Direct 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 direct reconstructor tomobar.methodsDIR:

from tomobar.methodsDIR import RecToolsDIR

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

Rectools = RecToolsDIR(
    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
    device_projector="gpu", # Device to perform reconstruction on
    )

• Now we have an access to all methods of this particular reconstructor, let us use the standard Filtered Backprojection algorithm (FBP).

data_axes_labels3D = ["detY", "angles", "detX"]
FBP_Rec = Rectools.FBP(data_norm, data_axes_labels_order=data_axes_labels3D)

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