def get_norm_wf(name="norm_wf"):
wf = Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['t1w', 't1w_brain']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(
fields=['t1w_2_MNI_mat', 't1w_2_MNI_warp', 't1w_MNIspace']),
name='outputnode')
# otherwise fnirt is trying to write to the sourcedata dir
t1w_dummy = pe.Node(fsl.ImageMaths(), name="t1w_dummy")
wf.connect(inputnode, "t1w", t1w_dummy, "in_file")
t1w_2_MNI_flirt = pe.Node(fsl.FLIRT(dof=12), name='t1w_2_MNI_flirt')
t1w_2_MNI_flirt.inputs.reference = fsl.Info.standard_image(
'MNI152_T1_1mm_brain.nii.gz')
wf.connect(inputnode, 't1w_brain', t1w_2_MNI_flirt, 'in_file')
wf.connect(t1w_2_MNI_flirt, 'out_matrix_file', outputnode, 't1w_2_MNI_mat')
# 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
t1w_2_MNI_fnirt = pe.Node(fsl.FNIRT(), name='t1w_2_MNI_fnirt')
t1w_2_MNI_fnirt.inputs.config_file = 'T1_2_MNI152_2mm'
t1w_2_MNI_fnirt.inputs.ref_file = fsl.Info.standard_image(
'MNI152_T1_2mm.nii.gz')
t1w_2_MNI_fnirt.inputs.field_file = True
t1w_2_MNI_fnirt.plugin_args = {'submit_specs': 'request_memory = 4000'}
wf.connect(t1w_dummy, 'out_file', t1w_2_MNI_fnirt, 'in_file')
wf.connect(t1w_2_MNI_flirt, 'out_matrix_file', t1w_2_MNI_fnirt,
'affine_file')
wf.connect(t1w_2_MNI_fnirt, 'field_file', outputnode, 't1w_2_MNI_warp')
wf.connect(t1w_2_MNI_fnirt, 'warped_file', outputnode, 't1w_MNIspace')
return wf
def register(ref_img, in_img, cmd=None):
"""Find the 4D affine transform that registers in_img to ref_img
Args:
ref_img -- (type: ndarray) reference image
in_img -- (type: ndarray) input image to be registered
cmd -- (default: None) override default flirt parameters
an instance of nipype.interfaces.fsl.FLIRT
Returns: warped input image
"""
assert in_img.ndim == ref_img.ndim, 'Input and reference images must have the same '\
'number of dimensions'
in_file = _ndarray_to_nifti(in_img)
ref_file = _ndarray_to_nifti(ref_img)
out_file = NamedTemporaryFile(suffix='.nii.gz')
log_file = NamedTemporaryFile(suffix='.txt')
cmd = cmd or fsl.FNIRT()
cmd.inputs.in_file = in_file.name
cmd.inputs.ref_file = ref_file.name
cmd.inputs.output_type = 'NIFTI_GZ'
cmd.inputs.log_file = log_file.name
cmd.inputs.warped_file = out_file.name
log.debug('cmdline: {}'.format(cmd.cmdline))
res = cmd.run()
out_img = nib.load(out_file.name).get_data()
return out_img
def FNIRT(self, in_file: str, ref: str, out_dir: str, type: int, aff: str):
"""
FNIRT non-linear transformation
@param in_file: image to be transformed
@param ref: image to transform into
@param out_dir: directory for outputs
@param type: 0 for highres2lowres, 1 for atlasHighres2highres, 2 for atlasLabels2highres
@return:
"""
fnt = fsl.FNIRT()
fnt.inputs.in_file = in_file
fnt.inputs.ref_file = ref
fnt.inputs.affine_file = aff
if type == 0:
fnt.inputs.warped_file = f"{out_dir}/Highres2Lowres.nii"
print("Running non-linear transformation from Highres to Lowres:")
elif type == 1:
fnt.inputs.warped_file = f"{out_dir}/HighresAtlas2Highres.nii"
print("Running non-linear transformation from Atlas to Highres:")
elif type == 2:
fnt.inputs.warped_file = f"{out_dir}/LabelsAtlas2Highres.nii"
print("Running nonlinear transformation from Atlas to Highres:")
fnt.inputs.output_type = "NIFTI"
print(fnt.cmdline)
fnt.run()
return fnt.inputs.warped_file
def __init__(self, in_file='path', ref_file='path', **options):
from nipype.interfaces import fsl
fnt = fsl.FNIRT()
fnt.inputs.in_file = in_file
fnt.inputs.ref_file = ref_file
for ef in options:
setattr(fnt.inputs, ef, options[ef])
self.res = fnt.run()
def init_fsl(self):
fnt = fsl.FNIRT()
fnt.inputs.in_file = self.atlas
fnt.inputs.affine_file = self.aff
fnt.inputs.ref_file = self.highres
fnt.inputs.warped_file = str(self.out_dir / "atlas2highres.nii")
fnt.inputs.fieldcoeff_file = str(self.out_dir / "atlas2highres_fieldcoeff.nii")
fnt.inputs.field_file = str(self.out_dir / "atlas2highres_field.nii")
return fnt
def create_workflow(config: AttrDict, resource_pool: ResourcePool,
context: Context):
for _, rp in resource_pool[['brain', 'label-reorient_T1w']]:
# TODO: disable skullstrip
linear_reg = NipypeJob(interface=fsl.FLIRT(cost='corratio'),
reference='linear_reg_0')
inv_flirt_xfm = NipypeJob(
interface=fsl.utils.ConvertXFM(invert_xfm=True),
reference='inv_linear_reg0_xfm')
linear_reg.in_file = rp[R('brain')]
linear_reg.reference = config.template_brain
linear_reg.interp = config.interpolation
inv_flirt_xfm.in_file = linear_reg.out_matrix_file
if config.linear_only:
rp[R('brain', space='MNI')] = linear_reg.out_file
# other xfm
return
else:
nonlinear_reg = NipypeJob(interface=fsl.FNIRT(fieldcoeff_file=True,
jacobian_file=True),
reference='nonlinear_reg_1')
brain_warp = NipypeJob(interface=fsl.ApplyWarp(),
reference='brain_warp')
nonlinear_reg.in_file = rp[R('T1w', label='reorient')]
nonlinear_reg.ref_file = config.template_skull
nonlinear_reg.refmask_file = config.ref_mask
nonlinear_reg.config_file = config.fnirt_config
nonlinear_reg.affine_file = linear_reg.out_matrix_file
brain_warp.interp = config.interpolation
brain_warp.in_file = rp[R('brain')]
brain_warp.field_file = nonlinear_reg.fieldcoeff_file
brain_warp.ref_file = config.template_brain
rp[R('brain', space='MNI')] = brain_warp.out_file
def register_atlas(atlas_file, reference_file, output_dir, recalculate=True):
"""Registers the atlas to your reference image.
This uses FNIRT to register the atlas to your reference image (for example the mean FA of the TBSS results).
Args:
atlas_file (str): the location of the atlas file we will register to the reference image.
reference_file (str): the location of the reference image.
output_dir (str): where to write the output
recalculate (boolean): if we recalculate if the output already exists. Set this to False to easily get the
results dictionary.
Returns:
dict: a dictionary with the filenames of the results. All files are in the output dir. Dir content:
fieldcoeff_file: the path to the field coefficients file. To be used input to the 'warp' parameter of
'applywarp'.
warped_image: the warped image.
"""
fieldcoeff_file = os.path.join(output_dir, 'fieldcoeff.nii.gz')
warped_image = os.path.join(output_dir, 'warped.nii.gz')
results_dict = {
'fieldcoeff_file': fieldcoeff_file,
'warped_image': warped_image
}
if not recalculate:
if all(os.path.isfile(f) for f in [fieldcoeff_file, warped_image]):
return results_dict
fnirt = pe.Node(fsl.FNIRT(ref_file=reference_file,
in_file=atlas_file,
fieldcoeff_file=fieldcoeff_file,
warped_file=warped_image),
name='fnirt')
fnirt.base_dir = os.path.join(output_dir, '_nipype_work_dir')
fnirt.run()
return results_dict
def create_register_func_to_epi(name='register_func_to_epi',
reg_option='ANTS'):
register_func_to_epi = pe.Workflow(name=name)
inputspec = pe.Node(
util.IdentityInterface(fields=['func_4d', 'func_3d', 'epi']),
name='inputspec')
outputspec = pe.Node(util.IdentityInterface(fields=[
'ants_initial_xfm', 'ants_rigid_xfm', 'ants_affine_xfm',
'ants_nonlinear_xfm', 'fsl_flirt_xfm', 'fsl_fnirt_xfm',
'invlinear_xfm', 'func_in_epi'
]),
name='outputspec')
if reg_option == 'ANTS':
# linear + non-linear registration
func_to_epi_ants = create_wf_calculate_ants_warp(
name='func_to_epi_ants')
func_to_epi_ants.inputs.inputspec.interp = 'LanczosWindowedSinc'
register_func_to_epi.connect([
(inputspec, func_to_epi_ants, [
('func_3d', 'inputspec.anatomical_brain'),
('epi', 'inputspec.reference_brain'),
('func_3d', 'inputspec.anatomical_skull'),
('epi', 'inputspec.reference_skull'),
]),
])
func_to_epi_ants.inputs.inputspec.set(
dimension=3,
use_histogram_matching=True,
winsorize_lower_quantile=0.01,
winsorize_upper_quantile=0.99,
metric=['MI', 'MI', 'CC'],
metric_weight=[[1, 32], [1, 32], [1, 5]],
radius_or_number_of_bins=[32, 32, 4],
sampling_strategy=['Regular', 'Regular', None],
sampling_percentage=[0.25, 0.25, None],
number_of_iterations=[[1000, 500, 250, 100], [1000, 500, 250, 100],
[100, 100, 70, 20]],
convergence_threshold=[1e-8, 1e-8, 1e-9],
convergence_window_size=[10, 10, 15],
transforms=['Rigid', 'Affine', 'SyN'],
transform_parameters=[[0.1], [0.1], [0.1, 3, 0]],
shrink_factors=[[8, 4, 2, 1], [8, 4, 2, 1], [4, 2, 1]],
smoothing_sigmas=[[3, 2, 1, 0], [3, 2, 1, 0], [0.6, 0.2, 0.0]])
register_func_to_epi.connect([
(func_to_epi_ants, outputspec, [
('outputspec.ants_initial_xfm', 'ants_initial_xfm'),
('outputspec.ants_rigid_xfm', 'ants_rigid_xfm'),
('outputspec.ants_affine_xfm', 'ants_affine_xfm'),
('outputspec.warp_field', 'ants_nonlinear_xfm'),
]),
])
# combine transforms
collect_transforms = pe.Node(util.Merge(4),
name='collect_transforms_ants')
register_func_to_epi.connect([
(func_to_epi_ants, collect_transforms, [
('outputspec.ants_initial_xfm', 'in1'),
('outputspec.ants_rigid_xfm', 'in2'),
('outputspec.ants_affine_xfm', 'in3'),
('outputspec.warp_field', 'in4'),
]),
])
# apply transform
func_in_epi = pe.Node(interface=ants.ApplyTransforms(),
name='func_in_epi_ants')
func_in_epi.inputs.input_image_type = 3
func_in_epi.inputs.interpolation = 'LanczosWindowedSinc'
register_func_to_epi.connect(inputspec, 'func_4d', func_in_epi,
'input_image')
register_func_to_epi.connect(inputspec, 'epi', func_in_epi,
'reference_image')
register_func_to_epi.connect(collect_transforms, 'out', func_in_epi,
'transforms')
register_func_to_epi.connect(func_in_epi, 'output_image', outputspec,
'func_in_epi')
elif reg_option == 'FSL':
# flirt linear registration
func_to_epi_linear = pe.Node(interface=fsl.FLIRT(),
name='func_to_epi_linear_fsl')
func_to_epi_linear.inputs.dof = 6
register_func_to_epi.connect(inputspec, 'func_3d', func_to_epi_linear,
'in_file')
register_func_to_epi.connect(inputspec, 'epi', func_to_epi_linear,
'reference')
register_func_to_epi.connect(func_to_epi_linear, 'out_matrix_file',
outputspec, 'fsl_flirt_xfm')
inv_flirt_xfm = pe.Node(interface=fsl.utils.ConvertXFM(),
name='inv_linear_reg0_xfm')
inv_flirt_xfm.inputs.invert_xfm = True
# fnirt non-linear registration
func_to_epi_nonlinear = pe.Node(interface=fsl.FNIRT(),
name='func_to_epi_nonlinear_fsl')
func_to_epi_nonlinear.inputs.fieldcoeff_file = True
register_func_to_epi.connect(inputspec, 'func_3d',
func_to_epi_nonlinear, 'in_file')
register_func_to_epi.connect(inputspec, 'epi', func_to_epi_nonlinear,
'ref_file')
register_func_to_epi.connect(func_to_epi_linear, 'out_matrix_file',
func_to_epi_nonlinear, 'affine_file')
register_func_to_epi.connect(func_to_epi_nonlinear, 'fieldcoeff_file',
outputspec, 'fsl_fnirt_xfm')
register_func_to_epi.connect(func_to_epi_linear, 'out_matrix_file',
inv_flirt_xfm, 'in_file')
register_func_to_epi.connect(inv_flirt_xfm, 'out_file', outputspec,
'invlinear_xfm')
# apply warp
func_in_epi = pe.Node(interface=fsl.ApplyWarp(),
name='func_in_epi_fsl')
func_in_epi.inputs.interp = 'sinc'
register_func_to_epi.connect(inputspec, 'func_4d', func_in_epi,
'in_file')
register_func_to_epi.connect(inputspec, 'epi', func_in_epi, 'ref_file')
# --premat input disabled because it was throwing off the transform
# application quality --- why, though?
#register_func_to_epi.connect(func_to_epi_linear, 'out_matrix_file', func_in_epi, 'premat')
register_func_to_epi.connect(func_to_epi_nonlinear, 'fieldcoeff_file',
func_in_epi, 'field_file')
register_func_to_epi.connect(func_in_epi, 'out_file', outputspec,
'func_in_epi')
return register_func_to_epi
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 img2img_register(
img_file,
ref_file,
wf_base_dir,
wf_name,
input_reorient2std=False,
ref_reorient2std=False,
method='linear',
flirt_out_reg_file='linear_reg.mat',
flirt_out_file='img2img_linear.nii.gz',
fnirt_out_file='img2img_nonlinear.nii.gz',
fnirt_fieldcoeff_file='img2img_nonlinear_fieldcoeff.nii.gz'):
img_file = os.path.abspath(img_file)
ref_file = os.path.abspath(ref_file)
wf_base_dir = os.path.abspath(wf_base_dir)
if not img_file.endswith(('.nii', '.nii.gz')):
raise ("input file should be in nifti format (.nii or .nii.gz)!")
if not ref_file.endswith(('.nii', '.nii.gz')):
raise (
"destination file (ref_file) should be in nifti format (.nii or .nii.gz)!"
)
### reorient2std
### reorient img_file
if input_reorient2std:
reorient = fsl.Reorient2Std()
reorient.inputs.in_file = img_file
reorient.inputs.out_file = 'input_img_reorient2std.nii.gz'
reorient_Node = pe.Node(reorient, 'input_file')
wf = pe.Workflow(name='reorient2std', base_dir=wf_base_dir)
wf.add_nodes([reorient_Node])
wf.run()
img_file = os.path.join(wf_base_dir, 'reorient2std', 'input_file',
'input_img_reorient2std.nii.gz')
### reorient ref_file if config is not MNI
if ref_reorient2std and config != 'MNI':
reorient = fsl.Reorient2Std()
reorient.inputs.in_file = ref_file
reorient.inputs.out_file = 'ref_img_reorient2std.nii.gz'
reorient_Node = pe.Node(reorient, 'ref_file')
wf = pe.Workflow(name='reorient2std', base_dir=wf_base_dir)
wf.add_nodes([reorient_Node])
wf.run()
ref_file = os.path.join(wf_base_dir, 'reorient2std', 'ref_file',
'ref_img_reorient2std.nii.gz')
if method == 'linear':
flirt = pe.Node(interface=fsl.FLIRT(in_file=img_file,
reference=ref_file,
out_matrix_file=flirt_out_reg_file,
out_file=flirt_out_file),
name='linear')
wf = pe.Workflow(name=wf_name, base_dir=wf_base_dir)
wf.add_nodes([flirt])
wf.run()
if method == 'nonlinear':
flirt = pe.Node(interface=fsl.FLIRT(in_file=img_file,
reference=ref_file,
out_matrix_file=flirt_out_reg_file,
out_file=flirt_out_file),
name='linear')
fnirt = pe.Node(interface=fsl.FNIRT(
in_file=img_file,
ref_file=ref_file,
warped_file=fnirt_out_file,
fieldcoeff_file=fnirt_fieldcoeff_file),
name='nonlinear')
wf = pe.Workflow(name=wf_name, base_dir=wf_base_dir)
## adding nodes to workflows
wf.add_nodes([flirt, fnirt])
wf.connect([(flirt, fnirt, [('out_matrix_file', 'affine_file')])])
wf.run()
linear_reg_file = os.path.join(wf_base_dir, wf_name, method,
flirt_out_reg_file)
nonlinear_warp_field_file = os.path.join(wf_base_dir, wf_name, method,
fnirt_fieldcoeff_file)
return {
'img_file': img_file,
'ref_file': ref_file,
'linear_reg_file': linear_reg_file,
'warp_field_file': nonlinear_warp_field_file
}
# Segment with FSL FAST #tissue priors #tissue_path = '/usr/share/fsl/5.0/data/standard/tissuepriors/2mm/' #csf_prior = tissue_path + 'avg152T1_csf_bin.nii.gz' #white_prior = tissue_path + 'avg152T1_white_bin.nii.gz' #gray_prior = tissue_path + 'avg152T1_gray_bin.nii.gz' #segmentation = pe.Node(interface=fsl.FAST(number_classes=3, use_priors=True, img_type=1), name='segmentation') #workflow.connect(skullstrip, 'out_file', segmentation, 'in_files') # Register to standard MNI template #1. linear linear_reg = pe.Node(interface=fsl.FLIRT(cost='corratio', reference=ref_brain), name='linear_reg') #2.nonlinear nonlinear_reg = pe.Node(interface=fsl.FNIRT(fieldcoeff_file=True, jacobian_file=True, ref_file=ref_brain, refmask_file=ref_mask), name='nonlinear_reg') inv_flirt_xfm = pe.Node(interface=fsl.utils.ConvertXFM(invert_xfm=True), name='inv_linear_xfm') workflow.connect(reorient, 'out_file', linear_reg, 'in_file') workflow.connect(linear_reg, 'out_matrix_file', nonlinear_reg, 'affine_file') workflow.connect(reorient, 'out_file', nonlinear_reg, 'in_file') workflow.connect(linear_reg, 'out_matrix_file', inv_flirt_xfm, 'in_file') # Run workflow workflow.write_graph() workflow.run() print "ANATOMICAL PREPROCESSING DONE! Results in ", results_path+subj
def create_register_func_to_epi(name='register_func_to_epi', reg_option='ANTS', reg_ants_skull=1):
register_func_to_epi = pe.Workflow(name=name)
inputspec = pe.Node(util.IdentityInterface(fields=['func_4d',
'func_3d',
'func_3d_mask',
'epi',
'interp',
'ants_para']),
name='inputspec')
outputspec = pe.Node(util.IdentityInterface(fields=['ants_initial_xfm',
'ants_rigid_xfm',
'ants_affine_xfm',
'warp_field',
'inverse_warp_field',
'fsl_flirt_xfm',
'fsl_fnirt_xfm',
'invlinear_xfm',
'func_in_epi']),
# 'func_mask_in_epi']),
name='outputspec')
if reg_option == 'ANTS':
# linear + non-linear registration
func_to_epi_ants = \
create_wf_calculate_ants_warp(
name='func_to_epi_ants',
num_threads=1,
reg_ants_skull=1)
register_func_to_epi.connect([
(inputspec, func_to_epi_ants, [
('func_3d', 'inputspec.moving_brain'),
('epi', 'inputspec.reference_brain'),
('func_3d', 'inputspec.moving_skull'),
('epi', 'inputspec.reference_skull'),
('interp', 'inputspec.interp'),
('ants_para', 'inputspec.ants_para')
]),
])
register_func_to_epi.connect([
(func_to_epi_ants, outputspec, [
('outputspec.ants_initial_xfm', 'ants_initial_xfm'),
('outputspec.ants_rigid_xfm', 'ants_rigid_xfm'),
('outputspec.ants_affine_xfm', 'ants_affine_xfm'),
('outputspec.warp_field', 'warp_field'),
('outputspec.inverse_warp_field', 'inverse_warp_field'),
]),
])
# combine transforms
collect_transforms = pe.Node(util.Merge(4), name='collect_transforms_ants')
register_func_to_epi.connect([
(func_to_epi_ants, collect_transforms, [
('outputspec.ants_initial_xfm', 'in1'),
('outputspec.ants_rigid_xfm', 'in2'),
('outputspec.ants_affine_xfm', 'in3'),
('outputspec.warp_field', 'in4'),
]),
])
# check transform list to exclude Nonetype (missing) init/rig/affine
check_transform = pe.Node(util.Function(input_names=['transform_list'],
output_names=['checked_transform_list', 'list_length'],
function=check_transforms), name='{0}_check_transforms'.format(name))
register_func_to_epi.connect(collect_transforms, 'out', check_transform, 'transform_list')
# apply transform to func
func_in_epi = pe.Node(interface=ants.ApplyTransforms(), name='func_in_epi_ants')
func_in_epi.inputs.dimension = 3
func_in_epi.inputs.input_image_type = 3
register_func_to_epi.connect(inputspec, 'func_4d', func_in_epi, 'input_image')
register_func_to_epi.connect(inputspec, 'epi', func_in_epi, 'reference_image')
register_func_to_epi.connect(check_transform, 'checked_transform_list', func_in_epi, 'transforms')
register_func_to_epi.connect(func_in_epi, 'output_image', outputspec, 'func_in_epi')
# # apply transform to functional mask
# func_mask_in_epi = pe.Node(interface=ants.ApplyTransforms(), name='func_mask_in_epi_ants')
# func_mask_in_epi.inputs.dimension = 3
# func_mask_in_epi.inputs.input_image_type = 0
# register_func_to_epi.connect(inputspec, 'func_3d_mask', func_mask_in_epi, 'input_image')
# register_func_to_epi.connect(inputspec, 'epi', func_mask_in_epi, 'reference_image')
# register_func_to_epi.connect(check_transform, 'checked_transform_list', func_mask_in_epi, 'transforms')
# register_func_to_epi.connect(func_mask_in_epi, 'output_image', outputspec, 'func_mask_in_epi')
elif reg_option == 'FSL':
# flirt linear registration
func_to_epi_linear = pe.Node(interface=fsl.FLIRT(), name='func_to_epi_linear_fsl')
func_to_epi_linear.inputs.dof = 6
register_func_to_epi.connect(inputspec, 'func_3d', func_to_epi_linear, 'in_file')
register_func_to_epi.connect(inputspec, 'epi', func_to_epi_linear, 'reference')
register_func_to_epi.connect(func_to_epi_linear, 'out_matrix_file', outputspec, 'fsl_flirt_xfm')
inv_flirt_xfm = pe.Node(interface=fsl.utils.ConvertXFM(), name='inv_linear_reg0_xfm')
inv_flirt_xfm.inputs.invert_xfm = True
# fnirt non-linear registration
func_to_epi_nonlinear = pe.Node(interface=fsl.FNIRT(), name='func_to_epi_nonlinear_fsl')
func_to_epi_nonlinear.inputs.fieldcoeff_file = True
register_func_to_epi.connect(inputspec, 'func_3d', func_to_epi_nonlinear, 'in_file')
register_func_to_epi.connect(inputspec, 'epi', func_to_epi_nonlinear, 'ref_file')
register_func_to_epi.connect(func_to_epi_linear, 'out_matrix_file', func_to_epi_nonlinear, 'affine_file')
register_func_to_epi.connect(func_to_epi_nonlinear, 'fieldcoeff_file', outputspec, 'fsl_fnirt_xfm')
register_func_to_epi.connect(func_to_epi_linear, 'out_matrix_file', inv_flirt_xfm, 'in_file')
register_func_to_epi.connect(inv_flirt_xfm, 'out_file', outputspec, 'invlinear_xfm')
# apply warp
func_in_epi = pe.Node(interface=fsl.ApplyWarp(), name='func_in_epi_fsl')
func_in_epi.inputs.interp = 'sinc'
register_func_to_epi.connect(inputspec, 'func_4d', func_in_epi, 'in_file')
register_func_to_epi.connect(inputspec, 'epi', func_in_epi, 'ref_file')
# --premat input disabled because it was throwing off the transform
# application quality --- why, though?
#register_func_to_epi.connect(func_to_epi_linear, 'out_matrix_file', func_in_epi, 'premat')
register_func_to_epi.connect(func_to_epi_nonlinear, 'fieldcoeff_file', func_in_epi, 'field_file')
register_func_to_epi.connect(func_in_epi, 'out_file', outputspec, 'func_in_epi')
return register_func_to_epi
def create_reg_workflow(name='registration'):
"""Create a FEAT preprocessing workflow together with freesurfer
Parameters
----------
::
name : name of workflow (default: 'registration')
Inputs::
inputspec.source_files : files (filename or list of filenames to register)
inputspec.mean_image : reference image to use
inputspec.anatomical_image : anatomical image to coregister to
inputspec.target_image : registration target
Outputs::
outputspec.func2anat_transform : FLIRT transform
outputspec.anat2target_transform : FLIRT+FNIRT transform
outputspec.transformed_files : transformed files in target space
outputspec.transformed_mean : mean image in target space
Example
-------
"""
register = pe.Workflow(name=name)
inputnode = pe.Node(interface=util.IdentityInterface(fields=['source_files',
'mean_image',
'anatomical_image',
'target_image']),
name='inputspec')
outputnode = pe.Node(interface=util.IdentityInterface(fields=['func2anat_transform',
'anat2target_transform',
'transformed_files',
'transformed_mean',
]),
name='outputspec')
"""
Estimate the tissue classes from the anatomical image. But use spm's segment
as FSL appears to be breaking.
"""
#fast = pe.Node(fsl.FAST(), name='fast')
#fast.config = {'execution': {'keep_unncessary_outputs': True}}
#register.connect(inputnode, 'anatomical_image', fast, 'in_files')
convert = pe.Node(fs.MRIConvert(out_type='nii'), name='convert')
register.connect(inputnode, 'anatomical_image', convert, 'in_file')
segment = pe.Node(spm.Segment(), name='segment')
segment.inputs.wm_output_type = [False, False, True]
segment.config = {'execution': {'keep_unnecessary_outputs': 'true'}}
register.connect(convert, 'out_file', segment, 'data')
"""
Binarize the segmentation
"""
binarize = pe.Node(fsl.ImageMaths(op_string='-nan -thr 0.5 -bin'),
name='binarize')
#pickindex = lambda x, i: x[i]
#register.connect(fast, ('partial_volume_files', pickindex, 1),
# binarize, 'in_file')
register.connect(segment, 'native_wm_image', binarize, 'in_file')
"""
Calculate rigid transform from mean image to anatomical image
"""
mean2anat = pe.Node(fsl.FLIRT(), name='mean2anat')
mean2anat.inputs.dof = 6
register.connect(inputnode, 'mean_image', mean2anat, 'in_file')
register.connect(inputnode, 'anatomical_image', mean2anat, 'reference')
"""
Now use bbr cost function to improve the transform
"""
mean2anatbbr = pe.Node(fsl.FLIRT(), name='mean2anatbbr')
mean2anatbbr.inputs.dof = 6
mean2anatbbr.inputs.cost = 'bbr'
os.environ['FSLDIR']
mean2anatbbr.inputs.schedule = os.path.join(os.environ['FSLDIR'],'etc/flirtsch/bbr.sch')
register.connect(inputnode, 'mean_image', mean2anatbbr, 'in_file')
register.connect(binarize, 'out_file', mean2anatbbr, 'wm_seg')
register.connect(inputnode, 'anatomical_image', mean2anatbbr, 'reference')
register.connect(mean2anat, 'out_matrix_file', mean2anatbbr, 'in_matrix_file')
"""
Calculate affine transform from anatomical to target
"""
anat2target_affine = pe.Node(fsl.FLIRT(), name='anat2target_linear')
register.connect(inputnode, 'anatomical_image', anat2target_affine, 'in_file')
register.connect(inputnode, 'target_image', anat2target_affine, 'reference')
"""
Calculate nonlinear transform from anatomical to target
"""
anat2target_nonlinear = pe.Node(fsl.FNIRT(), name='anat2target_nonlinear')
register.connect(anat2target_affine, 'out_matrix_file',
anat2target_nonlinear, 'affine_file')
#anat2target_nonlinear.inputs.in_fwhm = [8, 4, 2, 2]
#anat2target_nonlinear.inputs.subsampling_scheme = [4, 2, 1, 1]
anat2target_nonlinear.inputs.warp_resolution = (8, 8, 8)
register.connect(inputnode, 'anatomical_image', anat2target_nonlinear, 'in_file')
register.connect(inputnode, 'target_image',
anat2target_nonlinear, 'ref_file')
"""
Transform the mean image. First to anatomical and then to target
"""
warp2anat = pe.Node(fsl.ApplyWarp(interp='spline'), name='warp2anat')
register.connect(inputnode, 'mean_image', warp2anat, 'in_file')
register.connect(inputnode, 'anatomical_image', warp2anat, 'ref_file')
register.connect(mean2anatbbr, 'out_matrix_file', warp2anat, 'premat')
warpmean = pe.Node(fsl.ApplyWarp(interp='spline'), name='warpmean')
register.connect(warp2anat, 'out_file', warpmean, 'in_file')
register.connect(inputnode, 'target_image', warpmean, 'ref_file')
register.connect(anat2target_affine, 'out_matrix_file', warpmean, 'premat')
register.connect(anat2target_nonlinear, 'field_file',
warpmean, 'field_file')
"""
Transform the remaining images. First to anatomical and then to target
"""
warpall2anat = pe.MapNode(fsl.ApplyWarp(interp='spline'),
iterfield=['in_file'],
name='warpall2anat')
register.connect(inputnode, 'source_files', warpall2anat, 'in_file')
register.connect(inputnode, 'anatomical_image', warpall2anat, 'ref_file')
register.connect(mean2anatbbr, 'out_matrix_file', warpall2anat, 'premat')
warpall = pe.MapNode(fsl.ApplyWarp(interp='spline'), name='warpall',
iterfield=['in_file'])
register.connect(warpall2anat, 'out_file', warpall, 'in_file')
register.connect(inputnode, 'target_image', warpall, 'ref_file')
register.connect(anat2target_affine, 'out_matrix_file', warpall, 'premat')
register.connect(anat2target_nonlinear, 'field_file',
warpall, 'field_file')
"""
Assign all the output files
"""
register.connect(warpmean, 'out_file', outputnode, 'transformed_mean')
register.connect(warpall, 'out_file', outputnode, 'transformed_files')
register.connect(mean2anatbbr, 'out_matrix_file',
outputnode, 'func2anat_transform')
register.connect(anat2target_nonlinear, 'field_file',
outputnode, 'anat2target_transform')
return register
def create_reg_workflow(name='registration'):
"""Create a FEAT preprocessing workflow together with freesurfer
Parameters
----------
::
name : name of workflow (default: 'registration')
Inputs::
inputspec.source_files : files (filename or list of filenames to register)
inputspec.mean_image : reference image to use
inputspec.anatomical_image : anatomical image to coregister to
inputspec.target_image : registration target
Outputs::
outputspec.func2anat_transform : FLIRT transform
outputspec.anat2target_transform : FLIRT+FNIRT transform
outputspec.transformed_files : transformed files in target space
outputspec.transformed_mean : mean image in target space
Example
-------
"""
register = pe.Workflow(name=name)
inputnode = pe.Node(interface=util.IdentityInterface(fields=['source_files',
'mean_image',
'anatomical_image',
'target_image']),
name='inputspec')
outputnode = pe.Node(interface=util.IdentityInterface(fields=['func2anat_transform',
'anat2target_transform',
'transformed_files',
'transformed_mean',
]),
name='outputspec')
"""
Estimate the tissue classes from the anatomical image. But use spm's segment
as FSL appears to be breaking.
"""
stripper = pe.Node(fsl.BET(), name='stripper')
register.connect(inputnode, 'anatomical_image', stripper, 'in_file')
fast = pe.Node(fsl.FAST(), name='fast')
register.connect(stripper, 'out_file', fast, 'in_files')
"""
Binarize the segmentation
"""
binarize = pe.Node(fsl.ImageMaths(op_string='-nan -thr 0.5 -bin'),
name='binarize')
pickindex = lambda x, i: x[i]
register.connect(fast, ('partial_volume_files', pickindex, 2),
binarize, 'in_file')
"""
Calculate rigid transform from mean image to anatomical image
"""
mean2anat = pe.Node(fsl.FLIRT(), name='mean2anat')
mean2anat.inputs.dof = 6
register.connect(inputnode, 'mean_image', mean2anat, 'in_file')
register.connect(stripper, 'out_file', mean2anat, 'reference')
"""
Now use bbr cost function to improve the transform
"""
mean2anatbbr = pe.Node(fsl.FLIRT(), name='mean2anatbbr')
mean2anatbbr.inputs.dof = 6
mean2anatbbr.inputs.cost = 'bbr'
mean2anatbbr.inputs.schedule = os.path.join(os.getenv('FSLDIR'),
'etc/flirtsch/bbr.sch')
register.connect(inputnode, 'mean_image', mean2anatbbr, 'in_file')
register.connect(binarize, 'out_file', mean2anatbbr, 'wm_seg')
register.connect(inputnode, 'anatomical_image', mean2anatbbr, 'reference')
register.connect(mean2anat, 'out_matrix_file', mean2anatbbr, 'in_matrix_file')
"""
Calculate affine transform from anatomical to target
"""
anat2target_affine = pe.Node(fsl.FLIRT(), name='anat2target_linear')
register.connect(inputnode, 'anatomical_image', anat2target_affine, 'in_file')
register.connect(inputnode, 'target_image', anat2target_affine, 'reference')
"""
Calculate nonlinear transform from anatomical to target
"""
anat2target_nonlinear = pe.Node(fsl.FNIRT(), name='anat2target_nonlinear')
anat2target_nonlinear.inputs.fieldcoeff_file=True
register.connect(anat2target_affine, 'out_matrix_file',
anat2target_nonlinear, 'affine_file')
anat2target_nonlinear.inputs.warp_resolution = (8, 8, 8)
register.connect(inputnode, 'anatomical_image', anat2target_nonlinear, 'in_file')
register.connect(inputnode, 'target_image',
anat2target_nonlinear, 'ref_file')
"""
Transform the mean image. First to anatomical and then to target
"""
warp2anat = pe.Node(fsl.ApplyWarp(interp='spline'), name='warp2anat')
register.connect(inputnode, 'mean_image', warp2anat, 'in_file')
register.connect(inputnode, 'anatomical_image', warp2anat, 'ref_file')
register.connect(mean2anatbbr, 'out_matrix_file', warp2anat, 'premat')
warpmean = warp2anat.clone(name='warpmean')
register.connect(warp2anat, 'out_file', warpmean, 'in_file')
register.connect(inputnode, 'target_image', warpmean, 'ref_file')
register.connect(anat2target_nonlinear, 'fieldcoeff_file',
warpmean, 'field_file')
"""
Transform the remaining images. First to anatomical and then to target
"""
warpall2anat = pe.MapNode(fsl.ApplyWarp(interp='spline'),
iterfield=['in_file'],
name='warpall2anat')
register.connect(inputnode, 'source_files', warpall2anat, 'in_file')
register.connect(inputnode, 'anatomical_image', warpall2anat, 'ref_file')
register.connect(mean2anatbbr, 'out_matrix_file', warpall2anat, 'premat')
warpall = warpall2anat.clone(name='warpall')
register.connect(warpall2anat, 'out_file', warpall, 'in_file')
register.connect(inputnode, 'target_image', warpall, 'ref_file')
register.connect(anat2target_nonlinear, 'fieldcoeff_file',
warpall, 'field_file')
"""
Assign all the output files
"""
register.connect(warpmean, 'out_file', outputnode, 'transformed_mean')
register.connect(warpall, 'out_file', outputnode, 'transformed_files')
register.connect(mean2anatbbr, 'out_matrix_file',
outputnode, 'func2anat_transform')
register.connect(anat2target_nonlinear, 'fieldcoeff_file',
outputnode, 'anat2target_transform')
return register
def create_nonlinear_register(name='nonlinear_register'):
"""
Performs non-linear registration of an input file to a reference file.
Parameters
----------
name : string, optional
Name of the workflow.
Returns
-------
nonlinear_register : nipype.pipeline.engine.Workflow
Notes
-----
Workflow Inputs::
inputspec.input_brain : string (nifti file)
File of brain to be normalized (registered)
inputspec.input_skull : string (nifti file)
File of input brain with skull
inputspec.reference_brain : string (nifti file)
Target brain file to normalize to
inputspec.reference_skull : string (nifti file)
Target brain with skull to normalize to
inputspec.fnirt_config : string (fsl fnirt config file)
Configuration file containing parameters that can be specified in fnirt
Workflow Outputs::
outputspec.output_brain : string (nifti file)
Normalizion of input brain file
outputspec.linear_xfm : string (.mat file)
Affine matrix of linear transformation of brain file
outputspec.invlinear_xfm : string
Inverse of affine matrix of linear transformation of brain file
outputspec.nonlinear_xfm : string
Nonlinear field coefficients file of nonlinear transformation
Registration Procedure:
1. Perform a linear registration to get affine transformation matrix.
2. Perform a nonlinear registration on an input file to the reference file utilizing affine
transformation from the previous step as a starting point.
3. Invert the affine transformation to provide the user a transformation (affine only) from the
space of the reference file to the input file.
Workflow Graph:
.. image:: ../images/nonlinear_register.dot.png
:width: 500
Detailed Workflow Graph:
.. image:: ../images/nonlinear_register_detailed.dot.png
:width: 500
"""
nonlinear_register = pe.Workflow(name=name)
inputspec = pe.Node(util.IdentityInterface(fields=[
'input_brain', 'input_skull', 'reference_brain', 'reference_skull',
'fnirt_config'
]),
name='inputspec')
outputspec = pe.Node(util.IdentityInterface(fields=[
'output_brain', 'linear_xfm', 'invlinear_xfm', 'nonlinear_xfm'
]),
name='outputspec')
linear_reg = pe.Node(interface=fsl.FLIRT(), name='linear_reg_0')
linear_reg.inputs.cost = 'corratio'
nonlinear_reg = pe.Node(interface=fsl.FNIRT(), name='nonlinear_reg_1')
nonlinear_reg.inputs.fieldcoeff_file = True
nonlinear_reg.inputs.jacobian_file = True
brain_warp = pe.Node(interface=fsl.ApplyWarp(), name='brain_warp')
inv_flirt_xfm = pe.Node(interface=fsl.utils.ConvertXFM(),
name='inv_linear_reg0_xfm')
inv_flirt_xfm.inputs.invert_xfm = True
nonlinear_register.connect(inputspec, 'input_brain', linear_reg, 'in_file')
nonlinear_register.connect(inputspec, 'reference_brain', linear_reg,
'reference')
nonlinear_register.connect(inputspec, 'input_skull', nonlinear_reg,
'in_file')
nonlinear_register.connect(inputspec, 'reference_skull', nonlinear_reg,
'ref_file')
# FNIRT parameters are specified by FSL config file
# ${FSLDIR}/etc/flirtsch/TI_2_MNI152_2mm.cnf (or user-specified)
nonlinear_register.connect(inputspec, 'fnirt_config', nonlinear_reg,
'config_file')
nonlinear_register.connect(linear_reg, 'out_matrix_file', nonlinear_reg,
'affine_file')
nonlinear_register.connect(nonlinear_reg, 'fieldcoeff_file', outputspec,
'nonlinear_xfm')
nonlinear_register.connect(inputspec, 'input_brain', brain_warp, 'in_file')
nonlinear_register.connect(nonlinear_reg, 'fieldcoeff_file', brain_warp,
'field_file')
nonlinear_register.connect(inputspec, 'reference_brain', brain_warp,
'ref_file')
nonlinear_register.connect(brain_warp, 'out_file', outputspec,
'output_brain')
nonlinear_register.connect(linear_reg, 'out_matrix_file', inv_flirt_xfm,
'in_file')
nonlinear_register.connect(inv_flirt_xfm, 'out_file', outputspec,
'invlinear_xfm')
nonlinear_register.connect(linear_reg, 'out_matrix_file', outputspec,
'linear_xfm')
return nonlinear_register
def create_tbss_2_reg(name="tbss_2_reg"):
"""TBSS nonlinear registration:
A pipeline that does the same as tbss_2_reg script in FSL.
Example
------
>>> tbss2 = create_tbss_2_reg(name="tbss2")
>>> tbss2.inputs.inputnode.target = fsl.Info.standard_image("FMRIB58_FA_1mm.nii.gz")
>>> ...
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 the FA image to the target
config_file = os.path.join(os.environ["FSLDIR"],
"etc/flirtsch/FA_2_FMRIB58_1mm.cnf")
fnirt = pe.MapNode(interface=fsl.FNIRT(config_file=config_file,
fieldcoeff_file=True),
iterfield=['in_file', 'inmask_file', 'affine_file'],
name="fnirt")
# 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 modulation_( self ):
"""Modulation funcion is a correction of the volume change multiplying the voxel by the Jacobian determinant derived from the normalization process."""
try:
#
# Use the non-linear template built in the non-linear_registration_ funstion
temp_T1_brain_nlin = os.path.join(self.ana_dir_, "temp_T1_brain_nlin.nii.gz")
#
# Loop on the tasks
while True:
# get the item
item = self.queue_[2].get()
# registration estimation
flt = fsl.FLIRT()
flt.inputs.in_file = os.path.join(self.T1_brain_dir_, item )
flt.inputs.reference = temp_T1_brain_nlin
flt.inputs.out_file = os.path.join( self.template_dir_, "%s_li_template.nii.gz"%item[:-7] )
flt.inputs.out_matrix_file = os.path.join( self.template_dir_, "%s_li_template.mat"%item[:-7] )
flt.inputs.dof = 12
res = flt.run()
#
# Find the matching T1 image
PIDN = [int(s) for s in item.split("_") if s.isdigit()]
#
T1_item = [ s for s in self.list_T1_ if str(PIDN[0]) in s]
#
# apply registration using T1_2_MNI152_2mm configuration file
T1_2_MNI152_2mm = ""
if os.environ.get('FSLDIR'):
T1_2_MNI152_2mm = os.path.join( os.environ.get('FSLDIR'),
"etc","flirtsch","T1_2_MNI152_2mm.cnf" )
#
fnt = fsl.FNIRT()
fnt.inputs.in_file = os.path.join(self.T1_dir_, T1_item[0])
fnt.inputs.ref_file = self.template_
fnt.inputs.warped_file = os.path.join( self.template_dir_,
"%s_non_li_template_fnirt.nii.gz"%item[:-7] )
fnt.inputs.affine_file = os.path.join( self.template_dir_,
"%s_li_template.mat"%item[:-7] )
fnt.inputs.config_file = T1_2_MNI152_2mm
fnt.inputs.fieldcoeff_file = os.path.join( self.template_dir_,
"%s_non_li_template_coeff.nii.gz"%item[:-7] )
fnt.inputs.jacobian_file = os.path.join( self.template_dir_,
"%s_non_li_template_jac.nii.gz"%item[:-7] )
res = fnt.run()
# apply warp
aw = fsl.ApplyWarp()
aw.inputs.in_file = os.path.join(self.T1_dir_, T1_item[0])
aw.inputs.ref_file = self.template_
aw.inputs.out_file = os.path.join( self.template_dir_,
"%s_non_li_template_warped.nii.gz"%item[:-7] )
aw.inputs.field_file = os.path.join( self.template_dir_,
"%s_non_li_template_coeff.nii.gz"%item[:-7] )
res = aw.run()
# modulation
maths = fsl.ImageMaths()
maths.inputs.in_file = os.path.join(self.template_dir_,
"%s_non_li_template_warped.nii.gz"%item[:-7])
maths.inputs.op_string = '-mul %s'%(os.path.join(self.template_dir_,
"%s_non_li_template_jac.nii.gz"%item[:-7]))
maths.inputs.out_file = os.path.join( self.template_dir_,
"%s_modulated.nii.gz"%item[:-7] )
maths.inputs.out_data_type = "float"
maths.run();
# lock and add the file
singlelock.acquire()
self.warped_template_.append( aw.inputs.out_file )
self.modulated_template_.append( os.path.join(self.template_dir_,
"%s_modulated.nii.gz"%item[:-7]) )
singlelock.release()
# job is done
self.queue_[2].task_done()
#
#
except Exception as inst:
print inst
_log.error(inst)
quit(-1)
except IOError as e:
print "I/O error({0}): {1}".format(e.errno, e.strerror)
quit(-1)
except:
print "Unexpected error:", sys.exc_info()[0]
quit(-1)
def create_vmhc(use_ants):
"""
Compute the map of brain functional homotopy, the high degree of synchrony in spontaneous activity between geometrically corresponding interhemispheric (i.e., homotopic) regions.
Parameters
----------
None
Returns
-------
vmhc_workflow : workflow
Voxel Mirrored Homotopic Connectivity Analysis Workflow
Notes
-----
`Source <https://github.com/FCP-INDI/C-PAC/blob/master/CPAC/vmhc/vmhc.py>`_
Workflow Inputs::
inputspec.brain : string (existing nifti file)
Anatomical image(without skull)
inputspec.symmetric_brain : string (existing nifti file)
MNI152_T1_2mm_brain_symmetric.nii.gz
inputspec.rest_res_filt : string (existing nifti file)
Band passed Image with nuisance signal regressed out(and optionally scrubbed). Recommended bandpass filter (0.001,0.1) )
inputspec.reorient : string (existing nifti file)
RPI oriented anatomical data
inputspec.example_func2highres_mat : string (existing affine transformation .mat file)
Specifies an affine transform that should be applied to the example_func before non linear warping
inputspec.standard_for_func : string (existing nifti file)
MNI152_T1_standard_resolution_brain.nii.gz
inputspec.symmetric_skull : string (existing nifti file)
MNI152_T1_2mm_symmetric.nii.gz
inputspec.twomm_brain_mask_dil : string (existing nifti file)
MNI152_T1_2mm_brain_mask_symmetric_dil.nii.gz
inputspec.config_file_twomm_symmetric : string (existing .cnf file)
T1_2_MNI152_2mm_symmetric.cnf
inputspec.rest_mask : string (existing nifti file)
A mask functional volume(derived by dilation from motion corrected functional volume)
fwhm_input.fwhm : list (float)
For spatial smoothing the Z-transformed correlations in MNI space.
Generally the value of this parameter is 1.5 or 2 times the voxel size of the input Image.
inputspec.mean_functional : string (existing nifti file)
The mean functional image for use in the func-to-anat registration matrix conversion
to ITK (ANTS) format, if the user selects to use ANTS.
Workflow Outputs::
outputspec.highres2symmstandard : string (nifti file)
Linear registration of T1 image to symmetric standard image
outputspec.highres2symmstandard_mat : string (affine transformation .mat file)
An affine transformation .mat file from linear registration and used in non linear registration
outputspec.highres2symmstandard_warp : string (nifti file)
warp file from Non Linear registration of T1 to symmetrical standard brain
outputspec.fnirt_highres2symmstandard : string (nifti file)
Non Linear registration of T1 to symmetrical standard brain
outputspec.highres2symmstandard_jac : string (nifti file)
jacobian determinant image from Non Linear registration of T1 to symmetrical standard brain
outputspec.rest_res_2symmstandard : string (nifti file)
nonlinear registration (func to standard) image
outputspec.VMHC_FWHM_img : string (nifti file)
pearson correlation between res2standard and flipped res2standard
outputspec.VMHC_Z_FWHM_img : string (nifti file)
Fisher Z transform map
outputspec.VMHC_Z_stat_FWHM_img : string (nifti file)
Z statistic map
Order of commands:
- Perform linear registration of Anatomical brain in T1 space to symmetric standard space. For details see `flirt <http://www.fmrib.ox.ac.uk/fsl/flirt/index.html>`_::
flirt
-ref MNI152_T1_2mm_brain_symmetric.nii.gz
-in mprage_brain.nii.gz
-out highres2symmstandard.nii.gz
-omat highres2symmstandard.mat
-cost corratio
-searchcost corratio
-dof 12
-interp trilinear
- Perform nonlinear registration (higres to standard) to symmetric standard brain. For details see `fnirt <http://fsl.fmrib.ox.ac.uk/fsl/fnirt/>`_::
fnirt
--in=head.nii.gz
--aff=highres2symmstandard.mat
--cout=highres2symmstandard_warp.nii.gz
--iout=fnirt_highres2symmstandard.nii.gz
--jout=highres2symmstandard_jac.nii.gz
--config=T1_2_MNI152_2mm_symmetric.cnf
--ref=MNI152_T1_2mm_symmetric.nii.gz
--refmask=MNI152_T1_2mm_brain_mask_symmetric_dil.nii.gz
--warpres=10,10,10
- Perform spatial smoothing on the input functional image(inputspec.rest_res_filt). For details see `PrinciplesSmoothing <http://imaging.mrc-cbu.cam.ac.uk/imaging/PrinciplesSmoothing>`_ `fslmaths <http://www.fmrib.ox.ac.uk/fslcourse/lectures/practicals/intro/index.htm>`_::
fslmaths rest_res_filt.nii.gz
-kernel gauss FWHM/ sqrt(8-ln(2))
-fmean -mas rest_mask.nii.gz
rest_res_filt_FWHM.nii.gz
- Apply nonlinear registration (func to standard). For details see `applywarp <http://www.fmrib.ox.ac.uk/fsl/fnirt/warp_utils.html#applywarp>`_::
applywarp
--ref=MNI152_T1_2mm_symmetric.nii.gz
--in=rest_res_filt_FWHM.nii.gz
--out=rest_res_2symmstandard.nii.gz
--warp=highres2symmstandard_warp.nii.gz
--premat=example_func2highres.mat
- Copy and L/R swap the output of applywarp command (rest_res_2symmstandard.nii.gz). For details see `fslswapdim <http://fsl.fmrib.ox.ac.uk/fsl/fsl4.0/avwutils/index.html>`_::
fslswapdim
rest_res_2symmstandard.nii.gz
-x y z
tmp_LRflipped.nii.gz
- Calculate pearson correlation between rest_res_2symmstandard.nii.gz and flipped rest_res_2symmstandard.nii.gz(tmp_LRflipped.nii.gz). For details see `3dTcorrelate <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dTcorrelate.html>`_::
3dTcorrelate
-pearson
-polort -1
-prefix VMHC_FWHM.nii.gz
rest_res_2symmstandard.nii.gz
tmp_LRflipped.nii.gz
- Fisher Z Transform the correlation. For details see `3dcalc <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dcalc.html>`_::
3dcalc
-a VMHC_FWHM.nii.gz
-expr 'log((a+1)/(1-a))/2'
-prefix VMHC_FWHM_Z.nii.gz
- Calculate the number of volumes(nvols) in flipped rest_res_2symmstandard.nii.gz(tmp_LRflipped.nii.gz) ::
-Use Nibabel to do this
- Compute the Z statistic map ::
3dcalc
-a VMHC_FWHM_Z.nii.gz
-expr 'a*sqrt('${nvols}'-3)'
-prefix VMHC_FWHM_Z_stat.nii.gz
Workflow:
.. image:: ../images/vmhc_graph.dot.png
:width: 500
Workflow Detailed:
.. image:: ../images/vmhc_detailed_graph.dot.png
:width: 500
References
----------
.. [1] Zuo, X.-N., Kelly, C., Di Martino, A., Mennes, M., Margulies, D. S., Bangaru, S., Grzadzinski, R., et al. (2010). Growing together and growing apart: regional and sex differences in the lifespan developmental trajectories of functional homotopy. The Journal of neuroscience : the official journal of the Society for Neuroscience, 30(45), 15034-43. doi:10.1523/JNEUROSCI.2612-10.2010
Examples
--------
>>> vmhc_w = create_vmhc()
>>> vmhc_w.inputs.inputspec.symmetric_brain = 'MNI152_T1_2mm_brain_symmetric.nii.gz'
>>> vmhc_w.inputs.inputspec.symmetric_skull = 'MNI152_T1_2mm_symmetric.nii.gz'
>>> vmhc_w.inputs.inputspec.twomm_brain_mask_dil = 'MNI152_T1_2mm_brain_mask_symmetric_dil.nii.gz'
>>> vmhc_w.inputs.inputspec.config_file_twomm = 'T1_2_MNI152_2mm_symmetric.cnf'
>>> vmhc_w.inputs.inputspec.standard_for_func = 'MNI152_T1_2mm.nii.gz'
>>> vmhc_w.inputs.fwhm_input.fwhm = [4.5, 6]
>>> vmhc_w.get_node('fwhm_input').iterables = ('fwhm', [4.5, 6])
>>> vmhc_w.inputs.inputspec.rest_res = os.path.abspath('/home/data/Projects/Pipelines_testing/Dickstein/subjects/s1001/func/original/rest_res_filt.nii.gz')
>>> vmhc_w.inputs.inputspec.reorient = os.path.abspath('/home/data/Projects/Pipelines_testing/Dickstein/subjects/s1001/anat/mprage_RPI.nii.gz')
>>> vmhc_w.inputs.inputspec.brain = os.path.abspath('/home/data/Projects/Pipelines_testing/Dickstein/subjects/s1001/anat/mprage_brain.nii.gz')
>>> vmhc_w.inputs.inputspec.example_func2highres_mat = os.path.abspath('/home/data/Projects/Pipelines_testing/Dickstein/subjects/s1001/func/original/reg/example_func2highres.mat')
>>> vmhc_w.inputs.inputspec.rest_mask = os.path.abspath('/home/data/Projects/Pipelines_testing/Dickstein/subjects/s1001/func/original/rest_mask.nii.gz')
>>> vmhc_w.run() # doctest: +SKIP
"""
vmhc = pe.Workflow(name='vmhc_workflow')
inputNode = pe.Node(util.IdentityInterface(fields=['brain',
'symmetric_brain',
'rest_res',
'reorient',
'example_func2highres_mat',
'symmetric_skull',
'twomm_brain_mask_dil',
'config_file_twomm',
'rest_mask',
'standard_for_func',
'mean_functional']),
name='inputspec')
outputNode = pe.Node(util.IdentityInterface(fields=['highres2symmstandard',
'highres2symmstandard_mat',
'highres2symmstandard_warp',
'fnirt_highres2symmstandard',
'highres2symmstandard_jac',
'rest_res_2symmstandard',
'VMHC_FWHM_img',
'VMHC_Z_FWHM_img',
'VMHC_Z_stat_FWHM_img'
]),
name='outputspec')
inputnode_fwhm = pe.Node(util.IdentityInterface(fields=['fwhm']),
name='fwhm_input')
if use_ants == False:
## Linear registration of T1 --> symmetric standard
linear_T1_to_symmetric_standard = pe.Node(interface=fsl.FLIRT(),
name='linear_T1_to_symmetric_standard')
linear_T1_to_symmetric_standard.inputs.cost = 'corratio'
linear_T1_to_symmetric_standard.inputs.cost_func = 'corratio'
linear_T1_to_symmetric_standard.inputs.dof = 12
linear_T1_to_symmetric_standard.inputs.interp = 'trilinear'
## Perform nonlinear registration
##(higres to standard) to symmetric standard brain
nonlinear_highres_to_symmetric_standard = pe.Node(interface=fsl.FNIRT(),
name='nonlinear_highres_to_symmetric_standard')
nonlinear_highres_to_symmetric_standard.inputs.fieldcoeff_file = True
nonlinear_highres_to_symmetric_standard.inputs.jacobian_file = True
nonlinear_highres_to_symmetric_standard.inputs.warp_resolution = (10, 10, 10)
# needs new inputs. needs input from resources for the field coeff of the template->symmetric.
# and needs the field coeff of the anatomical-to-template registration
## Apply nonlinear registration (func to standard)
nonlinear_func_to_standard = pe.Node(interface=fsl.ApplyWarp(),
name='nonlinear_func_to_standard')
elif use_ants == True:
# ANTS warp image etc.
calculate_ants_xfm_vmhc = create_wf_calculate_ants_warp(name='calculate_ants_xfm_vmhc')
fsl_to_itk_vmhc = create_wf_c3d_fsl_to_itk(0, name='fsl_to_itk_vmhc')
collect_transforms_vmhc = create_wf_collect_transforms(0, name='collect_transforms_vmhc')
apply_ants_xfm_vmhc = create_wf_apply_ants_warp(0,name='apply_ants_xfm_vmhc')
calculate_ants_xfm_vmhc.inputs.inputspec.dimension = 3
calculate_ants_xfm_vmhc.inputs.inputspec. \
use_histogram_matching = True
calculate_ants_xfm_vmhc.inputs.inputspec. \
winsorize_lower_quantile = 0.01
calculate_ants_xfm_vmhc.inputs.inputspec. \
winsorize_upper_quantile = 0.99
calculate_ants_xfm_vmhc.inputs.inputspec. \
metric = ['MI','MI','CC']
calculate_ants_xfm_vmhc.inputs.inputspec.metric_weight = [1,1,1]
calculate_ants_xfm_vmhc.inputs.inputspec. \
radius_or_number_of_bins = [32,32,4]
calculate_ants_xfm_vmhc.inputs.inputspec. \
sampling_strategy = ['Regular','Regular',None]
calculate_ants_xfm_vmhc.inputs.inputspec. \
sampling_percentage = [0.25,0.25,None]
calculate_ants_xfm_vmhc.inputs.inputspec. \
number_of_iterations = [[1000,500,250,100], \
[1000,500,250,100], [100,100,70,20]]
calculate_ants_xfm_vmhc.inputs.inputspec. \
convergence_threshold = [1e-8,1e-8,1e-9]
calculate_ants_xfm_vmhc.inputs.inputspec. \
convergence_window_size = [10,10,15]
calculate_ants_xfm_vmhc.inputs.inputspec. \
transforms = ['Rigid','Affine','SyN']
calculate_ants_xfm_vmhc.inputs.inputspec. \
transform_parameters = [[0.1],[0.1],[0.1,3,0]]
calculate_ants_xfm_vmhc.inputs.inputspec. \
shrink_factors = [[8,4,2,1],[8,4,2,1],[6,4,2,1]]
calculate_ants_xfm_vmhc.inputs.inputspec. \
smoothing_sigmas = [[3,2,1,0],[3,2,1,0],[3,2,1,0]]
apply_ants_xfm_vmhc.inputs.inputspec.interpolation = 'Linear'
apply_ants_xfm_vmhc.inputs.inputspec.input_image_type = 3
## copy and L/R swap file
copy_and_L_R_swap = pe.Node(interface=fsl.SwapDimensions(),
name='copy_and_L_R_swap')
copy_and_L_R_swap.inputs.new_dims = ('-x', 'y', 'z')
## caculate vmhc
pearson_correlation = pe.Node(interface=preprocess.TCorrelate(),
name='pearson_correlation')
pearson_correlation.inputs.pearson = True
pearson_correlation.inputs.polort = -1
pearson_correlation.inputs.outputtype = 'NIFTI_GZ'
z_trans = pe.Node(interface=preprocess.Calc(),
name='z_trans')
z_trans.inputs.expr = 'log((1+a)/(1-a))/2'
z_trans.inputs.outputtype = 'NIFTI_GZ'
z_stat = pe.Node(interface=preprocess.Calc(),
name='z_stat')
z_stat.inputs.outputtype = 'NIFTI_GZ'
NVOLS = pe.Node(util.Function(input_names=['in_files'],
output_names=['nvols'],
function=get_img_nvols),
name='NVOLS')
generateEXP = pe.Node(util.Function(input_names=['nvols'],
output_names=['expr'],
function=get_operand_expression),
name='generateEXP')
smooth = pe.Node(interface=fsl.MultiImageMaths(),
name='smooth')
if use_ants == False:
vmhc.connect(inputNode, 'brain',
linear_T1_to_symmetric_standard, 'in_file')
vmhc.connect(inputNode, 'symmetric_brain',
linear_T1_to_symmetric_standard, 'reference')
vmhc.connect(inputNode, 'reorient',
nonlinear_highres_to_symmetric_standard, 'in_file')
vmhc.connect(linear_T1_to_symmetric_standard, 'out_matrix_file',
nonlinear_highres_to_symmetric_standard, 'affine_file')
vmhc.connect(inputNode, 'symmetric_skull',
nonlinear_highres_to_symmetric_standard, 'ref_file')
vmhc.connect(inputNode, 'twomm_brain_mask_dil',
nonlinear_highres_to_symmetric_standard, 'refmask_file')
vmhc.connect(inputNode, 'config_file_twomm',
nonlinear_highres_to_symmetric_standard, 'config_file')
vmhc.connect(inputNode, 'rest_res',
smooth, 'in_file')
vmhc.connect(inputnode_fwhm, ('fwhm', set_gauss),
smooth, 'op_string')
vmhc.connect(inputNode, 'rest_mask',
smooth, 'operand_files')
vmhc.connect(smooth, 'out_file',
nonlinear_func_to_standard, 'in_file')
vmhc.connect(inputNode, 'standard_for_func',
nonlinear_func_to_standard, 'ref_file')
vmhc.connect(nonlinear_highres_to_symmetric_standard, 'fieldcoeff_file',
nonlinear_func_to_standard, 'field_file')
## func->anat matrix (bbreg)
vmhc.connect(inputNode, 'example_func2highres_mat',
nonlinear_func_to_standard, 'premat')
vmhc.connect(nonlinear_func_to_standard, 'out_file',
copy_and_L_R_swap, 'in_file')
vmhc.connect(nonlinear_func_to_standard, 'out_file',
pearson_correlation, 'xset')
elif use_ants == True:
# connections for ANTS stuff
# registration calculation stuff -- might go out the window
vmhc.connect(inputNode, 'brain',
calculate_ants_xfm_vmhc, 'inputspec.anatomical_brain')
vmhc.connect(inputNode, 'symmetric_brain',
calculate_ants_xfm_vmhc, 'inputspec.reference_brain')
# functional apply warp stuff
vmhc.connect(inputNode, 'rest_res',
smooth, 'in_file')
vmhc.connect(inputnode_fwhm, ('fwhm', set_gauss),
smooth, 'op_string')
vmhc.connect(inputNode, 'rest_mask',
smooth, 'operand_files')
vmhc.connect(smooth, 'out_file',
apply_ants_xfm_vmhc, 'inputspec.input_image')
vmhc.connect(calculate_ants_xfm_vmhc, 'outputspec.ants_rigid_xfm',
collect_transforms_vmhc, 'inputspec.linear_rigid')
vmhc.connect(calculate_ants_xfm_vmhc, 'outputspec.ants_affine_xfm',
collect_transforms_vmhc, 'inputspec.linear_affine')
vmhc.connect(calculate_ants_xfm_vmhc, 'outputspec.warp_field',
collect_transforms_vmhc, 'inputspec.warp_file')
## func->anat matrix (bbreg)
vmhc.connect(inputNode, 'example_func2highres_mat',
fsl_to_itk_vmhc, 'inputspec.affine_file')
vmhc.connect(inputNode, 'brain', fsl_to_itk_vmhc,
'inputspec.reference_file')
vmhc.connect(inputNode, 'mean_functional', fsl_to_itk_vmhc,
'inputspec.source_file')
vmhc.connect(fsl_to_itk_vmhc, 'outputspec.itk_transform',
collect_transforms_vmhc, 'inputspec.fsl_to_itk_affine')
'''
vmhc.connect(inputNode, 'brain',
apply_ants_xfm_vmhc, 'inputspec.conversion_reference')
vmhc.connect(inputNode, 'mean_functional',
apply_ants_xfm_vmhc, 'inputspec.conversion_source')
'''
vmhc.connect(inputNode, 'standard_for_func',
apply_ants_xfm_vmhc, 'inputspec.reference_image')
vmhc.connect(collect_transforms_vmhc, \
'outputspec.transformation_series', \
apply_ants_xfm_vmhc, 'inputspec.transforms')
vmhc.connect(apply_ants_xfm_vmhc, 'outputspec.output_image',
copy_and_L_R_swap, 'in_file')
vmhc.connect(apply_ants_xfm_vmhc, 'outputspec.output_image',
pearson_correlation, 'xset')
vmhc.connect(copy_and_L_R_swap, 'out_file',
pearson_correlation, 'yset')
vmhc.connect(pearson_correlation, 'out_file',
z_trans, 'in_file_a')
vmhc.connect(copy_and_L_R_swap, 'out_file',
NVOLS, 'in_files')
vmhc.connect(NVOLS, 'nvols',
generateEXP, 'nvols')
vmhc.connect(z_trans, 'out_file',
z_stat, 'in_file_a')
vmhc.connect(generateEXP, 'expr',
z_stat, 'expr')
if use_ants == False:
vmhc.connect(linear_T1_to_symmetric_standard, 'out_file',
outputNode, 'highres2symmstandard')
vmhc.connect(linear_T1_to_symmetric_standard, 'out_matrix_file',
outputNode, 'highres2symmstandard_mat')
vmhc.connect(nonlinear_highres_to_symmetric_standard, 'jacobian_file',
outputNode, 'highres2symmstandard_jac')
vmhc.connect(nonlinear_highres_to_symmetric_standard, 'fieldcoeff_file',
outputNode, 'highres2symmstandard_warp')
vmhc.connect(nonlinear_highres_to_symmetric_standard, 'warped_file',
outputNode, 'fnirt_highres2symmstandard')
vmhc.connect(nonlinear_func_to_standard, 'out_file',
outputNode, 'rest_res_2symmstandard')
elif use_ants == True:
# ANTS warp outputs to outputnode
vmhc.connect(calculate_ants_xfm_vmhc, 'outputspec.ants_affine_xfm',
outputNode, 'highres2symmstandard_mat')
vmhc.connect(calculate_ants_xfm_vmhc, 'outputspec.warp_field',
outputNode, 'highres2symmstandard_warp')
vmhc.connect(calculate_ants_xfm_vmhc, 'outputspec.normalized_output_brain',
outputNode, 'fnirt_highres2symmstandard')
vmhc.connect(apply_ants_xfm_vmhc, 'outputspec.output_image',
outputNode, 'rest_res_2symmstandard')
vmhc.connect(pearson_correlation, 'out_file',
outputNode, 'VMHC_FWHM_img')
vmhc.connect(z_trans, 'out_file',
outputNode, 'VMHC_Z_FWHM_img')
vmhc.connect(z_stat, 'out_file',
outputNode, 'VMHC_Z_stat_FWHM_img')
return vmhc
parameter_source = 'FSL',
mask_type = 'file'),
iterfield=['realigned_files', 'realignment_parameters'],
name="art")
# Register structurals to a mni reference brain
# Use FLIRT first without skulls
mniFLIRT = pe.Node(interface=fsl.FLIRT(reference = strippedmfxTemplateBrain),
name = 'mniFLIRT')
# THen leave the skulls on both brains
# But apply the trasnformation to striped functionals later
mniFNIRT = pe.Node(interface=fsl.FNIRT(ref_file=mfxTemplateBrain,
config_file = mniConfig,
field_file = True,
fieldcoeff_file = True),
name = 'mniFNIRT')
func2MNI = pe.MapNode(interface = fsl.ApplyWarp(ref_file = mfxTemplateBrain),
iterfield=['in_file','premat'],
name = 'func2MNI')
#Generate a mean functional (it's quicker to check a mean then a timesearies)
meanfunc3 = pe.MapNode(interface=fsl.ImageMaths(op_string='-Tmean',
suffix='_mean'),
iterfield=['in_file'],
name='meanfunc3')
#Generate a mean functional (it's quicker to check a mean then a timesearies)
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 create_fsl_workflow(data_dir=None, subjects=None, name="fslwarp"):
"""Set up the anatomical normalzation workflow using FNIRT.
Your anatomical data must have been processed in Freesurfer.
Unlike most lyman workflows, the DataGrabber and DataSink
nodes are hardwired within the returned workflow, as this
tightly integrates with the Freesurfer subjects directory
structure.
Parameters
----------
data_dir : path
top level of data hierarchy/FS subjects directory
subjects : list of strings
list of subject IDs
name : alphanumeric string, optional
workflow name
"""
if data_dir is None:
data_dir = os.environ["SUBJECTS_DIR"]
if subjects is None:
subjects = []
# Get target images
target_brain = fsl.Info.standard_image("avg152T1_brain.nii.gz")
target_head = fsl.Info.standard_image("avg152T1.nii.gz")
hires_head = fsl.Info.standard_image("MNI152_T1_1mm.nii.gz")
target_mask = fsl.Info.standard_image(
"MNI152_T1_2mm_brain_mask_dil.nii.gz")
fnirt_cfg = os.path.join(os.environ["FSLDIR"],
"etc/flirtsch/T1_2_MNI152_2mm.cnf")
# Subject source node
subjectsource = Node(IdentityInterface(fields=["subject_id"]),
iterables=("subject_id", subjects),
name="subjectsource")
# Grab recon-all outputs
head_image = "T1"
templates = dict(aseg="{subject_id}/mri/aparc+aseg.mgz",
head="{subject_id}/mri/" + head_image + ".mgz")
datasource = Node(SelectFiles(templates, base_directory=data_dir),
"datasource")
# Convert images to nifti storage and float representation
cvtaseg = Node(fs.MRIConvert(out_type="niigz"), "convertaseg")
cvthead = Node(fs.MRIConvert(out_type="niigz", out_datatype="float"),
"converthead")
# Turn the aparc+aseg into a brainmask
makemask = Node(fs.Binarize(dilate=1, min=0.5), "makemask")
# Extract the brain from the orig.mgz using the mask
skullstrip = Node(fsl.ApplyMask(), "skullstrip")
# FLIRT brain to MNI152_brain
flirt = Node(fsl.FLIRT(reference=target_brain), "flirt")
sw = [-180, 180]
for dim in ["x", "y", "z"]:
setattr(flirt.inputs, "searchr_%s" % dim, sw)
# FNIRT head to MNI152
fnirt = Node(
fsl.FNIRT(ref_file=target_head,
refmask_file=target_mask,
config_file=fnirt_cfg,
fieldcoeff_file=True), "fnirt")
# Warp and rename the images
warpbrain = Node(
fsl.ApplyWarp(ref_file=target_head,
interp="spline",
out_file="brain_warp.nii.gz"), "warpbrain")
warpbrainhr = Node(
fsl.ApplyWarp(ref_file=hires_head,
interp="spline",
out_file="brain_warp_hires.nii.gz"), "warpbrainhr")
# Generate a png summarizing the registration
warpreport = Node(WarpReport(), "warpreport")
# Save relevant files to the data directory
fnirt_subs = [(head_image + "_out_masked_flirt.mat", "affine.mat"),
(head_image + "_out_fieldwarp", "warpfield"),
(head_image + "_out_masked", "brain"),
(head_image + "_out", "T1")]
datasink = Node(
DataSink(base_directory=data_dir,
parameterization=False,
substitutions=fnirt_subs), "datasink")
# Define and connect the workflow
# -------------------------------
normalize = Workflow(name=name)
normalize.connect([
(subjectsource, datasource, [("subject_id", "subject_id")]),
(datasource, cvtaseg, [("aseg", "in_file")]),
(datasource, cvthead, [("head", "in_file")]),
(cvtaseg, makemask, [("out_file", "in_file")]),
(cvthead, skullstrip, [("out_file", "in_file")]),
(makemask, skullstrip, [("binary_file", "mask_file")]),
(skullstrip, flirt, [("out_file", "in_file")]),
(flirt, fnirt, [("out_matrix_file", "affine_file")]),
(cvthead, fnirt, [("out_file", "in_file")]),
(skullstrip, warpbrain, [("out_file", "in_file")]),
(fnirt, warpbrain, [("fieldcoeff_file", "field_file")]),
(skullstrip, warpbrainhr, [("out_file", "in_file")]),
(fnirt, warpbrainhr, [("fieldcoeff_file", "field_file")]),
(warpbrain, warpreport, [("out_file", "in_file")]),
(subjectsource, datasink, [("subject_id", "container")]),
(skullstrip, datasink, [("out_file", "normalization.@brain")]),
(cvthead, datasink, [("out_file", "normalization.@t1")]),
(flirt, datasink, [("out_file", "normalization.@brain_flirted")]),
(flirt, datasink, [("out_matrix_file", "normalization.@affine")]),
(warpbrain, datasink, [("out_file", "normalization.@brain_warped")]),
(warpbrainhr, datasink, [("out_file", "normalization.@brain_hires")]),
(fnirt, datasink, [("fieldcoeff_file", "normalization.@warpfield")]),
(warpreport, datasink, [("out_file", "normalization.@report")]),
])
return normalize
def non_linear_registration_( self ):
"""Non-linear registration. Second part in the study template creation. Estimation of the registration parameters of brain to study oriented standard brain template."""
try:
#
#
#
# Use the linear template built in the linear_registration_ funstion
template_linear = os.path.join(self.ana_dir_, "temp_T1_lin.nii.gz")
template_T1_brain_lin = os.path.join(self.ana_dir_, "temp_T1_brain_lin.nii.gz")
# check if it has been built
if not os.path.exists(template_linear):
raise Exception( "Template %s has not been built yet. User has to process the linear step first"%template_linear )
#
# Loop on the tasks
while True:
# get the item
item = self.queue_[1].get()
# registration estimation
flt = fsl.FLIRT()
flt.inputs.in_file = os.path.join(self.T1_brain_dir_, item )
flt.inputs.reference = template_T1_brain_lin
flt.inputs.out_file = os.path.join( self.template_dir_, "%s_non_li_MNI.nii.gz"%item[:-7] )
flt.inputs.out_matrix_file = os.path.join( self.template_dir_, "%s_non_li_MNI.mat"%item[:-7] )
flt.inputs.dof = 12
res = flt.run()
#
# Find the matching T1 image
PIDN = [int(s) for s in item.split("_") if s.isdigit()]
#
T1_item = [ s for s in self.list_T1_ if str(PIDN[0]) in s]
#
# apply registration using T1_2_MNI152_2mm.cnf configuration file
T1_2_MNI152_2mm = ""
if os.environ.get('FSLDIR'):
T1_2_MNI152_2mm = os.path.join( os.environ.get('FSLDIR'),
"etc","flirtsch","T1_2_MNI152_2mm.cnf" )
#
fnt = fsl.FNIRT()
fnt.inputs.in_file = os.path.join( self.T1_dir_, T1_item[0] )
fnt.inputs.ref_file = template_linear
fnt.inputs.warped_file = os.path.join( self.template_dir_, "%s_non_li_MNI_fnirt.nii.gz"%item[:-7] )
fnt.inputs.affine_file = os.path.join( self.template_dir_, "%s_non_li_MNI.mat"%item[:-7] )
fnt.inputs.config_file = T1_2_MNI152_2mm
fnt.inputs.fieldcoeff_file = os.path.join( self.template_dir_, "%s_non_li_MNI_coeff.nii.gz"%item[:-7] )
res = fnt.run()
# apply warp
aw = fsl.ApplyWarp()
aw.inputs.in_file = os.path.join( self.T1_dir_, T1_item[0] )
aw.inputs.ref_file = template_linear
aw.inputs.out_file = os.path.join( self.template_dir_, "%s_non_li_MNI_warped.nii.gz"%item[:-7] )
aw.inputs.field_file = os.path.join( self.template_dir_, "%s_non_li_MNI_coeff.nii.gz"%item[:-7] )
res = aw.run()
# lock and add the file
singlelock.acquire()
self.non_linear_MNI_.append( aw.inputs.out_file )
singlelock.release()
# job is done
self.queue_[1].task_done()
#
#
except Exception as inst:
print inst
_log.error(inst)
quit(-1)
except IOError as e:
print "I/O error({0}): {1}".format(e.errno, e.strerror)
quit(-1)
except:
print "Unexpected error:", sys.exc_info()[0]
quit(-1)
def create_resting():
# main workflow
func_preproc = Workflow(name='resting')
inputnode = Node(util.IdentityInterface(fields=[
'subject_id', 'out_dir', 'freesurfer_dir', 'func', 'rs_mag', 'rs_ph',
'anat_head', 'anat_brain', 'anat_brain_mask', 'wmseg', 'csfseg',
'vol_to_remove', 'TR', 'highpass_freq', 'epi_resolution', 'echo_space',
'te_diff', 'fwhm', 'pe_dir', 'composite_transform', 'standard_brain',
'standard_downsampled'
]),
name='inputnode')
#Use correct subject ID from long timepoint for bbregister
def change_subject_id(subject):
import re
[subj, ses] = re.split("_", subject)
new_subject_id = subject + '.long.' + subj
return new_subject_id
change_subject_id = Node(util.Function(input_names=["subject"],
output_names=["new_subject_id"],
function=change_subject_id),
name="change_subject_id")
outputnode = Node(util.IdentityInterface(fields=[
'brain', 'brainmask', 'anat2std_transforms', 'std2anat_transforms',
'anat2std', 'anat_head', 'wmseg', 'csfseg', 'wmedge', 'subject_id'
]),
name='outputnode')
##PREPROCESSING FOR AROMA (Steps 1 - 7)
def merge_if_list(in_file):
if type(in_file) == list:
import numpy as np
import nibabel as nb
import os
from nipype.utils.filemanip import split_filename
nii1 = nb.load(in_file[0])
nii1d = nii1.get_data()
nii2 = nb.load(in_file[1])
nii2d = nii2.get_data()
x = np.concatenate((nii1d, nii2d), axis=3)
new_nii = nb.Nifti1Image(x, nii1.get_affine(), nii1.get_header())
new_nii.set_data_dtype(np.float32)
_, base, _ = split_filename(in_file[0])
nb.save(new_nii, base + "_merged.nii.gz")
return os.path.abspath(base + "_merged.nii.gz")
else:
return in_file
#if rsfmri is a list -> merge files, otherwise return single list.
merge_rs = Node(util.Function(input_names=['in_file'],
output_names=["out_file"],
function=merge_if_list),
name='merge_rs')
# node to remove first volumes
remove_vol = Node(util.Function(input_names=['in_file', 't_min'],
output_names=["out_file"],
function=strip_rois_func),
name='remove_vol')
# workflow for motion correction
moco = create_moco_pipeline()
# workflow for fieldmap correction and coregistration
fmap_coreg = create_fmap_coreg_pipeline()
# workflow for applying transformations to timeseries
transform_ts = create_transform_pipeline()
#mean intensity normalization
meanintensnorm = Node(fsl.ImageMaths(op_string='-ing 10000'),
name='meanintensnorm')
smoothing = create_smoothing_pipeline()
# connections
func_preproc.connect([
(inputnode, merge_rs, [('func', 'in_file')]),
(merge_rs, remove_vol, [('out_file', 'in_file')]),
(inputnode, remove_vol, [('vol_to_remove', 't_min')]),
(inputnode, moco, [('anat_brain_mask', 'inputnode.brainmask')]),
(remove_vol, moco, [('out_file', 'inputnode.epi')]),
(inputnode, change_subject_id, [('subject_id', 'subject')]),
(change_subject_id, fmap_coreg, [('new_subject_id',
'inputnode.fs_subject_id')]),
(inputnode, fmap_coreg, [('rs_mag', 'inputnode.mag'),
('rs_ph', 'inputnode.phase'),
('freesurfer_dir',
'inputnode.fs_subjects_dir'),
('echo_space', 'inputnode.echo_space'),
('te_diff', 'inputnode.te_diff'),
('pe_dir', 'inputnode.pe_dir'),
('anat_head', 'inputnode.anat_head'),
('anat_brain', 'inputnode.anat_brain')]),
(moco, fmap_coreg, [('outputnode.epi_mean', 'inputnode.epi_mean')]),
(remove_vol, transform_ts, [('out_file', 'inputnode.orig_ts')]),
(inputnode, transform_ts, [('anat_head', 'inputnode.anat_head')]),
(inputnode, transform_ts, [('anat_brain_mask', 'inputnode.brain_mask')
]),
(inputnode, transform_ts, [('epi_resolution', 'inputnode.resolution')
]),
(moco, transform_ts, [('outputnode.mat_moco', 'inputnode.mat_moco')]),
(fmap_coreg, transform_ts, [('outputnode.fmap_fullwarp',
'inputnode.fullwarp')]),
(transform_ts, meanintensnorm, [('outputnode.trans_ts', 'in_file')]),
(meanintensnorm, smoothing, [('out_file', 'inputnode.ts_transformed')
]),
(inputnode, smoothing, [('fwhm', 'inputnode.fwhm')])
])
##CALCULATE TRANSFORM from anatomical to standard space with FSL tools
# Anat > Standard
# register high-resolution to standard template with non-linear transform
# flirt serves as preparation for fnirt)
#reorient brain to standard (because Freesurfer space can cause problems)
reorient2std = Node(fsl.Reorient2Std(), name="reorient2std")
reorient2std_rs = Node(fsl.Reorient2Std(), name="reorient2std_rs")
reorient2std_mask = Node(fsl.Reorient2Std(), name="reorient2std_mask")
flirt_prep = Node(fsl.FLIRT(cost_func='mutualinfo', interp='trilinear'),
name='flirt_prep')
flirt_prep.inputs.interp = 'trilinear'
flirt_prep.inputs.dof = 12
fnirt = Node(fsl.FNIRT(), name='fnirt')
fnirt.inputs.field_file = True
fnirt.inputs.fieldcoeff_file = True
func_preproc.connect([
(inputnode, reorient2std, [('anat_brain', 'in_file')]),
(reorient2std, flirt_prep, [('out_file', 'in_file')]),
#(inputnode, flirt_prep, [('anat_brain', 'in_file')]),
(inputnode, flirt_prep, [('standard_brain', 'reference')]),
(flirt_prep, fnirt, [('out_matrix_file', 'affine_file')]),
(reorient2std, fnirt, [('out_file', 'in_file')]),
(inputnode, fnirt, [('standard_brain', 'ref_file')]),
])
def getcwd(subject_id):
import os
tmp = os.getcwd()
tmp = tmp[:-6]
tmp = tmp + 'ica_aroma/out' #%(subject_id)
return tmp
get_wd = Node(util.Function(input_names=['subject_id'],
output_names=["d"],
function=getcwd),
name='get_wd')
ica_aroma = Node(ICA_AROMA(), name="ica_aroma")
ica_aroma.inputs.denoise_type = 'both'
#ica_aroma.inputs.out_dir = os.getcwd()
func_preproc.connect([
(moco, ica_aroma, [('outputnode.par_moco', 'motion_parameters')]),
(smoothing, reorient2std_rs, [('outputnode.ts_smoothed', 'in_file')]),
(reorient2std_rs, ica_aroma, [('out_file', 'in_file')]),
(fnirt, ica_aroma, [('field_file', 'fnirt_warp_file')]),
(transform_ts, reorient2std_mask, [('outputnode.comb_mask_resamp',
'in_file')]),
(reorient2std_mask, ica_aroma, [('out_file', 'mask')]),
(inputnode, get_wd, [('subject_id', 'subject_id')]),
(get_wd, ica_aroma, [('d', 'out_dir')])
])
##POSTPROCESSING
postprocess = create_denoise_pipeline()
func_preproc.connect([
(reorient2std_mask, postprocess, [
('out_file', 'inputnode.brain_mask')
]), #use the correctly oriented mask
(ica_aroma, postprocess, [
('nonaggr_denoised_file', 'inputnode.epi_coreg')
]), #use the nonaggr_denoised_file
(inputnode, postprocess, [('TR', 'inputnode.tr')]),
(inputnode, postprocess, [('highpass_freq', 'inputnode.highpass_freq')
]),
(inputnode, postprocess, [('wmseg', 'inputnode.wmseg')]),
(inputnode, postprocess, [('csfseg', 'inputnode.csfseg')]),
])
#outputnode
outputnode = Node(util.IdentityInterface(fields=[
'par', 'rms', 'mean_epi', 'tsnr', 'stddev_file', 'realigned_ts',
'fmap', 'unwarped_mean_epi2fmap', 'coregistered_epi2fmap',
'fmap_fullwarp', 'epi2anat', 'epi2anat_mat', 'epi2anat_dat',
'epi2anat_mincost', 'full_transform_ts', 'full_transform_mean',
'resamp_t1', 'comb_mask_resamp', 'dvars_file', 'out_flirt_prep',
'out_matrix_flirt_prep', 'out_warped', 'out_warp_field',
'aggr_denoised_file', 'nonaggr_denoised_file', 'out_dir', 'wmcsf_mask',
'combined_motion', 'comp_regressor', 'comp_F', 'comp_pF', 'out_betas',
'ts_fullspectrum', 'ts_filtered'
]),
name='outputnode')
# connections
func_preproc.connect([
(
moco,
outputnode,
[ #('outputnode.epi_moco', 'realign.@realigned_ts'),
('outputnode.par_moco', 'par'),
('outputnode.rms_moco', 'rms'),
('outputnode.epi_moco', 'realigned_ts'),
('outputnode.epi_mean', 'mean_epi'),
('outputnode.tsnr_file', 'tsnr'),
('outputnode.stddev_file', 'stddev'),
]),
(fmap_coreg, outputnode,
[('outputnode.fmap', 'fmap'),
('outputnode.unwarped_mean_epi2fmap', 'unwarped_mean_epi2fmap'),
('outputnode.epi2fmap', 'coregistered_epi2fmap'),
('outputnode.fmap_fullwarp', 'fmap_fullwarp'),
('outputnode.epi2anat', 'epi2anat'),
('outputnode.epi2anat_mat', 'epi2anat_mat'),
('outputnode.epi2anat_dat', 'epi2anat_dat'),
('outputnode.epi2anat_mincost', 'epi2anat_mincost')]),
(transform_ts, outputnode,
[('outputnode.trans_ts', 'full_transform_ts'),
('outputnode.trans_ts_mean', 'full_transform_mean'),
('outputnode.resamp_t1', 'resamp_t1'),
('outputnode.comb_mask_resamp', 'comb_mask_resamp'),
('outputnode.out_dvars', 'dvars_file')]),
(flirt_prep, outputnode, [('out_file', 'out_flirt_prep'),
('out_matrix_file', 'out_matrix_flirt_prep')
]),
(fnirt, outputnode, [('warped_file', 'out_warped'),
('field_file', 'out_warp_field')]),
(ica_aroma, outputnode, [('aggr_denoised_file', 'aggr_denoised_file'),
('nonaggr_denoised_file',
'nonaggr_denoised_file'),
('out_dir', 'out_dir')]),
(postprocess, outputnode,
[('outputnode.wmcsf_mask', 'wmcsf_mask'),
('outputnode.combined_motion', 'combined_motion'),
('outputnode.comp_regressor', 'comp_regressor'),
('outputnode.comp_F', 'comp_F'), ('outputnode.comp_pF', 'comp_pF'),
('outputnode.out_betas', 'out_betas'),
('outputnode.ts_fullspectrum', 'ts_fullspectrum'),
('outputnode.ts_filtered', 'ts_filtered')])
])
return func_preproc
def get_ADC(LinADC):
"""
in the future, users will be able to specify which time point
to use as reference
"""
return LinADC[0]
get_Ref = pe.JoinNode(interface=util.Function(input_names=['LinADC'],
output_names=['REF'],
function=get_ADC),
name='get_Ref',
joinsource='data',
joinfield='LinADC')
ADCwarpHighRes = pe.Node(interface=fsl.FNIRT(), name='ADCwarpHighRes')
ADCwarpHighRes.inputs.field_file = True
ADCwarpHighRes.inputs.config_file = fnirt_config
TumorwarpHighRes = pe.Node(interface=fsl.ApplyWarp(),
name='TumorwarpHighRes')
REFwarpHighRes = pe.Node(interface=fsl.ApplyWarp(), name='REFwarpHighRes')
NormalwarpHighRes = pe.Node(interface=fsl.ApplyWarp(),
name='NormalwarpHighRes')
Warp = pe.Workflow(name='Warp')
Warp.base_dir = parent_dir + '/FDM'
Warp.connect([
(get_Ref, ADCwarpHighRes, [('REF', 'ref_file')]),
def anatomical_registration(self, subject, standard_image_name='MNI152_T1_2mm_brain.nii.gz' ):
"""
Anatomical Registration
1) Runs FLIRT on the brain extracted anatomy image with MNI152_T1_2mm_brain.nii.gz as reference
2) Runs FNIRT on the anatomy image
Outputs:
anatomy/reg/highres2standard.nii.gz
anatomy/reg/highres2standard.mat
anatomy/reg/highres2highres_jac
anatomy/reg/highres2standard_warp.nii.gz
Parameters
subject = Subject Dir object
standard_image_name = The anatomy reference image
"""
print ">>> Anatomical registration"
brain_image = subject.anatomical_nii('brain')
reg_dir = os.path.join(subject.anatomical_dir(), 'reg')
out_file = os.path.join(reg_dir, 'highres2standard.nii.gz')
out_mat_file = os.path.join(reg_dir, 'highres2standard.mat')
standard_image = fsl.Info.standard_image(standard_image_name)
if not os.path.isfile(out_mat_file):
print ">>>> FLIRT"
os.mkdir(reg_dir)
flirt = fsl.FLIRT(in_file=brain_image,
reference=standard_image,
out_file=out_file,
out_matrix_file=out_mat_file,
cost='corratio', # ('mutualinfo' or 'corratio' or 'normcorr' or 'normmi' or 'leastsq' or 'labeldiff' or 'bbr')
dof=12, # number of transform degrees of freedom
searchr_x=[-90, 90], # search angles along x-axis, in degrees
searchr_y=[-90, 90], # search angles along y-axis, in degrees
searchr_z=[-90, 90], # search angles along z-axis, in degrees
interp='trilinear') # 'trilinear' or 'nearestneighbour' or 'sinc' or 'spline'
flirt.run()
else:
print(">>>> FLIRT has already been performed")
anatomical_head = subject.anatomical_nii()
output_fielf_coeff = os.path.join(
reg_dir, 'highres2standard_warp.nii.gz')
output_jacobian = os.path.join(reg_dir, 'highres2highres_jac')
standard_head = fsl.Info.standard_image('MNI152_T1_2mm.nii.gz')
standard_mask = fsl.Info.standard_image(
'MNI152_T1_2mm_brain_mask_dil.nii.gz')
if not os.path.isfile(output_fielf_coeff):
print ">>>> FNIRT"
# non-linear registration.
fnirt = fsl.FNIRT(warped_file=out_file, # warped image
in_file=anatomical_head,
affine_file=out_mat_file, # Affine matrix to use
fieldcoeff_file=output_fielf_coeff, # name of output file with field coefficients or true
jacobian_file=output_jacobian, # name of file for writing out the Jacobianof the field (for diagnostic or VBM purposes)
config_file='T1_2_MNI152_2mm', # 'T1_2_MNI152_2mm' or 'FA_2_FMRIB58_1mm'
ref_file=standard_head, # name of reference image
refmask_file=standard_mask) # name of file with mask in reference space
fnirt.run()
cmd = 'fslview {} {} -t 0.5 '.format(standard_image, out_file)
pro = subprocess.Popen(cmd, stdout=subprocess.PIPE,
shell=True, preexec_fn=os.setsid)
else:
print(">>>> FNIRT has already been performed")
'reference')
register_flow.connect(func2highres, 'out_matrix_file', func2highres_bbr,
'in_matrix_file')
# Calculate affine transform from anatomical to target (default interpolation is trilinear)
highres2standard = Node(fsl.FLIRT(), name='highres2standard_affine')
highres2standard.inputs.dof = 12
highres2standard.inputs.searchr_x = [-90, 90]
highres2standard.inputs.searchr_y = [-90, 90]
highres2standard.inputs.searchr_z = [-90, 90]
register_flow.connect(stripper, 'out_file', highres2standard, 'in_file')
register_flow.connect(reg_inputnode, 'target_image_brain', highres2standard,
'reference')
# Calculate nonlinear transform from anatomical to target (default warp resolution is 10,10,10)
highres2standard_warp = Node(fsl.FNIRT(), name='highres2standard_nonlinear')
highres2standard_warp.inputs.fieldcoeff_file = True
register_flow.connect(highres2standard, 'out_matrix_file',
highres2standard_warp, 'affine_file')
register_flow.connect(reg_inputnode, 'anatomical_image', highres2standard_warp,
'in_file')
register_flow.connect(reg_inputnode, 'config_file', highres2standard_warp,
'config_file')
register_flow.connect(reg_inputnode, 'target_image', highres2standard_warp,
'ref_file')
register_flow.connect(reg_inputnode, 'target_mask', highres2standard_warp,
'refmask_file')
# Combine linear FLIRT transformations into one to create a transformation matrix from functional to target
func2standard = Node(fsl.ConvertXFM(concat_xfm=True),
name='func2standard_linear')
def FNIRT(in_file: str, ref: str, out_file: str, aff: str = None):
fnt = fsl.FNIRT()
fnt.inputs.in_file = in_file
fnt.inputs.ref_file = ref
#extract_b0.inputs.t_min = 0
get_T1_template.inputs.t_size = 1
get_T1_template.inputs.in_file = T1_Template
T1linTemplate = pe.Node(interface=fsl.FLIRT(), name='T1linTemplate')
#T1linTemplate.inputs.reference=Template
T1linTemplate.inputs.dof = 12
T1linTemplate.inputs.searchr_x = [-180, 180]
T1linTemplate.inputs.searchr_y = [-180, 180]
T1linTemplate.inputs.searchr_z = [-180, 180]
inverse_T1_matrix = pe.Node(interface=fsl.ConvertXFM(), name='inverse_T1_matrix')
inverse_T1_matrix.inputs.invert_xfm = True
T1warpTemplate=pe.Node(interface=fsl.FNIRT(), name='T1warpTemplate')
T1warpTemplate.inputs.field_file=True
T1warpTemplate.inputs.config_file=configfile
inverse_T1_warp=pe.Node(interface=tools.InvWarp(), name='inverse_T1_warp')
apply_T1_warp=pe.MapNode(interface=fsl.ApplyWarp(), name='apply_T1_warp',
iterfield=['in_file'])
apply_T1_warp.inputs.interp='nn'
get_masks = pe.MapNode(interface=fsl.ExtractROI(), name = 'get_masks',
iterfield=['in_file'])
get_masks.inputs.t_size = 1
get_masks.inputs.in_file = TissueList
def reg2std(name='standardization'):
"""
Linear and non-linear standardization for structural rat MRI images
Input: upscaled data
"""
reg = pe.Workflow(name=name)
"""
Set up a node to define all inputs required for the preprocessing workflow
"""
inputnode = pe.Node(interface=util.IdentityInterface(fields=['in_file_head', 'in_file_brain'], mandatory_inputs=True),
name='inputspec')
"""
Set up a node to define outputs for the preprocessing workflow
"""
outputnode = pe.Node(interface=util.IdentityInterface(fields=['out_nonlin_head', 'out_nonlin_brain', 'out_warpfield', 'out_lin_brain'], mandatory_inputs=True),
name='outputspec')
"""
Node for linear (12-param) reg
flirt -in $in -ref $std -dof 12 -omat $omat -bins 256 -cost $type -searchrx -90 90 -searchry -90 90 -searchrz -90 90 -interp trilinear
"""
flirt=pe.MapNode(interface=fsl.FLIRT(reference='/opt/shared/etc/std/new/standard-wistar_2mm_brain.nii.gz',
dof=12,
bins=256,
cost='corratio',
interp='trilinear'),
name='linear_standardization',
iterfield=['in_file']) #out_matrix_file?, searchr?
"""
Non-linear reg
fnirt --in=$in --ref=$std --aff=$init --iout=$out --miter=2,3 --infwhm=4,1 --reffwhm=2,0 --subsamp=8,1 --estint=1,1 --applyrefmask=1,1 --applyinmask=0,0 --lambda=200,500 --warpres=10,10,10 --intmod=global_non_linear --refmask=$refmask --fout=$warpingfield --interp=spline
"""
#TODO: parametrize standard template
fnirt=pe.MapNode(interface=fsl.FNIRT( \
ref_file='/opt/shared/etc/std/new/standard-wistar_2mm_brain.nii.gz', \
refmask_file='/opt/shared/etc/std/new/standard-wistar_2mm_brain_mask.nii.gz', \
max_nonlin_iter=[2,3], \
in_fwhm=[2,0], \
ref_fwhm=[2,0], \
subsampling_scheme=[8,1],\
apply_intensity_mapping=[1,1],\
apply_refmask=[1,1],\
apply_inmask=[0,0],\
regularization_lambda=[200,500],\
warp_resolution=(10,10,10),\
intensity_mapping_model='global_non_linear_with_bias',\
field_file=True),
name='nonlinear_standardization',
iterfield=['in_file', 'affine_file' ])
"""
Non-linear reg
fnirt --in=$in --ref=$std --aff=$init --iout=$out --miter=2,3 --infwhm=4,1 --reffwhm=2,0 --subsamp=8,1 --estint=1,1 --applyrefmask=1,1 --applyinmask=0,0 --lambda=200,500 --warpres=10,10,10 --intmod=global_non_linear --refmask=$refmask --fout=$warpingfield --interp=spline
"""
applyWarp=pe.MapNode(interface=fsl.ApplyWarp(), name="warp_brain",
iterfield=['in_file', 'ref_file', 'field_file'])
# TODO applywarp: brain
reg.connect(inputnode, "in_file_brain", flirt, "in_file")
reg.connect(flirt, "out_file", outputnode, "out_lin_brain")
reg.connect(inputnode, "in_file_head", fnirt, "in_file")
reg.connect(flirt, "out_matrix_file", fnirt, "affine_file")
reg.connect(inputnode, "in_file_brain", applyWarp, "in_file")
reg.connect(fnirt, "warped_file", applyWarp, "ref_file")
reg.connect(fnirt, "field_file", applyWarp, "field_file")
reg.connect(fnirt, "warped_file", outputnode, "out_nonlin_head")
reg.connect(applyWarp, "out_file", outputnode, "out_nonlin_brain")
reg.connect(fnirt, "field_file", outputnode, "out_warpfield")
return reg
def create_iterative_register_pipe(template_file,
template_brain_file,
template_mask_file,
gm_prob_file,
wm_prob_file,
csf_prob_file,
n_iter,
name="register_pipe"):
"""
Registration of template (NMT or other) according to Regis:
- The iterative FLIRT is between NMT_SS and subject's anat after a quick
skull-stripping but the anat is more and more refined to corresponds to the
brain
- there is also a FNIRT done once, for comparison of the quality of the
template on the subject's brain
Not used anymore: corresponds to the IterREGBET provided by Regis in bash
and wrapped node IterREGBET in nodes/register.py
#TODO: test if gives the same results as IterREGBET
"""
register_pipe = pe.Workflow(name=name)
# creating inputnode
inputnode = pe.Node(
niu.IdentityInterface(fields=['anat_file_BET', 'anat_file']),
name='inputnode')
# register node
register = pe.Node(niu.Function(
input_names=[
"anat_file", "anat_file_BET", "template_brain_file",
"template_mask_file", 'n_iter'
],
output_names=["anat_file_brain", "template_to_anat_file"],
function=interative_flirt),
name="register")
register.inputs.template_brain_file = template_brain_file
register.inputs.template_mask_file = template_mask_file
register.inputs.n_iter = n_iter
register_pipe.connect(inputnode, 'anat_file', register, 'anat_file')
register_pipe.connect(inputnode, 'anat_file_BET', register,
"anat_file_BET")
# apply transfo over the 3 tissues:
# gm
register_gm = pe.Node(fsl.ApplyXFM(), name="register_gm")
register_gm.inputs.in_file = gm_prob_file
register_gm.inputs.apply_xfm = True
register_gm.inputs.interp = "nearestneighbour"
register_gm.inputs.output_type = "NIFTI" # for SPM segment
register_pipe.connect(register, 'anat_file_brain', register_gm,
'reference')
register_pipe.connect(register, 'template_to_anat_file', register_gm,
"in_matrix_file")
# wm
register_wm = pe.Node(fsl.ApplyXFM(), name="register_wm")
register_wm.inputs.in_file = wm_prob_file
register_wm.inputs.apply_xfm = True
register_wm.inputs.interp = "nearestneighbour"
register_wm.inputs.output_type = "NIFTI" # for SPM segment
register_pipe.connect(register, 'anat_file_brain', register_wm,
'reference')
register_pipe.connect(register, 'template_to_anat_file', register_wm,
"in_matrix_file")
# csf
register_csf = pe.Node(fsl.ApplyXFM(), name="register_csf")
register_csf.inputs.in_file = csf_prob_file
register_csf.inputs.apply_xfm = True
register_csf.inputs.interp = "nearestneighbour"
register_csf.inputs.output_type = "NIFTI" # for SPM segment
register_pipe.connect(register, 'anat_file_brain', register_csf,
'reference')
register_pipe.connect(register, 'template_to_anat_file', register_csf,
"in_matrix_file")
def return_list(file1, file2, file3):
return [file1, file2, file3]
# merge 3 outputs to a list (...)
merge_3_files = pe.Node(niu.Function(
input_names=["file1", "file2", "file3"],
output_names=["list3files"],
function=return_list),
name="merge_3_files")
register_pipe.connect(register_gm, 'out_file', merge_3_files, "file1")
register_pipe.connect(register_wm, 'out_file', merge_3_files, "file2")
register_pipe.connect(register_csf, 'out_file', merge_3_files, "file3")
# same with non linear
# non linear register between anat and head
# (FNIRT work directly on head?
# I thought FLIRT was only skull-stripped brain, is it different? )
nl_register = pe.Node(fsl.FNIRT(), name="nl_register")
nl_register.inputs.in_file = template_file
register_pipe.connect(inputnode, 'anat_file', nl_register, 'ref_file')
register_pipe.connect(register, 'template_to_anat_file', nl_register,
'affine_file')
# apply non linear warp to NMT_SS
nl_apply = pe.Node(fsl.ApplyWarp(), name="nl_apply")
nl_apply.inputs.in_file = template_brain_file
register_pipe.connect(inputnode, 'anat_file', nl_apply, 'ref_file')
register_pipe.connect(nl_register, 'fieldcoeff_file', nl_apply,
'field_file') # iout from fnirt
return register_pipe