def volumeToSurface(image, interpolation='linear'):
fsaverageL = r"E:\brain\BNatlas\BN_Atlas_freesurfer\fsaverage\fsaverage_LR32k\fsaverage.L.inflated.32k_fs_LR.surf.gii"
fsaverageR = r"E:\brain\BNatlas\BN_Atlas_freesurfer\fsaverage\fsaverage_LR32k\fsaverage.R.inflated.32k_fs_LR.surf.gii"
textureL = surface.vol_to_surf(
image,
fsaverageL,
inner_mesh=
r"E:\brain\BNatlas\BN_Atlas_freesurfer\fsaverage\fsaverage_LR32k\fsaverage.L.white.32k_fs_LR.surf.gii"
)
textureR = surface.vol_to_surf(
image,
fsaverageR,
inner_mesh=
r"E:\brain\BNatlas\BN_Atlas_freesurfer\fsaverage\fsaverage_LR32k\fsaverage.R.white.32k_fs_LR.surf.gii"
)
#textureL = surface.vol_to_surf(image,fsaverageL,radius=4,interpolation=interpolation,kind='line',mask_img='Mask.nii')
#textureR = surface.vol_to_surf(image,fsaverageR,radius=4,interpolation=interpolation,kind='line',mask_img='Mask.nii')
newL = nib.GiftiImage(
header=nib.load(r"E:\brain\GS\surface\NC_tmap.nii.L.func.gii").header)
newR = nib.GiftiImage(
header=nib.load(r"E:\brain\GS\surface\NC_tmap.nii.R.func.gii").header)
dataArrayL = nib.gifti.gifti.GiftiDataArray(textureL.astype(np.float32))
dataArrayR = nib.gifti.gifti.GiftiDataArray(textureR.astype(np.float32))
newL.add_gifti_data_array(dataArrayL)
newR.add_gifti_data_array(dataArrayR)
return newL, newR
def annot_to_gifti(atlas):
"""
Converts FreeSurfer-style annotation file `atlas` to in-memory GIFTI image
Parameters
----------
annot : os.PathLike
Surface annotation file (.annot)
Returns
-------
gifti : nib.gifti.GiftiImage
Converted gifti image
"""
labels, ctab, names = nib.freesurfer.read_annot(atlas)
darr = nib.gifti.GiftiDataArray(labels,
intent='NIFTI_INTENT_LABEL',
datatype='NIFTI_TYPE_INT32')
labeltable = nib.gifti.GiftiLabelTable()
for key, label in enumerate(names):
(r, g, b), a = (ctab[key, :3] / 255), (1.0 if key != 0 else 0.0)
glabel = nib.gifti.GiftiLabel(key, r, g, b, a)
glabel.label = label.decode()
labeltable.labels.append(glabel)
return nib.GiftiImage(darrays=[darr], labeltable=labeltable)
def make_binary_gifti(img, label, out):
img = nib.load(img)
arr = img.agg_data().ravel()
labeltable = img.labeltable.get_labels_as_dict()
mask = np.where(arr == label, label, 0)
darray = nib.gifti.GiftiDataArray(mask,
intent='NIFTI_INTENT_LABEL',
datatype='NIFTI_TYPE_INT32')
# get only the labels that appear in the mask
mask_labels = np.unique(mask)
retained_labels = [
x for x in img.labeltable.labels if x.key in mask_labels
]
new_labels = nib.gifti.GiftiLabelTable()
for i in retained_labels:
new_labels.labels.append(i)
img = nib.GiftiImage(darrays=[darray], labeltable=new_labels)
img.to_filename(out)
return out
def annot_to_gifti(annot_file, out):
"""Converts FreeSurfer-style annotation file `atlas` to a label.gii
Based on Ross' code:
https://github.com/rmarkello/abagen/blob/28e238cf6a12ecb3a8fde0abb70ab0b6e9108394/abagen/images.py#L53-L79
Parameters
----------
annot_file : os.PathLike
Surface annotation file (.annot)
out : str
File name of output .label.gii
"""
labels, ctab, names = nib.freesurfer.read_annot(annot_file)
darr = nib.gifti.GiftiDataArray(labels,
intent='NIFTI_INTENT_LABEL',
datatype='NIFTI_TYPE_INT32')
labeltable = nib.gifti.GiftiLabelTable()
for key, label in enumerate(names):
(r, g, b), a = (ctab[key, :3] / 255), (1.0 if key != 0 else 0.0)
glabel = nib.gifti.GiftiLabel(key, r, g, b, a)
glabel.label = label.decode()
labeltable.labels.append(glabel)
img = nib.GiftiImage(darrays=[darr], labeltable=labeltable)
img.to_filename(out)
return out
def annot_to_func(annot_file, out, n=10):
"""Create a mock func.gii from an annotation file
All timepoints (darrays) in .func.gii are duplicates of the annotation
array. This enables a way to verify that expected data is correctly
extracted (e.g., label 1 should extract a timeseries of all 1's, etc).
Parameters
----------
annot_file : str
Freesurfer annotation file (.annot)
out : str
File name of output .func.gii
n : int, optional
Number of timepoints to generate, by default 100
"""
annot = nib.freesurfer.read_annot(annot_file)
darrays = []
for i in range(n):
x = nib.gifti.GiftiDataArray(annot[0],
intent='NIFTI_INTENT_TIME_SERIES',
datatype='NIFTI_TYPE_FLOAT32')
darrays.append(x)
img = nib.GiftiImage(darrays=darrays)
img.to_filename(out)
return out
def relabel_gifti(atlas, background=BACKGROUND, offset=None):
"""
Updates GIFTI images so label IDs are consecutive across hemispheres
Parameters
----------
atlas : (2,) tuple-of-str
Surface label files in GIFTI format (lh.label.gii, rh.label.gii)
background : list-of-str, optional
If provided, a list of IDs in `atlas` that should be set to 0 (the
presumptive background value). Other IDs will be shifted so they are
consecutive (i.e., 0--N). Default: `abagen.images.BACKGROUND`
offset : int, optional
What the lowest value in `atlas[1]` should be not including background
value. If not specified it will be purely consecutive from `atlas[0]`.
Default: None
Returns
-------
relabelled : (2,) tuple-of-nib.gifti.GiftiImage
Re-labelled `atlas` files
"""
out = tuple()
minval = 0
for hemi in atlas:
# get necessary info from file
img = load_gifti(hemi)
data = img.agg_data()
labels = img.labeltable.labels
lt = {v: k for k, v in img.labeltable.get_labels_as_dict().items()}
# get rid of labels we want to drop
if background is not None:
for val in background:
idx = lt.get(val, 0)
if idx == 0:
continue
data[data == idx] = 0
labels = [f for f in labels if f.key != idx]
# reset labels so they're consecutive and update label keys
data = _relabel(data, minval=minval, bgval=0)
ids = np.unique(data)
for n, i in enumerate(ids):
labels[n].key = i
minval = len(ids) - 1 if offset is None else int(offset) - 1
# make new gifti image with updated information
darr = nib.gifti.GiftiDataArray(data,
intent='NIFTI_INTENT_LABEL',
datatype='NIFTI_TYPE_INT32')
labeltable = nib.gifti.GiftiLabelTable()
labeltable.labels = labels
img = nib.GiftiImage(darrays=[darr], labeltable=labeltable)
out += (img, )
return out
def save_metric(self, data, path):
"""
Save vertex-wise data as a .func.gii at path
"""
if not self.n_points == data.shape[0]:
raise RuntimeError("Incorrect data shape")
if not path.endswith('.func.gii'):
print("appending .func.gii extension")
path += '.func.gii'
gii = nibabel.GiftiImage()
gii.add_gifti_data_array(
nibabel.gifti.GiftiDataArray(data.astype(NP_FLOAT)))
nibabel.save(gii, path)
def spm_write_vol(image_volume_info, image_voxels, image_name, file_type):
"""
Writes an image volume to disk
@image_volume_info - a structure containing image volume
information (see spm_vol)
@image_voxels - a one, two or three dimensional matrix
containing the image voxels
@image_name - name of the file to save the image in
"""
if file_type == ".nii" or file_type == ".nii.gz":
data = image_voxels
affine = image_volume_info.affine
image_volume_info = nib.Nifti1Image(data, affine)
nib.save(image_volume_info, image_name + file_type)
else:
data = image_voxels
gi = nib.GiftiImage()
gi.add_gifti_data_array(nib.gifti.GiftiDataArray(image_voxels))
nib.gifti.giftiio.write(gi, image_name + file_type)