def create_pial_mask(subject_id, subjects_dir, verbose=False):
ribbon = os.path.abspath(os.path.join(subjects_dir, subject_id, 'mri','ribbon'))
aseg = os.path.abspath(os.path.join(subjects_dir, subject_id, 'mri','aseg'))
mc = MRIConvert()
mc.inputs.in_file = ribbon + '.mgz'
mc.inputs.out_file = ribbon + '.nii.gz'
mc.inputs.out_type = 'nii'
mc.cmdline
mc.run()
mc = MRIConvert()
mc.inputs.in_file = aseg + '.mgz'
mc.inputs.out_file = aseg + '.nii.gz'
mc.inputs.out_type = 'nii'
mc.cmdline
mc.run()
#tic_labels_remove aseg.nii.gz --out_nii 1.mask.nii.gz --remove 3 42
#fslmaths 1.mask.nii.gz -bin 1.mask.nii.gz
#fslmaths 1.mask.nii.gz -add ribbon.nii.gz -bin 2.mask.nii.gz
#fslmaths 2.mask.nii.gz -kernel sphere 10 -dilM -ero -fillh 3.mask.nii.gz
return
def mgz2nii(file):
from nipype.interfaces.freesurfer import MRIConvert
mc = MRIConvert()
mc.inputs.in_file = file
mc.inputs.out_file = file[:-3] + 'nii'
mc.inputs.out_type = 'nii'
mc.run()
def convert_modalities(in_file=None, out_file=None):
"""Returns an undefined output if the in_file is not defined"""
from nipype.interfaces.freesurfer import MRIConvert
import os
if in_file:
convert = MRIConvert()
convert.inputs.in_file = in_file
convert.inputs.out_file = out_file
convert.inputs.no_scale = True
convert.run()
out_file = os.path.abspath(convert.outputs.out_file)
return out_file
def convert_modalities(in_file=None, out_file=None):
"""Returns an undefined output if the in_file is not defined"""
from nipype.interfaces.freesurfer import MRIConvert
import os
if in_file:
convert = MRIConvert()
convert.inputs.in_file = in_file
convert.inputs.out_file = out_file
convert.inputs.no_scale = True
convert.run()
out_file = os.path.abspath(convert.outputs.out_file)
return out_file
def convert_fs_scan(in_file, out_file, resample_type=None):
if not os.path.isfile(out_file):
from nipype.interfaces.freesurfer import MRIConvert
convert = MRIConvert()
convert.inputs.in_file = in_file
convert.inputs.out_file = out_file
convert.inputs.out_orientation = "LPS"
convert.inputs.conform = True
convert.inputs.no_change = True
if resample_type:
convert.inputs.resample_type = resample_type
convert.run()
return os.path.abspath(out_file)
def checkT1s(T1_files, cw256=False):
"""Verifying size of inputs and setting workflow parameters"""
import SimpleITK as sitk
import os
import sys
# check that the files are in a list
if not type(T1_files) == list:
T1_files = [T1_files]
if len(T1_files) == 0:
print("ERROR: No T1's Given")
sys.exit(-1)
for i, t1 in enumerate(T1_files):
if t1.endswith(".mgz"):
# convert input fs files to NIFTI
convert = MRIConvert()
convert.inputs.in_file = t1
convert.inputs.out_file = os.path.abspath(
os.path.basename(t1).replace('.mgz', '.nii.gz'))
convert.run()
T1_files[i] = convert.inputs.out_file
size = None
origvol_names = list()
for i, t1 in enumerate(T1_files):
# assign an input number
file_num = str(i + 1)
while len(file_num) < 3:
file_num = '0' + file_num
origvol_names.append("{0}.mgz".format(file_num))
# check the size of the image
img = sitk.ReadImage(t1)
if not size:
size = img.GetSize()
elif size != img.GetSize():
print(
"ERROR: T1s not the same size. Cannot process {0} {1} together"
.format(T1_files[0], otherFilename))
sys.exit(-1)
# check if cw256 is set to crop the images if size is larger than 256
if not cw256:
for dim in size:
if dim > 256:
print("Setting MRI Convert to crop images to 256 FOV")
cw256 = True
if len(T1_files) > 1:
resample_type = 'cubic'
else:
resample_type = 'interpolate'
return T1_files, cw256, resample_type, origvol_names
def change_orientation(image_file, out_file, orientation="LPS"):
convert = MRIConvert()
convert.inputs.in_file = image_file
convert.inputs.out_file = os.path.abspath(out_file)
convert.inputs.out_orientation = orientation
result = convert.run()
return result.outputs.out_file
def generate_ROI_file(FreeSurfer_ROI_file):
"""
This script generates a dictionary of ROIs found in the FreeSurfer parcellation filename
"""
from nipype.interfaces.freesurfer import MRIConvert
mc = MRIConvert()
mc.inputs.in_file = FreeSurfer_ROI_file
mc.inputs.out_type = 'niigz'
mc.run()
import nipype.interfaces.cmtk as cmtk
rg = cmtk.ROIGen()
rg.inputs.aparc_aseg_file = FreeSurfer_ROI_file.split('.')[0] + '_out.nii.gz'
rg.inputs.use_freesurfer_LUT = True
out_file = rg.run()
return out_file
def generate_ROI_file(FreeSurfer_ROI_file):
"""
This script generates a dictionary of ROIs found in the FreeSurfer parcellation filename
"""
from nipype.interfaces.freesurfer import MRIConvert
mc = MRIConvert()
mc.inputs.in_file = FreeSurfer_ROI_file
mc.inputs.out_type = 'niigz'
mc.run()
import nipype.interfaces.cmtk as cmtk
rg = cmtk.ROIGen()
rg.inputs.aparc_aseg_file = FreeSurfer_ROI_file.split('.')[0] + '_out.nii.gz'
rg.inputs.use_freesurfer_LUT = True
out_file = rg.run()
return out_file
def checkT1s(T1_files, cw256=False):
"""Verifying size of inputs and setting workflow parameters"""
import SimpleITK as sitk
import os
import sys
# check that the files are in a list
if not type(T1_files) == list:
T1_files = [T1_files]
if len(T1_files) == 0:
print("ERROR: No T1's Given")
sys.exit(-1)
for i, t1 in enumerate(T1_files):
if t1.endswith(".mgz"):
# convert input fs files to NIFTI
convert = MRIConvert()
convert.inputs.in_file = t1
convert.inputs.out_file = os.path.abspath(os.path.basename(t1).replace('.mgz', '.nii.gz'))
convert.run()
T1_files[i] = convert.inputs.out_file
size = None
origvol_names = list()
for i, t1 in enumerate(T1_files):
# assign an input number
file_num = str(i + 1)
while len(file_num) < 3:
file_num = '0' + file_num
origvol_names.append("{0}.mgz".format(file_num))
# check the size of the image
img = sitk.ReadImage(t1)
if not size:
size = img.GetSize()
elif size != img.GetSize():
print("ERROR: T1s not the same size. Cannot process {0} {1} together".format(T1_files[0],
otherFilename))
sys.exit(-1)
# check if cw256 is set to crop the images if size is larger than 256
if not cw256:
for dim in size:
if dim > 256:
print("Setting MRI Convert to crop images to 256 FOV")
cw256 = True
if len(T1_files) > 1:
resample_type = 'cubic'
else:
resample_type = 'interpolate'
return T1_files, cw256, resample_type, origvol_names
def convert_fs_scan(in_file, out_file, resample_type=None):
"""
This function...
:param in_file:
:param out_file:
:param resample_type:
:return:
"""
if not os.path.isfile(out_file):
from nipype.interfaces.freesurfer import MRIConvert
convert = MRIConvert()
convert.inputs.in_file = in_file
convert.inputs.out_file = out_file
convert.inputs.out_orientation = "LPS"
convert.inputs.conform = True
convert.inputs.no_change = True
if resample_type:
convert.inputs.resample_type = resample_type
convert.run()
return os.path.abspath(out_file)
def change_orientation(image_file, out_file, orientation="LPS"):
"""
This function...
:param image_file:
:param out_file:
:param orientation:
:return: result.outputs.out_file
"""
convert = MRIConvert()
convert.inputs.in_file = image_file
convert.inputs.out_file = os.path.abspath(out_file)
convert.inputs.out_orientation = orientation
result = convert.run()
return result.outputs.out_file
def change_orientation(image_file, out_file, orientation="LPS"):
"""
This function...
:param image_file:
:param out_file:
:param orientation:
:return:
"""
convert = MRIConvert()
convert.inputs.in_file = image_file
convert.inputs.out_file = os.path.abspath(out_file)
convert.inputs.out_orientation = orientation
result = convert.run()
return result.outputs.out_file
def create_pial_mask(subject_id,
subjects_dir,
output_filename,
output_directory,
reference=None,
mo_radius=7,
verbose=False,
save=False):
mri_directory = os.path.abspath(
os.path.join(subjects_dir, subject_id, 'mri'))
if False:
# I attempted to use nipype to call the mri_surf2vol. This did not work so I had to use the subprocess method.
surf2vol = Surface2VolTransform()
# surf2vol.inputs.reg_file = 'register.mat'
surf2vol.inputs.template_file = os.path.abspath(
os.path.join(mri_directory, 'brain.mgz'))
surf2vol.inputs.subjects_dir = subjects_dir
surf2vol.inputs.subject_id = subject_id
surf2vol.mkmask = True
surf2vol.projfrac = 0.05
surf2vol.surf_name = 'pial'
surf2vol.args = '--fillribbon --fill-projfrac -2 0 0.05'
for ii in ['lh', 'rh']:
surf2vol.inputs.hemi = 'lh'
surf2vol.transformed_file = os.path.abspath(
os.path.join(output_directory,
'_cpm_mask_surf2vol.pial_' + ii + '.nii.gz'))
if verbose:
surf2vol.cmdline
else:
for ii in ['lh', 'rh']:
ii_output = os.path.abspath(
os.path.join(output_directory,
'_cpm_mask_surf2vol.pial_' + ii + '.nii.gz'))
command = [
'mri_surf2vol', '--hemi', ii, '--surf', 'pial', '--o',
ii_output, '--identity', subject_id, '--template',
os.path.abspath(
os.path.join(subjects_dir, subject_id, 'mri',
'brain.mgz')), '--mkmask', '--fillribbon',
'--fill-projfrac', '-2', '0', '0.05'
]
if not os.path.isfile(ii_output):
util.iw_subprocess(command, verbose_flag=verbose)
if not os.path.isfile(ii_output):
print(ii_output + ' does not exist. Exiting!')
exit()
# Convert aseg.nii.gz
aseg_nii_gz = os.path.abspath(
os.path.join(output_directory, '_cpm_aseg.nii.gz'))
if not os.path.isfile(aseg_nii_gz):
mri_convert = MRIConvert(in_file=os.path.abspath(
os.path.join(mri_directory, 'aseg.mgz')),
out_file=aseg_nii_gz,
out_type='niigz')
mri_convert.run()
# Remove GM Cortex from aseg.nii.gz
mask_aseg_without_gm_cortex = os.path.join(
output_directory, '_cpm_mask.aseg_without_gm_cortex.nii.gz')
if not os.path.isfile(mask_aseg_without_gm_cortex):
labels.remove(aseg_nii_gz, [3, 42], [],
mask_aseg_without_gm_cortex,
merge=1)
# 1) Combine masks into a single mask
# 2) Threshold and invert mask
# 3) Grab largest component
# 4) Threshold image to remove negative numbers
# 5) ImageMath Opening operation with radius 3
# 6) Threshold image to remove negative numbers
# 7) ImageMath Closing operation with radius 3
# 8) Threshold and invert mask.
if not os.path.isfile(os.path.join(output_directory,
'_cpm_01.mask.nii.gz')):
os.chdir(output_directory)
commands = [[
'fslmaths',
os.path.join(output_directory,
'_cpm_mask.aseg_without_gm_cortex.nii.gz'), '-add',
os.path.abspath(
os.path.join(output_directory,
'_cpm_mask_surf2vol.pial_lh.nii.gz')), '-add',
os.path.abspath(
os.path.join(output_directory,
'_cpm_mask_surf2vol.pial_rh.nii.gz')), '-bin',
'-fillh26',
os.path.abspath(
os.path.join(output_directory, '_cpm_01.mask.nii.gz'))
],
[
'fslmaths',
os.path.join(output_directory, '_cpm_01.mask.nii.gz'),
'-thr', 0, '-binv',
os.path.join(output_directory, '_cpm_02.mask.nii.gz')
],
[
'ImageMath', 3, '_cpm_03.mask.nii.gz',
'GetLargestComponent', '_cpm_02.mask.nii.gz'
],
[
'ImageMath', 3, '_cpm_04.mask.nii.gz', 'MO',
'_cpm_03.mask.nii.gz', 7
],
[
'fslmaths', '_cpm_04.mask.nii.gz', '-thr', 0, '-bin',
'_cpm_05.mask.nii.gz'
],
[
'ImageMath', 3, '_cpm_06.mask.nii.gz', 'MC',
'_cpm_05.mask.nii.gz', 7
],
[
'fslmaths', '_cpm_06.mask.nii.gz', '-thr', 0, '-binv',
'_cpm_07.mask.nii.gz'
]]
for ii in commands:
util.iw_subprocess(ii, verbose_flag=verbose)
if reference is not None:
command = [
'antsApplyTransforms', '-i',
os.path.join(output_directory, '_cpm_07.mask.nii.gz'), '-r',
reference, '-n', 'Multilabel', '-o',
os.path.join(output_directory, output_filename), '-t', 'identity'
]
util.iw_subprocess(command, verbose_flag=verbose)
else:
shutil.copy2(os.path.join(output_directory, '_cpm_07.mask.nii.gz'),
os.path.join(output_directory, out_filename))
if not save:
remove_files = [
os.path.join(output_directory, '_cpm_01.mask.nii.gz'),
os.path.join(output_directory, '_cpm_02.mask.nii.gz'),
os.path.join(output_directory, '_cpm_03.mask.nii.gz'),
os.path.join(output_directory, '_cpm_04.mask.nii.gz'),
os.path.join(output_directory, '_cpm_05.mask.nii.gz'),
os.path.join(output_directory, '_cpm_06.mask.nii.gz'),
os.path.join(output_directory, '_cpm_07.mask.nii.gz'),
os.path.join(output_directory, '_cpm_aseg.nii.gz'),
os.path.join(output_directory,
'_cpm_mask.aseg_without_gm_cortex.nii.gz'),
os.path.abspath(
os.path.join(output_directory,
'_cpm_mask_surf2vol.pial_lh.nii.gz')),
os.path.abspath(
os.path.join(output_directory,
'_cpm_mask_surf2vol.pial_rh.nii.gz'))
]
clean(remove_files)
return
def __init__(self,coords, img_file, subject=None, coord_type='ras', working_dir=None, **traits):
""" General class to work with coordinates
Parameters
-----------
coords: numpy array
x,y,z coordinates matrix (npoints x 3)
img_file: str
path to image file
subject: str
subject name
coord_type: str, {'ras', 'voxel'}
coordinate system
working_dir:
the path to working directory
**traits: other traits of the coordinates;
the traits size should be equal to the number of npoints
for instance, one can add "name" or "color" as extra traits for each coordinate
Returns
--------
An object of Coords class
"""
coords = np.atleast_2d(coords)
self.img_file = img_file
self.subject=subject
self.coord_type = coord_type
self.img = nib.load(img_file)
self.working_dir = working_dir
self.vox2ras = self.img.affine
self.ras2vox = np.linalg.inv(self.vox2ras)
self.npoints = coords.shape[0]
self.coordinates = {}
affineM= self._to_affine_matrix(coords)
self._count=0
if coord_type=='ras':
self.coordinates['ras_coord'] = coords
self.coordinates['voxel_coord'] = np.round(np.dot(self.ras2vox,affineM).T[:,:3])
elif coord_type=='voxel':
self.coordinates['voxel_coord'] = coords
self.coordinates['ras_coord'] = np.dot(self.vox2ras,affineM).T[:,:3]
else:
raise ValueError('type should be either "ras" or "voxel"')
### to freesurfer coords
rnum1 = np.random.randint(10**15,10**16)
rnum2 = np.random.randint(10**10,10**11)
rnum = '{rnum1}_{rnum2}'.format(rnum1=rnum1,rnum2=rnum2)
os.makedirs(os.path.join(os.path.abspath('.'),'temp_{rnum}'.format(rnum=rnum)))
wf_dir = os.path.join(os.path.abspath('.'),'temp_{rnum}'.format(rnum=rnum))
## creating rawavg.mgz
mc = MRIConvert()
mc.inputs.in_file = img_file
mc.inputs.out_file = os.path.join(wf_dir,'rawavg.mgz')
mc.inputs.out_type = 'mgz'
mc.run()
## creating orig.mgz
mc = MRIConvert()
mc.inputs.in_file = os.path.join(wf_dir,'rawavg.mgz')
mc.inputs.out_file = os.path.join(wf_dir,'orig.mgz')
mc.inputs.out_type = 'mgz'
mc.inputs.conform = True
mc.run()
rawavg_file = os.path.join(wf_dir,'rawavg.mgz')
orig_file = os.path.join(wf_dir,'orig.mgz')
### loading
orig_img = nib.freesurfer.load(orig_file)
self.ras2fsvox = orig_img.header.get_ras2vox()
self.fsvox2ras_tkr = orig_img.header.get_vox2ras_tkr()
self.ras2ras_tkr = np.dot(self.fsvox2ras_tkr,self.ras2fsvox)
ras_affineM = self._to_affine_matrix(self.coordinates['ras_coord'])
self.coordinates['ras_tkr_coord'] = np.dot(self.fsvox2ras_tkr,np.dot(self.ras2fsvox, ras_affineM)).T[:,:3]
self.coordinates['fsvoxel_coord'] = np.round(np.dot(self.ras2fsvox,ras_affineM).T[:,:3])
shutil.rmtree(wf_dir)
##3 adding traits
self.traits_list = []
for trait in traits:
self.add_trait(trait,traits[trait])
def calculate_equivolumetric_layer(path, sub, n_layers, path_output):
"""
This script computes the volumetric layering from the FreeSurfer ribbon mask.
Inputs:
*path: path to the freesurfer segmentation folder.
*sub: name of the freesurfer segmentation folder.
*n_layers: number of layers.
*path_output: path where output is saved.
created by Daniel Haenelt
Date created: 20-11-2018
Last modified: 02-09-2018
"""
import os
import nibabel as nb
from nipype.interfaces.freesurfer import MRIConvert
from nighres.surface import probability_to_levelset
from nighres.laminar import volumetric_layering
# make output folder
if not os.path.exists(path_output):
os.mkdir(path_output)
if not os.path.exists(os.path.join(path_output,"binary")):
os.mkdir(os.path.join(path_output,"binary"))
if not os.path.exists(os.path.join(path_output,"levelset")):
os.mkdir(os.path.join(path_output,"levelset"))
# convert ribbon to nifti
ribbon = MRIConvert()
ribbon.inputs.in_file = os.path.join(path,sub,"mri","ribbon.mgz")
ribbon.inputs.out_file = os.path.join(path,sub,"mri","ribbon.nii.gz")
ribbon.inputs.out_type = "niigz"
ribbon.run()
# get GM/WM and GM/CSF boundaries
ribbon_img = nb.load(os.path.join(path,sub,"mri","ribbon.nii.gz"))
ribbon_array = ribbon_img.get_fdata()
# layering using nighres
hemi = ["lh", "rh"]
wm = [2, 41]
gm = [3, 42]
for i in range(len(hemi)):
wm_array = ribbon_array.copy()
wm_array[wm_array == wm[i]] = 1
wm_array[wm_array != 1] = 0
csf_array = ribbon_array.copy()
csf_array[csf_array == gm[i]] = 1
csf_array[csf_array == wm[i]] = 1
csf_array[csf_array != 1] = 0
output = nb.Nifti1Image(wm_array, ribbon_img.affine, ribbon_img.header)
nb.save(output,os.path.join(path_output,"binary",hemi[i]+"_wm.nii"))
output = nb.Nifti1Image(csf_array, ribbon_img.affine, ribbon_img.header)
nb.save(output,os.path.join(path_output,"binary",hemi[i]+"_csf.nii"))
# probability to levelset
probability_to_levelset(os.path.join(path_output,"binary",hemi[i]+"_wm.nii"),
save_data=True,
output_dir=os.path.join(path_output,"levelset"),
file_name=hemi[i]+"_wm")
# probability to levelset
probability_to_levelset(os.path.join(path_output,"binary",hemi[i]+"_csf.nii"),
save_data=True,
output_dir=os.path.join(path_output,"levelset"),
file_name=hemi[i]+"_csf")
# layering
suffix = "_p2l-surf"
volumetric_layering(os.path.join(path_output,"levelset",hemi[i]+"_wm"+suffix+".nii.gz"),
os.path.join(path_output,"levelset",hemi[i]+"_csf"+suffix+".nii.gz"),
n_layers=n_layers,
topology_lut_dir=None,
save_data=True,
output_dir=path_output,
file_name=hemi[i])
from nipype.interfaces.freesurfer import MRIConvert
import os
import shutil
import csv
import sys
import csv
from os import listdir
from os.path import isfile, join
############# MRI CONVERT ##############
# path of subjects in .mgz format
sub_path = '/media/Ali/8A9E6F039E6EE6E3/freesurfer/subjects'
# path of output converted files to .nii format
output_dir = '/media/Ali/8A9E6F039E6EE6E3/MRI/MRI-Converted'
dirs = [f for f in listdir(sub_path) if f.startswith('sub') == True]
mc = MRIConvert()
for sub in dirs:
mgz_path = os.path.join(sub_path, sub, 'mri/brain.mgz')
nii_path = os.path.join(output_dir, '{}-brain.nii'.format(sub))
mc.inputs.in_file = mgz_path
mc.inputs.out_file = nii_path
mc.inputs.out_type = 'nii'
mc.run()