示例#1
0
def surf_convert(fsdir, t1files, surffiles, output_directory=None,
                 rm_orig=False,
                 fsconfig="/i2bm/local/freesurfer/SetUpFreeSurfer.sh"):
    """ Export FreeSurfer surfaces to the native space.

    Note that all the vetices are given in the index coordinate system.
    The subjecy id in the t1 and surf files must appear in the -3 position:
        xxx/subject_id/convert/t1.nii.gz

    <unit>
        <input name="fsdir" type="Directory" description="The
            freesurfer working directory with all the subjects."/>
        <input name="output_directory" type="Directory" description="The
            conversion destination folder."/>
        <input name="t1files" type="List_File" description="The t1 nifti
            files."/>
        <input name="surffiles" type="List_File" description="The surface
            to be converted."/>
        <input name="rm_orig" type="Bool" description="If true remove
            the input surfaces."/>
        <input name="fsconfig" type="File" description="The freesurfer
            configuration batch."/>
        <output name="csurffiles" type="List_File" description="The converted
            surfaces in the native space."/>
    </unit>
    """
    # Create a t1 subject map
    t1map = {}
    for fname in t1files:
        subject_id = fname.split("/")[-3]
        if subject_id in t1map:
            raise ("Can't map two t1 for subject '{0}'.".format(subject_id))
        t1map[subject_id] = fname

    # Convert all the surfaces
    csurffiles = []
    for fname in surffiles:

        # Get the t1 reference image
        subject_id = fname.split("/")[-3]
        t1file = t1map[subject_id]
        t1_image = nibabel.load(t1file)

        # Compute the conformed space to the native anatomical deformation
        asegfile = os.path.join(fsdir, subject_id, "mri", "aseg.mgz")
        physical_to_index = numpy.linalg.inv(t1_image.get_affine())
        translation = tkregister_translation(asegfile, fsconfig)
        deformation = numpy.dot(physical_to_index, translation)

        # Load and warp the mesh
        # The mesh: a 2-uplet with vertex (x, y, z) coordinates and
        # mesh triangles
        mesh = freesurfer.read_geometry(fname)
        surf = TriSurface(vertices=apply_affine_on_mesh(mesh[0], deformation),
                          triangles=mesh[1])

        # Save the mesh in the native space
        outputfile = fname + ".native"
        surf.save(os.path.dirname(outputfile), os.path.basename(outputfile))
        csurffiles.append(outputfile)

        # Clean input surface if specified
        if rm_orig:
            os.remove(fname)

    return csurffiles
示例#2
0
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
示例#3
0
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
示例#4
0
mesharray = numpy.zeros(t1shape, dtype=numpy.uint)
for basename in surf:
    # > construc path
    fpath = os.path.join(mripath, basename)
    if not os.path.isfile(fpath):
        raise ValueError("'{0}' is not a valid file name.".format(fpath))
    surf[basename] = fpath

    # > load mesh
    name = basename.split(".")[1]
    annot_basename = basename.replace(
        name, "aparc.annot").replace(".native", "")
    annotfile = os.path.join(mripath, annot_basename)
    if not os.path.isfile(fpath):
        raise ValueError("'{0}' is not a valid file name.".format(fpath))
    surface = TriSurface.load(fpath, annotfile=annotfile)

    # > binarize mesh
    indices = numpy.round(surface.vertices).astype(int).T
    indices[0, numpy.where(indices[0] >= t1shape[0])] = 0
    indices[1, numpy.where(indices[1] >= t1shape[1])] = 0
    indices[2, numpy.where(indices[2] >= t1shape[2])] = 0
    mesharray[indices.tolist()] = colors[basename]

# Save the mesh volume
meshim = nibabel.Nifti1Image(mesharray, t1affine)
nibabel.save(meshim, meshfile)

# Need to reorient the image to MNI standard space
reomeshfile = os.path.join(qcdir, "reo." + os.path.basename(meshfile))
fslreorient2std(meshfile, reomeshfile, shfile=fslconfig)
示例#5
0
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
示例#6
0
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)
示例#7
0
def surf_convert(fsdir,
                 t1files,
                 surffiles,
                 output_directory=None,
                 rm_orig=False,
                 fsconfig="/i2bm/local/freesurfer/SetUpFreeSurfer.sh"):
    """ Export FreeSurfer surfaces to the native space.

    Note that all the vetices are given in the index coordinate system.
    The subjecy id in the t1 and surf files must appear in the -3 position:
        xxx/subject_id/convert/t1.nii.gz

    <unit>
        <input name="fsdir" type="Directory" description="The
            freesurfer working directory with all the subjects."/>
        <input name="output_directory" type="Directory" description="The
            conversion destination folder."/>
        <input name="t1files" type="List_File" description="The t1 nifti
            files."/>
        <input name="surffiles" type="List_File" description="The surface
            to be converted."/>
        <input name="rm_orig" type="Bool" description="If true remove
            the input surfaces."/>
        <input name="fsconfig" type="File" description="The freesurfer
            configuration batch."/>
        <output name="csurffiles" type="List_File" description="The converted
            surfaces in the native space."/>
    </unit>
    """
    # Create a t1 subject map
    t1map = {}
    for fname in t1files:
        subject_id = fname.split("/")[-3]
        if subject_id in t1map:
            raise ("Can't map two t1 for subject '{0}'.".format(subject_id))
        t1map[subject_id] = fname

    # Convert all the surfaces
    csurffiles = []
    for fname in surffiles:

        # Get the t1 reference image
        subject_id = fname.split("/")[-3]
        t1file = t1map[subject_id]
        t1_image = nibabel.load(t1file)

        # Compute the conformed space to the native anatomical deformation
        asegfile = os.path.join(fsdir, subject_id, "mri", "aseg.mgz")
        physical_to_index = numpy.linalg.inv(t1_image.get_affine())
        translation = tkregister_translation(asegfile, fsconfig)
        deformation = numpy.dot(physical_to_index, translation)

        # Load and warp the mesh
        # The mesh: a 2-uplet with vertex (x, y, z) coordinates and
        # mesh triangles
        mesh = freesurfer.read_geometry(fname)
        surf = TriSurface(vertices=apply_affine_on_mesh(mesh[0], deformation),
                          triangles=mesh[1])

        # Save the mesh in the native space
        outputfile = fname + ".native"
        surf.save(os.path.dirname(outputfile), os.path.basename(outputfile))
        csurffiles.append(outputfile)

        # Clean input surface if specified
        if rm_orig:
            os.remove(fname)

    return csurffiles
示例#8
0
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
示例#9
0
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))

# Go through all the hemisphere vertices
textures = {}
for index in vertices_indices:

    # Select the seeding vertex
    point = white_diff_vertices[index]
示例#10
0
mesharray = numpy.zeros(t1shape, dtype=numpy.uint)
for basename in surf:
    # > construc path
    fpath = os.path.join(mripath, basename)
    if not os.path.isfile(fpath):
        raise ValueError("'{0}' is not a valid file name.".format(fpath))
    surf[basename] = fpath

    # > load mesh
    name = basename.split(".")[1]
    annot_basename = basename.replace(name,
                                      "aparc.annot").replace(".native", "")
    annotfile = os.path.join(mripath, annot_basename)
    if not os.path.isfile(fpath):
        raise ValueError("'{0}' is not a valid file name.".format(fpath))
    surface = TriSurface.load(fpath, annotfile=annotfile)

    # > binarize mesh
    indices = numpy.round(surface.vertices).astype(int).T
    indices[0, numpy.where(indices[0] >= t1shape[0])] = 0
    indices[1, numpy.where(indices[1] >= t1shape[1])] = 0
    indices[2, numpy.where(indices[2] >= t1shape[2])] = 0
    mesharray[indices.tolist()] = colors[basename]

# Save the mesh volume
meshim = nibabel.Nifti1Image(mesharray, t1affine)
nibabel.save(meshim, meshfile)

# Need to reorient the image to MNI standard space
reomeshfile = os.path.join(qcdir, "reo." + os.path.basename(meshfile))
fslreorient2std(meshfile, reomeshfile, shfile=fslconfig)
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))

# Go through all the hemisphere vertices
textures = {}
for index in vertices_indices:

    # Select the seeding vertex
    point = white_diff_vertices[index]