def probtrackx2_connectogram(outdir,
bedpostx_dir,
seed_masks,
tracto_mask,
stop_mask = None,
avoid_mask = None,
subdir = "probtrackx2",
nsamples = 5000,
nsteps = 2000,
#cthr=0.2,
#loopcheck=None,
#onewaycondition=None,
#usef=None,
#simple=None,
#seedref=None,
#steplength=0.5,
#fibthresh=0.01,
#distthresh=0.0,
#sampvox=0.0,
#network=None
):
"""
<unit>
<output name="proba_matrix" type="File" />
<output name="network_matrix" type="File" />
<input name="outdir" type="Directory" />
<input name="bedpostx_dir" type="Directory" />
<input name="seed_masks" type="List" content="File" />
<input name="tracto_mask" type="File" />
<input name="stop_mask" type="File" />
<input name="avoid_mask" type="File" />
<input name="subdir" type="Str" />
<input name="nsamples" type="Int" />
<input name="nsteps" type="Int" />
</unit>
"""
if subdir:
outdir = os.path.join(outdir, subdir)
if not os.path.isdir(outdir):
os.makedirs(outdir)
file_listing_masks = os.path.join(outdir, "masks.txt")
np.savetxt(file_listing_masks, np.asarray(seed_masks), delimiter=" ", fmt="%s")
basepath_samples = os.path.join(bedpostx_dir, "merged")
proba_files, network_matrix = probtrackx2(network = True,
seed = file_listing_masks,
loopcheck = True,
onewaycondition = True,
samples = basepath_samples,
mask = tracto_mask,
dir = outdir,
nsamples = nsamples,
nsteps = nsteps)
proba_matrix = proba_files[0]
return proba_matrix, network_matrix
We compute the fibers starting at a specific gyrus, connecting the other
gyri only with the 'network' option. The result is stored in the 'fdt_paths'
image. The 'fdt_network_matrix' is a 2D region-by-region connectivity matrix.
"""
probtrackx2_outdir = os.path.join(outdir, "proba_region_connectivity")
masks_file = os.path.join(probtrackx2_outdir, "masks.txt")
if not os.path.isdir(probtrackx2_outdir):
os.mkdir(probtrackx2_outdir)
if not os.path.isfile(masks_file):
numpy.savetxt(masks_file, numpy.asarray(seeds), delimiter=" ", fmt="%s")
proba_files, network_file = probtrackx2(
network=True,
seed=masks_file,
loopcheck=True,
onewaycondition=True,
samples=os.path.join(bedpostx_outdir, "merged"),
mask=mask_file,
dir=probtrackx2_outdir)
else:
proba_file = glob.glob(os.path.join(probtrackx2_outdir, "fdt_paths*"))[0]
network_file = os.path.join(probtrackx2_outdir, "fdt_network_matrix")
weights_file = os.path.join(probtrackx2_outdir, "waytotal")
for restored_file in [network_file, proba_file, weights_file]:
if not os.path.isfile(restored_file):
raise IOError("FileDoesNotExist: '{0}'.".format(restored_file))
snap_file = os.path.join(qcdir, "fiber_density_map.pdf")
plot_image(b0_file, overlay_file=proba_file, snap_file=snap_file,
name="density", overlay_cmap="cold_hot")
snap_file = os.path.join(qcdir, "prob_gyri_connectogram.pdf")
def probtrackx2_connectogram_seeding_wm(outdir,
bedpostx_dir,
txt_roi_masks,
tracto_mask,
seed_mask,
stop_mask,
avoid_mask,
nsamples,
nsteps,
steplength,
cthr=None,
loopcheck=True,
fibthresh=None,
distthresh=None,
sampvox=None,
waypoints=None,
subdir="probtrackx2"):
"""
Generate a connectogram using probtrackx2 with the following approach:
- seed the white matter with <nsamples> in each voxel
- for each sample, bidirectionally propagate a fiber until both ends
of the fiber reach the stop mask.
Parameters
----------
outdir: str
Path to the directory where to output.
bedpostx_dir: str
Path to the bedpostx output dir.
txt_roi_masks: str
Path to the txt file listing the paths to the ROI masks.
tracto_mask: str
Path to the tractography mask (nodif_brain_mask.nii.gz).
seed_mask: str
Path to the mask where to start the tractography. It corresponds to
the voxels of white matter where the FA > 0.2.
stop_mask: str
Path to the stop mask (inverse of white matter).
subdir: str, default "probtrackx2"
If set, the outputs are written in a subdirectory of outdir.
<other args>:
'probtrackx2 --help'
"""
if subdir:
outdir = os.path.join(outdir, subdir)
if not os.path.isdir(outdir):
os.makedirs(outdir)
probtrackx2(dir=outdir,
samples=os.path.join(bedpostx_dir, "merged"),
mask=tracto_mask,
seed=seed_mask,
omatrix3=True,
target3=txt_roi_masks,
stop=stop_mask,
avoid=avoid_mask,
nsamples=nsamples,
nsteps=nsteps,
cthr=cthr,
loopcheck=loopcheck,
steplength=steplength,
fibthresh=fibthresh,
distthresh=distthresh,
sampvox=sampvox,
waypoints=waypoints)
fiber_density = os.path.join(outdir, "fdt_paths.nii.gz")
# Convert omatrix3 output (VOXELxVOXEL fiber count) to ROIxROI fiber count
txt_matrix = omatrix3_to_roi_network(outdir)
# Create a list of labels from the list of mask paths
roi_masks = np.loadtxt(txt_roi_masks, dtype=str)
labels = [os.path.basename(x).split(".nii")[0] for x in roi_masks]
txt_labels = os.path.join(outdir, "labels.txt")
np.savetxt(txt_labels, labels, fmt="%s")
# Create a normalized connectogram in the same directory
txt_matrix_normalized, _ = normalize_connectogram_by_target_surf_area(
outdir, txt_roi_masks, txt_matrix, txt_labels)
return fiber_density, txt_matrix, txt_matrix_normalized, txt_labels
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
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
