# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
#
# Copyright 2021 The NiPreps Developers <nipreps@gmail.com>
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"""Spatial normalization workflows."""
from collections import defaultdict
from nipype.interfaces import ants
from nipype.interfaces import utility as niu
from nipype.interfaces.ants.base import Info as ANTsInfo
from nipype.pipeline import engine as pe
from niworkflows.engine.workflows import LiterateWorkflow as Workflow
from niworkflows.interfaces.norm import SpatialNormalization
from templateflow import __version__ as tf_ver
from templateflow.api import get_metadata
from ...interfaces.templateflow import TemplateDesc, TemplateFlowSelect
[docs]
def init_register_template_wf(
*,
sloppy: bool,
omp_nthreads: int,
templates: list[str],
image_type: str = 'T1w',
name='register_template_wf',
):
"""
Build an individual spatial normalization workflow using ``antsRegistration``.
Workflow Graph
.. workflow ::
:graph2use: orig
:simple_form: yes
from smriprep.workflows.fit.registration import init_register_template_wf
wf = init_register_template_wf(
sloppy=False,
omp_nthreads=1,
templates=['MNI152NLin2009cAsym', 'MNI152NLin6Asym'],
)
.. important::
This workflow defines an iterable input over the input parameter ``templates``,
so Nipype will produce one copy of the downstream workflows which connect
``poutputnode.template`` or ``poutputnode.template_spec`` to their inputs
(``poutputnode`` stands for *parametric output node*).
Nipype refers to this expansion of the graph as *parameterized execution*.
If a joint list of values is required (and thus cutting off parameterization),
please use the equivalent outputs of ``outputnode`` (which *joins* all the
parameterized execution paths).
Parameters
----------
sloppy : :obj:`bool`
Apply sloppy arguments to speed up processing. Use with caution,
registration processes will be very inaccurate.
omp_nthreads : :obj:`int`
Maximum number of threads an individual process may use.
templates : :obj:`list` of :obj:`str`
List of standard space fullnames (e.g., ``MNI152NLin6Asym``
or ``MNIPediatricAsym:cohort-4``) which are targets for spatial
normalization.
Inputs
------
moving_image
The input image that will be normalized to standard space.
lesion_mask
(optional) A mask to exclude regions from the cost-function
input domain to enable standardization of lesioned brains.
template
Template name and specification
image_type
Moving image modality
Outputs
-------
anat2std_xfm
The T1w-to-template transform.
std2anat_xfm
The template-to-T1w transform.
template
Template name extracted from the input parameter ``template``, for further
use in downstream nodes.
template_spec
Template specifications extracted from the input parameter ``template``, for
further use in downstream nodes.
"""
ntpls = len(templates)
workflow = Workflow(name=name)
if templates:
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} were selected for spatial normalization
and accessed with *TemplateFlow* [{tf_ver}, @templateflow]:
""".format(
ants_ver=ANTsInfo.version() or '(version unknown)',
targets='{} standard space{}'.format(
defaultdict('several'.format, {1: 'one', 2: 'two', 3: 'three', 4: 'four'})[ntpls],
's' * (ntpls != 1),
),
targets_id=', '.join(templates),
tf_ver=tf_ver,
tpls=(' was', 's were')[ntpls != 1],
)
# Append template citations to description
for template in templates:
template_meta = get_metadata(template.split(':')[0])
template_refs = ['@{}'.format(template.split(':')[0].lower())]
if template_meta.get('RRID', None):
template_refs += [f'RRID:{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__ += '.\n' if template == templates[-1] else ', '
inputnode = pe.Node(
niu.IdentityInterface(
fields=[
'lesion_mask',
'moving_image',
'moving_mask',
'template',
]
),
name='inputnode',
)
inputnode.iterables = [('template', templates)]
out_fields = [
'anat2std_xfm',
'std2anat_xfm',
'template',
'template_spec',
]
outputnode = _make_outputnode(workflow, out_fields, joinsource='inputnode')
split_desc = pe.Node(TemplateDesc(), run_without_submitting=True, name='split_desc')
tf_select = pe.Node(
TemplateFlowSelect(resolution=1 + sloppy),
name='tf_select',
run_without_submitting=True,
)
set_reference = pe.Node(
niu.Function(
function=_set_reference,
output_names=['reference_type', 'use_histogram_matching'],
),
name='set_reference',
)
set_reference.inputs.image_type = image_type
# With the improvements from nipreps/niworkflows#342 this truncation is now necessary
trunc_mov = pe.Node(
ants.ImageMath(operation='TruncateImageIntensity', op2='0.01 0.999 256'),
name='trunc_mov',
)
registration = pe.Node(
SpatialNormalization(
float=True,
flavor=['precise', 'testing'][sloppy],
),
name='registration',
n_procs=omp_nthreads,
mem_gb=2,
)
fmt_cohort = pe.Node(
niu.Function(function=_fmt_cohort, output_names=['template', 'spec']),
name='fmt_cohort',
run_without_submitting=True,
)
# fmt:off
workflow.connect([
(inputnode, split_desc, [('template', 'template')]),
(inputnode, trunc_mov, [('moving_image', 'op1')]),
(inputnode, registration, [
('moving_mask', 'moving_mask'),
('lesion_mask', 'lesion_mask')]),
(split_desc, tf_select, [
('name', 'template'),
('spec', 'template_spec'),
]),
(tf_select, set_reference, [
('t1w_file', 'template_t1w'),
('t2w_file', 'template_t2w'),
]),
(set_reference, registration, [
('reference_type', 'reference'),
('use_histogram_matching', 'use_histogram_matching'),
]),
(split_desc, registration, [
('name', 'template'),
('spec', 'template_spec'),
]),
(trunc_mov, registration, [
('output_image', 'moving_image')]),
(split_desc, fmt_cohort, [
('name', 'template'),
('spec', 'spec'),
]),
(fmt_cohort, outputnode, [
('template', 'template'),
('spec', 'template_spec'),
]),
(registration, outputnode, [
('composite_transform', 'anat2std_xfm'),
('inverse_composite_transform', 'std2anat_xfm'),
]),
])
# fmt:on
return workflow
def _make_outputnode(workflow, out_fields, joinsource):
if joinsource:
pout = pe.Node(niu.IdentityInterface(fields=out_fields), name='poutputnode')
out = pe.JoinNode(
niu.IdentityInterface(fields=out_fields), name='outputnode', joinsource=joinsource
)
workflow.connect([(pout, out, [(f, f) for f in out_fields])])
return pout
return pe.Node(niu.IdentityInterface(fields=out_fields), name='outputnode')
def _fmt_cohort(template, spec):
cohort = spec.pop('cohort', None)
if cohort is not None:
template = f'{template}:cohort-{cohort}'
return template, spec
def _set_reference(image_type, template_t1w, template_t2w=None):
"""
Determine the normalization reference and whether histogram matching will be used.
Parameters
----------
image_type : MR image type of anatomical reference (T1w, T2w)
template_t1w : T1w file
template_t2w : T2w file or undefined
Returns
-------
reference_type : modality of template reference (T1w, T2w)
histogram_matching : False when image_type does not match reference image, otherwise Undefined
"""
from nipype.interfaces.base import Undefined
if image_type == 'T2w':
if template_t2w:
return 'T2w', Undefined
return 'T1w', False
return 'T1w', Undefined