tomobar.methodsIR_CuPy

tomobar.methodsIR_CuPy#

Reconstruction class for regularised iterative methods using CuPy library.

class tomobar.methodsIR_CuPy.RecToolsIRCuPy(DetectorsDimH, DetectorsDimH_pad, DetectorsDimV, CenterRotOffset, AnglesVec, ObjSize, datafidelity='LS', device_projector=0, cupyrun=True)[source]#

Bases: object

CuPy-enabled iterative reconstruction algorithms using ASTRA toolbox for forward/back projection. Parameters for reconstruction algorithms should be provided in three dictionaries: _data_, _algorithm_, and _regularisation_. See tomobar.supp.dicts function of ToMoBAR’s API reference for all parameters explained.

This implementation is typically several times faster than the one in RecToolsIR.FISTA() of tomobar.methodsIR, but not all functionality is supported yet.

Parameters:

  • DetectorsDimH (int) – Horizontal detector dimension size.

  • DetectorsDimH_pad (int) – The amount of padding for the horizontal detector.

  • DetectorsDimV (int) – Vertical detector dimension size.

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

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

  • ObjSize (int) – The size of the reconstructed object (a slice) defined as [recon_size, recon_size].

  • datafidelity (str, optional) – Data fidelity, choose from LS and PWLS. Defaults to LS.

  • device_projector (int, optional) – Provide a GPU index of a specific GPU device. Defaults to 0.

  • cupyrun (bool, optional) – instantiate CuPy modules.

CGLS(_data_: dict, _algorithm_: dict | None = None) → <cp.ndarray>[source]#

Conjugate Gradients Least Squares iterative technique to reconstruct projection data given as a CuPy array. See more about the method here.

Parameters:

  • _data_ (dict) – Data dictionary, where projection data is provided.

  • _algorithm_ (dict, optional) – Algorithm dictionary where algorithm parameters are provided.

Returns:

CGLS-reconstructed volume as a CuPy array.

Return type:

cp.ndarray

FISTA(_data_: dict, _algorithm_: dict | None = None, _regularisation_: dict | None = None) → <cp.ndarray>[source]#

A Fast Iterative Shrinkage-Thresholding Algorithm [BT2009] with various types of regularisation from the regularisation toolkit [KAZ2019] (currently accepts ROF_TV and PD_TV only). See more about the method here.

All parameters for the algorithm should be provided in three dictionaries: _data_, _algorithm_, and _regularisation_. See tomobar.supp.dicts function of ToMoBAR’s API reference for all parameters explained. Please note that not all of the functionality supported in this CuPy implementation compared to RecToolsIR.FISTA() from tomobar.methodsIR.

Parameters:

  • _data_ (dict) – Data dictionary, where input data is provided.

  • _algorithm_ (dict, optional) – Algorithm dictionary where algorithm parameters are provided.

  • _regularisation_ (dict, optional) – Regularisation dictionary, currently accepts ROF_TV and PD_TV only.

Returns:

FISTA-reconstructed 3D CuPy array

Return type:

cp.ndarray

Landweber(_data_: dict, _algorithm_: dict | None = None) → <cp.ndarray>[source]#

Using Landweber iterative technique to reconstruct projection data given as a CuPy array.

Parameters:

  • _data_ (dict) – Data dictionary, where projection data is provided.

  • _algorithm_ (dict, optional) – Algorithm dictionary where algorithm parameters are provided.

Returns:

The Landweber-reconstructed volume as a CuPy array.

Return type:

cp.ndarray

SIRT(_data_: dict, _algorithm_: dict | None = None) → <cp.ndarray>[source]#

Using Simultaneous Iterations Reconstruction Technique (SIRT) iterative technique to reconstruct projection data given as a CuPy array. See more about the method here.

Parameters:

  • _data_ (dict) – Data dictionary, where projection data is provided.

  • _algorithm_ (dict, optional) – Algorithm dictionary where algorithm parameters are provided.

Returns:

SIRT-reconstructed volume as a CuPy array.

Return type:

cp.ndarray

property cupyrun: int#
property datafidelity: int#
property objsize_user_given: int#
powermethod(_data_: dict) → float[source]#

Power iteration algorithm to calculate the eigenvalue of the operator (projection matrix). projection_raw_data is required for the PWLS fidelity, otherwise will be ignored.

Parameters:

_data_ (dict) – Data dictionary, where input data is provided.

Returns:

the Lipschitz constant

Return type:

float

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