cedalion.dot.head_model

Functions

get_standard_headmodel(model)

Create a TwoSurfaceHeadmodel for common atlases.

Classes

TwoSurfaceHeadModel(segmentation_masks, ...)

Head Model class to represent a segmented head.

class cedalion.dot.head_model.TwoSurfaceHeadModel( segmentation_masks: xr.DataArray, brain: cdc.Surface, scalp: cdc.Surface, landmarks: cdt.LabeledPointCloud, t_ijk2ras: cdt.AffineTransform, t_ras2ijk: cdt.AffineTransform, voxel_to_vertex_brain: scipy.sparse.spmatrix, voxel_to_vertex_scalp: scipy.sparse.spmatrix, )[source]

Bases: object

Head Model class to represent a segmented head.

Its main functions are reduced to work on voxel projections to scalp and cortex surfaces.

segmentation_masks[source]

xr.DataArray Segmentation masks of the head for each tissue type.

Type:: xarray.core.dataarray.DataArray

brain[source]

cdc.Surface Surface of the brain.

Type:: cedalion.dataclasses.geometry.Surface

scalp[source]

cdc.Surface Surface of the scalp.

Type:: cedalion.dataclasses.geometry.Surface

landmarks[source]

cdt.LabeledPointCloud Anatomical landmarks in RAS space.

Type:: xarray.core.dataarray.DataArray

t_ijk2ras[source]

cdt.AffineTransform Affine transformation from ijk to RAS space.

Type:: xarray.core.dataarray.DataArray

t_ras2ijk[source]

cdt.AffineTransform Affine transformation from RAS to ijk space.

Type:: xarray.core.dataarray.DataArray

voxel_to_vertex_brain[source]

scipy.sparse.spmatrix Mapping from voxel to brain vertices.

Type:: scipy.sparse._matrix.spmatrix

voxel_to_vertex_scalp[source]

scipy.sparse.spmatrix Mapping from voxel to scalp vertices.

Type:: scipy.sparse._matrix.spmatrix

crs[source]: str Coordinate reference system of the head model.

from_segmentation(cls, segmentation_dir, mask_files, landmarks_ras_file,: brain_seg_types, scalp_seg_types, smoothing, brain_face_count, scalp_face_count): Construct instance from segmentation masks in NIfTI format.

apply_transform(transform)[source]: Apply a coordinate transformation to the head model.

save(foldername)[source]: Save the head model to a folder.

load(foldername)[source]: Load the head model from a folder.

align_and_snap_to_scalp(points)[source]: Align and snap optodes or points to the scalp surface.

segmentation_masks: DataArray[source]

brain: Surface[source]

scalp: Surface[source]

landmarks: Annotated[DataArray, DataArraySchema(dims='label', coords='label', 'label', 'type')][source]

t_ijk2ras: DataArray[source]

t_ras2ijk: DataArray[source]

voxel_to_vertex_brain: spmatrix[source]

voxel_to_vertex_scalp: spmatrix[source]

classmethod from_segmentation( segmentation_dir: str, mask_files: dict[str, str] = {'csf': 'csf.nii', 'gm': 'gm.nii', 'scalp': 'scalp.nii', 'skull': 'skull.nii', 'wm': 'wm.nii'}, landmarks_ras_file: str | None = None, brain_seg_types: list[str] = ['gm', 'wm'], scalp_seg_types: list[str] = ['scalp'], smoothing: float = 0.5, brain_face_count: int | None = 180000, scalp_face_count: int | None = 60000, fill_holes: bool = True, ) → TwoSurfaceHeadModel[source]

Constructor from binary masks as gained from segmented MRI scans.

Parameters:

segmentation_dir – Folder containing the segmentation masks in NIFTI format.
mask_files – Dictionary mapping segmentation types to NIFTI filenames.
landmarks_ras_file – Filename of the landmarks in RAS space.
brain_seg_types – List of segmentation types to be included in the brain surface.
scalp_seg_types – List of segmentation types to be included in the scalp surface.
smoothing – Smoothing factor for the brain and scalp surfaces.
brain_face_count – Number of faces for the brain surface.
scalp_face_count – Number of faces for the scalp surface.
fill_holes – Whether to fill holes in the segmentation masks.

classmethod from_surfaces( segmentation_dir: str, mask_files: dict[str, str] = {'csf': 'csf.nii', 'gm': 'gm.nii', 'scalp': 'scalp.nii', 'skull': 'skull.nii', 'wm': 'wm.nii'}, brain_surface_file: str = None, scalp_surface_file: str = None, landmarks_ras_file: str | None = None, brain_seg_types: list[str] = ['gm', 'wm'], scalp_seg_types: list[str] = ['scalp'], smoothing: float = 0.5, brain_face_count: int | None = 180000, scalp_face_count: int | None = 60000, fill_holes: bool = False, parcel_file: Path | str | None = None, ) → TwoSurfaceHeadModel[source]

Constructor from seg.masks, brain and head surfaces as gained from MRI scans.

Parameters:

segmentation_dir – Folder containing the segmentation masks in NIFTI format.
mask_files – Dictionary mapping segmentation types to NIFTI filenames.
brain_surface_file – Path to the brain surface.
scalp_surface_file – Path to the scalp surface.
landmarks_ras_file – Filename of the landmarks in RAS space.
brain_seg_types – List of segmentation types to be included in the brain surface.
scalp_seg_types – List of segmentation types to be included in the scalp surface.
smoothing – Smoothing factor for the brain and scalp surfaces.
brain_face_count – Number of faces for the brain surface.
scalp_face_count – Number of faces for the scalp surface.
fill_holes – Whether to fill holes in the segmentation masks.
parcel_file – path to the json file mapping vertices to parcels.

Returns:

An instance of the TwoSurfaceHeadModel class.

Return type:

TwoSurfaceHeadModel

property crs[source]: Coordinate reference system of the head model.

apply_transform( transform: cdt.AffineTransform, ) → TwoSurfaceHeadModel[source]

Apply a coordinate transformation to the head model.

Parameters:: transform – Affine transformation matrix (4x4) to be applied.
Returns:: Transformed head model.

save(foldername: str)[source]

Save the head model to a folder.

Parameters:: foldername (str) – Folder to save the head model into.
Returns:: None

classmethod load(foldername: str)[source]

Load the head model from a folder.

Parameters:: foldername (str) – Folder to load the head model from.
Returns:: Loaded head model.
Return type:: TwoSurfaceHeadModel

align_and_snap_to_scalp( points: cdt.LabeledPointCloud, mode: str = 'trans_rot_isoscale', ) → cdt.LabeledPointCloud[source]

Align and snap optodes or points to the scalp surface.

Parameters:

points (cdt.LabeledPointCloud) – Points to be aligned and snapped to the scalp surface.
mode – method to derive the affine transform. Could be either ‘trans_rot_isoscale’ or ‘general’. See cedalion.geometry.registraion for details.

Returns:

Points aligned and snapped to the scalp surface.

Return type:

cdt.LabeledPointCloud

snap_to_scalp_voxels( points: cdt.LabeledPointCloud, ) → cdt.LabeledPointCloud[source]

Snap optodes or points to the closest scalp voxel.

Parameters:

points (cdt.LabeledPointCloud) – Points to be snapped to the closest scalp voxel.

Returns:

Points aligned and snapped to the closest scalp: voxel.

Return type:

cdt.LabeledPointCloud

scale_to_landmarks( target_landmarks: cdt.LabeledPointCloud, ) → TwoSurfaceHeadModel[source]

scale_to_headsize( circumference: cdt.QLength, nz_cz_iz: cdt.QLength, lpa_cz_rpa: cdt.QLength, ) → TwoSurfaceHeadModel[source]

cedalion.dot.head_model.get_standard_headmodel(model: str) → TwoSurfaceHeadModel[source]

Create a TwoSurfaceHeadmodel for common atlases.

Onc created, this function caches a head model.

Parameters:: model – either colin27 or icbm152
Returns:: The loaded head model with 1010-landmarks and parcel labels assigned.