FBP_CUDA

This is a GPU implementation of the Filtered Backprojection (FBP) algorithm for 2D data sets. It takes projection data as input, and returns the reconstruction.

Supported geometries: parallel, fanflat

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.FilterType	Type of projection filter. Options: * ‘none’ * ‘ram-lak’ (default) * ‘shepp-logan’ * ‘cosine’ * ‘hamming’ * ‘hann’ * ‘tukey’ * ‘lanczos’ * ‘triangular’ * ‘gaussian’ * ‘barlett-hann’ * ‘blackman’ * ‘nuttall’ * ‘blackman-harris’ * ‘blackman-nuttall’ * ‘flat-top’ * ‘kaiser’ * ‘parzen’ * ‘projection’ (Fourier space filter, all projection directions share one filter) * ‘sinogram’ (Fourier space filter, every projection direction has its own filter) * ‘rprojection’ (real space filter, all projection directions share one filter) * ‘rsinogram’ (real space filter, every projection direction has its own filter)
option.FilterParameter	Parameter value for the ‘tukey’, ‘gaussian’, ‘blackman’ and ‘kaiser’ filter types.
option.FilterD	“D” parameter value for ‘shepp-logan’, ‘cosine’, ‘hamming’ and ‘hann’ filter types.
option.FilterSinogramId	The data object ID of the filter data for ‘projection’, ‘sinogram’, ‘rprojection’ and ‘rsinogram’ filter types.
option.ShortScan	Only for use with the fanflat geometry. If enabled, do Parker weighting to support non-360-degree data. This needs an angle range of at least 180 degrees plus twice the fan angle (default: false).
option.PixelSuperSampling	Each pixel in the volume will be subdivided by this factor along each dimension. This should only be used if pixels in the volume are larger than the detector elements (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 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('cuda', 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)

# Reconstruct
recon_id = astra.data2d.create('-vol', vol_geom, data=1.0)
cfg = astra.astra_dict('FBP_CUDA')
cfg['ProjectionDataId'] = sinogram_id
cfg['ReconstructionDataId'] = recon_id
algorithm_id = astra.algorithm.create(cfg)

astra.algorithm.run(algorithm_id)

reconstruction = astra.data2d.get(recon_id)
plt.imshow(reconstruction, cmap='gray')

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

%% Create phantom
N = 256;
phantom = phantom(N);

%% Create geometries and projector
det_spacing = 1.0;
N_ANGLES = 180;
angles = linspace(0, 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('cuda', proj_geom, vol_geom);

%% Create forward projection
[sinogram_id, sinogram] = astra_create_sino(phantom, projector_id);

%% Reconstruct
recon_id = astra_mex_data2d('create', '-vol', vol_geom, 1.0);
cfg = astra_struct('FBP_CUDA');
cfg.ProjectionDataId = sinogram_id;
cfg.ReconstructionDataId = recon_id;
algorithm_id = astra_mex_algorithm('create', cfg);

astra_mex_algorithm('run', algorithm_id);

reconstruction = astra_mex_data2d('get', recon_id);
imshow(reconstruction, []);

%% Clean up
astra_mex_data2d('delete', sinogram_id, recon_id);
astra_mex_projector('delete', projector_id);
astra_mex_algorithm('delete', algorithm_id);