# 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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"""Brain extraction workflows."""
from nipype.interfaces import afni, utility as niu
from nipype.pipeline import engine as pe
from ..interfaces.nibabel import Binarize
from ..interfaces.fixes import FixN4BiasFieldCorrection as N4BiasFieldCorrection
[docs]
def afni_wf(name="AFNISkullStripWorkflow", unifize=False, n4_nthreads=1):
"""
Create a skull-stripping workflow based on AFNI's tools.
Originally derived from the `codebase of the QAP
<https://github.com/preprocessed-connectomes-project/quality-assessment-protocol/blob/master/qap/anatomical_preproc.py#L105>`_.
Now, this workflow includes :abbr:`INU (intensity non-uniformity)` correction
using the N4 algorithm and (optionally) intensity harmonization using
ANFI's ``3dUnifize``.
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from niworkflows.anat.skullstrip import afni_wf
wf = afni_wf()
Parameters
----------
n4_nthreads : int
number of cpus N4 bias field correction can utilize.
unifize : bool
whether AFNI's ``3dUnifize`` should be applied (default: ``False``).
name : str
name for the workflow hierarchy of Nipype
Inputs
------
in_file : str
input T1w image.
Outputs
-------
bias_corrected : str
path to the bias corrected input MRI.
out_file : str
path to the skull-stripped image.
out_mask : str
path to the generated brain mask.
bias_image : str
path to the B1 inhomogeneity field.
"""
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=["in_file"]), name="inputnode")
outputnode = pe.Node(
niu.IdentityInterface(
fields=["bias_corrected", "out_file", "out_mask", "bias_image"]
),
name="outputnode",
)
inu_n4 = pe.Node(
N4BiasFieldCorrection(
dimension=3,
save_bias=True,
num_threads=n4_nthreads,
rescale_intensities=True,
copy_header=True,
),
n_procs=n4_nthreads,
name="inu_n4",
)
sstrip = pe.Node(afni.SkullStrip(outputtype="NIFTI_GZ"), name="skullstrip")
sstrip_orig_vol = pe.Node(
afni.Calc(expr="a*step(b)", outputtype="NIFTI_GZ"), name="sstrip_orig_vol"
)
binarize = pe.Node(Binarize(thresh_low=0.0), name="binarize")
if unifize:
# Add two unifize steps, pre- and post- skullstripping.
inu_uni_0 = pe.Node(
afni.Unifize(outputtype="NIFTI_GZ"), name="unifize_pre_skullstrip"
)
inu_uni_1 = pe.Node(
afni.Unifize(gm=True, outputtype="NIFTI_GZ"), name="unifize_post_skullstrip"
)
# fmt: off
workflow.connect([
(inu_n4, inu_uni_0, [("output_image", "in_file")]),
(inu_uni_0, sstrip, [("out_file", "in_file")]),
(inu_uni_0, sstrip_orig_vol, [("out_file", "in_file_a")]),
(sstrip_orig_vol, inu_uni_1, [("out_file", "in_file")]),
(inu_uni_1, outputnode, [("out_file", "out_file")]),
(inu_uni_0, outputnode, [("out_file", "bias_corrected")]),
])
# fmt: on
else:
# fmt: off
workflow.connect([
(inputnode, sstrip_orig_vol, [("in_file", "in_file_a")]),
(inu_n4, sstrip, [("output_image", "in_file")]),
(sstrip_orig_vol, outputnode, [("out_file", "out_file")]),
(inu_n4, outputnode, [("output_image", "bias_corrected")]),
])
# fmt: on
# Remaining connections
# fmt: off
workflow.connect([
(sstrip, sstrip_orig_vol, [("out_file", "in_file_b")]),
(inputnode, inu_n4, [("in_file", "input_image")]),
(sstrip_orig_vol, binarize, [("out_file", "in_file")]),
(binarize, outputnode, [("out_mask", "out_mask")]),
(inu_n4, outputnode, [("bias_image", "bias_image")]),
])
# fmt: on
return workflow