-thr ' + str(threshold) + ' -bin'
fslmaths = fsl.ImageMaths(
in_file=os.path.join(oDir, 'mean_FA_skeleton.nii.gz'),
op_string=op_str,
out_file=os.path.join(oDir,
'mean_FA_skeleton_mask.nii.gz'),
output_type='NIFTI_GZ')
fslmaths.run()
write_log(fslmaths.cmdline, logFile)
print 'Creating skeleton distancemap (for use in projection search)'
op_str = '-mul 1 -add 1 -add ' + os.path.join(
oDir, 'mean_FA_skeleton_mask.nii.gz')
fslmaths = fsl.ImageMaths(
in_file=os.path.join(oDir, 'mean_FA_mask.nii.gz'),
op_string=op_str,
out_file=os.path.join(oDir,
'mean_FA_skeleton_mask_dst.nii.gz'),
output_type='NIFTI_GZ')
fslmaths.run()
write_log(fslmaths.cmdline, logFile)
distancemap = fsl.DistanceMap(
in_file=os.path.join(oDir,
'mean_FA_skeleton_mask_dst.nii.gz'),
distance_map=os.path.join(
oDir, 'mean_FA_skeleton_mask_dst.nii.gz'))
distancemap.run()
write_log(distancemap.cmdline, logFile)
#to make things easier below, make a generic-named copy of the 4d fa map. needed for skeletonization.
shutil.copyfile(merged4d.replace('.nii.gz', '_masked.nii.gz'),
os.path.join(oDir, 'all_FA.nii.gz'))
else:
if not os.path.exists(os.path.join(oDir,
'mean_FA_mask.nii.gz')):
print oDir, 'mean_FA_mask.nii.gz does not exist'
print 'Make sure FA is the first scalar in your list. Must exit'
sys.exit(0)
#mask other scalars
op_str = '-mas ' + os.path.join(oDir, 'mean_FA_mask.nii.gz')
fslmaths = fsl.ImageMaths(in_file=merged4d,
op_string=op_str,
out_file=merged4d.replace(
'.nii.gz', '_masked.nii.gz'),
output_type='NIFTI_GZ')
fslmaths.run()
write_log(fslmaths.cmdline, logFile)
#skeletenize
nb_template = nb.load(template)
x = abs(nb_template.get_affine()[0][0])
y = abs(nb_template.get_affine()[1][1])
z = abs(nb_template.get_affine()[2][2])
cing_file1mm = os.path.join(os.environ['FSLDIR'], 'data',
'standard', 'LowerCingulum_1mm.nii.gz')
if x == 1 and y == 1 and z == 1:
cing_file = cing_file1mm
elif x == 2 and y == 2 and z == 2:
cing_file = os.path.join(os.environ['FSLDIR'], 'data',
'standard',
'LowerCingulum_2mm.nii.gz')
if not os.path.exists(cing_file):
cing_file = os.path.join(oDir, 'LowerCingulum_2mm.nii.gz')
flirt = fsl.FLIRT(in_file=cing_file1mm,
reference=os.path.join(
os.environ['FSLDIR'], 'data',
'standard', 'MNI152_T1_2mm.nii.gz'),
out_file=cing_file,
args='-applyisoxfm 2')
flirt.run()
write_log(flirt.cmdline, logFile)
else:
cing_file = False
if not cing_file:
print 'Cannot find cingulum file.'
return
print 'Projecting all FA data onto Skeleton'
#run the tbss skeletonize thing
if not os.path.exists(os.path.join(oDir, 'mean_FA_mask.nii.gz')):
print oDir, 'mean_FA_mask.nii.gz does not exist'
print 'Make sure FA is the first scalar in your list. Must exit'
sys.exit(0)
final4d = merged4d.replace('.nii.gz', '_skeletonized.nii.gz')
tbss_skeleton = fsl.TractSkeleton(
threshold=threshold,
distance_map=os.path.join(oDir,
'mean_FA_skeleton_mask_dst.nii.gz'),
alt_data_file=merged4d.replace('.nii.gz', '_masked.nii.gz'),
data_file=os.path.join(oDir, 'all_FA.nii.gz'),
projected_data=final4d,
in_file=os.path.join(oDir, 'mean_FA.nii.gz'),
project_data=True)
tbss_skeleton.inputs.use_cingulum_mask = Undefined
tbss_skeleton.inputs.search_mask_file = cing_file
tbss_skeleton.run()
write_log(tbss_skeleton.cmdline, logFile)
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