def _check_dtype(self, nii: nib.Nifti1Image) -> nib.Nifti1Image:
"""
Checks the NIfTI header datatype and converts the data to the matching
numpy dtype.
Parameters
----------
nii : nib.Nifti1Image
Input image
Returns
-------
nib.Nifti1Image
Converted input image
"""
header = nii.header.copy()
datatype = int(header["datatype"])
self._warn_suspicious_dtype(datatype)
try:
dtype = NUMPY_DTYPE[datatype]
except KeyError:
return nii
else:
header.set_data_dtype(dtype)
converted = nii.get_data().astype(dtype)
return nib.Nifti1Image(converted, nii.affine, header)
def resample_nii(nifti: nib.Nifti1Image, new_pix_dims: Tuple[float, ...],
num_workers: int) -> Tuple[np.ndarray, ...]:
"Resamples nifti pixels to new_pix_dims and returns the new array and new affine using num_workers threads."
return reslice(nifti.get_data(),
nifti.affine,
nifti.header.get_zooms(),
new_pix_dims,
num_processes=num_workers)
def _interpolate_data(stc, morph, mri_resolution=True, mri_space=True,
output='nifti1'):
"""Interpolate source estimate data to MRI."""
_check_dep(nibabel='2.1.0', dipy=False)
if output not in ('nifti', 'nifti1', 'nifti2'):
raise ValueError("invalid output specifier %s. Must be 'nifti1' or"
" 'nifti2'" % output)
if output in ('nifti', 'nifti1'):
from nibabel import (Nifti1Image as NiftiImage,
Nifti1Header as NiftiHeader)
else:
assert output == 'nifti2'
from nibabel import (Nifti2Image as NiftiImage,
Nifti2Header as NiftiHeader)
assert morph.kind == 'volume'
voxel_size_defined = False
if isinstance(mri_resolution, (int, float)) and not isinstance(
mri_resolution, bool):
# use iso voxel size
mri_resolution = (float(mri_resolution),) * 3
if isinstance(mri_resolution, tuple):
_check_dep(nibabel=False, dipy='0.10.1') # nibabel was already checked
from dipy.align.reslice import reslice
voxel_size = mri_resolution
voxel_size_defined = True
mri_resolution = True
# if data wasn't morphed yet - necessary for call of
# stc_unmorphed.as_volume. Since only the shape of src is known, it cannot
# be resliced to a given voxel size without knowing the original.
if isinstance(morph, SourceSpaces):
assert morph.kind == 'volume'
if voxel_size_defined:
raise ValueError(
"Cannot infer original voxel size for reslicing... "
"set mri_resolution to boolean value or apply morph first.")
from mne.io.constants import BunchConst
morph = BunchConst(src_data=_get_src_data(morph)[0])
# setup volume parameters
n_times = stc.data.shape[1]
shape3d = morph.src_data['src_shape']
shape = (n_times,) + shape3d
vols = np.zeros(shape)
mask3d = morph.src_data['inuse'].reshape(shape3d).astype(np.bool)
n_vertices = np.sum(mask3d)
n_vertices_seen = 0
for k, vol in enumerate(vols): # loop over time instants
stc_slice = slice(n_vertices_seen, n_vertices_seen + n_vertices)
vol[mask3d] = stc.data[stc_slice, k]
n_vertices_seen += n_vertices
# use mri resolution as represented in src
if mri_resolution:
mri_shape3d = morph.src_data['src_shape_full']
mri_shape = (n_times,) + mri_shape3d
mri_vol = np.zeros(mri_shape)
interpolator = morph.src_data['interpolator']
for k, vol in enumerate(vols):
mri_vol[k] = (interpolator * vol.ravel()).reshape(mri_shape3d)
vols = mri_vol
vols = vols.T
# set correct space
affine = morph.src_data['src_affine_vox']
if not mri_resolution:
affine = morph.src_data['src_affine_src']
if mri_space:
affine = np.dot(morph.src_data['src_affine_ras'], affine)
affine[:3] *= 1e3
# pre-define header
header = NiftiHeader()
header.set_xyzt_units('mm', 'msec')
header['pixdim'][4] = 1e3 * stc.tstep
with warnings.catch_warnings(): # nibabel<->numpy warning
img = NiftiImage(vols, affine, header=header)
# if a specific voxel size was targeted (only possible after morphing)
if voxel_size_defined:
# reslice mri
img, img_affine = reslice(
img.get_data(), img.affine, _get_zooms_orig(morph), voxel_size)
with warnings.catch_warnings(): # nibabel<->numpy warning
img = NiftiImage(img, img_affine, header=header)
return img
def _morphed_stc_as_volume(morph, stc, mri_resolution=False, mri_space=True,
output='nifti1'):
"""Return volume source space as Nifti1Image and/or save to disk."""
if not isinstance(stc, VolSourceEstimate):
raise ValueError('Only volume source estimates can be converted to '
'volumes')
_check_dep(nibabel='2.1.0', dipy=False)
known_types = ('nifti', 'nifti1', 'nifti2')
if output not in known_types:
raise ValueError('output must be one of %s, got %s'
% (known_types, output))
if output in ('nifti', 'nifti1'):
from nibabel import (Nifti1Image as NiftiImage,
Nifti1Header as NiftiHeader)
else:
assert output == 'nifti2'
from nibabel import (Nifti2Image as NiftiImage,
Nifti2Header as NiftiHeader)
new_zooms = None
# if full MRI resolution, compute zooms from shape and MRI zooms
if isinstance(mri_resolution, bool) and mri_resolution:
new_zooms = _get_zooms_orig(morph)
# if MRI resolution is set manually as a single value, convert to tuple
if isinstance(mri_resolution, (int, float)) and not isinstance(
mri_resolution, bool):
# use iso voxel size
new_zooms = (float(mri_resolution),) * 3
# if MRI resolution is set manually as a tuple, use it
if isinstance(mri_resolution, tuple):
new_zooms = mri_resolution
# create header
hdr = NiftiHeader()
hdr.set_xyzt_units('mm', 'msec')
hdr['pixdim'][4] = 1e3 * stc.tstep
# setup empty volume
img = np.zeros(morph.shape + (stc.shape[1],)).reshape(-1, stc.shape[1])
img[stc.vertices, :] = stc.data
img = img.reshape(morph.shape + (-1,))
# make nifti from data
with warnings.catch_warnings(): # nibabel<->numpy warning
img = NiftiImage(img, morph.affine, header=hdr)
# reslice in case of manually defined voxel size
zooms = morph.zooms[:3]
if new_zooms is not None:
from dipy.align.reslice import reslice
new_zooms = new_zooms[:3]
img, affine = reslice(img.get_data(),
img.affine, # MRI to world registration
zooms, # old voxel size in mm
new_zooms) # new voxel size in mm
with warnings.catch_warnings(): # nibabel<->numpy warning
img = NiftiImage(img, affine)
zooms = new_zooms
# set zooms in header
img.header.set_zooms(tuple(zooms) + (1,))
return img
def _interpolate_data(stc, morph, mri_resolution=True, mri_space=True,
output='nifti1'):
"""Interpolate source estimate data to MRI."""
_check_dep(nibabel='2.1.0', dipy=False)
if output not in ('nifti', 'nifti1', 'nifti2'):
raise ValueError("invalid output specifier %s. Must be 'nifti1' or"
" 'nifti2'" % output)
if output in ('nifti', 'nifti1'):
from nibabel import (Nifti1Image as NiftiImage,
Nifti1Header as NiftiHeader)
else:
assert output == 'nifti2'
from nibabel import (Nifti2Image as NiftiImage,
Nifti2Header as NiftiHeader)
assert morph.kind == 'volume'
voxel_size_defined = False
if isinstance(mri_resolution, (int, float)) and not isinstance(
mri_resolution, bool):
# use iso voxel size
mri_resolution = (float(mri_resolution),) * 3
if isinstance(mri_resolution, tuple):
_check_dep(nibabel=False, dipy='0.10.1') # nibabel was already checked
from dipy.align.reslice import reslice
voxel_size = mri_resolution
voxel_size_defined = True
mri_resolution = True
# if data wasn't morphed yet - necessary for call of
# stc_unmorphed.as_volume. Since only the shape of src is known, it cannot
# be resliced to a given voxel size without knowing the original.
if isinstance(morph, SourceSpaces):
assert morph.kind == 'volume'
if voxel_size_defined:
raise ValueError(
"Cannot infer original voxel size for reslicing... "
"set mri_resolution to boolean value or apply morph first.")
from mne.io.constants import BunchConst
# Now deal with the fact that we may have multiple sub-volumes
inuse = [morph[k]['inuse'] for k in range(len(morph))]
src_shape = [morph[k]['shape'] for k in range(len(morph))]
assert len(set(map(tuple, src_shape))) == 1
morph = BunchConst(src_data=_get_src_data(morph)[0])
else:
# Make a list as we may have many inuse when using multiple sub-volumes
inuse = [morph.src_data['inuse']]
shape3d = morph.src_data['src_shape']
# setup volume parameters
n_times = stc.data.shape[1]
shape = (n_times,) + shape3d
vols = np.zeros(shape)
n_vertices_seen = 0
for this_inuse in inuse:
mask3d = this_inuse.reshape(shape3d).astype(np.bool)
n_vertices = np.sum(mask3d)
stc_slice = slice(n_vertices_seen, n_vertices_seen + n_vertices)
for k, vol in enumerate(vols): # loop over time instants
vol[mask3d] = stc.data[stc_slice, k]
n_vertices_seen += n_vertices
# use mri resolution as represented in src
if mri_resolution:
mri_shape3d = morph.src_data['src_shape_full']
mri_shape = (n_times,) + mri_shape3d
mri_vol = np.zeros(mri_shape)
interpolator = morph.src_data['interpolator']
for k, vol in enumerate(vols):
mri_vol[k] = (interpolator * vol.ravel()).reshape(mri_shape3d)
vols = mri_vol
vols = vols.T
# set correct space
affine = morph.src_data['src_affine_vox']
if not mri_resolution:
affine = morph.src_data['src_affine_src']
if mri_space:
affine = np.dot(morph.src_data['src_affine_ras'], affine)
affine[:3] *= 1e3
# pre-define header
header = NiftiHeader()
header.set_xyzt_units('mm', 'msec')
header['pixdim'][4] = 1e3 * stc.tstep
with warnings.catch_warnings(): # nibabel<->numpy warning
img = NiftiImage(vols, affine, header=header)
# if a specific voxel size was targeted (only possible after morphing)
if voxel_size_defined:
# reslice mri
img, img_affine = reslice(
img.get_data(), img.affine, _get_zooms_orig(morph), voxel_size)
with warnings.catch_warnings(): # nibabel<->numpy warning
img = NiftiImage(img, img_affine, header=header)
return img
def _morphed_stc_as_volume(morph, stc, mri_resolution=False, mri_space=True,
output='nifti1'):
"""Return volume source space as Nifti1Image and/or save to disk."""
if not isinstance(stc, VolSourceEstimate):
raise ValueError('Only volume source estimates can be converted to '
'volumes')
_check_dep(nibabel='2.1.0', dipy=False)
known_types = ('nifti', 'nifti1', 'nifti2')
if output not in known_types:
raise ValueError('output must be one of %s, got %s'
% (known_types, output))
if output in ('nifti', 'nifti1'):
from nibabel import (Nifti1Image as NiftiImage,
Nifti1Header as NiftiHeader)
else:
assert output == 'nifti2'
from nibabel import (Nifti2Image as NiftiImage,
Nifti2Header as NiftiHeader)
new_zooms = None
# if full MRI resolution, compute zooms from shape and MRI zooms
if isinstance(mri_resolution, bool) and mri_resolution:
new_zooms = _get_zooms_orig(morph)
# if MRI resolution is set manually as a single value, convert to tuple
if isinstance(mri_resolution, (int, float)) and not isinstance(
mri_resolution, bool):
# use iso voxel size
new_zooms = (float(mri_resolution),) * 3
# if MRI resolution is set manually as a tuple, use it
if isinstance(mri_resolution, tuple):
new_zooms = mri_resolution
# create header
hdr = NiftiHeader()
hdr.set_xyzt_units('mm', 'msec')
hdr['pixdim'][4] = 1e3 * stc.tstep
# setup empty volume
img = np.zeros(morph.shape + (stc.shape[1],)).reshape(-1, stc.shape[1])
img[stc.vertices, :] = stc.data
img = img.reshape(morph.shape + (-1,))
# make nifti from data
with warnings.catch_warnings(): # nibabel<->numpy warning
img = NiftiImage(img, morph.affine, header=hdr)
# reslice in case of manually defined voxel size
zooms = morph.zooms[:3]
if new_zooms is not None:
from dipy.align.reslice import reslice
new_zooms = new_zooms[:3]
img, affine = reslice(img.get_data(),
img.affine, # MRI to world registration
zooms, # old voxel size in mm
new_zooms) # new voxel size in mm
with warnings.catch_warnings(): # nibabel<->numpy warning
img = NiftiImage(img, affine)
zooms = new_zooms
# set zooms in header
img.header.set_zooms(tuple(zooms) + (1,))
return img
def weight(echo: nib.Nifti1Image, TE: float):
data = echo.get_data()
mean = data[..., -n_vols:].mean(axis=-1)
std = data[..., -n_vols:].std(axis=-1)
return TE * mean / std
