def process(self):
volume = self.get_input()
volume_axes = volume.get_data_axes_order(new_order=self.default_image_axes_order)
if not volume_axes == [0,1,2]:
volume.array = numpy.transpose(volume.array, volume_axes)
pixel_per_voxel = 1 # should be estimated from image_geometry and
# acquisition_geometry
if self.acquisition_geometry.geom_type == 'parallel':
pixels = pbalg.pb_forward_project(volume.as_array(),
self.acquisition_geometry.angles,
pixel_per_voxel)
out = AcquisitionData(geometry=self.acquisition_geometry,
label_dimensions=self.default_acquisition_axes_order)
out.fill(pixels)
out_axes = out.get_data_axes_order(new_order=self.output_axes_order)
if not out_axes == [0,1,2]:
out.array = numpy.transpose(out.array, out_axes)
return out
else:
raise ValueError('Cannot process cone beam')
from ccpi.optimisation.algs import CGLS
import numpy
# Set up reader object and read the data
datareader = XTEKReader("REPLACE_THIS_BY_PATH_TO_DATASET/SophiaBeads_256_averaged.xtekct")
data = datareader.get_acquisition_data()
# Crop data and fix dimension labels
data = AcquisitionData(data.array[:,:,901:1101],
geometry=data.geometry,
dimension_labels=['angle','horizontal','vertical'])
data.geometry.pixel_num_v = 200
# Scale and negative-log transform
data.array = -np.log(data.as_array()/60000.0)
# Apply centering correction by zero padding, amount found manually
cor_pad = 30
data_pad = np.zeros((data.shape[0],data.shape[1]+cor_pad,data.shape[2]))
data_pad[:,cor_pad:,:] = data.array
data.geometry.pixel_num_h = data.geometry.pixel_num_h + cor_pad
data.array = data_pad
# Choose the number of voxels to reconstruct onto as number of detector pixels
N = data.geometry.pixel_num_h
# Geometric magnification
mag = (np.abs(data.geometry.dist_center_detector) + \
np.abs(data.geometry.dist_source_center)) / \
np.abs(data.geometry.dist_source_center)
