Real data reconstruction

This tutorial covers real data reconstruction using ToMoBAR software. The data is obtained at Diamond Light Source facility (UK synchrotron), i12 beamline. The sample is a magnesium allow which undergoes some thermal changes in which the dendritic growth occurs. See more details about the experiment in [GUO2018] and more on reconstruction using ToMoBAR in [KAZ2017].

This tutorial loosely follows Demo_RealData.py demo.

• We will extract a 2D sinogram out of 3D projection data and reconstruct it using the FBP method.

from tomobar.methodsDIR import RecToolsDIR

Rectools = RecToolsDIR(
DetectorsDimH=detectorHoriz,  # Horizontal detector dimension
DetectorsDimV=None,  # Vertical detector dimension
CenterRotOffset=None,  # Center of Rotation scalar
AnglesVec=angles_rad,  # A vector of projection angles in radians
ObjSize=N_size,  # Reconstructed object dimensions (scalar)
device_projector="gpu",
)

FBPrec = Rectools.FBP(sinogram, data_axes_labels_order=["detX", "angles"])

• Next we reconstruct using ordered-subsets FISTA with Total Variation regularisation.

from tomobar.methodsIR import RecToolsIR

Rectools = RecToolsIR(
    DetectorsDimH=detectorHoriz,  # Horizontal detector dimension
    DetectorsDimV=None,  # Vertical detector dimension (3D case)
    CenterRotOffset=None,  # Center of Rotation scalar
    AnglesVec=angles_rad,  # A vector of projection angles in radians
    ObjSize=N_size,  # Reconstructed object dimensions (scalar)
    datafidelity="PWLS",  # Data fidelity term
    device_projector="gpu",
)

_data_ = {
    "projection_norm_data": sinogram,  # Normalised projection data
    "projection_raw_data": sinogram_raw,  # Raw projection data
    "OS_number": 6,  # The number of subsets
    "data_axes_labels_order": ["detX", "angles"],
}
lc = Rectools.powermethod(_data_)  # calculate Lipschitz constant

_algorithm_ = {"iterations": 25, "lipschitz_const": lc}

_regularisation_ = {
    "method": "PD_TV",  # Regularisation method
    "regul_param": 0.000002,  # Regularisation parameter
    "iterations": 60,  # The number of regularisation iterations
    "device_regulariser": "gpu",
}

RecFISTA = Rectools.FISTA(_data_, _algorithm_, _regularisation_)

• Then we will add the Group-Huber data fidelity model [PM2015] to minimise the ring artefacts. The only change needed is the update of the _data_ dictionary.

 _data_.update({"ringGH_lambda": 0.000015})
 _data_.update({"ringGH_accelerate": 6})

RecFISTA = Rectools.FISTA(_data_, _algorithm_, _regularisation_)

• We also can try the Stripe-Weighted Least Squares (SWLS) data model [HOA2017]. As we change the data fidelity, we need to re-initialise the class object.

Rectools = RecToolsIR(
    DetectorsDimH=detectorHoriz,  # Horizontal detector dimension
    DetectorsDimV=None,  # Vertical detector dimension (3D case)
    CenterRotOffset=None,  # Center of Rotation scalar
    AnglesVec=angles_rad,  # A vector of projection angles in radians
    ObjSize=N_size,  # Reconstructed object dimensions (scalar)
    datafidelity="SWLS",  # Data fidelity term
    device_projector="gpu",
)

_data_ = {
    "projection_norm_data": sinogram,  # Normalised projection data
    "projection_raw_data": sinogram_raw,  # Raw projection data
    "OS_number": 6,  # The number of subsets
    "beta_SWLS": 0.2,  #  parameter for the SWLS model
    "data_axes_labels_order": ["detX", "angles"],
}

RecFISTA = Rectools.FISTA(_data_, _algorithm_, _regularisation_)

As one can see that visually the SWLS model produced the best reconstruction here. This model is indeed works very well when the stripes (rings) are full and not partial.