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rf: Split premask workflow out of enhance_and_skullstrip_wf #1001

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rf: Split premask workflow out of enhance_and_skullstrip_wf #1001

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204 changes: 124 additions & 80 deletions niworkflows/func/util.py
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Original file line number Diff line number Diff line change
Expand Up @@ -279,6 +279,119 @@ def init_bold_reference_wf(
return workflow


def init_bold_premask_wf(
name='bold_premask_wf',
omp_nthreads=1,
):
"""
Calculate a preliminary brain mask for BOLD images.

This workflow calculates a tentative mask by registering (9-parameters) to
*fMRIPrep*'s :abbr:`EPI (echo-planar imaging)` -*boldref* template, which
is in MNI space.
The tentative mask is obtained by resampling the MNI template's brainmask
into *boldref*-space.

Workflow graph
.. workflow ::
:graph2use: orig
:simple_form: yes

from niworkflows.func.util import init_bold_premask_wf
wf = init_bold_premask_wf(omp_nthreads=1)

Parameters
----------
name : str
Name of workflow (default: ``enhance_and_skullstrip_bold_wf``)
omp_nthreads : int
number of threads available to parallel nodes

Inputs
------
in_file : str
BOLD image (single volume)


Outputs
-------
mask_file : str
mask of the skull-stripped input file

"""
from nipype.interfaces.ants.utils import AI

workflow = Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['in_file']), name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['mask_file']), name='outputnode')

bold_template = get_template(
'MNI152NLin2009cAsym', resolution=2, desc='fMRIPrep', suffix='boldref'
)
brain_mask = get_template('MNI152NLin2009cAsym', resolution=2, desc='brain', suffix='mask')

# Initialize transforms with antsAI
init_aff = pe.Node(
AI(
fixed_image=str(bold_template),
fixed_image_mask=str(brain_mask),
metric=('Mattes', 32, 'Regular', 0.2),
transform=('Affine', 0.1),
search_factor=(20, 0.12),
principal_axes=False,
convergence=(10, 1e-6, 10),
verbose=True,
),
name='init_aff',
n_procs=omp_nthreads,
)

# Registration().version may be None
if parseversion(Registration().version or '0.0.0') > Version('2.2.0'):
init_aff.inputs.search_grid = (40, (0, 40, 40))

# Set up spatial normalization
norm = pe.Node(
Registration(from_file=data.load('epi_atlasbased_brainmask.json')),
name='norm',
n_procs=omp_nthreads,
)
norm.inputs.fixed_image = str(bold_template)
map_brainmask = pe.Node(
ApplyTransforms(
interpolation='Linear',
# Use the higher resolution and probseg for numerical stability in rounding
input_image=str(
get_template(
'MNI152NLin2009cAsym',
resolution=1,
label='brain',
suffix='probseg',
)
),
),
name='map_brainmask',
)
# Ensure mask's header matches reference's
fix_header = pe.Node(CopyHeader(), name='fix_header', run_without_submitting=True)

workflow.connect([
(inputnode, fix_header, [('in_file', 'hdr_file')]),
(inputnode, init_aff, [('in_file', 'moving_image')]),
(inputnode, map_brainmask, [('in_file', 'reference_image')]),
(inputnode, norm, [('in_file', 'moving_image')]),
(init_aff, norm, [('output_transform', 'initial_moving_transform')]),
(norm, map_brainmask, [
('reverse_invert_flags', 'invert_transform_flags'),
('reverse_transforms', 'transforms'),
]),
(map_brainmask, fix_header, [('output_image', 'in_file')]),
(fix_header, outputnode, [('out_file', 'mask_file')]),
]) # fmt: skip

return workflow


def init_enhance_and_skullstrip_bold_wf(
brainmask_thresh=0.5,
name='enhance_and_skullstrip_bold_wf',
Expand All @@ -297,17 +410,14 @@ def init_enhance_and_skullstrip_bold_wf(

Steps of this workflow are:

1. Calculate a tentative mask by registering (9-parameters) to *fMRIPrep*'s
:abbr:`EPI (echo-planar imaging)` -*boldref* template, which
is in MNI space.
The tentative mask is obtained by resampling the MNI template's
brainmask into *boldref*-space.
2. Binary dilation of the tentative mask with a sphere of 3mm diameter.
3. Run ANTs' ``N4BiasFieldCorrection`` on the input
1. Calculate a tentative mask via
:func:`~niworkflows.func.util.init_bold_premask_wf`.
2. Run ANTs' ``N4BiasFieldCorrection`` on the input
:abbr:`BOLD (blood-oxygen level-dependant)` average, using the
mask generated in 1) instead of the internal Otsu thresholding.
4. Calculate a loose mask using FSL's ``bet``, with one mathematical morphology
3. Calculate a loose mask using FSL's ``bet``, with one mathematical morphology
dilation of one iteration and a sphere of 6mm as structuring element.
4. Binary dilation of the loose mask with a sphere of 3mm diameter.
5. Mask the :abbr:`INU (intensity non-uniformity)`-corrected image
with the latest mask calculated in 3), then use AFNI's ``3dUnifize``
to *standardize* the T2* contrast distribution.
Expand All @@ -320,7 +430,6 @@ def init_enhance_and_skullstrip_bold_wf(
a tentative mask is passed in to the workflow through the ``pre_mask``
Nipype input.


Workflow graph
.. workflow ::
:graph2use: orig
Expand Down Expand Up @@ -415,81 +524,17 @@ def init_enhance_and_skullstrip_bold_wf(
apply_mask = pe.Node(ApplyMask(), name='apply_mask')

if not pre_mask:
from nipype.interfaces.ants.utils import AI

bold_template = get_template(
'MNI152NLin2009cAsym', resolution=2, desc='fMRIPrep', suffix='boldref'
)
brain_mask = get_template('MNI152NLin2009cAsym', resolution=2, desc='brain', suffix='mask')

# Initialize transforms with antsAI
init_aff = pe.Node(
AI(
fixed_image=str(bold_template),
fixed_image_mask=str(brain_mask),
metric=('Mattes', 32, 'Regular', 0.2),
transform=('Affine', 0.1),
search_factor=(20, 0.12),
principal_axes=False,
convergence=(10, 1e-6, 10),
verbose=True,
),
name='init_aff',
n_procs=omp_nthreads,
)

# Registration().version may be None
if parseversion(Registration().version or '0.0.0') > Version('2.2.0'):
init_aff.inputs.search_grid = (40, (0, 40, 40))

# Set up spatial normalization
norm = pe.Node(
Registration(from_file=data.load('epi_atlasbased_brainmask.json')),
name='norm',
n_procs=omp_nthreads,
)
norm.inputs.fixed_image = str(bold_template)
map_brainmask = pe.Node(
ApplyTransforms(
interpolation='Linear',
# Use the higher resolution and probseg for numerical stability in rounding
input_image=str(
get_template(
'MNI152NLin2009cAsym',
resolution=1,
label='brain',
suffix='probseg',
)
),
),
name='map_brainmask',
)
# Ensure mask's header matches reference's
fix_header = pe.Node(CopyHeader(), name='fix_header', run_without_submitting=True)
bold_premask_wf = init_bold_premask_wf(omp_nthreads=omp_nthreads)

# fmt: off
workflow.connect([
(inputnode, fix_header, [('in_file', 'hdr_file')]),
(inputnode, init_aff, [('in_file', 'moving_image')]),
(inputnode, map_brainmask, [('in_file', 'reference_image')]),
(inputnode, norm, [('in_file', 'moving_image')]),
(init_aff, norm, [('output_transform', 'initial_moving_transform')]),
(norm, map_brainmask, [
('reverse_invert_flags', 'invert_transform_flags'),
('reverse_transforms', 'transforms'),
]),
(map_brainmask, fix_header, [('output_image', 'in_file')]),
(fix_header, n4_correct, [('out_file', 'weight_image')]),
])
# fmt: on
(inputnode, bold_premask_wf, [('in_file', 'inputnode.in_file')]),
(bold_premask_wf, n4_correct, [('outputnode.mask_file', 'weight_image')]),
]) # fmt: skip
else:
# fmt: off
workflow.connect([
(inputnode, n4_correct, [('pre_mask', 'weight_image')]),
])
# fmt: on
]) # fmt: skip

# fmt: off
workflow.connect([
(inputnode, n4_correct, [('in_file', 'input_image')]),
(inputnode, fixhdr_unifize, [('in_file', 'hdr_file')]),
Expand All @@ -509,8 +554,7 @@ def init_enhance_and_skullstrip_bold_wf(
(combine_masks, outputnode, [('out_file', 'mask_file')]),
(apply_mask, outputnode, [('out_file', 'skull_stripped_file')]),
(n4_correct, outputnode, [('output_image', 'bias_corrected_file')]),
])
# fmt: on
]) # fmt: skip

return workflow

Expand Down
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