def mesh_sampling_layer(surf_in,
file_in,
boundaries_in,
path_output,
layer,
r=[0.4, 0.4, 0.4],
interpolation="Cu",
average_layer=False,
write_profile=True,
write_upsampled=True):
"""
This function samples data from an image volume to a surface mesh from specific layers defined
by a levelset image. If average_layer is true, the parameter layer should contain only two
integers which denote the start and ending layer.
Inputs:
*surf_in: filename of input surface mesh.
*file_in: filename of input volume from which data is sampled.
*boundaries_in: filename of 4D levelset image.
*path_output: path where output is written.
*layer: which layers to sample (array of integers).
*r: destination voxel size after upsampling (performed if not None).
*interpolation: interpolation method for upsampling of file from whic data is sampled.
*average_layer: average across cortex.
*write_profile: write sampled profile.
*write_upsampled: write upsampled file.
created by Daniel Haenelt
Date created: 18-12-2019
Last modified: 24-07-2020
"""
import os
import sys
import shutil as sh
import numpy as np
import nibabel as nb
from os.path import join, exists, basename, splitext
from nighres.laminar import profile_sampling
from lib.io.get_filename import get_filename
from lib.utils.upsample_volume import upsample_volume
from lib.mapping import map2surface
# make output folder
if not exists(path_output):
os.makedirs(path_output)
# filenames
_, name_file, ext_file = get_filename(file_in)
_, hemi, name_surf = get_filename(surf_in)
name_surf = name_surf[1:]
name_profile = splitext(basename(file_in))[0] + "_profile"
# check hemi
if not hemi == "lh" and not hemi == "rh":
sys.exit("Could not identify hemi from filename!")
# upsample volume
if not r == None:
name_file = name_file + "_upsampled"
upsample_volume(file_in, join(path_output, name_file + ext_file), r,
interpolation)
else:
if file_in != join(path_output, name_file + ext_file):
sh.copyfile(file_in, join(path_output, name_file + ext_file))
# get profile sampling
tmp = np.random.randint(0, 10, 5)
tmp_string = ''.join(str(i) for i in tmp)
profile = profile_sampling(boundaries_in,
join(path_output, name_file + ext_file),
save_data=write_profile,
overwrite=write_profile,
output_dir=path_output,
file_name="profile_" + tmp_string)
# rename profile sampling output
if write_profile:
os.rename(
join(path_output, "profile_" + tmp_string + "_lps-data.nii.gz"),
join(path_output, name_profile + ".nii.gz"))
# load profile
if write_profile:
data = nb.load(join(path_output, name_profile + ".nii.gz"))
else:
data = profile["result"]
data.header["dim"][0] = 3
# do mapping
tmp2 = np.random.randint(0, 10, 5)
tmp2_string = ''.join(str(i) for i in tmp2)
if not average_layer:
for i in range(len(layer)):
data_array = data.get_fdata()[:, :, :, layer[i]]
out = nb.Nifti1Image(data_array, data.affine, data.header)
nb.save(out, join(path_output, "temp_" + tmp2_string + ".nii"))
# do the mapping
map2surface(surf_in,
join(path_output, "temp_" + tmp2_string + ".nii"),
hemi,
path_output,
input_white=None,
input_ind=None,
cleanup=True)
# rename mapping file
os.rename(
join(
path_output, hemi + ".temp_" + tmp2_string + "_" +
name_surf + "_def.mgh"),
join(
path_output, hemi + "." + name_file + "_layer" +
str(layer[i]) + ".mgh"))
else:
if len(layer) != 2:
sys.exit("For averaging, layer should only contain two elements!")
data_array = data.get_fdata()[:, :, :, layer[0]:layer[1]]
data_array = np.mean(data_array, axis=3)
out = nb.Nifti1Image(data_array, data.affine, data.header)
nb.save(out, join(path_output, "temp_" + tmp2_string + ".nii"))
# do the mapping
map2surface(surf_in,
join(path_output, "temp_" + tmp2_string + ".nii"),
hemi,
path_output,
input_white=None,
input_ind=None,
cleanup=True)
# rename mapping file
os.rename(
join(path_output,
hemi + ".temp_" + tmp2_string + "_" + name_surf + "_def.mgh"),
join(
path_output, hemi + "." + name_file + "_avg_layer" +
str(layer[0]) + "_" + str(layer[1]) + ".mgh"))
# clean temp
os.remove(join(path_output, "temp_" + tmp2_string + ".nii"))
# clean file
if not write_upsampled:
os.remove(join(path_output, name_file + ext_file))
def get_thickness(boundaries_in, ref_in, hemi, path_output, r=[0.4, 0.4, 0.4]):
"""
This function computes the cortical thickness as euclidean distances between vertex ras
coordinates from outer levelset boundaries.
Inputs:
*boundaries_in: filename of 4D levelset image.
*ref_in: filename of reference volume for coordinate transformation.
*hemi: hemisphere.
*path_output: path where output is written.
*r: destination voxel size after upsampling (performed if not None).
created by Daniel Haenelt
Date created: 18-12-2019
Last modified: 24-07-2020
"""
import os
import shutil as sh
import numpy as np
import nibabel as nb
from nibabel.affines import apply_affine
from nighres.laminar import profile_sampling
from lib.cmap.generate_coordinate_mapping import generate_coordinate_mapping
from lib.utils.upsample_volume import upsample_volume
from lib.surface.vox2ras import vox2ras
# make output folder
if not os.path.exists(path_output):
os.makedirs(path_output)
# upsample volume
if not r == None:
upsample_volume(ref_in, os.path.join(path_output, "ref.nii"), r, "Cu")
else:
sh.copyfile(ref_in, os.path.join(path_output, "ref.nii"))
# make coordinate mapping
cmap = generate_coordinate_mapping(boundaries_in, pad=0)
cmap.header["dim"][0] = 1
# get voxel to vertex ras coordinate transformation
vox2ras_tkr, _ = vox2ras(os.path.join(path_output, "ref.nii"))
# apply transformation to cmap
ras_array = apply_affine(vox2ras_tkr, cmap.get_fdata())
# split coordinates into single dimensions
x_ras = nb.Nifti1Image(ras_array[:, :, :, 0], cmap.affine, cmap.header)
y_ras = nb.Nifti1Image(ras_array[:, :, :, 1], cmap.affine, cmap.header)
z_ras = nb.Nifti1Image(ras_array[:, :, :, 2], cmap.affine, cmap.header)
# get profile sampling
x_profile = profile_sampling(boundaries_in, x_ras)
y_profile = profile_sampling(boundaries_in, y_ras)
z_profile = profile_sampling(boundaries_in, z_ras)
# compute euclidean distance between outer levelset boundaries
x_array = x_profile["result"].get_fdata()
y_array = y_profile["result"].get_fdata()
z_array = z_profile["result"].get_fdata()
x_diff_array = np.square(x_array[:, :, :, -1] - x_array[:, :, :, 0])
y_diff_array = np.square(y_array[:, :, :, -1] - y_array[:, :, :, 0])
z_diff_array = np.square(z_array[:, :, :, -1] - z_array[:, :, :, 0])
r = np.sqrt(x_diff_array + y_diff_array + z_diff_array)
# set unrealistic values to zero
r[r > 10] = 0
# hemi suffix
if hemi == "lh":
hemi_suffix = "_left"
elif hemi == "rh":
hemi_suffix = "_right"
else:
hemi_suffix = None
# write nifti
output = nb.Nifti1Image(r, cmap.affine, cmap.header)
nb.save(output,
os.path.join(path_output, "thickness" + hemi_suffix + ".nii"))
# remove temporary reference volume
os.remove(os.path.join(path_output, "ref.nii"))
def calculate_equidistant_epi(input_white, input_pial, input_vol, path_output, n_layers, pathLAYNII,
r=[0.4,0.4,0.4], n_iter=2, debug=False):
"""
This function computes equidistant layers in volume space from input pial and white surfaces
in freesurfer format using the laynii function LN_GROW_LAYERS. The input surfaces do not have
to cover the whole brain. Number of vertices and indices do not have to correspond between
surfaces.
Inputs:
*input_white: filename of white surface.
*input_pial: filename of pial surface.
*input_vol: filename of reference volume.
*path_output: path where output is written.
*n_layers: number of generated layers + 1.
*pathLAYNII: path to laynii folder.
*r: array of new voxel sizes for reference volume upsampling.
*n_iter: number of surface upsampling iterations.
*debug: write out some intermediate files (boolean).
created by Daniel Haenelt
Date created: 31-05-2020
Last modified: 24-07-2020
"""
import os
import sys
import numpy as np
import nibabel as nb
from nibabel.affines import apply_affine
from nibabel.freesurfer.io import read_geometry
from skimage import measure
from nighres.surface import probability_to_levelset
from scipy.ndimage.morphology import binary_fill_holes
from collections import Counter
from lib.utils.upsample_volume import upsample_volume
from lib.surface.vox2ras import vox2ras
from lib.surface.upsample_surf_mesh import upsample_surf_mesh
# make output folder
if not os.path.exists(path_output):
os.makedirs(path_output)
# get hemi from filename
hemi = os.path.splitext(os.path.basename(input_white))[0]
if not hemi == "lh" and not hemi == "rh":
sys.exit("Could not identify hemi from filename!")
# new filenames in output folder
res_white = os.path.join(path_output,hemi+".white")
res_pial = os.path.join(path_output,hemi+".pial")
res_vol = os.path.join(path_output,"epi_upsampled.nii")
# upsample reference volume and input surface
upsample_volume(input_vol, res_vol, dxyz=r, rmode="Cu")
upsample_surf_mesh(input_white, res_white, n_iter, "linear")
upsample_surf_mesh(input_pial, res_pial, n_iter, "linear")
# get affine ras2vox-tkr transformation to reference volume
_, ras2vox_tkr = vox2ras(res_vol)
# load surface
vtx_white, _ = read_geometry(res_white)
vtx_pial, _ = read_geometry(res_pial)
# load volume
vol = nb.load(res_vol)
# apply ras2vox to coords
vtx_white = np.round(apply_affine(ras2vox_tkr, vtx_white)).astype(int)
vtx_pial = np.round(apply_affine(ras2vox_tkr, vtx_pial)).astype(int)
# surfaces to lines in volume
white_array = np.zeros(vol.header["dim"][1:4])
white_array[vtx_white[:,0],vtx_white[:,1],vtx_white[:,2]] = 1
white = nb.Nifti1Image(white_array, vol.affine, vol.header)
pial_array = np.zeros(vol.header["dim"][1:4])
pial_array[vtx_pial[:,0],vtx_pial[:,1],vtx_pial[:,2]] = 1
pial = nb.Nifti1Image(pial_array, vol.affine, vol.header)
"""
make wm
"""
white_label_array = np.zeros_like(white_array)
for i in range(np.shape(white_label_array)[2]):
white_label_array[:,:,i] = binary_fill_holes(white_array[:,:,i])
white_label_array = white_label_array - white_array
white_label_array = measure.label(white_label_array, connectivity=1)
white_label_flatten = np.ndarray.flatten(white_label_array)
white_label_flatten = white_label_flatten[white_label_flatten > 0]
label_number = Counter(white_label_flatten).most_common(1)[0][0]
white_label_array[white_label_array != label_number] = 0
white_label_array[white_label_array > 0] = 1
white_label = nb.Nifti1Image(white_label_array, vol.affine, vol.header)
"""
make csf
"""
pial_label_array = np.zeros_like(pial_array)
for i in range(np.shape(pial_label_array)[2]):
pial_label_array[:,:,i] = binary_fill_holes(pial_array[:,:,i])
pial_label_array = pial_label_array - pial_array
pial_label_array = measure.label(pial_label_array, connectivity=1)
pial_label_flatten = np.ndarray.flatten(pial_label_array)
pial_label_flatten = pial_label_flatten[pial_label_flatten > 0]
label_number = Counter(pial_label_flatten).most_common(1)[0][0]
pial_label_array[pial_label_array != label_number] = 0
pial_label_array[pial_label_array > 0] = 1
pial_label_array = pial_label_array + pial_array # add csf line again
pial_label = nb.Nifti1Image(pial_label_array, vol.affine, vol.header)
"""
make gm
"""
ribbon_label_array = pial_label_array - white_label_array
ribbon_label_array[ribbon_label_array != 1] = 0
ribbon_label = nb.Nifti1Image(ribbon_label_array, vol.affine, vol.header)
"""
make rim
"""
rim_array = np.zeros_like(ribbon_label_array)
rim_array[ribbon_label_array == 1] = 3
rim_array[pial_array == 1] = 1
rim_array[white_array == 1] = 2
output = nb.Nifti1Image(rim_array, vol.affine, vol.header)
nb.save(output, os.path.join(path_output,"rim.nii"))
"""
grow layers using laynii
"""
os.chdir(pathLAYNII)
vinc = 40
os.system("./LN_GROW_LAYERS" + \
" -rim " + os.path.join(path_output,"rim.nii") + \
" -vinc " + str(vinc) + \
" -N " + str(n_layers) + \
" -threeD" + \
" -output " + os.path.join(path_output,"layers.nii"))
"""
tranform label to levelset
"""
binary_array = white_label_array + ribbon_label_array + pial_label_array
binary_array[binary_array != 0] = 1
layer_array = nb.load(os.path.join(path_output,"layers.nii")).get_fdata()
layer_array += 1
layer_array[layer_array == 1] = 0
layer_array[white_label_array == 1] = 1 # fill wm
if debug:
out_debug = nb.Nifti1Image(layer_array, vol.affine, vol.header)
nb.save(out_debug, os.path.join(path_output,"layer_plus_white_debug.nii"))
level_array = np.zeros(np.append(vol.header["dim"][1:4],n_layers + 1))
for i in range(n_layers+1):
print("Probabilty to levelset for layer: "+str(i+1))
temp_layer_array = binary_array.copy()
temp_layer_array[layer_array > i+1] = 0
temp_layer = nb.Nifti1Image(temp_layer_array, vol.affine, vol.header)
# write control output
if debug:
nb.save(temp_layer, os.path.join(path_output,"layer_"+str(i)+"_debug.nii"))
# transform binary image to levelset image
res = probability_to_levelset(temp_layer)
# sort levelset image into 4d array
level_array[:,:,:,i] = res["result"].get_fdata()
# levelset image
vol.header["dim"][0] = 4
vol.header["dim"][4] = n_layers + 1
levelset = nb.Nifti1Image(level_array, vol.affine, vol.header)
# write niftis
nb.save(white, os.path.join(path_output,"wm_line.nii"))
nb.save(pial, os.path.join(path_output,"csf_line.nii"))
nb.save(white_label,os.path.join(path_output,"wm_label.nii"))
nb.save(pial_label,os.path.join(path_output,"csf_label.nii"))
nb.save(ribbon_label, os.path.join(path_output,"gm_label.nii"))
nb.save(levelset, os.path.join(path_output,"boundaries.nii"))
def calculate_equivolumetric_epi2(input_white,
input_pial,
input_vol,
path_output,
n_layers,
r=[0.4, 0.4, 0.4],
n_iter=2):
"""
This function computes equivolumetric layers in volume space from input pial and white surfaces
in freesurfer format. The input surfaces do not have to cover the whole brain. Number of
vertices and indices do not have to correspond between surfaces.
Inputs:
*input_white: filename of white surface.
*input_pial: filename of pial surface.
*input_vol: filename of reference volume.
*path_output: path where output is written.
*n_layers: number of generated layers + 1.
*r: array of new voxel sizes for reference volume upsampling.
*n_iter: number of surface upsampling iterations.
created by Daniel Haenelt
Date created: 17-12-2019
Last modified: 24-07-2020
"""
import os
import sys
import numpy as np
import nibabel as nb
from nibabel.affines import apply_affine
from nibabel.freesurfer.io import read_geometry
from skimage import measure
from nighres.surface import probability_to_levelset
from nighres.laminar import volumetric_layering
from scipy.ndimage.morphology import binary_fill_holes
from collections import Counter
from lib.utils.upsample_volume import upsample_volume
from lib.surface.vox2ras import vox2ras
from lib.surface.upsample_surf_mesh import upsample_surf_mesh
# make output folder
if not os.path.exists(path_output):
os.makedirs(path_output)
# get hemi from filename
hemi = os.path.splitext(os.path.basename(input_white))[0]
if not hemi == "lh" and not hemi == "rh":
sys.exit("Could not identify hemi from filename!")
# new filenames in output folder
res_white = os.path.join(path_output, hemi + ".white")
res_pial = os.path.join(path_output, hemi + ".pial")
res_vol = os.path.join(path_output, "epi_upsampled.nii")
# upsample reference volume and input surface
upsample_volume(input_vol, res_vol, dxyz=r, rmode="Cu")
upsample_surf_mesh(input_white, res_white, n_iter, "linear")
upsample_surf_mesh(input_pial, res_pial, n_iter, "linear")
# get affine ras2vox-tkr transformation to reference volume
_, ras2vox_tkr = vox2ras(res_vol)
# load surface
vtx_white, fac_white = read_geometry(res_white)
vtx_pial, _ = read_geometry(res_pial)
# load volume
vol = nb.load(res_vol)
# apply ras2vox to coords
vtx_white = np.round(apply_affine(ras2vox_tkr, vtx_white)).astype(int)
vtx_pial = np.round(apply_affine(ras2vox_tkr, vtx_pial)).astype(int)
# surfaces to lines in volume
white_array = np.zeros(vol.header["dim"][1:4])
white_array[vtx_white[:, 0], vtx_white[:, 1], vtx_white[:, 2]] = 1
white = nb.Nifti1Image(white_array, vol.affine, vol.header)
pial_array = np.zeros(vol.header["dim"][1:4])
pial_array[vtx_pial[:, 0], vtx_pial[:, 1], vtx_pial[:, 2]] = 1
pial = nb.Nifti1Image(pial_array, vol.affine, vol.header)
"""
make wm
"""
white_label_array = np.zeros_like(white_array)
for i in range(np.shape(white_label_array)[2]):
white_label_array[:, :, i] = binary_fill_holes(white_array[:, :, i])
white_label_array = white_label_array - white_array
white_label_array = measure.label(white_label_array, connectivity=1)
white_label_flatten = np.ndarray.flatten(white_label_array)
white_label_flatten = white_label_flatten[white_label_flatten > 0]
label_number = Counter(white_label_flatten).most_common(1)[0][0]
white_label_array[white_label_array != label_number] = 0
white_label_array[white_label_array > 0] = 1
white_label = nb.Nifti1Image(white_label_array, vol.affine, vol.header)
"""
make csf
"""
pial_label_array = np.zeros_like(pial_array)
for i in range(np.shape(pial_label_array)[2]):
pial_label_array[:, :, i] = binary_fill_holes(pial_array[:, :, i])
pial_label_array = pial_label_array - pial_array
pial_label_array = measure.label(pial_label_array, connectivity=1)
pial_label_flatten = np.ndarray.flatten(pial_label_array)
pial_label_flatten = pial_label_flatten[pial_label_flatten > 0]
label_number = Counter(pial_label_flatten).most_common(1)[0][0]
pial_label_array[pial_label_array != label_number] = 0
pial_label_array[pial_label_array > 0] = 1
#pial_label_array = pial_label_array + pial_array # add csf line again (worsen layering)
pial_label = nb.Nifti1Image(pial_label_array, vol.affine, vol.header)
"""
make gm
"""
ribbon_label_array = pial_label_array - white_label_array
ribbon_label_array[ribbon_label_array != 1] = 0
ribbon_label = nb.Nifti1Image(ribbon_label_array, vol.affine, vol.header)
"""
layers
"""
csf_level = probability_to_levelset(pial_label)
wm_level = probability_to_levelset(white_label)
volumetric_layering(wm_level["result"],
csf_level["result"],
n_layers=n_layers,
topology_lut_dir=None,
save_data=True,
overwrite=True,
output_dir=path_output,
file_name="epi")
# write niftis
nb.save(white, os.path.join(path_output, "wm_line.nii"))
nb.save(pial, os.path.join(path_output, "csf_line.nii"))
nb.save(white_label, os.path.join(path_output, "wm_label.nii"))
nb.save(pial_label, os.path.join(path_output, "csf_label.nii"))
nb.save(ribbon_label, os.path.join(path_output, "gm_label.nii"))
def apply_registration(file_in,
cmap_in,
file_out,
interpolation="linear",
r=[0.4, 0.4, 0.4]):
"""
This function applies a coordinate mapping to a volume. Optionally, the voxel size of the output
volume can be changed. This is achieved by adjusting the coordinate mapping to the new voxel
size before application.
Inputs:
*file_in: filename of input volume.
*cmap_in: filename of coordinate mapping.
*file_out: filename of output volume.
*interpolation: interpolation type (linear or nearest).
*r: destination voxel size after upsampling (performed if not None).
Outputs:
*nibabel object instance of transformed input.
created by Daniel Haenelt
Date created: 30-05-2020
Last modified: 02-06-2020
"""
import os
import numpy as np
import nibabel as nb
from nighres.registration import apply_coordinate_mappings
from lib.io.get_filename import get_filename
from lib.utils.upsample_volume import upsample_volume
# make output folder
path_output = os.path.dirname(file_out)
if not os.path.exists(path_output):
os.makedirs(path_output)
# filename for temporary cmap copy
_, _, ext_cmap = get_filename(cmap_in)
tmp = np.random.randint(0, 10, 5)
tmp_string = ''.join(str(i) for i in tmp)
file_tmp = os.path.join(path_output, "tmp_" + tmp_string + ext_cmap)
file_tmp2 = os.path.join(path_output, "tmp2_" + tmp_string + ext_cmap)
# adjust coordinate mapping
if r:
# upsample cmap
upsample_volume(cmap_in, file_tmp, dxyz=r, rmode="Linear")
upsample_volume(cmap_in, file_tmp2, dxyz=r, rmode="NN")
# mask upsampled cmap
cmap = nb.load(file_tmp)
mask = nb.load(file_tmp2)
cmap_array = cmap.get_fdata()
mask_array = mask.get_fdata()
mask_array = np.sum(mask_array, axis=3)
mask_array[mask_array != 0] = 1
cmap_array[:, :, :, 0][mask_array == 0] = 0
cmap_array[:, :, :, 1][mask_array == 0] = 0
cmap_array[:, :, :, 2][mask_array == 0] = 0
cmap = nb.Nifti1Image(cmap_array, cmap.affine, cmap.header)
else:
cmap = nb.load(cmap_in)
# apply coordinate mapping
res = apply_coordinate_mappings(
image=file_in, # input
mapping1=cmap, # cmap
interpolation=interpolation, # nearest or linear
padding="zero", # closest, zero or max
save_data=False, # save output data to file (boolean)
overwrite=False, # overwrite existing results (boolean)
output_dir=None, # output directory
file_name=None, # base name with file extension for output
)
# write output
nb.save(res["result"], file_out)
# remove temporary files
if r:
os.remove(file_tmp)
os.remove(file_tmp2)
return res["result"]
def mesh_sampling_layer_other(surf_in,
file_in,
target2source_in,
source2target_in,
boundaries_in,
path_output,
layer,
smooth_iter=0,
r=[0.4, 0.4, 0.4],
interpolation="Cu",
average_layer=False,
write_profile=False,
write_upsampled=True,
cleanup=True):
"""
This function samples data from an image volume to a surface mesh which is located in a
different space. Boundaries and surface mesh are first transformed to the space of the image
volume using coordinate mappings before data sampling. If average_layer is true, the parameter
layer should contain only two integers which denote the start and ending layer. The basename
of the surface file should have no file extension and the hemisphere should be stated as prefix.
Inputs:
*surf_in: filename of input surface mesh.
*file_in: filename of input volume from which data is sampled.
*target2source_in: target to source coordinate mapping.
*source2target_in: source to target coordinate mapping.
*boundaries_in: filename of 4D levelset image.
*path_output: path where output is written.
*layer: which layers to sample (array of integers).
*smooth_iter: number of smoothing iterations after mesh deformation.
*r: destination voxel size after upsampling (performed if not None).
*interpolation: interpolation method for upsampling of file from which data is sampled.
*average_layer: average across cortex.
*write_profile: write sampled profile.
*write_upsampled: write upsampled file.
*cleanup: remove intermediate files.
created by Daniel Haenelt
Date created: 13-01-2020
Last modified: 24-06-2020
"""
import os
import shutil as sh
import numpy as np
import nibabel as nb
from nighres.registration import apply_coordinate_mappings
from lib.io.get_filename import get_filename
from lib.utils.upsample_volume import upsample_volume
from lib.surface.deform_surface import deform_surface
from lib.surface.mesh_sampling_layer import mesh_sampling_layer
"""
set folder structure
"""
tmp = np.random.randint(0, 10, 5)
tmp_string = ''.join(str(i) for i in tmp)
path_temp = os.path.join(path_output, "temp_" + tmp_string)
path_cmap = os.path.join(path_temp, "cmap")
path_data = os.path.join(path_temp, "data")
path_surf = os.path.join(path_temp, "surf")
if not os.path.exists(path_output):
os.makedirs(path_output)
if not os.path.exists(path_temp):
os.makedirs(path_temp)
if not os.path.exists(path_cmap):
os.makedirs(path_cmap)
if not os.path.exists(path_data):
os.makedirs(path_data)
if not os.path.exists(path_surf):
os.makedirs(path_surf)
# get filenames
_, hemi, name_surf = get_filename(surf_in)
_, name_file, ext_file = get_filename(file_in)
_, name_t2s, ext_t2s = get_filename(target2source_in)
_, name_s2t, ext_s2t = get_filename(source2target_in)
# copy input files
sh.copyfile(target2source_in, os.path.join(path_cmap, "t2s" + ext_t2s))
sh.copyfile(source2target_in, os.path.join(path_cmap, "s2t" + ext_s2t))
sh.copyfile(file_in, os.path.join(path_data, name_file + ext_file))
# set filenames
data = os.path.join(path_data, name_file + ext_file)
data_upsampled = os.path.join(path_data,
name_file + "_upsampled" + ext_file)
t2s = os.path.join(path_cmap, "t2s" + ext_t2s)
t2s_upsampled = os.path.join(path_cmap, "t2s_upsampled" + ext_t2s)
t2s_rescaled = os.path.join(path_cmap, "t2s_upsampled_rescaled" + ext_t2s)
s2t = os.path.join(path_cmap, "s2t" + ext_s2t)
s2t_upsampled = os.path.join(path_cmap, "s2t_upsampled" + ext_s2t)
s2t_rescaled = os.path.join(path_cmap, "s2t_upsampled_rescaled" + ext_s2t)
"""
upsample data
"""
if r:
upsample_volume(data, data_upsampled, dxyz=r, rmode=interpolation)
upsample_volume(t2s, t2s_upsampled, dxyz=r, rmode="Linear")
upsample_volume(s2t, s2t_upsampled, dxyz=r, rmode="Linear")
"""
rescale cmap
"""
dim_target = nb.load(s2t).header["dim"][1:4] - 1
dim_source = nb.load(t2s).header["dim"][1:4] - 1
dim_target_upsampled = nb.load(s2t_upsampled).header["dim"][1:4] - 1
dim_source_upsampled = nb.load(t2s_upsampled).header["dim"][1:4] - 1
cmap_t2s = nb.load(t2s_upsampled)
cmap_s2t = nb.load(s2t_upsampled)
cmap_t2s_array = cmap_t2s.get_fdata()
cmap_s2t_array = cmap_s2t.get_fdata()
for i in range(3):
cmap_t2s_array[:, :, :, i] = cmap_t2s_array[:, :, :, i] / dim_target[
i] * dim_target_upsampled[i]
cmap_s2t_array[:, :, :, i] = cmap_s2t_array[:, :, :, i] / dim_source[
i] * dim_source_upsampled[i]
cmap_t2s_rescaled = nb.Nifti1Image(cmap_t2s_array, cmap_t2s.affine,
cmap_t2s.header)
cmap_s2t_rescaled = nb.Nifti1Image(cmap_s2t_array, cmap_s2t.affine,
cmap_s2t.header)
nb.save(cmap_t2s_rescaled, t2s_rescaled)
nb.save(cmap_s2t_rescaled, s2t_rescaled)
"""
deform boundaries
"""
apply_coordinate_mappings(
image=boundaries_in, # input
mapping1=t2s_rescaled, # cmap
interpolation="linear", # nearest or linear
padding="zero", # closest, zero or max
save_data=True, # save output data to file (boolean)
overwrite=True, # overwrite existing results (boolean)
output_dir=path_data, # output directory
file_name="boundaries" # base name with file extension for output
)
"""
deform mesh
"""
deform_surface(input_surf=surf_in,
input_orig=s2t_upsampled,
input_deform=s2t_rescaled,
input_target=data_upsampled,
hemi=hemi,
path_output=path_surf,
input_mask=None,
interp_method="trilinear",
smooth_iter=smooth_iter,
flip_faces=False,
cleanup=True)
"""
sample data
"""
if smooth_iter:
surf_in = os.path.join(path_surf, hemi + name_surf + "_def_smooth")
else:
surf_in = os.path.join(path_surf, hemi + name_surf + "_def")
mesh_sampling_layer(surf_in=surf_in,
file_in=data_upsampled,
boundaries_in=os.path.join(
path_data, "boundaries_def-img.nii.gz"),
path_output=path_output,
layer=layer,
r=None,
average_layer=average_layer,
write_profile=write_profile,
write_upsampled=write_upsampled)
# delete intermediate files
if cleanup:
sh.rmtree(path_temp, ignore_errors=True)
def calculate_equivolumetric_epi(input_white,
input_pial,
input_vol,
path_output,
n_start,
n_end,
n_layers,
r=[0.4, 0.4, 0.4],
n_iter=2):
"""
This function computes equivolumetric layers in volume space from input pial and white surfaces
in freesurfer format. The input surfaces do not have to cover the whole brain. Number of
vertices and indices do not have to correspond between surfaces.
Inputs:
*input_white: filename of white surface.
*input_pial: filename of pial surface.
*input_vol: filename of reference volume.
*path_output: path where output is written.
*n_start: number of slices (axis=2) to discard at the beginning of the upsampled volume.
*n_end: number of slices (axis=2) to discard at the end of the upsampled volume.
*n_layers: number of generated layers + 1.
*r: array of new voxel sizes for reference volume upsampling.
*n_iter: number of surface upsampling iterations.
created by Daniel Haenelt
Date created: 17-12-2019
Last modified: 24-07-2020
"""
import os
import sys
import numpy as np
import nibabel as nb
from nibabel.affines import apply_affine
from nibabel.freesurfer.io import read_geometry
from skimage import measure
from nighres.surface import probability_to_levelset
from nighres.laminar import volumetric_layering
from lib.utils.upsample_volume import upsample_volume
from lib.surface.vox2ras import vox2ras
from lib.surface.upsample_surf_mesh import upsample_surf_mesh
# make output folder
if not os.path.exists(path_output):
os.makedirs(path_output)
# get hemi from filename
hemi = os.path.splitext(os.path.basename(input_white))[0]
if not hemi == "lh" or hemi == "rh":
sys.exit("Could not identify hemi from filename!")
# new filenames in output folder
res_white = os.path.join(path_output, hemi + ".white")
res_pial = os.path.join(path_output, hemi + ".pial")
res_vol = os.path.join(path_output, "epi_upsampled.nii")
# upsample reference volume and input surface
upsample_volume(input_vol, res_vol, dxyz=r, rmode="Cu")
upsample_surf_mesh(input_white, res_white, n_iter, "linear")
upsample_surf_mesh(input_pial, res_pial, n_iter, "linear")
# get affine ras2vox-tkr transformation to reference volume
_, ras2vox_tkr = vox2ras(res_vol)
# load surface
vtx_white, fac_white = read_geometry(res_white)
vtx_pial, _ = read_geometry(res_pial)
# load volume
vol = nb.load(res_vol)
# apply ras2vox to coords
vtx_white = np.round(apply_affine(ras2vox_tkr, vtx_white)).astype(int)
vtx_pial = np.round(apply_affine(ras2vox_tkr, vtx_pial)).astype(int)
# surfaces to lines in volume
white_array = np.zeros(vol.header["dim"][1:4])
white_array[vtx_white[:, 0], vtx_white[:, 1], vtx_white[:, 2]] = 1
white = nb.Nifti1Image(white_array, vol.affine, vol.header)
pial_array = np.zeros(vol.header["dim"][1:4])
pial_array[vtx_pial[:, 0], vtx_pial[:, 1], vtx_pial[:, 2]] = 1
pial = nb.Nifti1Image(pial_array, vol.affine, vol.header)
# lines to levelset
white_level = probability_to_levelset(white)
white_level_array = white_level["result"].get_fdata()
pial_level = probability_to_levelset(pial)
pial_level_array = pial_level["result"].get_fdata()
"""
make wm
"""
white_label_array = np.zeros_like(white_level_array)
white_label_array[white_level_array > pial_level_array] = 1
white_label_array[white_label_array != 1] = 0
white_label_array -= 1
white_label_array = np.abs(white_label_array).astype(int)
white_label_array = white_label_array - white_array
white_label_array[white_label_array < 0] = 0
if n_start > 0:
white_label_array[:, :, :n_start] = 0
if n_end > 0:
white_label_array[:, :, -n_end:] = 0
white_label_array = measure.label(white_label_array, connectivity=1)
white_label_array[white_label_array == 1] = 0
white_label_array[white_label_array > 0] = 1
white_label = nb.Nifti1Image(white_label_array, vol.affine, vol.header)
"""
make csf
"""
pial_label_array = np.zeros_like(pial_level_array)
pial_label_array[pial_level_array < white_level_array] = 1
pial_label_array[pial_label_array != 1] = 0
pial_label_array = np.abs(pial_label_array).astype(int)
pial_label_array = pial_label_array - pial_array
pial_label_array[pial_label_array < 0] = 0
if n_start > 0:
pial_label_array[:, :, :n_start] = 0
if n_end > 0:
pial_label_array[:, :, -n_end:] = 0
pial_label_array = measure.label(pial_label_array, connectivity=1)
pial_label_array[pial_label_array != 1] = 0
pial_label_array -= 1
pial_label_array = np.abs(pial_label_array)
pial_label_array[pial_array == 1] = 0
if n_start > 0:
pial_label_array[:, :, :n_start] = 0
if n_end > 0:
pial_label_array[:, :, -n_end:] = 0
pial_label = nb.Nifti1Image(pial_label_array, vol.affine, vol.header)
"""
make gm
"""
ribbon_label_array = pial_label_array.copy()
ribbon_label_array -= 1
ribbon_label_array = np.abs(ribbon_label_array)
ribbon_label_array = ribbon_label_array + white_label_array
ribbon_label_array -= 1
ribbon_label_array = np.abs(ribbon_label_array).astype(int)
if n_start > 0:
ribbon_label_array[:, :, :n_start] = 0
if n_end > 0:
ribbon_label_array[:, :, -n_end:] = 0
ribbon_label = nb.Nifti1Image(ribbon_label_array, vol.affine, vol.header)
"""
layers
"""
#csf_level = probability_to_levelset(pial_label)
#wm_level = probability_to_levelset(white_label)
#volumetric_layering(wm_level["result"],
# csf_level["result"],
# n_layers=n_layers,
# topology_lut_dir=None,
# save_data=True,
# overwrite=True,
# output_dir=path_output,
# file_name="epi")
# write niftis
nb.save(white, os.path.join(path_output, "wm_line.nii"))
nb.save(pial, os.path.join(path_output, "csf_line.nii"))
nb.save(white_label, os.path.join(path_output, "wm_label.nii"))
nb.save(pial_label, os.path.join(path_output, "csf_label.nii"))
nb.save(ribbon_label, os.path.join(path_output, "gm_label.nii"))
def mesh_sampling(surf_in, vol_in, source2target_in, path_output, r=[0.4,0.4,0.4],
interpolation="Cu", cleanup=True):
"""
This function samples data onto a surface mesh. Optionally, the volume can be upsampled and a
coordinate mapping can be applied to transform the surface mesh to the space of the input
volume.
Inputs:
*surf_in: filename of input surface mesh.
*vol_in: filename of input volume from which data is sampled.
*source2target_in: source to target coordinate mapping.
*path_output: path where output is written.
*r: destination voxel size after upsampling (performed if not None).
*interpolation: interpolation method for upsampling of file from which data is sampled.
*cleanup: remove intermediate files.
created by Daniel Haenelt
Date created: 24-06-2020
Last modified: 24-06-2020
"""
import os
import numpy as np
import nibabel as nb
import shutil as sh
from sh import gunzip
from lib.io.get_filename import get_filename
from lib.utils.upsample_volume import upsample_volume
from lib.surface.deform_surface import deform_surface
from lib.mapping import map2surface
# make output folder
if not os.path.exists(path_output):
os.makedirs(path_output)
tmp = np.random.randint(0, 10, 5)
tmp_string = ''.join(str(i) for i in tmp)
path_temp = os.path.join(path_output, "temp_"+tmp_string)
if not os.path.exists(path_temp):
os.makedirs(path_temp)
# get filenames
_, hemi, name_mesh = get_filename(surf_in)
name_mesh = name_mesh[1:]
_, name_vol, _ = get_filename(vol_in)
# set filenames
vol_upsampled = os.path.join(path_temp, "vol_upsampled.nii")
s2t_upsampled = os.path.join(path_temp, "s2t_upsampled.nii")
# upsample volumes and rescale cmap
if r:
upsample_volume(vol_in, vol_upsampled, dxyz=r, rmode=interpolation)
upsample_volume(source2target_in, s2t_upsampled, dxyz=r, rmode="Linear")
# rescale cmap
dim = nb.load(vol_in).header["dim"][1:4] - 1
dim_upsampled = nb.load(vol_upsampled).header["dim"][1:4] - 1
cmap_s2t = nb.load(s2t_upsampled)
cmap_s2t_array = cmap_s2t.get_fdata()
for i in range(3):
cmap_s2t_array[:,:,:,i] = cmap_s2t_array[:,:,:,i] / dim[i] * dim_upsampled[i]
cmap_s2t_upsampled = nb.Nifti1Image(cmap_s2t_array, cmap_s2t.affine, cmap_s2t.header)
nb.save(cmap_s2t_upsampled, s2t_upsampled)
else:
_, _, ext = get_filename(vol_in)
sh.copy(vol_in, os.path.join(path_temp, "vol_upsampled"+ext))
if ext[-3:] == ".gz":
gunzip(os.path.join(path_temp, "vol_upsampled"+ext))
_, _, ext = get_filename(source2target_in)
sh.copy(source2target_in, os.path.join(path_temp, "s2t_upsampled"+ext))
if ext[-3:] == ".gz":
gunzip(os.path.join(path_temp, "s2t_upsampled"+ext))
# deform mesh
deform_surface(input_surf=surf_in,
input_orig=s2t_upsampled,
input_deform=s2t_upsampled,
input_target=vol_upsampled,
hemi=hemi,
path_output=path_temp,
input_mask=None,
interp_method="trilinear",
smooth_iter=0,
flip_faces=False,
cleanup=True)
# do mapping
map2surface(input_surf=os.path.join(path_temp, hemi+"."+name_mesh+"_def"),
input_vol=vol_upsampled,
hemi=hemi,
path_output=path_temp,
input_white=None,
input_ind=None,
cleanup=True)
# rename output surface
os.rename(os.path.join(path_temp, hemi+".vol_upsampled_"+name_mesh+"_def_def.mgh"),
os.path.join(path_output, hemi+"."+name_vol+"_"+name_mesh+".mgh"))
# delete intermediate files
if cleanup:
sh.rmtree(path_temp, ignore_errors=True)
