def qc(t1files, wmfiles, asegfiles, whitefiles, pialfiles, annotfiles,
actor_ang=[0., 0., 0.], output_directory=None,
fsconfig="/i2bm/local/freesurfer/SetUpFreeSurfer.sh"):
""" Compute some quality check plots on the converted FrreSurfer
outputs.
The subjecy id in the input files must appear in the -3 position:
xxx/subject_id/convert/t1.nii.gz
Steps:
* t1-images overlays
* 3d surface segmentation snaps
* t1-surfaces overlays
actor_ang: float (optional, default 0)
the actor rotation in the z direction.
<unit>
<input name="t1files" type="List_File" description="The
t1 subject files."/>
<input name="wmfiles" type="List_File" description="The
white matter subject files."/>
<input name="asegfiles" type="List_File" description="The
subcortical segmentation subject files."/>
<input name="output_directory" type="Directory" description="The
conversion destination folder."/>
<input name="whitefiles" type="List_File" description="The subject
cortex surfaces."/>
<input name="pialfiles" type="List_File" description="The subject pial
surfaces."/>
<input name="annotfiles" type="List_File" description="The pial/white
surface annotations."/>
<input name="actor_ang" type="List_Float" description="The actor x, y,
z position (in degrees)."/>
<input name="fsconfig" type="File" description="The freesurfer
configuration batch."/>
<output name="qcfiles" type="List_File" description="The quality check
snaps."/>
</unit>
"""
import clindmri.plot.pvtk as pvtk
from clindmri.plot.slicer import plot_image
# Create a t1 subject map
t1map = {}
for fname in t1files:
subject_id = fname.split("/")[-3]
if subject_id in t1map:
raise Exception("Can't map two t1 for subject '{0}'"
".".format(subject_id))
t1map[subject_id] = fname
# Create the output list that will contain all the qc files
qcfiles = []
# Construct the t1-surfaces overlays and the 3d surface segmentation snaps
ren = pvtk.ren()
for name, files in [("white", whitefiles), ("pial", pialfiles)]:
for fname in files:
# Get the t1 reference image
subject_id = fname.split("/")[-3]
t1file = t1map[subject_id]
t1_image = nibabel.load(t1file)
# Get the qc output directory
qcdir = os.path.join(os.path.dirname(fname), "qc")
qcname = os.path.basename(fname)
if not os.path.isdir(qcdir):
os.makedirs(qcdir)
# Get the triangular mesh
basename = os.path.basename(fname).replace(
name, "aparc.annot").replace(".native", "")
annotfile = os.path.join(os.path.dirname(fname), basename)
if annotfile not in annotfiles:
raise ValueError(
"Annotation file '{0}' can't be found.".format(annotfile))
surface = TriSurface.load(fname, annotfile=annotfile)
# Construct the surfaces binarized volume
binarizedfile = os.path.join(qcdir, qcname + ".nii.gz")
overlay = numpy.zeros(t1_image.shape, dtype=numpy.uint)
indices = numpy.round(surface.vertices).astype(int).T
indices[0, numpy.where(indices[0] >= t1_image.shape[0])] = 0
indices[1, numpy.where(indices[1] >= t1_image.shape[1])] = 0
indices[2, numpy.where(indices[2] >= t1_image.shape[2])] = 0
overlay[indices.tolist()] = 1
overlay_image = nibabel.Nifti1Image(overlay, t1_image.get_affine())
nibabel.save(overlay_image, binarizedfile)
snap_file = os.path.join(qcdir, qcname + ".png")
plot_image(t1file, overlay_file=binarizedfile, snap_file=snap_file,
name=qcname, overlay_cmap="cold_hot")
qcfiles.append(snap_file)
# Create a vtk surface actor of the cortex surface with the
# associated labels
ctab = [item["color"] for _, item in surface.metadata.items()]
actor = pvtk.surface(
surface.vertices, surface.triangles, surface.labels, ctab)
actor.RotateX(actor_ang[0])
actor.RotateY(actor_ang[1])
actor.RotateZ(actor_ang[2])
# Create a 3d surface segmentation snap
pvtk.add(ren, actor)
snaps = pvtk.record(ren, qcdir, qcname, n_frames=36,
az_ang=10, animate=True, delay=50)
qcfiles.append(snaps[0])
snaps = pvtk.record(ren, qcdir, qcname + ".3d", n_frames=1)
qcfiles.append(snaps[0])
pvtk.clear(ren)
# Get the FreeSurfer lookup table
fs_lut_names, fs_lut_colors = parse_fs_lut(os.path.join(
os.path.dirname(fsconfig), "FreeSurferColorLUT.txt"))
cmap = []
nb_values = numpy.asarray(fs_lut_colors.keys()).max()
cmap = numpy.zeros((nb_values, 4), dtype=numpy.single)
for key, color in fs_lut_colors.items():
if key > 0:
cmap[key - 1, :3] = color
cmap[:, 3] = 200.
cmap /= 255.
# Compute t1-images overlays
for name, files in [("aseg", asegfiles), ("wm", wmfiles)]:
for fname in files:
# Get the t1 reference image
subject_id = fname.split("/")[-3]
t1file = t1map[subject_id]
t1_image = nibabel.load(t1file)
# Get the qc output directory
qcdir = os.path.join(os.path.dirname(fname), "qc")
if not os.path.isdir(qcdir):
os.makedirs(qcdir)
# Troncate the color map based on the label max
array = nibabel.load(fname).get_data()
order = sorted(set(array.flatten()))
ccmap = cmap[order[1]: order[-1] + 1]
# Overlay the current image with the t1 image
qcname = "t1-{0}".format(name)
snap_file = os.path.join(qcdir, qcname + ".png")
plot_image(t1file, overlay_file=fname, snap_file=snap_file,
name=qcname, overlay_cmap=ccmap, cut_coords=(0, 0, 0))
qcfiles.append(snap_file)
return qcfiles
if fsdir is None:
raise NotImplementedError()
# recon-all -s 0001 -i T1.nii.gz
if use_vtk:
physical_to_index = numpy.linalg.inv(nibabel.load(t1_file).get_affine())
hemi_surfaces = read_cortex_surface_segmentation(fsdir, physical_to_index)
ren = pvtk.ren()
for hemi in ["lh", "rh"]:
surface = hemi_surfaces[hemi]
ctab = [item["color"] for _, item in surface.metadata.items()]
actor = pvtk.surface(surface.vertices, surface.triangles,
surface.labels, ctab)
pvtk.add(ren, actor)
pvtk.record(ren, qcdir, hemi + "_white")
pvtk.clear(ren)
actor = pvtk.surface(surface.inflated_vertices, surface.triangles,
surface.labels, ctab)
pvtk.add(ren, actor)
pvtk.record(ren, qcdir, hemi + "_inflated")
pvtk.clear(ren)
"""
Diffusion Processing
====================
At this point we have a motion- & artifact-corrected image, the corrected
gradient table and a mask of the non-diffusion-weighted image.
From our DTI data, we need to produce the following information:
if fsdir is None:
raise NotImplementedError()
# recon-all -s 0001 -i T1.nii.gz
if use_vtk:
physical_to_index = numpy.linalg.inv(nibabel.load(t1_file).get_affine())
hemi_surfaces = read_cortex_surface_segmentation(fsdir, physical_to_index)
ren = pvtk.ren()
for hemi in ["lh", "rh"]:
surface = hemi_surfaces[hemi]
ctab = [item["color"] for _, item in surface.metadata.items()]
actor = pvtk.surface(surface.vertices, surface.triangles,
surface.labels, ctab)
pvtk.add(ren, actor)
pvtk.record(ren, qcdir, hemi + "_white")
pvtk.clear(ren)
actor = pvtk.surface(surface.inflated_vertices, surface.triangles,
surface.labels, ctab)
pvtk.add(ren, actor)
pvtk.record(ren, qcdir, hemi + "_inflated")
pvtk.clear(ren)
"""
Diffusion Processing
====================
At this point we have a motion- & artifact-corrected image, the corrected
gradient table and a mask of the non-diffusion-weighted image.
From our DTI data, we need to produce the following information:
def qc_profile(nodif_file,
proba_file,
proba_texture,
ico_order,
fsdir,
sid,
outdir,
fsconfig,
actor_ang=(0., 0., 0.)):
""" Connectivity profile QC.
Generates views of:
- the superposition of the nodif image with tractography result volume.
- the connected points on the cortical surface
Resample cortical meshes if needed.
Results output are available as gif and png.
Parameters
----------
nodif_file: str (mandatory)
file for probtrackx2 containing the no diffusion volume and associated
space information.
proba_file: str (mandatory)
the protrackx2 output seeding probabilistic path volume.
proba_texture: dict (mandatory)
the FreeSurfer mri_vol2surf '.mgz' 'lh' and 'rh' textrue that contains
the cortiacal connection strength.
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).
fsdir: str (mandatory)
FreeSurfer subjects directory 'SUBJECTS_DIR'.
sid: str (mandatory)
FreeSurfer subject identifier.
outdir: str (mandatory)
The QC output directory.
fsconfig: str (mandatory)
the FreeSurfer '.sh' config file.
actor_ang: 3-uplet (optinal, default (0, 0, 0))
the actor x, y, z position (in degrees).
Returns
-------
snaps: list of str
two gifs images, one showing the connection profile as a texture on
the cortical surface, the other a volumic representation of the
deterministic tractography.
"""
import clindmri.plot.pvtk as pvtk
from clindmri.plot.slicer import animate_image
# Construct/check the subject directory
subjectdir = os.path.join(fsdir, sid)
if not os.path.isdir(subjectdir):
raise ValueError(
"'{0}' is not a FreeSurfer subject directory.".format(subjectdir))
# Check that the output QC directory exists
if not os.path.isdir(outdir):
os.makedirs(outdir)
# Superpose the nodif and probabilistic tractography volumes
proba_shape = nibabel.load(proba_file).shape
snaps = []
snaps.append(
animate_image(nodif_file,
overlay_file=proba_file,
clean=True,
overlay_cmap="Spectral",
cut_coords=proba_shape[2],
outdir=outdir))
# Define a renderer
ren = pvtk.ren()
# For each hemisphere
for hemi in ["lh", "rh"]:
# Get the the white mesh on the desired icosphere
meshfile = os.path.join(subjectdir, "convert",
"{0}.white.{1}.native".format(hemi, ico_order))
if not os.path.isfile(meshfile):
raise ValueError(
"'{0}' is not a valid white mesh. Generate it through the "
"'clindmri.scripts.freesurfer_conversion' script.".format(
meshfile))
# Check texture has the expected extension, size
texture_file = proba_texture[hemi]
if not texture_file.endswith(".mgz"):
raise ValueError("'{0}' is not a '.mgz' file. Format not "
"supported.".format(texture_file))
profile_array = nibabel.load(texture_file).get_data()
profile_dim = profile_array.ndim
profile_shape = profile_array.shape
if profile_dim != 3:
raise ValueError(
"Expected profile texture array of dimension 3 not "
"'{0}'".format(profile_dim))
if (profile_shape[1] != 1) or (profile_shape[2] != 1):
raise ValueError(
"Expected profile texture array of shape (*, 1, 1) not "
"'{0}'.".format(profile_shape))
# Flatten the profile texture array
texture = profile_array.ravel()
# Load the white mesh
surface = TriSurface.load(meshfile)
# Define a textured surface actor
actor = pvtk.surface(surface.vertices, surface.triangles, texture)
actor.RotateX(actor_ang[0])
actor.RotateY(actor_ang[1])
actor.RotateZ(actor_ang[2])
pvtk.add(ren, actor)
# Create a animaton with the generated surface
qcname = "profile_as_texture"
snaps.extend(
pvtk.record(ren,
outdir,
qcname,
n_frames=36,
az_ang=10,
animate=True,
delay=10))
return snaps
def display_folds(folds_file, labels, weights, white_file=None,
interactive=True, snap=False, animate=False, outdir=None,
name="folds", actor_ang=(0., 0., 0.)):
""" Display the folds computed by morphologist.
The scene supports one feature activated via the keystroke:
* 'p': Pick the data at the current mouse point. This will pop-up a window
with information on the current pick (ie. the fold name).
Parameters
----------
folds_file: str( mandatory)
the folds '.gii' file.
labels: dict (mandatory)
a mapping between a mesh id and its label.
weights: dict (mandatory)
a mapping between a mesh label and its wheight in [0, 1].
white_file: str (optional, default None)
if specified the white surface will be displayed.
interactive: bool (optional, default True)
if True display the renderer.
snap: bool (optional, default False)
if True create a snap of the scene: need a valid outdir.
animate: bool (optional, default False)
if True create a gif 360 degrees animation of the scene: need a valid
outdir.
outdir: str (optional, default None)
an existing directory.
name: str (optional, default 'folds')
the basename of the generated files.
actor_ang: 3-uplet (optinal, default (0, 0, 0))
the actors x, y, z position (in degrees).
"""
# Load the folds file
image = gio.read(folds_file)
nb_of_surfs = len(image.darrays)
if nb_of_surfs % 2 != 0:
raise ValueError("Need an odd number of arrays (vertices, triangles).")
# Create an actor for each fold
ren = pvtk.ren()
ren.SetBackground(1, 1, 1)
for vertindex in range(0, nb_of_surfs, 2):
vectices = image.darrays[vertindex].data
triangles = image.darrays[vertindex + 1].data
labelindex = image.darrays[vertindex].get_metadata()["Timestep"]
if labelindex != image.darrays[vertindex + 1].get_metadata()[
"Timestep"]:
raise ValueError("Gifti arrays '{0}' and '{1}' do not share the "
"same label.".format(vertindex, vertindex + 1))
labelindex = int(labelindex)
if labelindex in labels:
label = labels[labelindex]
if label in weights:
weight = weights[label] * 256.
else:
weight = 0
else:
label = "NC"
weight = 0
surf = TriSurface(vectices, triangles, labels=None)
actor = pvtk.surface(surf.vertices, surf.triangles,
surf.labels + weight)
actor.label = label
actor.RotateX(actor_ang[0])
actor.RotateY(actor_ang[1])
actor.RotateZ(actor_ang[2])
pvtk.add(ren, actor)
# Add the white surface if specified
if white_file is not None:
image = gio.read(white_file)
nb_of_surfs = len(image.darrays)
if nb_of_surfs != 2:
raise ValueError("'{0}' does not a contain a valid white "
"mesh.".format(white_file))
vectices = image.darrays[0].data
triangles = image.darrays[1].data
surf = TriSurface(vectices, triangles, labels=None)
actor = pvtk.surface(surf.vertices, surf.triangles, surf.labels,
opacity=1, set_lut=False)
actor.label = "white"
actor.RotateX(actor_ang[0])
actor.RotateY(actor_ang[1])
actor.RotateZ(actor_ang[2])
pvtk.add(ren, actor)
# Show the renderer
if interactive:
actor = pvtk.text("!!!!", font_size=15, position=(10, 10),
is_visible=False)
pvtk.add(ren, actor)
obs = LabelsOnPick(actor, static_position=True,
to_keep_actors=["white"])
pvtk.show(ren, title="morphologist folds", observers=[obs])
# Create a snap
if snap:
if not os.path.isdir(outdir):
raise ValueError("'{0}' is not a valid directory.".format(outdir))
pvtk.record(ren, outdir, name, n_frames=1)
# Create an animation
if animate:
if not os.path.isdir(outdir):
raise ValueError("'{0}' is not a valid directory.".format(outdir))
pvtk.record(ren, outdir, name, n_frames=36, az_ang=10, animate=True,
delay=25)
def display_folds(folds_file,
labels,
weights,
white_file=None,
interactive=True,
snap=False,
animate=False,
outdir=None,
name="folds",
actor_ang=(0., 0., 0.)):
""" Display the folds computed by morphologist.
The scene supports one feature activated via the keystroke:
* 'p': Pick the data at the current mouse point. This will pop-up a window
with information on the current pick (ie. the fold name).
Parameters
----------
folds_file: str( mandatory)
the folds '.gii' file.
labels: dict (mandatory)
a mapping between a mesh id and its label.
weights: dict (mandatory)
a mapping between a mesh label and its wheight in [0, 1].
white_file: str (optional, default None)
if specified the white surface will be displayed.
interactive: bool (optional, default True)
if True display the renderer.
snap: bool (optional, default False)
if True create a snap of the scene: need a valid outdir.
animate: bool (optional, default False)
if True create a gif 360 degrees animation of the scene: need a valid
outdir.
outdir: str (optional, default None)
an existing directory.
name: str (optional, default 'folds')
the basename of the generated files.
actor_ang: 3-uplet (optinal, default (0, 0, 0))
the actors x, y, z position (in degrees).
"""
# Load the folds file
image = gio.read(folds_file)
nb_of_surfs = len(image.darrays)
if nb_of_surfs % 2 != 0:
raise ValueError("Need an odd number of arrays (vertices, triangles).")
# Create an actor for each fold
ren = pvtk.ren()
ren.SetBackground(1, 1, 1)
for vertindex in range(0, nb_of_surfs, 2):
vectices = image.darrays[vertindex].data
triangles = image.darrays[vertindex + 1].data
labelindex = image.darrays[vertindex].get_metadata()["Timestep"]
if labelindex != image.darrays[vertindex +
1].get_metadata()["Timestep"]:
raise ValueError("Gifti arrays '{0}' and '{1}' do not share the "
"same label.".format(vertindex, vertindex + 1))
labelindex = int(labelindex)
if labelindex in labels:
label = labels[labelindex]
if label in weights:
weight = weights[label] * 256.
else:
weight = 0
else:
label = "NC"
weight = 0
surf = TriSurface(vectices, triangles, labels=None)
actor = pvtk.surface(surf.vertices, surf.triangles,
surf.labels + weight)
actor.label = label
actor.RotateX(actor_ang[0])
actor.RotateY(actor_ang[1])
actor.RotateZ(actor_ang[2])
pvtk.add(ren, actor)
# Add the white surface if specified
if white_file is not None:
image = gio.read(white_file)
nb_of_surfs = len(image.darrays)
if nb_of_surfs != 2:
raise ValueError("'{0}' does not a contain a valid white "
"mesh.".format(white_file))
vectices = image.darrays[0].data
triangles = image.darrays[1].data
surf = TriSurface(vectices, triangles, labels=None)
actor = pvtk.surface(surf.vertices,
surf.triangles,
surf.labels,
opacity=1,
set_lut=False)
actor.label = "white"
actor.RotateX(actor_ang[0])
actor.RotateY(actor_ang[1])
actor.RotateZ(actor_ang[2])
pvtk.add(ren, actor)
# Show the renderer
if interactive:
actor = pvtk.text("!!!!",
font_size=15,
position=(10, 10),
is_visible=False)
pvtk.add(ren, actor)
obs = LabelsOnPick(actor,
static_position=True,
to_keep_actors=["white"])
pvtk.show(ren, title="morphologist folds", observers=[obs])
# Create a snap
if snap:
if not os.path.isdir(outdir):
raise ValueError("'{0}' is not a valid directory.".format(outdir))
pvtk.record(ren, outdir, name, n_frames=1)
# Create an animation
if animate:
if not os.path.isdir(outdir):
raise ValueError("'{0}' is not a valid directory.".format(outdir))
pvtk.record(ren,
outdir,
name,
n_frames=36,
az_ang=10,
animate=True,
delay=25)
points_space="voxel")
else:
trackvis_fibers, trackvis_header = nibabel.trackvis.read(
track_filtered_file, as_generator=False, points_space="voxel")
fibers = [track_item[0] for track_item in trackvis_fibers]
if use_vtk:
ren = pvtk.ren()
colors = line_colors(fibers)
actor = pvtk.tubes(fibers, colors)
actor.RotateX(actor_ang[0])
actor.RotateY(actor_ang[1])
actor.RotateZ(actor_ang[2])
pvtk.add(ren, actor)
ren.SetBackground(1, 1, 1)
pvtk.record(ren, qcdir, "fibers", az_ang=45, n_frames=2)
pvtk.record(ren, qcdir, "fibers", n_frames=36, az_ang=10, animate=True,
delay=25)
pvtk.show(ren)
pvtk.clear(ren)
"""
Fiber clustering
----------------
Based on an agglomerative clustering, and a geometric distance.
"""
clustering_outdir = os.path.join(outdir, "clustering")
cluster_file = os.path.join(clustering_outdir, "clusters.json")
def qc(t1files,
wmfiles,
asegfiles,
whitefiles,
pialfiles,
annotfiles,
actor_ang=[0., 0., 0.],
output_directory=None,
fsconfig="/i2bm/local/freesurfer/SetUpFreeSurfer.sh"):
""" Compute some quality check plots on the converted FrreSurfer
outputs.
The subjecy id in the input files must appear in the -3 position:
xxx/subject_id/convert/t1.nii.gz
Steps:
* t1-images overlays
* 3d surface segmentation snaps
* t1-surfaces overlays
actor_ang: float (optional, default 0)
the actor rotation in the z direction.
<unit>
<input name="t1files" type="List_File" description="The
t1 subject files."/>
<input name="wmfiles" type="List_File" description="The
white matter subject files."/>
<input name="asegfiles" type="List_File" description="The
subcortical segmentation subject files."/>
<input name="output_directory" type="Directory" description="The
conversion destination folder."/>
<input name="whitefiles" type="List_File" description="The subject
cortex surfaces."/>
<input name="pialfiles" type="List_File" description="The subject pial
surfaces."/>
<input name="annotfiles" type="List_File" description="The pial/white
surface annotations."/>
<input name="actor_ang" type="List_Float" description="The actor x, y,
z position (in degrees)."/>
<input name="fsconfig" type="File" description="The freesurfer
configuration batch."/>
<output name="qcfiles" type="List_File" description="The quality check
snaps."/>
</unit>
"""
import clindmri.plot.pvtk as pvtk
from clindmri.plot.slicer import plot_image
# Create a t1 subject map
t1map = {}
for fname in t1files:
subject_id = fname.split("/")[-3]
if subject_id in t1map:
raise Exception("Can't map two t1 for subject '{0}'"
".".format(subject_id))
t1map[subject_id] = fname
# Create the output list that will contain all the qc files
qcfiles = []
# Construct the t1-surfaces overlays and the 3d surface segmentation snaps
ren = pvtk.ren()
for name, files in [("white", whitefiles), ("pial", pialfiles)]:
for fname in files:
# Get the t1 reference image
subject_id = fname.split("/")[-3]
t1file = t1map[subject_id]
t1_image = nibabel.load(t1file)
# Get the qc output directory
qcdir = os.path.join(os.path.dirname(fname), "qc")
qcname = os.path.basename(fname)
if not os.path.isdir(qcdir):
os.makedirs(qcdir)
# Get the triangular mesh
basename = os.path.basename(fname).replace(name,
"aparc.annot").replace(
".native", "")
annotfile = os.path.join(os.path.dirname(fname), basename)
if annotfile not in annotfiles:
raise ValueError(
"Annotation file '{0}' can't be found.".format(annotfile))
surface = TriSurface.load(fname, annotfile=annotfile)
# Construct the surfaces binarized volume
binarizedfile = os.path.join(qcdir, qcname + ".nii.gz")
overlay = numpy.zeros(t1_image.shape, dtype=numpy.uint)
indices = numpy.round(surface.vertices).astype(int).T
indices[0, numpy.where(indices[0] >= t1_image.shape[0])] = 0
indices[1, numpy.where(indices[1] >= t1_image.shape[1])] = 0
indices[2, numpy.where(indices[2] >= t1_image.shape[2])] = 0
overlay[indices.tolist()] = 1
overlay_image = nibabel.Nifti1Image(overlay, t1_image.get_affine())
nibabel.save(overlay_image, binarizedfile)
snap_file = os.path.join(qcdir, qcname + ".png")
plot_image(t1file,
overlay_file=binarizedfile,
snap_file=snap_file,
name=qcname,
overlay_cmap="cold_hot")
qcfiles.append(snap_file)
# Create a vtk surface actor of the cortex surface with the
# associated labels
ctab = [item["color"] for _, item in surface.metadata.items()]
actor = pvtk.surface(surface.vertices, surface.triangles,
surface.labels, ctab)
actor.RotateX(actor_ang[0])
actor.RotateY(actor_ang[1])
actor.RotateZ(actor_ang[2])
# Create a 3d surface segmentation snap
pvtk.add(ren, actor)
snaps = pvtk.record(ren,
qcdir,
qcname,
n_frames=36,
az_ang=10,
animate=True,
delay=50)
qcfiles.append(snaps[0])
snaps = pvtk.record(ren, qcdir, qcname + ".3d", n_frames=1)
qcfiles.append(snaps[0])
pvtk.clear(ren)
# Get the FreeSurfer lookup table
fs_lut_names, fs_lut_colors = parse_fs_lut(
os.path.join(os.path.dirname(fsconfig), "FreeSurferColorLUT.txt"))
cmap = []
nb_values = numpy.asarray(fs_lut_colors.keys()).max()
cmap = numpy.zeros((nb_values, 4), dtype=numpy.single)
for key, color in fs_lut_colors.items():
if key > 0:
cmap[key - 1, :3] = color
cmap[:, 3] = 200.
cmap /= 255.
# Compute t1-images overlays
for name, files in [("aseg", asegfiles), ("wm", wmfiles)]:
for fname in files:
# Get the t1 reference image
subject_id = fname.split("/")[-3]
t1file = t1map[subject_id]
t1_image = nibabel.load(t1file)
# Get the qc output directory
qcdir = os.path.join(os.path.dirname(fname), "qc")
if not os.path.isdir(qcdir):
os.makedirs(qcdir)
# Troncate the color map based on the label max
array = nibabel.load(fname).get_data()
order = sorted(set(array.flatten()))
ccmap = cmap[order[1]:order[-1] + 1]
# Overlay the current image with the t1 image
qcname = "t1-{0}".format(name)
snap_file = os.path.join(qcdir, qcname + ".png")
plot_image(t1file,
overlay_file=fname,
snap_file=snap_file,
name=qcname,
overlay_cmap=ccmap,
cut_coords=(0, 0, 0))
qcfiles.append(snap_file)
return qcfiles
def qc_profile(nodif_file, proba_file, proba_texture, ico_order,
fsdir, sid, outdir, fsconfig, actor_ang=(0., 0., 0.)):
""" Connectivity profile QC.
Generates views of:
- the superposition of the nodif image with tractography result volume.
- the connected points on the cortical surface
Resample cortical meshes if needed.
Results output are available as gif and png.
Parameters
----------
nodif_file: str (mandatory)
file for probtrackx2 containing the no diffusion volume and associated
space information.
proba_file: str (mandatory)
the protrackx2 output seeding probabilistic path volume.
proba_texture: dict (mandatory)
the FreeSurfer mri_vol2surf '.mgz' 'lh' and 'rh' textrue that contains
the cortiacal connection strength.
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).
fsdir: str (mandatory)
FreeSurfer subjects directory 'SUBJECTS_DIR'.
sid: str (mandatory)
FreeSurfer subject identifier.
outdir: str (mandatory)
The QC output directory.
fsconfig: str (mandatory)
the FreeSurfer '.sh' config file.
actor_ang: 3-uplet (optinal, default (0, 0, 0))
the actor x, y, z position (in degrees).
Returns
-------
snaps: list of str
two gifs images, one showing the connection profile as a texture on
the cortical surface, the other a volumic representation of the
deterministic tractography.
"""
import clindmri.plot.pvtk as pvtk
from clindmri.plot.slicer import animate_image
# Construct/check the subject directory
subjectdir = os.path.join(fsdir, sid)
if not os.path.isdir(subjectdir):
raise ValueError(
"'{0}' is not a FreeSurfer subject directory.".format(subjectdir))
# Check that the output QC directory exists
if not os.path.isdir(outdir):
os.makedirs(outdir)
# Superpose the nodif and probabilistic tractography volumes
proba_shape = nibabel.load(proba_file).shape
snaps = []
snaps.append(
animate_image(nodif_file, overlay_file=proba_file, clean=True,
overlay_cmap="Spectral", cut_coords=proba_shape[2],
outdir=outdir))
# Define a renderer
ren = pvtk.ren()
# For each hemisphere
for hemi in ["lh", "rh"]:
# Get the the white mesh on the desired icosphere
meshfile = os.path.join(
subjectdir, "convert", "{0}.white.{1}.native".format(
hemi, ico_order))
if not os.path.isfile(meshfile):
raise ValueError(
"'{0}' is not a valid white mesh. Generate it through the "
"'clindmri.scripts.freesurfer_conversion' script.".format(
meshfile))
# Check texture has the expected extension, size
texture_file = proba_texture[hemi]
if not texture_file.endswith(".mgz"):
raise ValueError("'{0}' is not a '.mgz' file. Format not "
"supported.".format(texture_file))
profile_array = nibabel.load(texture_file).get_data()
profile_dim = profile_array.ndim
profile_shape = profile_array.shape
if profile_dim != 3:
raise ValueError(
"Expected profile texture array of dimension 3 not "
"'{0}'".format(profile_dim))
if (profile_shape[1] != 1) or (profile_shape[2] != 1):
raise ValueError(
"Expected profile texture array of shape (*, 1, 1) not "
"'{0}'.".format(profile_shape))
# Flatten the profile texture array
texture = profile_array.ravel()
# Load the white mesh
surface = TriSurface.load(meshfile)
# Define a textured surface actor
actor = pvtk.surface(surface.vertices, surface.triangles, texture)
actor.RotateX(actor_ang[0])
actor.RotateY(actor_ang[1])
actor.RotateZ(actor_ang[2])
pvtk.add(ren, actor)
# Create a animaton with the generated surface
qcname = "profile_as_texture"
snaps.extend(
pvtk.record(ren, outdir, qcname, n_frames=36, az_ang=10, animate=True,
delay=10))
return snaps
if fsdir is None:
raise NotImplementedError()
# recon-all -s 0001 -i T1.nii.gz
if use_vtk:
physical_to_index = numpy.linalg.inv(nibabel.load(t1_file).get_affine())
hemi_surfaces = read_cortex_surface_segmentation(fsdir, physical_to_index)
ren = pvtk.ren()
for hemi in ["lh", "rh"]:
surface = hemi_surfaces[hemi]
ctab = [item["color"] for _, item in surface.metadata.items()]
actor = pvtk.surface(surface.vertices, surface.triangles, surface.labels,
ctab)
pvtk.add(ren, actor)
pvtk.record(ren, qcdir, hemi + "_white")
pvtk.clear(ren)
actor = pvtk.surface(surface.inflated_vertices, surface.triangles,
surface.labels, ctab)
pvtk.add(ren, actor)
pvtk.record(ren, qcdir, hemi + "_inflated")
pvtk.clear(ren)
"""
Diffusion Processing
====================
At this point we have a motion- & artifact-corrected image, the corrected
gradient table and a mask of the non-diffusion-weighted image.
From our DTI data, we need to produce the following information: