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registration.py
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registration.py
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# -*- coding: utf-8 -*-
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
ReportCapableInterfaces for registration tools
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import os
from distutils.version import LooseVersion
import nibabel as nb
import numpy as np
from nilearn import image as nli
from nilearn.image import index_img
from nipype.utils.filemanip import fname_presuffix
from nipype.interfaces.base import (
traits, isdefined, TraitedSpec, BaseInterfaceInputSpec, File, SimpleInterface)
from nipype.interfaces.mixins import reporting
from nipype.interfaces import freesurfer as fs
from nipype.interfaces import fsl, ants, afni
from .. import NIWORKFLOWS_LOG
from . import report_base as nrc
from .mni import (
RobustMNINormalizationInputSpec,
RobustMNINormalizationOutputSpec,
RobustMNINormalization
)
from .fixes import (FixHeaderApplyTransforms as ApplyTransforms,
FixHeaderRegistration as Registration)
class RobustMNINormalizationInputSpecRPT(
nrc.SVGReportCapableInputSpec, RobustMNINormalizationInputSpec):
pass
class RobustMNINormalizationOutputSpecRPT(
reporting.ReportCapableOutputSpec, RobustMNINormalizationOutputSpec):
pass
class RobustMNINormalizationRPT(
nrc.RegistrationRC, RobustMNINormalization):
input_spec = RobustMNINormalizationInputSpecRPT
output_spec = RobustMNINormalizationOutputSpecRPT
def _post_run_hook(self, runtime):
# We need to dig into the internal ants.Registration interface
self._fixed_image = self._get_ants_args()['fixed_image']
if isinstance(self._fixed_image, (list, tuple)):
self._fixed_image = self._fixed_image[0] # get first item if list
if self._get_ants_args().get('fixed_image_mask') is not None:
self._fixed_image_mask = self._get_ants_args().get('fixed_image_mask')
self._moving_image = self.aggregate_outputs(runtime=runtime).warped_image
NIWORKFLOWS_LOG.info('Report - setting fixed (%s) and moving (%s) images',
self._fixed_image, self._moving_image)
return super(RobustMNINormalizationRPT, self)._post_run_hook(runtime)
class ANTSRegistrationInputSpecRPT(nrc.SVGReportCapableInputSpec,
ants.registration.RegistrationInputSpec):
pass
class ANTSRegistrationOutputSpecRPT(reporting.ReportCapableOutputSpec,
ants.registration.RegistrationOutputSpec):
pass
class ANTSRegistrationRPT(nrc.RegistrationRC, Registration):
input_spec = ANTSRegistrationInputSpecRPT
output_spec = ANTSRegistrationOutputSpecRPT
def _post_run_hook(self, runtime):
self._fixed_image = self.inputs.fixed_image[0]
self._moving_image = self.aggregate_outputs(runtime=runtime).warped_image
NIWORKFLOWS_LOG.info('Report - setting fixed (%s) and moving (%s) images',
self._fixed_image, self._moving_image)
return super(ANTSRegistrationRPT, self)._post_run_hook(runtime)
class ANTSApplyTransformsInputSpecRPT(nrc.SVGReportCapableInputSpec,
ants.resampling.ApplyTransformsInputSpec):
pass
class ANTSApplyTransformsOutputSpecRPT(reporting.ReportCapableOutputSpec,
ants.resampling.ApplyTransformsOutputSpec):
pass
class ANTSApplyTransformsRPT(nrc.RegistrationRC, ApplyTransforms):
input_spec = ANTSApplyTransformsInputSpecRPT
output_spec = ANTSApplyTransformsOutputSpecRPT
def _post_run_hook(self, runtime):
self._fixed_image = self.inputs.reference_image
self._moving_image = self.aggregate_outputs(runtime=runtime).output_image
NIWORKFLOWS_LOG.info('Report - setting fixed (%s) and moving (%s) images',
self._fixed_image, self._moving_image)
return super(ANTSApplyTransformsRPT, self)._post_run_hook(runtime)
class ApplyTOPUPInputSpecRPT(nrc.SVGReportCapableInputSpec,
fsl.epi.ApplyTOPUPInputSpec):
wm_seg = File(argstr='-wmseg %s',
desc='reference white matter segmentation mask')
class ApplyTOPUPOutputSpecRPT(reporting.ReportCapableOutputSpec,
fsl.epi.ApplyTOPUPOutputSpec):
pass
class ApplyTOPUPRPT(nrc.RegistrationRC, fsl.ApplyTOPUP):
input_spec = ApplyTOPUPInputSpecRPT
output_spec = ApplyTOPUPOutputSpecRPT
def _post_run_hook(self, runtime):
self._fixed_image_label = "after"
self._moving_image_label = "before"
self._fixed_image = index_img(self.aggregate_outputs(runtime=runtime).out_corrected, 0)
self._moving_image = index_img(self.inputs.in_files[0], 0)
self._contour = self.inputs.wm_seg if isdefined(self.inputs.wm_seg) else None
NIWORKFLOWS_LOG.info('Report - setting corrected (%s) and warped (%s) images',
self._fixed_image, self._moving_image)
return super(ApplyTOPUPRPT, self)._post_run_hook(runtime)
class FUGUEInputSpecRPT(nrc.SVGReportCapableInputSpec,
fsl.preprocess.FUGUEInputSpec):
wm_seg = File(argstr='-wmseg %s',
desc='reference white matter segmentation mask')
class FUGUEOutputSpecRPT(reporting.ReportCapableOutputSpec,
fsl.preprocess.FUGUEOutputSpec):
pass
class FUGUERPT(nrc.RegistrationRC, fsl.FUGUE):
input_spec = FUGUEInputSpecRPT
output_spec = FUGUEOutputSpecRPT
def _post_run_hook(self, runtime):
self._fixed_image_label = "after"
self._moving_image_label = "before"
self._fixed_image = self.aggregate_outputs(runtime=runtime).unwarped_file
self._moving_image = self.inputs.in_file
self._contour = self.inputs.wm_seg if isdefined(self.inputs.wm_seg) else None
NIWORKFLOWS_LOG.info(
'Report - setting corrected (%s) and warped (%s) images',
self._fixed_image, self._moving_image)
return super(FUGUERPT, self)._post_run_hook(runtime)
class FLIRTInputSpecRPT(nrc.SVGReportCapableInputSpec,
fsl.preprocess.FLIRTInputSpec):
pass
class FLIRTOutputSpecRPT(reporting.ReportCapableOutputSpec,
fsl.preprocess.FLIRTOutputSpec):
pass
class FLIRTRPT(nrc.RegistrationRC, fsl.FLIRT):
input_spec = FLIRTInputSpecRPT
output_spec = FLIRTOutputSpecRPT
def _post_run_hook(self, runtime):
self._fixed_image = self.inputs.reference
self._moving_image = self.aggregate_outputs(runtime=runtime).out_file
self._contour = self.inputs.wm_seg if isdefined(self.inputs.wm_seg) else None
NIWORKFLOWS_LOG.info(
'Report - setting fixed (%s) and moving (%s) images',
self._fixed_image, self._moving_image)
return super(FLIRTRPT, self)._post_run_hook(runtime)
class ApplyXFMInputSpecRPT(nrc.SVGReportCapableInputSpec,
fsl.preprocess.ApplyXFMInputSpec):
pass
class ApplyXFMRPT(FLIRTRPT, fsl.ApplyXFM):
input_spec = ApplyXFMInputSpecRPT
output_spec = FLIRTOutputSpecRPT
if LooseVersion("0.0.0") < fs.Info.looseversion() < LooseVersion("6.0.0"):
_BBRegisterInputSpec = fs.preprocess.BBRegisterInputSpec
else:
_BBRegisterInputSpec = fs.preprocess.BBRegisterInputSpec6
class BBRegisterInputSpecRPT(nrc.SVGReportCapableInputSpec,
_BBRegisterInputSpec):
# Adds default=True, usedefault=True
out_lta_file = traits.Either(traits.Bool, File, default=True, usedefault=True,
argstr="--lta %s", min_ver='5.2.0',
desc="write the transformation matrix in LTA format")
class BBRegisterOutputSpecRPT(reporting.ReportCapableOutputSpec,
fs.preprocess.BBRegisterOutputSpec):
pass
class BBRegisterRPT(nrc.RegistrationRC, fs.BBRegister):
input_spec = BBRegisterInputSpecRPT
output_spec = BBRegisterOutputSpecRPT
def _post_run_hook(self, runtime):
outputs = self.aggregate_outputs(runtime=runtime)
mri_dir = os.path.join(self.inputs.subjects_dir,
self.inputs.subject_id, 'mri')
target_file = os.path.join(mri_dir, 'brainmask.mgz')
# Apply transform for simplicity
mri_vol2vol = fs.ApplyVolTransform(
source_file=self.inputs.source_file,
target_file=target_file,
lta_file=outputs.out_lta_file,
interp='nearest')
res = mri_vol2vol.run()
self._fixed_image = target_file
self._moving_image = res.outputs.transformed_file
self._contour = os.path.join(mri_dir, 'ribbon.mgz')
NIWORKFLOWS_LOG.info(
'Report - setting fixed (%s) and moving (%s) images',
self._fixed_image, self._moving_image)
return super(BBRegisterRPT, self)._post_run_hook(runtime)
class MRICoregInputSpecRPT(nrc.SVGReportCapableInputSpec,
fs.registration.MRICoregInputSpec):
pass
class MRICoregOutputSpecRPT(reporting.ReportCapableOutputSpec,
fs.registration.MRICoregOutputSpec):
pass
class MRICoregRPT(nrc.RegistrationRC, fs.MRICoreg):
input_spec = MRICoregInputSpecRPT
output_spec = MRICoregOutputSpecRPT
def _post_run_hook(self, runtime):
outputs = self.aggregate_outputs(runtime=runtime)
mri_dir = None
if isdefined(self.inputs.subject_id):
mri_dir = os.path.join(self.inputs.subjects_dir,
self.inputs.subject_id, 'mri')
if isdefined(self.inputs.reference_file):
target_file = self.inputs.reference_file
else:
target_file = os.path.join(mri_dir, 'brainmask.mgz')
# Apply transform for simplicity
mri_vol2vol = fs.ApplyVolTransform(
source_file=self.inputs.source_file,
target_file=target_file,
lta_file=outputs.out_lta_file,
interp='nearest')
res = mri_vol2vol.run()
self._fixed_image = target_file
self._moving_image = res.outputs.transformed_file
if mri_dir is not None:
self._contour = os.path.join(mri_dir, 'ribbon.mgz')
NIWORKFLOWS_LOG.info(
'Report - setting fixed (%s) and moving (%s) images',
self._fixed_image, self._moving_image)
return super(MRICoregRPT, self)._post_run_hook(runtime)
class SimpleBeforeAfterInputSpecRPT(nrc.SVGReportCapableInputSpec):
before = File(exists=True, mandatory=True, desc='file before')
after = File(exists=True, mandatory=True, desc='file after')
wm_seg = File(desc='reference white matter segmentation mask')
before_label = traits.Str("before", usedefault=True)
after_label = traits.Str("after", usedefault=True)
class SimpleBeforeAfterRPT(nrc.RegistrationRC, nrc.ReportingInterface):
input_spec = SimpleBeforeAfterInputSpecRPT
def _post_run_hook(self, runtime):
""" there is not inner interface to run """
self._fixed_image_label = self.inputs.after_label
self._moving_image_label = self.inputs.before_label
self._fixed_image = self.inputs.after
self._moving_image = self.inputs.before
self._contour = self.inputs.wm_seg if isdefined(self.inputs.wm_seg) else None
NIWORKFLOWS_LOG.info(
'Report - setting before (%s) and after (%s) images',
self._fixed_image, self._moving_image)
return super(SimpleBeforeAfterRPT, self)._post_run_hook(runtime)
class ResampleBeforeAfterInputSpecRPT(SimpleBeforeAfterInputSpecRPT):
base = traits.Enum('before', 'after', usedefault=True, mandatory=True)
class ResampleBeforeAfterRPT(SimpleBeforeAfterRPT):
input_spec = ResampleBeforeAfterInputSpecRPT
def _post_run_hook(self, runtime):
self._fixed_image = self.inputs.after
self._moving_image = self.inputs.before
if self.inputs.base == 'before':
resampled_after = nli.resample_to_img(self._fixed_image, self._moving_image)
fname = fname_presuffix(self._fixed_image, suffix='_resampled', newpath=runtime.cwd)
resampled_after.to_filename(fname)
self._fixed_image = fname
else:
resampled_before = nli.resample_to_img(self._moving_image, self._fixed_image)
fname = fname_presuffix(self._moving_image, suffix='_resampled', newpath=runtime.cwd)
resampled_before.to_filename(fname)
self._moving_image = fname
self._contour = self.inputs.wm_seg if isdefined(self.inputs.wm_seg) else None
NIWORKFLOWS_LOG.info(
'Report - setting before (%s) and after (%s) images',
self._fixed_image, self._moving_image)
runtime = super(ResampleBeforeAfterRPT, self)._post_run_hook(runtime)
NIWORKFLOWS_LOG.info('Successfully created report (%s)', self._out_report)
os.unlink(fname)
return runtime
class EstimateReferenceImageInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc="4D EPI file")
sbref_file = File(exists=True, desc="Single band reference image")
mc_method = traits.Enum(
"AFNI", "FSL", usedefault=True,
desc="Which software to use to perform motion correction")
class EstimateReferenceImageOutputSpec(TraitedSpec):
ref_image = File(exists=True, desc="3D reference image")
n_volumes_to_discard = traits.Int(desc="Number of detected non-steady "
"state volumes in the beginning of "
"the input file")
class EstimateReferenceImage(SimpleInterface):
"""
Given an 4D EPI file estimate an optimal reference image that could be later
used for motion estimation and coregistration purposes. If detected uses
T1 saturated volumes (non-steady state) otherwise a median of
of a subset of motion corrected volumes is used.
"""
input_spec = EstimateReferenceImageInputSpec
output_spec = EstimateReferenceImageOutputSpec
def _run_interface(self, runtime):
ref_name = self.inputs.in_file
ref_nii = nb.load(ref_name)
n_volumes_to_discard = _get_vols_to_discard(ref_nii)
self._results["n_volumes_to_discard"] = n_volumes_to_discard
out_ref_fname = os.path.join(runtime.cwd, "ref_bold.nii.gz")
if isdefined(self.inputs.sbref_file):
out_ref_fname = os.path.join(runtime.cwd, "ref_sbref.nii.gz")
ref_name = self.inputs.sbref_file
ref_nii = nb.squeeze_image(nb.load(ref_name))
# If reference is only 1 volume, return it directly
if len(ref_nii.shape) == 3:
ref_nii.header.extensions.clear()
ref_nii.to_filename(out_ref_fname)
self._results['ref_image'] = out_ref_fname
return runtime
else:
# Reset this variable as it no longer applies
# and value for the output is stored in self._results
n_volumes_to_discard = 0
# Slicing may induce inconsistencies with shape-dependent values in extensions.
# For now, remove all. If this turns out to be a mistake, we can select extensions
# that don't break pipeline stages.
ref_nii.header.extensions.clear()
if n_volumes_to_discard == 0:
if ref_nii.shape[-1] > 40:
ref_name = os.path.join(runtime.cwd, "slice.nii.gz")
nb.Nifti1Image(ref_nii.dataobj[:, :, :, 20:40], ref_nii.affine,
ref_nii.header).to_filename(ref_name)
if self.inputs.mc_method == "AFNI":
res = afni.Volreg(in_file=ref_name, args='-Fourier -twopass',
zpad=4, outputtype='NIFTI_GZ').run()
elif self.inputs.mc_method == "FSL":
res = fsl.MCFLIRT(in_file=ref_name,
ref_vol=0, interpolation='sinc').run()
mc_slice_nii = nb.load(res.outputs.out_file)
median_image_data = np.median(mc_slice_nii.get_data(), axis=3)
else:
median_image_data = np.median(
ref_nii.dataobj[:, :, :, :n_volumes_to_discard], axis=3)
nb.Nifti1Image(median_image_data, ref_nii.affine,
ref_nii.header).to_filename(out_ref_fname)
self._results["ref_image"] = out_ref_fname
return runtime
def _get_vols_to_discard(img):
from nipype.algorithms.confounds import is_outlier
data_slice = img.dataobj[:, :, :, :50]
global_signal = data_slice.mean(axis=0).mean(axis=0).mean(axis=0)
return is_outlier(global_signal)