multi-material CT scan: avoid saving data to disk between gVXR and CIL

[paskino]

One could skip saving the data to disk between gVXR and CIL

source_position = [0.0, sod, 0.0];
gvxr.setSourcePosition(*source_position, "mm");

# Set the detector properties
detector_position = (0.0, -(sdd - sod), 0.0)
gvxr.setDetectorPosition(*detector_position, "mm")

detectorUp = (0, 0, 1)
gvxr.setDetectorUpVector(*detectorUp)

detectorNumberOfPixels = (raw_reference.shape[1] // scaling_factor, raw_reference.shape[0] // scaling_factor)
gvxr.setDetectorNumberOfPixels(*detectorNumberOfPixels);

ctscan = np.asarray(gvxr.getLastProjectionSet(), dtype=np.float32)


###
scaling_factor = 8
detectorSpacing = (imager_pixel_spacing_in_um[0] * scaling_factor, imager_pixel_spacing_in_um[1] * scaling_factor);
gvxr.setDetectorPixelSize(*detectorSpacing, "um");

gvxr.computeCTAcquisition("", "",
                          number_of_projections, # The total number of projections to simulate.
                          0, # The rotation angle corresponding to the first projection.
                          True, # A boolean flag to include or exclude the last angle. It is used to calculate the angular step between successive projections.
                          360,
                          0, # The number of white images used to perform the flat-field correction. If zero, then no correction will be performed.
                          *translation_vector_in_mm, # The location of the rotation centre.
                          "mm", # The corresponding unit of length.
                          *gvxr.getDetectorUpVector(), # The rotation axis
                          True # If true the energy fluence is returned, otherwise the number of photons is returned
                               # (default value: true)
);


from cil.framework import AcquisitionGeometry, ImageGeometry
from cil.utilities.display import show_geometry

ag = AcquisitionGeometry.create_Cone3D(source_position=source_position,
                                       detector_position=detector_position,
                                        # detector_direction=detectorUp,
                                        rotation_axis_direction=[0, 0, 1],
                                        )
ag.set_panel(detectorNumberOfPixels, [el/1000 for el in detectorSpacing], 'bottom-right')
ag.set_angles(np.linspace(0, 2 * np.pi, number_of_projections, endpoint=False), 
              angle_unit='radian')

data = AcquisitionData(ctscan, geometry=ag, deep_copy=False)

These changes correctly reconstruct the simulated data: