def _run_interface(self, runtime):
in_files = self.inputs.in_files
if not isinstance(in_files, list):
in_files = [self.inputs.in_files]
# Generate output average name early
self._results['out_avg'] = genfname(self.inputs.in_files[0],
suffix='avg')
if self.inputs.to_ras:
in_files = [reorient(inf) for inf in in_files]
if len(in_files) == 1:
filenii = nb.load(in_files[0])
filedata = filenii.get_data()
# magnitude files can have an extra dimension empty
if filedata.ndim == 5:
sqdata = np.squeeze(filedata)
if sqdata.ndim == 5:
raise RuntimeError('Input image (%s) is 5D' % in_files[0])
else:
in_files = [genfname(in_files[0], suffix='squeezed')]
nb.Nifti1Image(sqdata, filenii.get_affine(),
filenii.get_header()).to_filename(
in_files[0])
if np.squeeze(nb.load(in_files[0]).get_data()).ndim < 4:
self._results['out_file'] = in_files[0]
self._results['out_avg'] = in_files[0]
# TODO: generate identity out_mats and zero-filled out_movpar
return runtime
in_files = in_files[0]
else:
magmrg = fsl.Merge(dimension='t', in_files=self.inputs.in_files)
in_files = magmrg.run().outputs.merged_file
mcflirt = fsl.MCFLIRT(cost='normcorr',
save_mats=True,
save_plots=True,
ref_vol=0,
in_file=in_files)
mcres = mcflirt.run()
self._results['out_mats'] = mcres.outputs.mat_file
self._results['out_movpar'] = mcres.outputs.par_file
self._results['out_file'] = mcres.outputs.out_file
hmcnii = nb.load(mcres.outputs.out_file)
hmcdat = hmcnii.get_data().mean(axis=3)
if self.inputs.zero_based_avg:
hmcdat -= hmcdat.min()
nb.Nifti1Image(hmcdat, hmcnii.get_affine(),
hmcnii.get_header()).to_filename(
self._results['out_avg'])
return runtime
def _run_interface(self, runtime):
in_files = self.inputs.in_files
if not isinstance(in_files, list):
in_files = [self.inputs.in_files]
# Generate output average name early
self._results['out_avg'] = genfname(self.inputs.in_files[0],
suffix='avg')
if self.inputs.to_ras:
in_files = [reorient(inf) for inf in in_files]
if len(in_files) == 1:
filenii = nb.load(in_files[0])
filedata = filenii.get_data()
# magnitude files can have an extra dimension empty
if filedata.ndim == 5:
sqdata = np.squeeze(filedata)
if sqdata.ndim == 5:
raise RuntimeError('Input image (%s) is 5D' % in_files[0])
else:
in_files = [genfname(in_files[0], suffix='squeezed')]
nb.Nifti1Image(sqdata, filenii.get_affine(),
filenii.get_header()).to_filename(in_files[0])
if np.squeeze(nb.load(in_files[0]).get_data()).ndim < 4:
self._results['out_file'] = in_files[0]
self._results['out_avg'] = in_files[0]
# TODO: generate identity out_mats and zero-filled out_movpar
return runtime
in_files = in_files[0]
else:
magmrg = fsl.Merge(dimension='t', in_files=self.inputs.in_files)
in_files = magmrg.run().outputs.merged_file
mcflirt = fsl.MCFLIRT(cost='normcorr', save_mats=True, save_plots=True,
ref_vol=0, in_file=in_files)
mcres = mcflirt.run()
self._results['out_mats'] = mcres.outputs.mat_file
self._results['out_movpar'] = mcres.outputs.par_file
self._results['out_file'] = mcres.outputs.out_file
hmcnii = nb.load(mcres.outputs.out_file)
hmcdat = hmcnii.get_data().mean(axis=3)
if self.inputs.zero_based_avg:
hmcdat -= hmcdat.min()
nb.Nifti1Image(
hmcdat, hmcnii.get_affine(), hmcnii.get_header()).to_filename(
self._results['out_avg'])
return runtime
def _run_interface(self, runtime):
target_affine = None
target_shape = None
if isdefined(self.inputs.target_affine):
target_affine = self.inputs.target_affine
if isdefined(self.inputs.target_shape):
target_shape = self.inputs.target_shape
masknii = compute_epi_mask(
self.inputs.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=target_affine,
target_shape=target_shape
)
self._results['out_mask'] = genfname(
self.inputs.in_files[0], suffix='mask')
masknii.to_filename(self._results['out_mask'])
return runtime
def _run_interface(self, runtime):
out_file = genfname(self.inputs.in_file, 'brainmask')
nii = nb.load(self.inputs.in_file)
data = nii.get_data()
data[nb.load(self.inputs.in_mask).get_data() <= 0] = 0
nb.Nifti1Image(data, nii.affine, nii.header).to_filename(out_file)
self._results['out_file'] = out_file
return runtime
def _run_interface(self, runtime):
out_file = genfname(self.inputs.in_file, 'brainmask')
nii = nb.load(self.inputs.in_file)
data = nii.get_data()
data[nb.load(self.inputs.in_mask).get_data() <= 0] = 0
nb.Nifti1Image(data, nii.affine, nii.header).to_filename(out_file)
self._results['out_file'] = out_file
return runtime
def _hz2rads(in_file, out_file=None):
"""Transform a fieldmap in Hz into rad/s"""
from math import pi
import nibabel as nb
from fmriprep.utils.misc import genfname
if out_file is None:
out_file = genfname(in_file, 'rads')
nii = nb.load(in_file)
data = nii.get_data() * 2.0 * pi
nb.Nifti1Image(data, nii.get_affine(),
nii.get_header()).to_filename(out_file)
return out_file
def _hz2rads(in_file, out_file=None):
"""Transform a fieldmap in Hz into rad/s"""
from math import pi
import nibabel as nb
from fmriprep.utils.misc import genfname
if out_file is None:
out_file = genfname(in_file, 'rads')
nii = nb.load(in_file)
data = nii.get_data() * 2.0 * pi
nb.Nifti1Image(data, nii.get_affine(),
nii.get_header()).to_filename(out_file)
return out_file
def reorient(in_file, out_file=None):
import nibabel as nb
from fmriprep.utils.misc import genfname
from builtins import (str, bytes)
if out_file is None:
out_file = genfname(in_file, suffix='ras')
if isinstance(in_file, (str, bytes)):
nii = nb.load(in_file)
nii = nb.as_closest_canonical(nii)
nii.to_filename(out_file)
return out_file
def _gen_reference(fixed_image, moving_image, out_file=None):
import numpy
from nilearn.image import resample_img, load_img
if out_file is None:
out_file = genfname(fixed_image, suffix='reference')
new_zooms = load_img(moving_image).header.get_zooms()[:3]
# Avoid small differences in reported resolution to cause changes to
# FOV. See https://github.com/poldracklab/fmriprep/issues/512
new_zooms_round = numpy.round(new_zooms, 3)
resample_img(fixed_image, target_affine=numpy.diag(new_zooms_round),
interpolation='nearest').to_filename(out_file)
return out_file
def reorient(in_file, out_file=None):
import nibabel as nb
from fmriprep.utils.misc import genfname
from builtins import (str, bytes)
if out_file is None:
out_file = genfname(in_file, suffix='ras')
if isinstance(in_file, (str, bytes)):
nii = nb.load(in_file)
nii = nb.as_closest_canonical(nii)
nii.to_filename(out_file)
return out_file
def _gen_reference(fixed_image, moving_image, out_file=None):
import numpy
from nilearn.image import resample_img, load_img
if out_file is None:
out_file = genfname(fixed_image, suffix='reference')
new_zooms = load_img(moving_image).header.get_zooms()[:3]
# Avoid small differences in reported resolution to cause changes to
# FOV. See https://github.com/poldracklab/fmriprep/issues/512
new_zooms_round = numpy.round(new_zooms, 3)
resample_img(fixed_image, target_affine=numpy.diag(new_zooms_round),
interpolation='nearest').to_filename(out_file)
return out_file
def _flatten_split_merge(in_files):
from builtins import bytes, str
if isinstance(in_files, (bytes, str)):
in_files = [in_files]
nfiles = len(in_files)
all_nii = []
for fname in in_files:
nii = nb.squeeze_image(nb.load(fname))
if nii.get_data().ndim > 3:
all_nii += nb.four_to_three(nii)
else:
all_nii.append(nii)
if len(all_nii) == 1:
LOGGER.warn('File %s cannot be split', all_nii[0])
return in_files[0], in_files
if len(all_nii) == nfiles:
flat_split = in_files
else:
splitname = genfname(in_files[0], suffix='split%04d')
flat_split = []
for i, nii in enumerate(all_nii):
flat_split.append(splitname % i)
nii.to_filename(flat_split[-1])
# Only one 4D file was supplied
if nfiles == 1:
merged = in_files[0]
else:
# More that one in_files - need merge
merged = genfname(in_files[0], suffix='merged')
nb.concat_images(all_nii).to_filename(merged)
return merged, flat_split
def _tohz(in_file, cutoff_hz, out_file=None):
from math import pi
import nibabel as nb
from fmriprep.utils.misc import genfname
if out_file is None:
out_file = genfname(in_file, suffix='hz')
fmapnii = nb.load(in_file)
fmapdata = fmapnii.get_data()
fmapdata *= (cutoff_hz / pi)
nb.Nifti1Image(fmapdata, fmapnii.affine,
fmapnii.header).to_filename(out_file)
return out_file
def _demean(in_file, in_mask, out_file=None):
import numpy as np
import nibabel as nb
from fmriprep.utils.misc import genfname
if out_file is None:
out_file = genfname(in_file, 'demeaned')
nii = nb.load(in_file)
msk = nb.load(in_mask).get_data()
data = nii.get_data()
data -= np.median(data[msk > 0])
nb.Nifti1Image(data, nii.affine, nii.header).to_filename(out_file)
return out_file
def _flatten_split_merge(in_files):
from builtins import bytes, str
if isinstance(in_files, (bytes, str)):
in_files = [in_files]
nfiles = len(in_files)
all_nii = []
for fname in in_files:
nii = nb.squeeze_image(nb.load(fname))
if nii.get_data().ndim > 3:
all_nii += nb.four_to_three(nii)
else:
all_nii.append(nii)
if len(all_nii) == 1:
LOGGER.warn('File %s cannot be split', all_nii[0])
return in_files[0], in_files
if len(all_nii) == nfiles:
flat_split = in_files
else:
splitname = genfname(in_files[0], suffix='split%04d')
flat_split = []
for i, nii in enumerate(all_nii):
flat_split.append(splitname % i)
nii.to_filename(flat_split[-1])
# Only one 4D file was supplied
if nfiles == 1:
merged = in_files[0]
else:
# More that one in_files - need merge
merged = genfname(in_files[0], suffix='merged')
nb.concat_images(all_nii).to_filename(merged)
return merged, flat_split
def _demean(in_file, in_mask, out_file=None):
import numpy as np
import nibabel as nb
from fmriprep.utils.misc import genfname
if out_file is None:
out_file = genfname(in_file, 'demeaned')
nii = nb.load(in_file)
msk = nb.load(in_mask).get_data()
data = nii.get_data()
data -= np.median(data[msk > 0])
nb.Nifti1Image(data, nii.affine, nii.header).to_filename(
out_file)
return out_file
def _run_interface(self, runtime):
self._results['out_file'] = genfname(
self.inputs.in_files[0], suffix='merged')
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
def _tohz(in_file, cutoff_hz, out_file=None):
from math import pi
import nibabel as nb
from fmriprep.utils.misc import genfname
if out_file is None:
out_file = genfname(in_file, suffix='hz')
fmapnii = nb.load(in_file)
fmapdata = fmapnii.get_data()
fmapdata = fmapdata * (cutoff_hz / pi)
out_img = nb.Nifti1Image(fmapdata, fmapnii.affine, fmapnii.header)
out_img.set_data_dtype('float32')
out_img.to_filename(out_file)
return out_file
def _run_interface(self, runtime):
self._results['out_file'] = genfname(self.inputs.in_files[0],
suffix='merged')
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
def _torads(in_file, out_file=None):
from math import pi
import nibabel as nb
import numpy as np
from fmriprep.utils.misc import genfname
if out_file is None:
out_file = genfname(in_file, suffix='rad')
fmapnii = nb.load(in_file)
fmapdata = fmapnii.get_data()
cutoff = max(abs(fmapdata.min()), fmapdata.max())
fmapdata *= (pi / cutoff)
nb.Nifti1Image(fmapdata, fmapnii.affine,
fmapnii.header).to_filename(out_file)
return out_file, cutoff
def _torads(in_file, out_file=None):
from math import pi
import nibabel as nb
from fmriprep.utils.misc import genfname
if out_file is None:
out_file = genfname(in_file, suffix='rad')
fmapnii = nb.load(in_file)
fmapdata = fmapnii.get_data()
cutoff = max(abs(fmapdata.min()), fmapdata.max())
fmapdata = fmapdata * (pi / cutoff)
out_img = nb.Nifti1Image(fmapdata, fmapnii.affine, fmapnii.header)
out_img.set_data_dtype('float32')
out_img.to_filename(out_file)
return out_file, cutoff
def _run_interface(self, runtime):
nii = nb.load(self.inputs.in_file)
phaseEncDim = {'i': 0, 'j': 1, 'k': 2}[self.inputs.pe_dir[0]]
if len(self.inputs.pe_dir) == 2:
phaseEncSign = 1.0
else:
phaseEncSign = -1.0
# Fix header
hdr = nii.header.copy()
hdr.set_data_dtype(np.dtype('<f4'))
hdr.set_intent('vector', (), '')
# Get data, convert to mm
data = nii.get_data()
aff = np.diag([1.0, 1.0, -1.0])
if np.linalg.det(aff) < 0 and phaseEncDim != 0:
# Reverse direction since ITK is LPS
aff *= -1.0
aff = aff.dot(nii.affine[:3, :3])
data *= phaseEncSign * nii.header.get_zooms()[phaseEncDim]
# Add missing dimensions
zeros = np.zeros_like(data)
field = [zeros, zeros]
field.insert(phaseEncDim, data)
field = np.stack(field, -1)
# Add empty axis
field = field[:, :, :, np.newaxis, :]
# Write out
self._results['out_file'] = genfname(
self.inputs.in_file, suffix='antswarp')
nb.Nifti1Image(
field.astype(np.dtype('<f4')), nii.affine, hdr).to_filename(
self._results['out_file'])
return runtime
def _run_interface(self, runtime):
target_affine = None
target_shape = None
if isdefined(self.inputs.target_affine):
target_affine = self.inputs.target_affine
if isdefined(self.inputs.target_shape):
target_shape = self.inputs.target_shape
masknii = compute_epi_mask(self.inputs.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=target_affine,
target_shape=target_shape)
self._results['out_mask'] = genfname(self.inputs.in_files[0],
suffix='mask')
masknii.to_filename(self._results['out_mask'])
return runtime
def _run_interface(self, runtime):
from scipy import ndimage as sim
fmap_nii = nb.load(self.inputs.in_file)
data = np.squeeze(fmap_nii.get_data().astype(np.float32))
# Despike / denoise (no-mask)
if self.inputs.despike:
data = _despike2d(data, self.inputs.despike_threshold)
mask = None
if isdefined(self.inputs.in_mask):
masknii = nb.load(self.inputs.in_mask)
mask = masknii.get_data().astype(np.uint8)
# Dilate mask
if self.inputs.mask_erode > 0:
struc = sim.iterate_structure(
sim.generate_binary_structure(3, 2), 1)
mask = sim.binary_erosion(
mask, struc,
iterations=self.inputs.mask_erode).astype(np.uint8) # pylint: disable=no-member
self._results['out_file'] = genfname(self.inputs.in_file, suffix='enh')
datanii = nb.Nifti1Image(data, fmap_nii.affine, fmap_nii.header)
if self.inputs.unwrap:
data = _unwrap(data, self.inputs.in_magnitude, mask)
self._results['out_unwrapped'] = genfname(self.inputs.in_file,
suffix='unwrap')
nb.Nifti1Image(data, fmap_nii.affine, fmap_nii.header).to_filename(
self._results['out_unwrapped'])
if not self.inputs.bspline_smooth:
datanii.to_filename(self._results['out_file'])
return runtime
else:
from fmriprep.utils import bspline as fbsp
from statsmodels.robust.scale import mad
# Fit BSplines (coarse)
bspobj = fbsp.BSplineFieldmap(datanii,
weights=mask,
njobs=self.inputs.njobs)
bspobj.fit()
smoothed1 = bspobj.get_smoothed()
# Manipulate the difference map
diffmap = data - smoothed1.get_data()
sderror = mad(diffmap[mask > 0])
LOGGER.info('SD of error after B-Spline fitting is %f', sderror)
errormask = np.zeros_like(diffmap)
errormask[np.abs(diffmap) > (10 * sderror)] = 1
errormask *= mask
nslices = 0
try:
errorslice = np.squeeze(
np.argwhere(errormask.sum(0).sum(0) > 0))
nslices = errorslice[-1] - errorslice[0]
except IndexError: # mask is empty, do not refine
pass
if nslices > 1:
diffmapmsk = mask[..., errorslice[0]:errorslice[-1]]
diffmapnii = nb.Nifti1Image(
diffmap[..., errorslice[0]:errorslice[-1]] * diffmapmsk,
datanii.affine, datanii.header)
bspobj2 = fbsp.BSplineFieldmap(diffmapnii,
knots_zooms=[24., 24., 4.],
njobs=self.inputs.njobs)
bspobj2.fit()
smoothed2 = bspobj2.get_smoothed().get_data()
final = smoothed1.get_data().copy()
final[..., errorslice[0]:errorslice[-1]] += smoothed2
else:
final = smoothed1.get_data()
nb.Nifti1Image(final, datanii.affine, datanii.header).to_filename(
self._results['out_file'])
return runtime
def _run_interface(self, runtime):
from scipy import ndimage as sim
fmap_nii = nb.load(self.inputs.in_file)
data = np.squeeze(fmap_nii.get_data().astype(np.float32))
# Despike / denoise (no-mask)
if self.inputs.despike:
data = _despike2d(data, self.inputs.despike_threshold)
mask = None
if isdefined(self.inputs.in_mask):
masknii = nb.load(self.inputs.in_mask)
mask = masknii.get_data().astype(np.uint8)
# Dilate mask
if self.inputs.mask_erode > 0:
struc = sim.iterate_structure(sim.generate_binary_structure(3, 2), 1)
mask = sim.binary_erosion(
mask, struc,
iterations=self.inputs.mask_erode
).astype(np.uint8) # pylint: disable=no-member
self._results['out_file'] = genfname(self.inputs.in_file, suffix='enh')
datanii = nb.Nifti1Image(data, fmap_nii.affine, fmap_nii.header)
if self.inputs.unwrap:
data = _unwrap(data, self.inputs.in_magnitude, mask)
self._results['out_unwrapped'] = genfname(self.inputs.in_file, suffix='unwrap')
nb.Nifti1Image(data, fmap_nii.affine, fmap_nii.header).to_filename(
self._results['out_unwrapped'])
if not self.inputs.bspline_smooth:
datanii.to_filename(self._results['out_file'])
return runtime
else:
from fmriprep.utils import bspline as fbsp
from statsmodels.robust.scale import mad
# Fit BSplines (coarse)
bspobj = fbsp.BSplineFieldmap(datanii, weights=mask,
njobs=self.inputs.njobs)
bspobj.fit()
smoothed1 = bspobj.get_smoothed()
# Manipulate the difference map
diffmap = data - smoothed1.get_data()
sderror = mad(diffmap[mask > 0])
LOGGER.info('SD of error after B-Spline fitting is %f', sderror)
errormask = np.zeros_like(diffmap)
errormask[np.abs(diffmap) > (10 * sderror)] = 1
errormask *= mask
nslices = 0
try:
errorslice = np.squeeze(np.argwhere(errormask.sum(0).sum(0) > 0))
nslices = errorslice[-1] - errorslice[0]
except IndexError: # mask is empty, do not refine
pass
if nslices > 1:
diffmapmsk = mask[..., errorslice[0]:errorslice[-1]]
diffmapnii = nb.Nifti1Image(
diffmap[..., errorslice[0]:errorslice[-1]] * diffmapmsk,
datanii.affine, datanii.header)
bspobj2 = fbsp.BSplineFieldmap(diffmapnii, knots_zooms=[24., 24., 4.],
njobs=self.inputs.njobs)
bspobj2.fit()
smoothed2 = bspobj2.get_smoothed().get_data()
final = smoothed1.get_data().copy()
final[..., errorslice[0]:errorslice[-1]] += smoothed2
else:
final = smoothed1.get_data()
nb.Nifti1Image(final, datanii.affine, datanii.header).to_filename(
self._results['out_file'])
return runtime
