Basic iterative reconstruction

We start by defining a 3D projection data Numpy array of unsigned integer 16-bit data type (optional) and with axes labels given as ["detY", "angles", "detX"]. We also provide the corresponding flats and darks fields (also 3D arrays of the same axes order).

• The first step is to normalise dataRaw using the normaliser function from tomobar.supp.suppTools.normaliser.

from tomobar.supp.suppTools import normaliser

data_norm = normaliser(dataRaw, flats, darks, log=True, method='mean', axis=1)

• Instantiate the iterative reconstructor tomobar.methodsIR:

from tomobar.methodsIR import RecToolsIR

detectorVert, angles_number, detectorHoriz = np.shape(data_norm)

Rectools = RecToolsIR(
    DetectorsDimH=detectorHoriz,  # Horizontal detector dimension
    DetectorsDimV=detectorVert,  # Vertical detector dimension
    CenterRotOffset=0.0,  # Center of Rotation (needs to be found)
    AnglesVec=angles_rad,  # A vector of projection angles in radians
    ObjSize=detectorHoriz,  # The reconstructed object dimensions
    datafidelity="LS",  # Least-Squares data fidelity for basic methods
    device_projector="gpu", # Device to perform reconstruction on
    )

• Now we have an access to all methods of this particular reconstructor.

  The basic iterative algorithms are wrapped directly from ASTRA-Toolbox, with an exception of CuPy-enabled ones. Let us use SIRT and CGLS reconstruction algorithms.

  Please note that the dictionaries needed for all iterative methods with exact keyword arguments defined in tomobar.supp.dicts.

_data_ = {"projection_norm_data": data_norm,
          "data_axes_labels_order": ["detY", "angles", "detX"],
}  # data dictionary

_algorithm_ = {"iterations": 300, "nonnegativity": True} # algorithm dict

SIRT_Rec = Rectools.SIRT(_data_, _algorithm_)
CGLS_Rec = Rectools.CGLS(_data_, _algorithm_)

One can also operate purely on CuPy arrays if Dependencies are satisfied for the CuPy package. For that one needs to use tomobar.methodsIR_CuPy class instead of tomobar.methodsIR. Note that the array of angles for the CuPy modules should be provided as a Numpy array.