def cortex(t1_file, fsdir, outdir, dest_file=None, prefix="cortex",
generate_mask=True, generate_seeds=True):
""" Compute a white matter mask and gyri labelization from the FreeSurfer
'white' surface.
Parameters
----------
t1_file: str (mandatory)
a file containing the t1 image used in FreeSurfer for the segmentation.
fsdir: str( mandatory)
the subject freesurfer segmentation directory.
outdir: str (mandatory)
the output directory.
dest_file: str (optional, default None)
a file containing an image where we want to project the segmentations:
an affine transform is used to align this image to the t1 image.
prefix: str (optional, default 'cortex')
the output files prefix.
generate_mask: bool (optional, default True)
if True generate a white matter binary mask.
generate_seeds: boll (optional, default False)
if True create a 'seeds' directory containing all the gyri mask as
idenpendent files.
Returns
-------
mask_file: str
the white matter mask image file.
label_file: str
the gyri label image file.
seeds: list of str
a list with the seed volumes.
"""
# Create the output directory if necessary
if not os.path.isdir(outdir):
os.makedirs(outdir)
# Load the dataset
t1_image = nibabel.load(t1_file)
t1_affine = t1_image.get_affine()
# If a destination file is specified register it to the t1
if dest_file is not None:
# Load dataset
dest_image = nibabel.load(dest_file)
dest_affine = dest_image.get_affine()
dest_shape = dest_image.get_shape()
# In case of temporal serie extract the first volume
if len(dest_shape) > 3:
temporal_dest_file = dest_file
dest_file = os.path.join(outdir, prefix + "_volume-0.nii.gz")
extract_image(temporal_dest_file, index=0, out_file=dest_file)
dest_shape = dest_shape[:3]
# Register destination image to t1 image
trf_file = os.path.join(outdir, prefix + "_dest_to_t1.trf")
reg_file = os.path.join(outdir, prefix + "_dest_to_t1.nii.gz")
flirt(dest_file, t1_file, omat=trf_file, out=reg_file, usesqform=False,
cost="normmi", dof=6)
voxel_dest_to_t1 = flirt2aff(trf_file, dest_file, t1_file)
voxel_t1_to_dest = numpy.linalg.inv(voxel_dest_to_t1)
# Otherwise use identity transformation
else:
trf_file = None
reg_file = None
dest_affine = t1_affine
dest_shape = t1_image.get_shape()
voxel_t1_to_dest = numpy.identity(4)
# Load the FreeSurfer surface in the 'dest_file' voxel coordinates or
# 't1_file' coordinates if not specified
t1_physical_to_voxel = numpy.linalg.inv(t1_affine)
seg = read_cortex_surface_segmentation(fsdir, t1_physical_to_voxel,
voxel_t1_to_dest)
# Create a mask of the white matter of both hemisphere
if generate_mask:
mask_array = seg["lh"].voxelize(dest_shape)
mask_array += seg["rh"].voxelize(dest_shape)
# Create a gyri label image of both hemisphere
label_array = {}
try:
label_array["lh"], shift_lh = seg["lh"].labelize(dest_shape)
label_array["rh"], shift_rh = seg["rh"].labelize(dest_shape, shift_lh)
except:
if reg_file is not None:
raise FSLResultError("flirt")
raise
# Create the seeds
seeds = []
if generate_seeds:
seedsdir = os.path.join(outdir, "gyri")
if not os.path.isdir(seedsdir):
os.mkdir(seedsdir)
for hemi in ["lh", "rh"]:
surf = seg[hemi]
hemi_label_array = label_array[hemi]
seed_array = numpy.zeros(hemi_label_array.shape,
dtype=hemi_label_array.dtype)
for index, item in surf.metadata.items():
if index != 0:
if hemi == "rh":
index += shift_lh
seed_array[numpy.where(hemi_label_array == index)] = 1
seed_file = os.path.join(
seedsdir,
"{0}-{1}.nii.gz".format(hemi, item["region"]))
seed_image = nibabel.Nifti1Image(seed_array, dest_affine)
nibabel.save(seed_image, seed_file)
seed_array[...] = 0
seeds.append(seed_file)
# Save the mask and label images
mask_file = None
if generate_mask:
mask_file = os.path.join(outdir, prefix + "_mask.nii.gz")
mask_image = nibabel.Nifti1Image(mask_array, dest_affine)
nibabel.save(mask_image, mask_file)
label_array = label_array["lh"] + label_array["rh"]
label_file = os.path.join(outdir, prefix + "_gyri_labels.nii.gz")
label_image = nibabel.Nifti1Image(label_array, dest_affine)
nibabel.save(label_image, label_file)
return mask_file, label_file, seeds, reg_file, trf_file
sname = label_to_shortname(label, vois)
if sname not in HABITAT:
continue
if tag == 'both':
cumul += img3d.get_data()
elif tag == sname: # should be edema or enhancement
cumul = img3d.get_data()
# resample lesion images in MNI 1mm to form a dict indexed by subject
tmp_lesion_name = os.path.basename(lesion)
res_lesion_name = 'res_{}'.format(tmp_lesion_name)
cumul[cumul != 0] = 1
ni.save(ni.Nifti1Image(cumul, affine=vois.affine), tmp_lesion_name)
flirt(in_file=tmp_lesion_name,
ref_file=aal_name,
applyxfm=True,
init=path_dict[s]['xfm'][0],
out=res_lesion_name,
interp='nearestneighbour')
resamp_lesion = ni.load(res_lesion_name).get_data()
# crawl the AAL labels to count lesions occ to form a dict indexed by l
for l in aal_label: # acummulate for the cases with multiples lesions
res_dict[s][l] += np.sum(resamp_lesion[aal_dict[l]])
# save as a json for manipulation in R
with open("stat_localisation.json", 'w') as fpout:
json.dump(res_dict, fpout, indent=4)
fpout.write('\n')
# move selected files
flist = ["stat_localisation.json"]
for f in flist:
def get_profile(ico_order, nodif_file, nodifmask_file, seed_file,
bedpostx_samples, outdir, t1_file, trf_file, dat_file, fsdir,
sid, fsconfig):
""" Probabilistic profile
Computes the tractography using FSL probtrackx2 and projects the result
on the cortical surface using FS mri_vol2surf.
Parameters
----------
ico_order: int (mandatory)
icosahedron order in [0, 7] that will be used to generate the cortical
surface texture at a specific tessalation (the corresponding cortical
surface can be resampled using the
'clindmri.segmentation.freesurfer.resample_cortical_surface' function).
nodif_file: str (mandatory)
file for probtrackx2 containing the no diffusion volume and associated
space information.
nodifmask_file: str (mandatory)
file for probtrackx2 containing the tractography mask (ie., a mask of
the white matter).
seed_file: str (mandatory)
text file for probtrackx2 containing seed coordinates.
bedpostx_samples: str (mandatory)
path prefix for bedpostX model samples files injected in probtrackx2
(eg., fsl.bedpostX/merged).
outdir: str (mandatory)
the output directory.
t1_file : str (mandatory)
T1 image file used to align the produced probabilitic tractography map
to the T1 space.
trf_file : str (mandatory)
diffusion to t1 space affine transformation matrix file.
dat_file: str (mandatory)
structural to FreeSurfer space affine transformation matrix '.dat'
file as computed by 'tkregister2'.
fsdir: str (mandatory)
FreeSurfer subjects directory 'SUBJECTS_DIR'.
sid: str (mandatory)
FreeSurfer subject identifier.
fsconfig: str (mandatory)
the FreeSurfer '.sh' config file.
Returns
-------
proba_file: str
the seed probabilistic tractography volume.
textures: dict
a dictionary containing the probabilist texture for each hemisphere.
"""
# Generates the diffusion probability map
proba_files, _ = probtrackx2(simple=True,
seedref=nodif_file,
out="fdt_paths",
seed=seed_file,
loopcheck=True,
onewaycondition=True,
samples=bedpostx_samples,
mask=nodifmask_file,
dir=outdir)
# Check that only one 'fdt_paths' has been generated
if len(proba_files) != 1:
raise Exception("One probabilistic tractography file expected at this "
"point: {0}".format(proba_files))
proba_file = proba_files[0]
proba_fname = os.path.basename(proba_file).replace(".nii.gz", "")
# Apply 'trf_file' affine transformation matrix using FSL flirt function:
# probability map (diffusion space) -> probability map (T1 space).
flirt_t1_file = os.path.join(outdir, proba_fname + "_t1_flirt.nii.gz")
flirt(proba_file, t1_file, out=flirt_t1_file, applyxfm=True, init=trf_file)
# Project the volumic probability map (T1 space) generated with FSL flirt
# on the cortical surface (Freesurfer space) (both hemispheres) using
# Freesurfer's mri_vol2surf and applying the 'dat_file' transformation.
textures = {}
for hemi in ["lh", "rh"]:
prob_texture_file = os.path.join(
outdir, "{0}.{1}_vol2surf.mgz".format(hemi, proba_fname))
mri_vol2surf(hemi,
flirt_t1_file,
prob_texture_file,
ico_order,
dat_file,
fsdir,
sid,
surface_name="white",
fsconfig=fsconfig)
textures[hemi] = prob_texture_file
return proba_file, textures
infile = args.infile
if not os.path.isdir(os.path.dirname(outfile)):
os.makedirs(os.path.dirname(outfile))
omatfile = os.path.splitext(outfile)[0]
if os.path.splitext(omatfile)[0] != '':
omatfile = '{}.txt'.format(os.path.splitext(outfile)[0])
else:
omatfile = '{}.txt'.format(omatfile)
outfileAxi = '{}/qc_axi.pdf'.format(os.path.dirname(outfile))
outfileSag = '{}/qc_sag.pdf'.format(os.path.dirname(outfile))
# Register : call to the wrapping function
flirt(in_file=infile,
ref_file=anat,
omat=omatfile,
out=outfile,
cost='mutualinfo',
interp='sinc',
datatype='float')
# QC : pdf sheet
bg = nibabel.load(outfile)
anat = nibabel.load(anat)
# image axial
display = plotting.plot_anat(bg,
title="T1 Gado contours",
display_mode='z',
cut_coords=10)
display.add_edges(anat)
display.savefig(outfileAxi)
display.close()
def cortex(t1_file,
fsdir,
outdir,
dest_file=None,
prefix="cortex",
generate_mask=True,
generate_seeds=True):
""" Compute a white matter mask and gyri labelization from the FreeSurfer
'white' surface.
Parameters
----------
t1_file: str (mandatory)
a file containing the t1 image used in FreeSurfer for the segmentation.
fsdir: str( mandatory)
the subject freesurfer segmentation directory.
outdir: str (mandatory)
the output directory.
dest_file: str (optional, default None)
a file containing an image where we want to project the segmentations:
an affine transform is used to align this image to the t1 image.
prefix: str (optional, default 'cortex')
the output files prefix.
generate_mask: bool (optional, default True)
if True generate a white matter binary mask.
generate_seeds: boll (optional, default False)
if True create a 'seeds' directory containing all the gyri mask as
idenpendent files.
Returns
-------
mask_file: str
the white matter mask image file.
label_file: str
the gyri label image file.
seeds: list of str
a list with the seed volumes.
"""
# Create the output directory if necessary
if not os.path.isdir(outdir):
os.makedirs(outdir)
# Load the dataset
t1_image = nibabel.load(t1_file)
t1_affine = t1_image.get_affine()
# If a destination file is specified register it to the t1
if dest_file is not None:
# Load dataset
dest_image = nibabel.load(dest_file)
dest_affine = dest_image.get_affine()
dest_shape = dest_image.get_shape()
# In case of temporal serie extract the first volume
if len(dest_shape) > 3:
temporal_dest_file = dest_file
dest_file = os.path.join(outdir, prefix + "_volume-0.nii.gz")
extract_image(temporal_dest_file, index=0, out_file=dest_file)
dest_shape = dest_shape[:3]
# Register destination image to t1 image
trf_file = os.path.join(outdir, prefix + "_dest_to_t1.trf")
reg_file = os.path.join(outdir, prefix + "_dest_to_t1.nii.gz")
flirt(dest_file,
t1_file,
omat=trf_file,
out=reg_file,
usesqform=False,
cost="normmi",
dof=6)
voxel_dest_to_t1 = flirt2aff(trf_file, dest_file, t1_file)
voxel_t1_to_dest = numpy.linalg.inv(voxel_dest_to_t1)
# Otherwise use identity transformation
else:
trf_file = None
reg_file = None
dest_affine = t1_affine
dest_shape = t1_image.get_shape()
voxel_t1_to_dest = numpy.identity(4)
# Load the FreeSurfer surface in the 'dest_file' voxel coordinates or
# 't1_file' coordinates if not specified
t1_physical_to_voxel = numpy.linalg.inv(t1_affine)
seg = read_cortex_surface_segmentation(fsdir, t1_physical_to_voxel,
voxel_t1_to_dest)
# Create a mask of the white matter of both hemisphere
if generate_mask:
mask_array = seg["lh"].voxelize(dest_shape)
mask_array += seg["rh"].voxelize(dest_shape)
# Create a gyri label image of both hemisphere
label_array = {}
try:
label_array["lh"], shift_lh = seg["lh"].labelize(dest_shape)
label_array["rh"], shift_rh = seg["rh"].labelize(dest_shape, shift_lh)
except:
if reg_file is not None:
raise FSLResultError("flirt")
raise
# Create the seeds
seeds = []
if generate_seeds:
seedsdir = os.path.join(outdir, "gyri")
if not os.path.isdir(seedsdir):
os.mkdir(seedsdir)
for hemi in ["lh", "rh"]:
surf = seg[hemi]
hemi_label_array = label_array[hemi]
seed_array = numpy.zeros(hemi_label_array.shape,
dtype=hemi_label_array.dtype)
for index, item in surf.metadata.items():
if index != 0:
if hemi == "rh":
index += shift_lh
seed_array[numpy.where(hemi_label_array == index)] = 1
seed_file = os.path.join(
seedsdir,
"{0}-{1}.nii.gz".format(hemi, item["region"]))
seed_image = nibabel.Nifti1Image(seed_array, dest_affine)
nibabel.save(seed_image, seed_file)
seed_array[...] = 0
seeds.append(seed_file)
# Save the mask and label images
mask_file = None
if generate_mask:
mask_file = os.path.join(outdir, prefix + "_mask.nii.gz")
mask_image = nibabel.Nifti1Image(mask_array, dest_affine)
nibabel.save(mask_image, mask_file)
label_array = label_array["lh"] + label_array["rh"]
label_file = os.path.join(outdir, prefix + "_gyri_labels.nii.gz")
label_image = nibabel.Nifti1Image(label_array, dest_affine)
nibabel.save(label_image, label_file)
return mask_file, label_file, seeds, reg_file, trf_file
def get_profile(ico_order, nodif_file, nodifmask_file, seed_file,
bedpostx_samples, outdir, t1_file, trf_file, dat_file, fsdir,
sid, fsconfig):
""" Probabilistic profile
Computes the tractography using FSL probtrackx2 and projects the result
on the cortical surface using FS mri_vol2surf.
Parameters
----------
ico_order: int (mandatory)
icosahedron order in [0, 7] that will be used to generate the cortical
surface texture at a specific tessalation (the corresponding cortical
surface can be resampled using the
'clindmri.segmentation.freesurfer.resample_cortical_surface' function).
nodif_file: str (mandatory)
file for probtrackx2 containing the no diffusion volume and associated
space information.
nodifmask_file: str (mandatory)
file for probtrackx2 containing the tractography mask (ie., a mask of
the white matter).
seed_file: str (mandatory)
text file for probtrackx2 containing seed coordinates.
bedpostx_samples: str (mandatory)
path prefix for bedpostX model samples files injected in probtrackx2
(eg., fsl.bedpostX/merged).
outdir: str (mandatory)
the output directory.
t1_file : str (mandatory)
T1 image file used to align the produced probabilitic tractography map
to the T1 space.
trf_file : str (mandatory)
diffusion to t1 space affine transformation matrix file.
dat_file: str (mandatory)
structural to FreeSurfer space affine transformation matrix '.dat'
file as computed by 'tkregister2'.
fsdir: str (mandatory)
FreeSurfer subjects directory 'SUBJECTS_DIR'.
sid: str (mandatory)
FreeSurfer subject identifier.
fsconfig: str (mandatory)
the FreeSurfer '.sh' config file.
Returns
-------
proba_file: str
the seed probabilistic tractography volume.
textures: dict
a dictionary containing the probabilist texture for each hemisphere.
"""
# Generates the diffusion probability map
proba_files, _ = probtrackx2(
simple=True, seedref=nodif_file, out="fdt_paths", seed=seed_file,
loopcheck=True, onewaycondition=True, samples=bedpostx_samples,
mask=nodifmask_file, dir=outdir)
# Check that only one 'fdt_paths' has been generated
if len(proba_files) != 1:
raise Exception("One probabilistic tractography file expected at this "
"point: {0}".format(proba_files))
proba_file = proba_files[0]
proba_fname = os.path.basename(proba_file).replace(".nii.gz", "")
# Apply 'trf_file' affine transformation matrix using FSL flirt function:
# probability map (diffusion space) -> probability map (T1 space).
flirt_t1_file = os.path.join(outdir, proba_fname + "_t1_flirt.nii.gz")
flirt(proba_file, t1_file, out=flirt_t1_file, applyxfm=True, init=trf_file)
# Project the volumic probability map (T1 space) generated with FSL flirt
# on the cortical surface (Freesurfer space) (both hemispheres) using
# Freesurfer's mri_vol2surf and applying the 'dat_file' transformation.
textures = {}
for hemi in ["lh", "rh"]:
prob_texture_file = os.path.join(
outdir, "{0}.{1}_vol2surf.mgz".format(hemi, proba_fname))
mri_vol2surf(hemi, flirt_t1_file, prob_texture_file, ico_order,
dat_file, fsdir, sid, surface_name="white",
fsconfig=fsconfig)
textures[hemi] = prob_texture_file
return proba_file, textures
dat_file = os.path.join(subjdir, "convert", "register.native.dat")
if not os.path.isfile(dat_file):
raise ValueError("'{0}' has not been generated with the "
"'clindmri.scripts.freesurfer_conversion' "
"script.".format(dat_file))
"""
If no '.trf' file is provided, register the nodif image on the t1 image
to get it.
"""
if trf_file is None:
trf_file = os.path.join(connectdir, "dmri_to_t1.trf")
reg_file = os.path.join(connectdir, "nodif_to_t1.nii.gz")
flirt(nodif_file,
t1_file,
omat=trf_file,
out=reg_file,
usesqform=False,
cost="normmi",
dof=6)
"""
Launch the tractography on the requested point of the cortical surface on the
selected hemisphere
"""
# Load the white mesh in the diffusion space
surface = TriSurface.load(whitefile)
voxel_diff_to_t1 = flirt2aff(trf_file, nodif_file, t1_file)
voxel_t1_to_diff = numpy.linalg.inv(voxel_diff_to_t1)
white_diff_vertices = apply_affine_on_mesh(surface.vertices, voxel_t1_to_diff)
# Select the vertices of interest
if vertices_indices is None:
if dat_file is None:
dat_file = os.path.join(subjdir, "convert", "register.native.dat")
if not os.path.isfile(dat_file):
raise ValueError("'{0}' has not been generated with the "
"'clindmri.scripts.freesurfer_conversion' "
"script.".format(dat_file))
"""
If no '.trf' file is provided, register the nodif image on the t1 image
to get it.
"""
if trf_file is None:
trf_file = os.path.join(connectdir, "dmri_to_t1.trf")
reg_file = os.path.join(connectdir, "nodif_to_t1.nii.gz")
flirt(nodif_file, t1_file, omat=trf_file, out=reg_file, usesqform=False,
cost="normmi", dof=6)
"""
Launch the tractography on the requested point of the cortical surface on the
selected hemisphere
"""
# Load the white mesh in the diffusion space
surface = TriSurface.load(whitefile)
voxel_diff_to_t1 = flirt2aff(trf_file, nodif_file, t1_file)
voxel_t1_to_diff = numpy.linalg.inv(voxel_diff_to_t1)
white_diff_vertices = apply_affine_on_mesh(surface.vertices, voxel_t1_to_diff)
# Select the vertices of interest
if vertices_indices is None:
vertices_indices = range(len(surface.vertices))