def fsl_name(obj, fname):
"""Create valid fsl name, including file extension for output type.
"""
ext = Info.output_type_to_ext(obj.inputs.output_type)
return fname + ext
@pytest.fixture()
def setup_infile(tmpdir):
ext = Info.output_type_to_ext(Info.output_type())
tmp_infile = tmpdir.join('foo' + ext)
tmp_infile.open("w")
return (tmp_infile.strpath, tmpdir.strpath)
@pytest.mark.skipif(no_fsl(), reason="fsl is not installed")
def test_bet(setup_infile):
tmp_infile, tp_dir = setup_infile
better = fsl.BET()
assert better.cmd == 'bet'
# Test raising error with mandatory args absent
with pytest.raises(ValueError):
better.run()
# Test generated outfile name
better.inputs.in_file = tmp_infile
outfile = fsl_name(better, 'foo_brain')
outpath = os.path.join(os.getcwd(), outfile)
realcmd = 'bet %s %s' % (tmp_infile, outpath)
assert better.cmdline == realcmd
def create_tbss_2_reg(name="tbss_2_reg"):
"""TBSS nonlinear registration:
A pipeline that does the same as 'tbss_2_reg -t' script in FSL. '-n' option
is not supported at the moment.
Example
-------
>>> from nipype.workflows.dmri.fsl import tbss
>>> tbss2 = create_tbss_2_reg(name="tbss2")
>>> tbss2.inputs.inputnode.target = fsl.Info.standard_image("FMRIB58_FA_1mm.nii.gz") # doctest: +SKIP
>>> tbss2.inputs.inputnode.fa_list = ['s1_FA.nii', 's2_FA.nii', 's3_FA.nii']
>>> tbss2.inputs.inputnode.mask_list = ['s1_mask.nii', 's2_mask.nii', 's3_mask.nii']
Inputs::
inputnode.fa_list
inputnode.mask_list
inputnode.target
Outputs::
outputnode.field_list
"""
# Define the inputnode
inputnode = pe.Node(interface=util.IdentityInterface(fields=["fa_list",
"mask_list",
"target"]),
name="inputnode")
# Flirt the FA image to the target
flirt = pe.MapNode(interface=fsl.FLIRT(dof=12),
iterfield=['in_file', 'in_weight'],
name="flirt")
fnirt = pe.MapNode(interface=fsl.FNIRT(fieldcoeff_file=True),
iterfield=['in_file', 'inmask_file', 'affine_file'],
name="fnirt")
# Fnirt the FA image to the target
if fsl.no_fsl():
warn('NO FSL found')
else:
config_file = os.path.join(os.environ["FSLDIR"],
"etc/flirtsch/FA_2_FMRIB58_1mm.cnf")
fnirt.inputs.config_file=config_file
# Define the registration workflow
tbss2 = pe.Workflow(name=name)
# Connect up the registration workflow
tbss2.connect([
(inputnode, flirt, [("fa_list", "in_file"),
("target", "reference"),
("mask_list", "in_weight")]),
(inputnode, fnirt, [("fa_list", "in_file"),
("mask_list", "inmask_file"),
("target", "ref_file")]),
(flirt, fnirt, [("out_matrix_file", "affine_file")]),
])
# Define the outputnode
outputnode = pe.Node(interface=util.IdentityInterface(fields=['field_list']),
name="outputnode")
tbss2.connect([
(fnirt, outputnode, [('fieldcoeff_file', 'field_list')])
])
return tbss2
def fsl_name(obj, fname):
"""Create valid fsl name, including file extension for output type.
"""
ext = Info.output_type_to_ext(obj.inputs.output_type)
return fname + ext
@pytest.fixture()
def setup_infile(tmpdir):
ext = Info.output_type_to_ext(Info.output_type())
tmp_infile = tmpdir.join('foo' + ext)
tmp_infile.open("w")
return (tmp_infile.strpath, tmpdir.strpath)
@pytest.mark.skipif(no_fsl(), reason="fsl is not installed")
def test_bet(setup_infile):
tmp_infile, tp_dir = setup_infile
better = fsl.BET()
assert better.cmd == 'bet'
# Test raising error with mandatory args absent
with pytest.raises(ValueError):
better.run()
# Test generated outfile name
better.inputs.in_file = tmp_infile
outfile = fsl_name(better, 'foo_brain')
outpath = os.path.join(os.getcwd(), outfile)
realcmd = 'bet %s %s' % (tmp_infile, outpath)
assert better.cmdline == realcmd
def create_tbss_all(name='tbss_all', estimate_skeleton=True):
"""Create a pipeline that combines create_tbss_* pipelines
Example
-------
>>> from nipype.workflows.dmri.fsl import tbss
>>> tbss = tbss.create_tbss_all('tbss')
>>> tbss.inputs.inputnode.skeleton_thresh = 0.2
Inputs::
inputnode.fa_list
inputnode.skeleton_thresh
Outputs::
outputnode.meanfa_file
outputnode.projectedfa_file
outputnode.skeleton_file
outputnode.skeleton_mask
"""
# Define the inputnode
inputnode = pe.Node(interface=util.IdentityInterface(fields=['fa_list',
'skeleton_thresh']),
name='inputnode')
tbss1 = create_tbss_1_preproc(name='tbss1')
tbss2 = create_tbss_2_reg(name='tbss2')
if fsl.no_fsl():
warn('NO FSL found')
else:
tbss2.inputs.inputnode.target = fsl.Info.standard_image("FMRIB58_FA_1mm.nii.gz")
tbss3 = create_tbss_3_postreg(name='tbss3', estimate_skeleton=estimate_skeleton)
tbss4 = create_tbss_4_prestats(name='tbss4')
tbss_all = pe.Workflow(name=name)
tbss_all.connect([
(inputnode, tbss1, [('fa_list', 'inputnode.fa_list')]),
(inputnode, tbss4, [('skeleton_thresh', 'inputnode.skeleton_thresh')]),
(tbss1, tbss2, [('outputnode.fa_list', 'inputnode.fa_list'),
('outputnode.mask_list', 'inputnode.mask_list')]),
(tbss1, tbss3, [('outputnode.fa_list', 'inputnode.fa_list')]),
(tbss2, tbss3, [('outputnode.field_list', 'inputnode.field_list')]),
(tbss3, tbss4, [
('outputnode.groupmask', 'inputnode.groupmask'),
('outputnode.skeleton_file', 'inputnode.skeleton_file'),
('outputnode.meanfa_file', 'inputnode.meanfa_file'),
('outputnode.mergefa_file', 'inputnode.mergefa_file')
])
])
# Define the outputnode
outputnode = pe.Node(interface=util.IdentityInterface(fields=['groupmask',
'skeleton_file3',
'meanfa_file',
'mergefa_file',
'projectedfa_file',
'skeleton_file4',
'skeleton_mask',
'distance_map']),
name='outputnode')
outputall_node = pe.Node(interface=util.IdentityInterface(
fields=['fa_list1',
'mask_list1',
'field_list2',
'groupmask3',
'skeleton_file3',
'meanfa_file3',
'mergefa_file3',
'projectedfa_file4',
'skeleton_mask4',
'distance_map4']),
name='outputall_node')
tbss_all.connect([
(tbss3, outputnode, [('outputnode.meanfa_file', 'meanfa_file'),
('outputnode.mergefa_file', 'mergefa_file'),
('outputnode.groupmask', 'groupmask'),
('outputnode.skeleton_file', 'skeleton_file3'),
]),
(tbss4, outputnode, [('outputnode.projectedfa_file', 'projectedfa_file'),
('outputnode.skeleton_file', 'skeleton_file4'),
('outputnode.skeleton_mask', 'skeleton_mask'),
('outputnode.distance_map', 'distance_map'),
]),
(tbss1, outputall_node, [('outputnode.fa_list', 'fa_list1'),
('outputnode.mask_list', 'mask_list1'),
]),
(tbss2, outputall_node, [('outputnode.field_list', 'field_list2'),
]),
(tbss3, outputall_node, [
('outputnode.meanfa_file', 'meanfa_file3'),
('outputnode.mergefa_file', 'mergefa_file3'),
('outputnode.groupmask', 'groupmask3'),
('outputnode.skeleton_file', 'skeleton_file3'),
]),
(tbss4, outputall_node, [
('outputnode.projectedfa_file', 'projectedfa_file4'),
('outputnode.skeleton_mask', 'skeleton_mask4'),
('outputnode.distance_map', 'distance_map4'),
]),
])
return tbss_all
def create_tbss_non_FA(name='tbss_non_FA'):
"""
A pipeline that implement tbss_non_FA in FSL
Example
-------
>>> from nipype.workflows.dmri.fsl import tbss
>>> tbss_MD = tbss.create_tbss_non_FA()
>>> tbss_MD.inputs.inputnode.file_list = []
>>> tbss_MD.inputs.inputnode.field_list = []
>>> tbss_MD.inputs.inputnode.skeleton_thresh = 0.2
>>> tbss_MD.inputs.inputnode.groupmask = './xxx'
>>> tbss_MD.inputs.inputnode.meanfa_file = './xxx'
>>> tbss_MD.inputs.inputnode.distance_map = []
>>> tbss_MD.inputs.inputnode.all_FA_file = './xxx'
Inputs::
inputnode.file_list
inputnode.field_list
inputnode.skeleton_thresh
inputnode.groupmask
inputnode.meanfa_file
inputnode.distance_map
inputnode.all_FA_file
Outputs::
outputnode.projected_nonFA_file
"""
# Define the inputnode
inputnode = pe.Node(interface=util.IdentityInterface(fields=['file_list',
'field_list',
'skeleton_thresh',
'groupmask',
'meanfa_file',
'distance_map',
'all_FA_file']),
name='inputnode')
# Apply the warpfield to the non FA image
applywarp = pe.MapNode(interface=fsl.ApplyWarp(),
iterfield=['in_file', 'field_file'],
name="applywarp")
if fsl.no_fsl():
warn('NO FSL found')
else:
applywarp.inputs.ref_file = fsl.Info.standard_image("FMRIB58_FA_1mm.nii.gz")
# Merge the non FA files into a 4D file
merge = pe.Node(fsl.Merge(dimension="t"), name="merge")
#merged_file="all_FA.nii.gz"
maskgroup = pe.Node(fsl.ImageMaths(op_string="-mas",
suffix="_masked"),
name="maskgroup")
projectfa = pe.Node(fsl.TractSkeleton(project_data=True,
#projected_data = 'test.nii.gz',
use_cingulum_mask=True
),
name="projectfa")
tbss_non_FA = pe.Workflow(name=name)
tbss_non_FA.connect([
(inputnode, applywarp, [('file_list', 'in_file'),
('field_list', 'field_file'),
]),
(applywarp, merge, [("out_file", "in_files")]),
(merge, maskgroup, [("merged_file", "in_file")]),
(inputnode, maskgroup, [('groupmask', 'in_file2')]),
(maskgroup, projectfa, [('out_file', 'alt_data_file')]),
(inputnode, projectfa, [('skeleton_thresh', 'threshold'),
("meanfa_file", "in_file"),
("distance_map", "distance_map"),
("all_FA_file", 'data_file')
]),
])
# Define the outputnode
outputnode = pe.Node(interface=util.IdentityInterface(
fields=['projected_nonFA_file']),
name='outputnode')
tbss_non_FA.connect([
(projectfa, outputnode, [('projected_data', 'projected_nonFA_file'),
]),
])
return tbss_non_FA
def create_tbss_3_postreg(name='tbss_3_postreg', estimate_skeleton=True):
"""Post-registration processing: derive mean_FA and mean_FA_skeleton from
mean of all subjects in study. Target is assumed to be FMRIB58_FA_1mm.
A pipeline that does the same as 'tbss_3_postreg -S' script from FSL
Setting 'estimate_skeleton to False will use precomputed FMRIB58_FA-skeleton_1mm
skeleton (same as 'tbss_3_postreg -T').
Example
-------
>>> from nipype.workflows.dmri.fsl import tbss
>>> tbss3 = tbss.create_tbss_3_postreg()
>>> tbss3.inputs.inputnode.fa_list = ['s1_wrapped_FA.nii', 's2_wrapped_FA.nii', 's3_wrapped_FA.nii']
Inputs::
inputnode.field_list
inputnode.fa_list
Outputs::
outputnode.groupmask
outputnode.skeleton_file
outputnode.meanfa_file
outputnode.mergefa_file
"""
# Create the inputnode
inputnode = pe.Node(interface=util.IdentityInterface(fields=['field_list',
'fa_list']),
name='inputnode')
# Apply the warpfield to the masked FA image
applywarp = pe.MapNode(interface=fsl.ApplyWarp(),
iterfield=['in_file', 'field_file'],
name="applywarp")
if fsl.no_fsl():
warn('NO FSL found')
else:
applywarp.inputs.ref_file = fsl.Info.standard_image("FMRIB58_FA_1mm.nii.gz")
# Merge the FA files into a 4D file
mergefa = pe.Node(fsl.Merge(dimension="t"),
name="mergefa")
# Get a group mask
groupmask = pe.Node(fsl.ImageMaths(op_string="-max 0 -Tmin -bin",
out_data_type="char",
suffix="_mask"),
name="groupmask")
maskgroup = pe.Node(fsl.ImageMaths(op_string="-mas",
suffix="_masked"),
name="maskgroup")
tbss3 = pe.Workflow(name=name)
tbss3.connect([
(inputnode, applywarp, [("fa_list", "in_file"),
("field_list", "field_file")]),
(applywarp, mergefa, [("out_file", "in_files")]),
(mergefa, groupmask, [("merged_file", "in_file")]),
(mergefa, maskgroup, [("merged_file", "in_file")]),
(groupmask, maskgroup, [("out_file", "in_file2")]),
])
# Create outputnode
outputnode = pe.Node(interface=util.IdentityInterface(fields=['groupmask',
'skeleton_file',
'meanfa_file',
'mergefa_file']),
name='outputnode')
if estimate_skeleton:
# Take the mean over the fourth dimension
meanfa = pe.Node(fsl.ImageMaths(op_string="-Tmean",
suffix="_mean"),
name="meanfa")
# Use the mean FA volume to generate a tract skeleton
makeskeleton = pe.Node(fsl.TractSkeleton(skeleton_file=True),
name="makeskeleton")
tbss3.connect([
(maskgroup, meanfa, [("out_file", "in_file")]),
(meanfa, makeskeleton, [("out_file", "in_file")]),
(groupmask, outputnode, [('out_file', 'groupmask')]),
(makeskeleton, outputnode, [('skeleton_file', 'skeleton_file')]),
(meanfa, outputnode, [('out_file', 'meanfa_file')]),
(maskgroup, outputnode, [('out_file', 'mergefa_file')])
])
else:
#$FSLDIR/bin/fslmaths $FSLDIR/data/standard/FMRIB58_FA_1mm -mas mean_FA_mask mean_FA
maskstd = pe.Node(fsl.ImageMaths(op_string="-mas",
suffix="_masked"),
name="maskstd")
maskstd.inputs.in_file = fsl.Info.standard_image("FMRIB58_FA_1mm.nii.gz")
#$FSLDIR/bin/fslmaths mean_FA -bin mean_FA_mask
binmaskstd = pe.Node(fsl.ImageMaths(op_string="-bin"),
name="binmaskstd")
#$FSLDIR/bin/fslmaths all_FA -mas mean_FA_mask all_FA
maskgroup2 = pe.Node(fsl.ImageMaths(op_string="-mas",
suffix="_masked"),
name="maskgroup2")
tbss3.connect([
(groupmask, maskstd, [("out_file", "in_file2")]),
(maskstd, binmaskstd, [("out_file", "in_file")]),
(maskgroup, maskgroup2, [("out_file", "in_file")]),
(binmaskstd, maskgroup2, [("out_file", "in_file2")])
])
outputnode.inputs.skeleton_file = fsl.Info.standard_image("FMRIB58_FA-skeleton_1mm.nii.gz")
tbss3.connect([
(binmaskstd, outputnode, [('out_file', 'groupmask')]),
(maskstd, outputnode, [('out_file', 'meanfa_file')]),
(maskgroup2, outputnode, [('out_file', 'mergefa_file')])
])
return tbss3
def create_tbss_2_reg(name="tbss_2_reg"):
"""TBSS nonlinear registration:
A pipeline that does the same as 'tbss_2_reg -t' script in FSL. '-n' option
is not supported at the moment.
Example
-------
>>> from nipype.workflows.dmri.fsl import tbss
>>> tbss2 = create_tbss_2_reg(name="tbss2")
>>> tbss2.inputs.inputnode.target = fsl.Info.standard_image("FMRIB58_FA_1mm.nii.gz") # doctest: +SKIP
>>> tbss2.inputs.inputnode.fa_list = ['s1_FA.nii', 's2_FA.nii', 's3_FA.nii']
>>> tbss2.inputs.inputnode.mask_list = ['s1_mask.nii', 's2_mask.nii', 's3_mask.nii']
Inputs::
inputnode.fa_list
inputnode.mask_list
inputnode.target
Outputs::
outputnode.field_list
"""
# Define the inputnode
inputnode = pe.Node(interface=util.IdentityInterface(
fields=["fa_list", "mask_list", "target"]),
name="inputnode")
# Flirt the FA image to the target
flirt = pe.MapNode(interface=fsl.FLIRT(dof=12),
iterfield=['in_file', 'in_weight'],
name="flirt")
fnirt = pe.MapNode(interface=fsl.FNIRT(fieldcoeff_file=True),
iterfield=['in_file', 'inmask_file', 'affine_file'],
name="fnirt")
# Fnirt the FA image to the target
if fsl.no_fsl():
warn('NO FSL found')
else:
config_file = os.path.join(os.environ["FSLDIR"],
"etc/flirtsch/FA_2_FMRIB58_1mm.cnf")
fnirt.inputs.config_file = config_file
# Define the registration workflow
tbss2 = pe.Workflow(name=name)
# Connect up the registration workflow
tbss2.connect([
(inputnode, flirt, [("fa_list", "in_file"), ("target", "reference"),
("mask_list", "in_weight")]),
(inputnode, fnirt, [("fa_list", "in_file"),
("mask_list", "inmask_file"),
("target", "ref_file")]),
(flirt, fnirt, [("out_matrix_file", "affine_file")]),
])
# Define the outputnode
outputnode = pe.Node(
interface=util.IdentityInterface(fields=['field_list']),
name="outputnode")
tbss2.connect([(fnirt, outputnode, [('fieldcoeff_file', 'field_list')])])
return tbss2
def create_tbss_non_FA(name='tbss_non_FA'):
"""
A pipeline that implement tbss_non_FA in FSL
Example
-------
>>> from nipype.workflows.dmri.fsl import tbss
>>> tbss_MD = tbss.create_tbss_non_FA()
>>> tbss_MD.inputs.inputnode.file_list = []
>>> tbss_MD.inputs.inputnode.field_list = []
>>> tbss_MD.inputs.inputnode.skeleton_thresh = 0.2
>>> tbss_MD.inputs.inputnode.groupmask = './xxx'
>>> tbss_MD.inputs.inputnode.meanfa_file = './xxx'
>>> tbss_MD.inputs.inputnode.distance_map = []
Inputs::
inputnode.file_list
inputnode.field_list
inputnode.skeleton_thresh
inputnode.groupmask
inputnode.meanfa_file
inputnode.distance_map
Outputs::
outputnode.projected_nonFA_file
"""
# Define the inputnode
inputnode = pe.Node(interface=util.IdentityInterface(fields=[
'file_list', 'field_list', 'skeleton_thresh', 'groupmask',
'meanfa_file', 'distance_map'
]),
name='inputnode')
# Apply the warpfield to the non FA image
applywarp = pe.MapNode(interface=fsl.ApplyWarp(),
iterfield=['in_file', 'field_file'],
name="applywarp")
if fsl.no_fsl():
warn('NO FSL found')
else:
applywarp.inputs.ref_file = fsl.Info.standard_image(
"FMRIB58_FA_1mm.nii.gz")
# Merge the non FA files into a 4D file
merge = pe.Node(fsl.Merge(dimension="t"), name="merge")
#merged_file="all_FA.nii.gz"
maskgroup = pe.Node(fsl.ImageMaths(op_string="-mas", suffix="_masked"),
name="maskgroup")
projectfa = pe.Node(
fsl.TractSkeleton(
project_data=True,
#projected_data = 'test.nii.gz',
use_cingulum_mask=True),
name="projectfa")
tbss_non_FA = pe.Workflow(name=name)
tbss_non_FA.connect([
(inputnode, applywarp, [
('file_list', 'in_file'),
('field_list', 'field_file'),
]),
(applywarp, merge, [("out_file", "in_files")]),
(merge, maskgroup, [("merged_file", "in_file")]),
(inputnode, maskgroup, [('groupmask', 'in_file2')]),
(maskgroup, projectfa, [('out_file', 'data_file')]),
(inputnode, projectfa, [
('skeleton_thresh', 'threshold'),
("meanfa_file", "in_file"),
("distance_map", "distance_map"),
]),
])
# Define the outputnode
outputnode = pe.Node(
interface=util.IdentityInterface(fields=['projected_nonFA_file']),
name='outputnode')
tbss_non_FA.connect([
(projectfa, outputnode, [
('projected_data', 'projected_nonFA_file'),
]),
])
return tbss_non_FA
def create_tbss_all(name='tbss_all', estimate_skeleton=True):
"""Create a pipeline that combines create_tbss_* pipelines
Example
-------
>>> from nipype.workflows.dmri.fsl import tbss
>>> tbss = tbss.create_tbss_all('tbss')
>>> tbss.inputs.inputnode.skeleton_thresh = 0.2
Inputs::
inputnode.fa_list
inputnode.skeleton_thresh
Outputs::
outputnode.meanfa_file
outputnode.projectedfa_file
outputnode.skeleton_file
outputnode.skeleton_mask
"""
# Define the inputnode
inputnode = pe.Node(interface=util.IdentityInterface(
fields=['fa_list', 'skeleton_thresh']),
name='inputnode')
tbss1 = create_tbss_1_preproc(name='tbss1')
tbss2 = create_tbss_2_reg(name='tbss2')
if fsl.no_fsl():
warn('NO FSL found')
else:
tbss2.inputs.inputnode.target = fsl.Info.standard_image(
"FMRIB58_FA_1mm.nii.gz")
tbss3 = create_tbss_3_postreg(name='tbss3',
estimate_skeleton=estimate_skeleton)
tbss4 = create_tbss_4_prestats(name='tbss4')
tbss_all = pe.Workflow(name=name)
tbss_all.connect([
(inputnode, tbss1, [('fa_list', 'inputnode.fa_list')]),
(inputnode, tbss4, [('skeleton_thresh', 'inputnode.skeleton_thresh')]),
(tbss1, tbss2, [('outputnode.fa_list', 'inputnode.fa_list'),
('outputnode.mask_list', 'inputnode.mask_list')]),
(tbss1, tbss3, [('outputnode.fa_list', 'inputnode.fa_list')]),
(tbss2, tbss3, [('outputnode.field_list', 'inputnode.field_list')]),
(tbss3, tbss4, [('outputnode.groupmask', 'inputnode.groupmask'),
('outputnode.skeleton_file',
'inputnode.skeleton_file'),
('outputnode.meanfa_file', 'inputnode.meanfa_file'),
('outputnode.mergefa_file', 'inputnode.mergefa_file')])
])
# Define the outputnode
outputnode = pe.Node(interface=util.IdentityInterface(fields=[
'groupmask', 'skeleton_file3', 'meanfa_file', 'mergefa_file',
'projectedfa_file', 'skeleton_file4', 'skeleton_mask', 'distance_map'
]),
name='outputnode')
outputall_node = pe.Node(interface=util.IdentityInterface(fields=[
'fa_list1', 'mask_list1', 'field_list2', 'groupmask3',
'skeleton_file3', 'meanfa_file3', 'mergefa_file3', 'projectedfa_file4',
'skeleton_mask4', 'distance_map4'
]),
name='outputall_node')
tbss_all.connect([
(tbss3, outputnode, [
('outputnode.meanfa_file', 'meanfa_file'),
('outputnode.mergefa_file', 'mergefa_file'),
('outputnode.groupmask', 'groupmask'),
('outputnode.skeleton_file', 'skeleton_file3'),
]),
(tbss4, outputnode, [
('outputnode.projectedfa_file', 'projectedfa_file'),
('outputnode.skeleton_file', 'skeleton_file4'),
('outputnode.skeleton_mask', 'skeleton_mask'),
('outputnode.distance_map', 'distance_map'),
]),
(tbss1, outputall_node, [
('outputnode.fa_list', 'fa_list1'),
('outputnode.mask_list', 'mask_list1'),
]),
(tbss2, outputall_node, [
('outputnode.field_list', 'field_list2'),
]),
(tbss3, outputall_node, [
('outputnode.meanfa_file', 'meanfa_file3'),
('outputnode.mergefa_file', 'mergefa_file3'),
('outputnode.groupmask', 'groupmask3'),
('outputnode.skeleton_file', 'skeleton_file3'),
]),
(tbss4, outputall_node, [
('outputnode.projectedfa_file', 'projectedfa_file4'),
('outputnode.skeleton_mask', 'skeleton_mask4'),
('outputnode.distance_map', 'distance_map4'),
]),
])
return tbss_all
def create_tbss_3_postreg(name='tbss_3_postreg', estimate_skeleton=True):
"""Post-registration processing: derive mean_FA and mean_FA_skeleton from
mean of all subjects in study. Target is assumed to be FMRIB58_FA_1mm.
A pipeline that does the same as 'tbss_3_postreg -S' script from FSL
Setting 'estimate_skeleton to False will use precomputed FMRIB58_FA-skeleton_1mm
skeleton (same as 'tbss_3_postreg -T').
Example
-------
>>> from nipype.workflows.dmri.fsl import tbss
>>> tbss3 = tbss.create_tbss_3_postreg()
>>> tbss3.inputs.inputnode.fa_list = ['s1_wrapped_FA.nii', 's2_wrapped_FA.nii', 's3_wrapped_FA.nii']
Inputs::
inputnode.field_list
inputnode.fa_list
Outputs::
outputnode.groupmask
outputnode.skeleton_file
outputnode.meanfa_file
outputnode.mergefa_file
"""
# Create the inputnode
inputnode = pe.Node(
interface=util.IdentityInterface(fields=['field_list', 'fa_list']),
name='inputnode')
# Apply the warpfield to the masked FA image
applywarp = pe.MapNode(interface=fsl.ApplyWarp(),
iterfield=['in_file', 'field_file'],
name="applywarp")
if fsl.no_fsl():
warn('NO FSL found')
else:
applywarp.inputs.ref_file = fsl.Info.standard_image(
"FMRIB58_FA_1mm.nii.gz")
# Merge the FA files into a 4D file
mergefa = pe.Node(fsl.Merge(dimension="t"), name="mergefa")
# Get a group mask
groupmask = pe.Node(fsl.ImageMaths(op_string="-max 0 -Tmin -bin",
out_data_type="char",
suffix="_mask"),
name="groupmask")
maskgroup = pe.Node(fsl.ImageMaths(op_string="-mas", suffix="_masked"),
name="maskgroup")
tbss3 = pe.Workflow(name=name)
tbss3.connect([
(inputnode, applywarp, [("fa_list", "in_file"),
("field_list", "field_file")]),
(applywarp, mergefa, [("out_file", "in_files")]),
(mergefa, groupmask, [("merged_file", "in_file")]),
(mergefa, maskgroup, [("merged_file", "in_file")]),
(groupmask, maskgroup, [("out_file", "in_file2")]),
])
# Create outputnode
outputnode = pe.Node(interface=util.IdentityInterface(
fields=['groupmask', 'skeleton_file', 'meanfa_file', 'mergefa_file']),
name='outputnode')
if estimate_skeleton:
# Take the mean over the fourth dimension
meanfa = pe.Node(fsl.ImageMaths(op_string="-Tmean", suffix="_mean"),
name="meanfa")
# Use the mean FA volume to generate a tract skeleton
makeskeleton = pe.Node(fsl.TractSkeleton(skeleton_file=True),
name="makeskeleton")
tbss3.connect([
(maskgroup, meanfa, [("out_file", "in_file")]),
(meanfa, makeskeleton, [("out_file", "in_file")]),
(groupmask, outputnode, [('out_file', 'groupmask')]),
(makeskeleton, outputnode, [('skeleton_file', 'skeleton_file')]),
(meanfa, outputnode, [('out_file', 'meanfa_file')]),
(maskgroup, outputnode, [('out_file', 'mergefa_file')])
])
else:
#$FSLDIR/bin/fslmaths $FSLDIR/data/standard/FMRIB58_FA_1mm -mas mean_FA_mask mean_FA
maskstd = pe.Node(fsl.ImageMaths(op_string="-mas", suffix="_masked"),
name="maskstd")
maskstd.inputs.in_file = fsl.Info.standard_image(
"FMRIB58_FA_1mm.nii.gz")
#$FSLDIR/bin/fslmaths mean_FA -bin mean_FA_mask
binmaskstd = pe.Node(fsl.ImageMaths(op_string="-bin"),
name="binmaskstd")
#$FSLDIR/bin/fslmaths all_FA -mas mean_FA_mask all_FA
maskgroup2 = pe.Node(fsl.ImageMaths(op_string="-mas",
suffix="_masked"),
name="maskgroup2")
tbss3.connect([(groupmask, maskstd, [("out_file", "in_file2")]),
(maskstd, binmaskstd, [("out_file", "in_file")]),
(maskgroup, maskgroup2, [("out_file", "in_file")]),
(binmaskstd, maskgroup2, [("out_file", "in_file2")])])
outputnode.inputs.skeleton_file = fsl.Info.standard_image(
"FMRIB58_FA-skeleton_1mm.nii.gz")
tbss3.connect([(binmaskstd, outputnode, [('out_file', 'groupmask')]),
(maskstd, outputnode, [('out_file', 'meanfa_file')]),
(maskgroup2, outputnode, [('out_file', 'mergefa_file')])
])
return tbss3
