BP

This is a CPU implementation of a simple backprojection algorithm for 2D data sets. It takes a projector and projection data as input, and returns the backprojection of this data.

Supported geometries: parallel, parallel_vec, fanflat, fanflat_vec, matrix.

Configuration options

Name

Description

ProjectorId

Projector object ID.

ProjectionDataId

Projection data object ID.

ReconstructionDataId

ID of data object to store the result. The content of this data is overwritten.

option.SinogramMaskId

If specified, data object ID of a projection-data-sized volume to be used as a mask.

option.ReconstructionMaskId

If specified, data object ID of a volume-data-sized volume to be used as a mask.

Example

import astra
import matplotlib.pyplot as plt
import numpy as np

# Create geometries and projector
N = 256
N_ANGLES = 180
det_spacing = 1.0
angles = np.linspace(0, np.pi, N_ANGLES)
proj_geom = astra.create_proj_geom('parallel', det_spacing, N, angles)
vol_geom = astra.create_vol_geom(N, N)
projector_id = astra.create_projector('linear', proj_geom, vol_geom)

# Generate phantom image
phantom_id, phantom = astra.data2d.shepp_logan(vol_geom)

# Create forward projection
sinogram_id, sinogram = astra.create_sino(phantom_id, projector_id)

# Calculate backprojection
backprojection_id = astra.data2d.create('-vol', vol_geom)
cfg = astra.astra_dict('BP')
cfg['ProjectorId'] = projector_id
cfg['ProjectionDataId'] = sinogram_id
cfg['ReconstructionDataId'] = backprojection_id
algorithm_id = astra.algorithm.create(cfg)

astra.algorithm.run(algorithm_id)

backprojection = astra.data2d.get(backprojection_id)
plt.imshow(backprojection, cmap='gray')

# Clean up
astra.data2d.delete([sinogram_id, backprojection_id, phantom_id])
astra.projector.delete(projector_id)
astra.algorithm.delete(algorithm_id)