tomobar.methodsDIR

Reconstruction class for direct reconstruction methods (2D/3D).

• RecToolsDIR.FORWPROJ() and RecToolsDIR.BACKPROJ() Forward/Backward 2D/3D projection (ASTRA-Toolbox)

• RecToolsDIR.FOURIER() Fourier Slice Theorem-based reconstruction in 2D only (adopted from the Tim Day’s code)

• RecToolsDIR.FBP() Filtered Back Projection 2D/3D (ASTRA with the custom built filter).

class tomobar.methodsDIR.RecToolsDIR(DetectorsDimH, DetectorsDimV, CenterRotOffset, AnglesVec, ObjSize, device_projector='gpu')

Bases: object

Reconstruction class using DIRect methods (FBP and Fourier).

Parameters:

• DetectorsDimH (int) – Horizontal detector dimension.

• DetectorsDimV (int) – Vertical detector dimension for 3D case, 0 or None for 2D case.

• CenterRotOffset (float, ndarray) – The Centre of Rotation (CoR) scalar or a vector for each angle.

• AnglesVec (np.ndarray) – Vector of projection angles in radians.

• ObjSize (int) – Reconstructed object dimensions (a scalar).

• device_projector (str, int, optional) – ‘cpu’ or ‘gpu’ device OR provide a GPU index (integer) of a specific GPU device.

• cupyrun (bool, optional) – instantiate CuPy class if True.

BACKPROJ(projdata: <cp.ndarray>, **kwargs) → <cp.ndarray>

Module to perform back-projection of 2d/3d data numpy array

Parameters:

projdata (np.ndarray) – 2D/3D projection data

Keyword Arguments:

data_axes_labels_order (Union[list, None], optional) – The order of the axes labels for the input data. When “None” we assume [“angles”, “detX”] for 2D and [“detY”, “angles”, “detX”] for 3D.

Returns:

Backprojected 2D/3D object

Return type:

np.ndarray

FBP(data: <cp.ndarray>, **kwargs) → <cp.ndarray>

Filtered backprojection reconstruction module for 2D or 3D data.

Parameters:

data (np.ndarray) – 2D or 3D projection data.

Keyword Arguments:

data_axes_labels_order (Union[list, None], optional) – The order of the axes labels for the input data. When “None” we assume [“angles”, “detX”] for 2D and [“detY”, “angles”, “detX”] for 3D.

Returns:

FBP reconstructed 2D or 3D object.

Return type:

np.ndarray

FORWPROJ(data: <cp.ndarray>, **kwargs) → <cp.ndarray>

Module to perform forward projection of 2d/3d data numpy array

Parameters:

data (np.ndarray) – 2D or 3D object

Keyword Arguments:

data_axes_labels_order (Union[list, None], optional) – The order of the axes labels for the OUTPUT data. When “None” we assume [“angles”, “detX”] for 2D and [“detY”, “angles”, “detX”] for 3D.

Returns:

Forward projected numpy array (projection data)

Return type:

np.ndarray

FOURIER(data: <cp.ndarray>, **kwargs) → <cp.ndarray>

2D Reconstruction using Fourier slice theorem (scipy required) for griddata interpolation module choose nearest, linear or cubic

Parameters:

data (np.ndarray) – 2D sinogram data

Keyword Arguments:

• data_axes_labels_order (Union[list, None], optional) – The order of the axes labels for the input data. When “None” we assume [“angles”, “detX”] for 2D and [“detY”, “angles”, “detX”] for 3D.

• method (str, optional) – Interpolation type (nearest, linear, or cubic). Defaults to “linear”.

Returns:

Reconstructed object

Return type:

np.ndarray