# 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>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# We support and encourage derived works from this project, please read
# about our expectations at
#
# https://www.nipreps.org/community/licensing/
#
"""Utilities based on nilearn."""
import os
import nibabel as nb
import numpy as np
from nipype import logging
from nipype.utils.filemanip import fname_presuffix
from nipype.interfaces.base import (
traits,
isdefined,
TraitedSpec,
BaseInterfaceInputSpec,
File,
InputMultiPath,
SimpleInterface,
)
from nipype.interfaces.mixins import reporting
from .reportlets import base as nrb
try:
from nilearn import __version__ as NILEARN_VERSION
except ImportError:
NILEARN_VERSION = "unknown"
LOGGER = logging.getLogger("nipype.interface")
__all__ = ["NILEARN_VERSION", "MaskEPI", "Merge", "ComputeEPIMask"]
class _MaskEPIInputSpec(BaseInterfaceInputSpec):
in_files = InputMultiPath(
File(exists=True), mandatory=True, desc="input EPI or list of files"
)
lower_cutoff = traits.Float(0.2, usedefault=True)
upper_cutoff = traits.Float(0.85, usedefault=True)
connected = traits.Bool(True, usedefault=True)
enhance_t2 = traits.Bool(
False, usedefault=True, desc="enhance T2 contrast on image"
)
opening = traits.Int(2, usedefault=True)
closing = traits.Bool(True, usedefault=True)
fill_holes = traits.Bool(True, usedefault=True)
exclude_zeros = traits.Bool(False, usedefault=True)
ensure_finite = traits.Bool(True, usedefault=True)
target_affine = traits.Either(
None, traits.File(exists=True), default=None, usedefault=True
)
target_shape = traits.Either(
None, traits.File(exists=True), default=None, usedefault=True
)
no_sanitize = traits.Bool(False, usedefault=True)
class _MaskEPIOutputSpec(TraitedSpec):
out_mask = File(exists=True, desc="output mask")
[docs]
class MaskEPI(SimpleInterface):
"""Run Nilearn's compute_epi_mask."""
input_spec = _MaskEPIInputSpec
output_spec = _MaskEPIOutputSpec
def _run_interface(self, runtime):
from skimage import morphology as sim
from scipy.ndimage.morphology import binary_fill_holes
from nilearn.masking import compute_epi_mask
in_files = self.inputs.in_files
if self.inputs.enhance_t2:
in_files = [_enhance_t2_contrast(f, newpath=runtime.cwd) for f in in_files]
masknii = compute_epi_mask(
in_files,
lower_cutoff=self.inputs.lower_cutoff,
upper_cutoff=self.inputs.upper_cutoff,
connected=self.inputs.connected,
opening=self.inputs.opening,
exclude_zeros=self.inputs.exclude_zeros,
ensure_finite=self.inputs.ensure_finite,
target_affine=self.inputs.target_affine,
target_shape=self.inputs.target_shape,
)
if self.inputs.closing:
closed = sim.binary_closing(
np.asanyarray(masknii.dataobj).astype(np.uint8), sim.ball(1)
).astype(np.uint8)
masknii = masknii.__class__(closed, masknii.affine, masknii.header)
if self.inputs.fill_holes:
filled = binary_fill_holes(
np.asanyarray(masknii.dataobj).astype(np.uint8), sim.ball(6)
).astype(np.uint8)
masknii = masknii.__class__(filled, masknii.affine, masknii.header)
if self.inputs.no_sanitize:
in_file = self.inputs.in_files
if isinstance(in_file, list):
in_file = in_file[0]
nii = nb.load(in_file)
qform, code = nii.get_qform(coded=True)
masknii.set_qform(qform, int(code))
sform, code = nii.get_sform(coded=True)
masknii.set_sform(sform, int(code))
self._results["out_mask"] = fname_presuffix(
self.inputs.in_files[0], suffix="_mask", newpath=runtime.cwd
)
masknii.to_filename(self._results["out_mask"])
return runtime
class _MergeInputSpec(BaseInterfaceInputSpec):
in_files = InputMultiPath(
File(exists=True), mandatory=True, desc="input list of files to merge"
)
dtype = traits.Enum(
"f4",
"f8",
"u1",
"u2",
"u4",
"i2",
"i4",
usedefault=True,
desc="numpy dtype of output image",
)
header_source = File(
exists=True, desc="a Nifti file from which the header should be copied"
)
compress = traits.Bool(
True, usedefault=True, desc="Use gzip compression on .nii output"
)
class _MergeOutputSpec(TraitedSpec):
out_file = File(exists=True, desc="output merged file")
[docs]
class Merge(SimpleInterface):
"""Run Nilearn's concat_imgs."""
input_spec = _MergeInputSpec
output_spec = _MergeOutputSpec
def _run_interface(self, runtime):
from nilearn.image import concat_imgs
ext = ".nii.gz" if self.inputs.compress else ".nii"
self._results["out_file"] = fname_presuffix(
self.inputs.in_files[0],
suffix="_merged" + ext,
newpath=runtime.cwd,
use_ext=False,
)
new_nii = concat_imgs(self.inputs.in_files, dtype=self.inputs.dtype)
if isdefined(self.inputs.header_source):
src_hdr = nb.load(self.inputs.header_source).header
new_nii.header.set_xyzt_units(t=src_hdr.get_xyzt_units()[-1])
new_nii.header.set_zooms(
list(new_nii.header.get_zooms()[:3]) + [src_hdr.get_zooms()[3]]
)
new_nii.to_filename(self._results["out_file"])
return runtime
class _ComputeEPIMaskInputSpec(nrb._SVGReportCapableInputSpec, BaseInterfaceInputSpec):
in_file = File(exists=True, desc="3D or 4D EPI file")
dilation = traits.Int(desc="binary dilation on the nilearn output")
class _ComputeEPIMaskOutputSpec(reporting.ReportCapableOutputSpec):
mask_file = File(exists=True, desc="Binary brain mask")
[docs]
class ComputeEPIMask(nrb.SegmentationRC):
input_spec = _ComputeEPIMaskInputSpec
output_spec = _ComputeEPIMaskOutputSpec
def _run_interface(self, runtime):
from scipy.ndimage.morphology import binary_dilation
from nilearn.masking import compute_epi_mask
orig_file_nii = nb.load(self.inputs.in_file)
in_file_data = orig_file_nii.get_fdata()
# pad the data to avoid the mask estimation running into edge effects
in_file_data_padded = np.pad(
in_file_data, (1, 1), "constant", constant_values=(0, 0)
)
padded_nii = nb.Nifti1Image(
in_file_data_padded, orig_file_nii.affine, orig_file_nii.header
)
mask_nii = compute_epi_mask(padded_nii, exclude_zeros=True)
mask_data = np.asanyarray(mask_nii.dataobj).astype(np.uint8)
if isdefined(self.inputs.dilation):
mask_data = binary_dilation(mask_data).astype(np.uint8)
# reverse image padding
mask_data = mask_data[1:-1, 1:-1, 1:-1]
# exclude zero and NaN voxels
mask_data[in_file_data == 0] = 0
mask_data[np.isnan(in_file_data)] = 0
better_mask = nb.Nifti1Image(
mask_data, orig_file_nii.affine, orig_file_nii.header
)
better_mask.set_data_dtype(np.uint8)
better_mask.to_filename("mask_file.nii.gz")
self._mask_file = os.path.join(runtime.cwd, "mask_file.nii.gz")
runtime.returncode = 0
return super()._run_interface(runtime)
def _list_outputs(self):
outputs = super()._list_outputs()
outputs["mask_file"] = self._mask_file
return outputs
def _post_run_hook(self, runtime):
"""Prepare report generation post-hook."""
self._anat_file = self.inputs.in_file
self._mask_file = self.aggregate_outputs(runtime=runtime).mask_file
self._seg_files = [self._mask_file]
self._masked = True
LOGGER.info(
'Generating report for nilearn.compute_epi_mask. file "%s", and mask file "%s"',
self._anat_file,
self._mask_file,
)
return super()._post_run_hook(runtime)
def _enhance_t2_contrast(in_file, newpath=None, offset=0.5):
"""
Enhance the T2* contrast of an EPI dataset.
Performs a logarithmic transformation of intensity that
effectively splits brain and background and makes the
overall distribution more Gaussian.
"""
out_file = fname_presuffix(in_file, suffix="_t1enh", newpath=newpath)
nii = nb.load(in_file)
data = nii.get_fdata()
maxd = data.max()
newdata = np.log(offset + data / maxd)
newdata -= newdata.min()
newdata *= maxd / newdata.max()
nii = nii.__class__(newdata, nii.affine, nii.header)
nii.to_filename(out_file)
return out_file