def parse_spaces(opts):
"""Ensures the spaces are correctly parsed"""
from sys import stderr
from collections import OrderedDict
from templateflow.api import templates as get_templates
# Set the default template to 'MNI152NLin2009cAsym'
output_spaces = opts.output_spaces or OrderedDict(
[('MNI152NLin2009cAsym', {})])
if opts.template:
print("""\
The ``--template`` option has been deprecated in version 1.4.0. Your selected template \
"%s" will be inserted at the front of the ``--output-spaces`` argument list. Please update \
your scripts to use ``--output-spaces``.""" % opts.template,
file=stderr)
deprecated_tpl_arg = [(opts.template, {})]
# If output_spaces is not set, just replate the default - append otherwise
if opts.output_spaces is not None:
deprecated_tpl_arg += list(output_spaces.items())
output_spaces = OrderedDict(deprecated_tpl_arg)
if opts.output_space:
print("""\
The ``--output_space`` option has been deprecated in version 1.4.0. Your selection of spaces \
"%s" will be inserted at the front of the ``--output-spaces`` argument list. Please update \
your scripts to use ``--output-spaces``.""" % ', '.join(opts.output_space),
file=stderr)
missing = set(opts.output_space)
if 'template' in missing:
missing.remove('template')
if not opts.template:
missing.add('MNI152NLin2009cAsym')
missing = missing - set(output_spaces.keys())
output_spaces.update({tpl: {} for tpl in missing})
FS_SPACES = set(['fsnative', 'fsaverage', 'fsaverage6', 'fsaverage5'])
if opts.run_reconall and not list(
FS_SPACES.intersection(output_spaces.keys())):
print("""\
Although ``--fs-no-reconall`` was not set (i.e., FreeSurfer is to be run), no FreeSurfer \
output space (valid values are: %s) was selected. Adding default "fsaverage5" to the \
list of output spaces.""" % ', '.join(FS_SPACES),
file=stderr)
output_spaces['fsaverage5'] = {}
# Validity of some inputs
# ERROR check if use_aroma was specified, but the correct template was not
if opts.use_aroma and 'MNI152NLin6Asym' not in output_spaces:
output_spaces['MNI152NLin6Asym'] = {'res': 2}
print("""\
Option "--use-aroma" requires functional images to be resampled to MNI152NLin6Asym space. \
The argument "MNI152NLin6Asym:res-2" has been automatically added to the list of output spaces \
(option ``--output-spaces``).""",
file=stderr)
if opts.cifti_output and 'MNI152NLin2009cAsym' not in output_spaces:
if 'MNI152NLin2009cAsym' not in output_spaces:
output_spaces['MNI152NLin2009cAsym'] = {'res': 2}
print(
"""Option ``--cifti-output`` requires functional images to be resampled to \
``MNI152NLin2009cAsym`` space. Such template identifier has been automatically added to the \
list of output spaces (option "--output-space").""",
file=stderr)
if not [s for s in output_spaces if s in ('fsaverage5', 'fsaverage6')]:
output_spaces['fsaverage5'] = {}
print(
"""Option ``--cifti-output`` requires functional images to be resampled to \
``fsaverage`` space. The argument ``fsaverage:den-10k`` (a.k.a ``fsaverage5``) has been \
automatically added to the list of output spaces (option ``--output-space``).""",
file=stderr)
if opts.template_resampling_grid is not None:
print(
"""Option ``--template-resampling-grid`` is deprecated, please specify \
resampling grid options as modifiers to templates listed in ``--output-spaces``. \
The configuration value will be applied to ALL output standard spaces.""")
if opts.template_resampling_grid != 'native':
for key in output_spaces.keys():
if key in get_templates():
output_spaces[key]['res'] = opts.template_resampling_grid[
0]
return output_spaces
def init_anat_norm_wf(
debug,
omp_nthreads,
templates,
):
"""
Build an individual spatial normalization workflow using ``antsRegistration``.
.. workflow ::
:graph2use: orig
:simple_form: yes
from smriprep.workflows.norm import init_anat_norm_wf
wf = init_anat_norm_wf(
debug=False,
omp_nthreads=1,
templates=[('MNI152NLin2009cAsym', {}), ('MNI152NLin6Asym', {})],
)
**Parameters**
debug : bool
Apply sloppy arguments to speed up processing. Use with caution,
registration processes will be very inaccurate.
omp_nthreads : int
Maximum number of threads an individual process may use.
templates : list of tuples
List of tuples containing TemplateFlow identifiers (e.g. ``MNI152NLin6Asym``)
and corresponding specs, which specify target templates
for spatial normalization.
**Inputs**
moving_image
The input image that will be normalized to standard space.
moving_mask
A precise brain mask separating skull/skin/fat from brain
structures.
moving_segmentation
A brain tissue segmentation of the ``moving_image``.
moving_tpms
tissue probability maps (TPMs) corresponding to the
``moving_segmentation``.
lesion_mask
(optional) A mask to exclude regions from the cost-function
input domain to enable standardization of lesioned brains.
orig_t1w
The original T1w image from the BIDS structure.
**Outputs**
standardized
The T1w after spatial normalization, in template space.
anat2std_xfm
The T1w-to-template transform.
std2anat_xfm
The template-to-T1w transform.
std_mask
The ``moving_mask`` in template space (matches ``standardized`` output).
std_dseg
The ``moving_segmentation`` in template space (matches ``standardized``
output).
std_tpms
The ``moving_tpms`` in template space (matches ``standardized`` output).
template
The input parameter ``template`` for further use in nodes depending
on this
workflow.
"""
templateflow = get_templates()
missing_tpls = [
template for template, _ in templates if template not in templateflow
]
if missing_tpls:
raise ValueError("""\
One or more templates were not found (%s). Please make sure TemplateFlow is \
correctly installed and contains the given template identifiers.""" %
', '.join(missing_tpls))
ntpls = len(templates)
workflow = Workflow('anat_norm_wf')
workflow.__desc__ = """\
Volume-based spatial normalization to {targets} ({targets_id}) was performed through
nonlinear registration with `antsRegistration` (ANTs {ants_ver}),
using brain-extracted versions of both T1w reference and the T1w template.
The following template{tpls} selected for spatial normalization:
""".format(ants_ver=ANTsInfo.version() or '(version unknown)',
targets='%s standard space%s' %
(defaultdict('several'.format, {
1: 'one',
2: 'two',
3: 'three',
4: 'four'
})[ntpls], 's' * (ntpls != 1)),
targets_id=', '.join((t for t, _ in templates)),
tpls=(' was', 's were')[ntpls != 1])
# Append template citations to description
for template, _ in templates:
template_meta = get_metadata(template)
template_refs = ['@%s' % template.lower()]
if template_meta.get('RRID', None):
template_refs += ['RRID:%s' % template_meta['RRID']]
workflow.__desc__ += """\
*{template_name}* [{template_refs}; TemplateFlow ID: {template}]""".format(
template=template,
template_name=template_meta['Name'],
template_refs=', '.join(template_refs))
workflow.__desc__ += (', ', '.')[template == templates[-1][0]]
inputnode = pe.Node(niu.IdentityInterface(fields=[
'moving_image', 'moving_mask', 'moving_segmentation', 'moving_tpms',
'lesion_mask', 'orig_t1w', 'template'
]),
name='inputnode')
inputnode.iterables = [('template', templates)]
out_fields = [
'standardized', 'anat2std_xfm', 'std2anat_xfm', 'std_mask', 'std_dseg',
'std_tpms', 'template'
]
poutputnode = pe.Node(niu.IdentityInterface(fields=out_fields),
name='poutputnode')
tf_select = pe.Node(TemplateFlowSelect(resolution=1 + debug),
name='tf_select',
run_without_submitting=True)
# With the improvements from poldracklab/niworkflows#342 this truncation is now necessary
trunc_mov = pe.Node(ImageMath(operation='TruncateImageIntensity',
op2='0.01 0.999 256'),
name='trunc_mov')
registration = pe.Node(RobustMNINormalization(
float=True,
flavor=['precise', 'testing'][debug],
),
name='registration',
n_procs=omp_nthreads,
mem_gb=2)
# Resample T1w-space inputs
tpl_moving = pe.Node(ApplyTransforms(dimension=3,
default_value=0,
float=True,
interpolation='LanczosWindowedSinc'),
name='tpl_moving')
std_mask = pe.Node(ApplyTransforms(dimension=3,
default_value=0,
float=True,
interpolation='MultiLabel'),
name='std_mask')
std_dseg = pe.Node(ApplyTransforms(dimension=3,
default_value=0,
float=True,
interpolation='MultiLabel'),
name='std_dseg')
std_tpms = pe.MapNode(ApplyTransforms(dimension=3,
default_value=0,
float=True,
interpolation='Gaussian'),
iterfield=['input_image'],
name='std_tpms')
workflow.connect([
(inputnode, tf_select, [(('template', _get_name), 'template'),
(('template', _get_spec), 'template_spec')]),
(inputnode, registration, [(('template', _get_name), 'template'),
(('template', _get_spec), 'template_spec')
]),
(inputnode, trunc_mov, [('moving_image', 'op1')]),
(inputnode, registration, [('moving_mask', 'moving_mask'),
('lesion_mask', 'lesion_mask')]),
(inputnode, tpl_moving, [('moving_image', 'input_image')]),
(inputnode, std_mask, [('moving_mask', 'input_image')]),
(tf_select, tpl_moving, [('t1w_file', 'reference_image')]),
(tf_select, std_mask, [('t1w_file', 'reference_image')]),
(tf_select, std_dseg, [('t1w_file', 'reference_image')]),
(tf_select, std_tpms, [('t1w_file', 'reference_image')]),
(trunc_mov, registration, [('output_image', 'moving_image')]),
(registration, tpl_moving, [('composite_transform', 'transforms')]),
(registration, std_mask, [('composite_transform', 'transforms')]),
(inputnode, std_dseg, [('moving_segmentation', 'input_image')]),
(registration, std_dseg, [('composite_transform', 'transforms')]),
(inputnode, std_tpms, [('moving_tpms', 'input_image')]),
(registration, std_tpms, [('composite_transform', 'transforms')]),
(registration, poutputnode, [('composite_transform', 'anat2std_xfm'),
('inverse_composite_transform',
'std2anat_xfm')]),
(tpl_moving, poutputnode, [('output_image', 'standardized')]),
(std_mask, poutputnode, [('output_image', 'std_mask')]),
(std_dseg, poutputnode, [('output_image', 'std_dseg')]),
(std_tpms, poutputnode, [('output_image', 'std_tpms')]),
(inputnode, poutputnode, [('template', 'template')]),
])
# Provide synchronized output
outputnode = pe.JoinNode(niu.IdentityInterface(fields=out_fields),
name='outputnode',
joinsource='inputnode')
workflow.connect([
(poutputnode, outputnode, [(f, f) for f in out_fields]),
])
return workflow