BP3D_CUDA

This is a GPU implementation of a simple backprojection algorithm for 3D data sets. It takes projection data as input, and returns the backprojection of this data.

Configuration options

Name

Description

ProjectionDataId

Projection data object ID.

ReconstructionDataId

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

option.VoxelSuperSampling

Each voxel in the volume will be subdivided by this factor along each dimension. This should only be used if voxels in the volume are larger than the detector pixels (default: 1).

option.GPUindex

The index of the GPU to use (default: 0).

Example

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

# Create geometries
N = 256
N_ANGLES = 180
det_spacing = 1.0
angles = np.linspace(0, np.pi, N_ANGLES)
proj_geom = astra.create_proj_geom('parallel3d', det_spacing, det_spacing, N, N, angles)
vol_geom = astra.create_vol_geom(N, N, N)

# Generate phantom image
phantom_id, phantom = astra.data3d.shepp_logan(vol_geom)

# Create forward projection
sinogram_id, sinogram = astra.create_sino3d_gpu(phantom_id, proj_geom, vol_geom)

# Calculate backprojection
backprojection_id = astra.data3d.create('-vol', vol_geom)
cfg = astra.astra_dict('BP3D_CUDA')
cfg['ProjectionDataId'] = sinogram_id
cfg['ReconstructionDataId'] = backprojection_id
algorithm_id = astra.algorithm.create(cfg)

astra.algorithm.run(algorithm_id)

backprojection = astra.data3d.get(backprojection_id)
plt.imshow(backprojection[N//2], cmap='gray')

# Clean up
astra.data3d.delete([sinogram_id, backprojection_id, phantom_id])
astra.algorithm.delete(algorithm_id)