tomobar.methodsDIR#

Reconstruction class for direct reconstructon methods.

  • Fourier Slice Theorem reconstruction (adopted from Tim Day’s code)

  • Forward/Backward projection (ASTRA)

  • Filtered Back Projection (ASTRA)

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

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: numpy.ndarray, **kwargs) → numpy.ndarray[source]#

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: numpy.ndarray, **kwargs) → numpy.ndarray[source]#

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: numpy.ndarray, **kwargs) → numpy.ndarray[source]#

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: numpy.ndarray, **kwargs) → numpy.ndarray[source]#

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