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	type	description
cfg.ProjectionDataId	required	The astra_mex_data2d ID of the projection data
cfg.ReconstructionDataId	required	The astra_mex_data2d ID of the reconstruction data. The content of this is overwritten.
cfg.FilterType	optional	Type of projection filter. Options: ‘ram-lak’ (default), ‘shepp-logan’, ‘cosine’, ‘hamming’, ‘hann’, ‘none’, ‘tukey’, ‘lanczos’, ‘triangular’, ‘gaussian’, ‘barlett-hann’, ‘blackman’, ‘nuttall’, ‘blackman-harris’, ‘blackman-nuttall’, ‘flat-top’, ‘kaiser’, ‘parzen’, ‘projection’, ‘sinogram’, ‘rprojection’, ‘rsinogram’.
cfg.FilterSinogramId	optional	Only for some FilterTypes.
cfg.FilterParameter	optional	Only for some FilterTypes.
cfg.FilterD	optional	Only for some FilterTypes.
cfg.option.GPUindex	optional	Specifies which GPU to use. Default = 0.
cfg.option.PixelSuperSampling	optional	Specifies the amount of pixel supersampling, i.e., how many (one dimension) subpixels are generated from a single parent pixel.
cfg.option.ShortScan	optional	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. Defaults to no.

Example

import astra
import matplotlib.pyplot as plt
import numpy

# create geometries and projector
proj_geom = astra.create_proj_geom('parallel', 1.0, 256, numpy.linspace(0, numpy.pi, 180, endpoint=False))
vol_geom = astra.create_vol_geom(256,256)
proj_id = astra.create_projector('cuda', proj_geom, vol_geom)

# generate phantom image
V_exact_id, V_exact = astra.data2d.shepp_logan(vol_geom)

# create forward projection
sinogram_id, sinogram = astra.create_sino(V_exact, proj_id)

# reconstruct
recon_id = astra.data2d.create('-vol', vol_geom, 0)
cfg = astra.astra_dict('FBP_CUDA')
cfg['ProjectorId'] = proj_id
cfg['ProjectionDataId'] = sinogram_id
cfg['ReconstructionDataId'] = recon_id
cfg['option'] = { 'MinConstraint': 0, 'MaxConstraint': 1 }
fbp_id = astra.algorithm.create(cfg)
astra.algorithm.run(fbp_id)
V = astra.data2d.get(recon_id)
plt.gray()
plt.imshow(V)
plt.show()

# garbage disposal
astra.data2d.delete([sinogram_id, recon_id, V_exact_id])
astra.projector.delete(proj_id)
astra.algorithm.delete(fbp_id)

%% create phantom
V_exact = phantom(256);

%% create geometries
proj_geom = astra_create_proj_geom('parallel', 1.0, 256, linspace2(0,pi,180));
vol_geom = astra_create_vol_geom(256,256);

%% create forward projection
[sinogram_id, sinogram] = astra_create_sino_cuda(V_exact, proj_geom, vol_geom);

%% reconstruct
recon_id = astra_mex_data2d('create', '-vol', vol_geom, 0);
cfg = astra_struct('FBP_CUDA');
cfg.ProjectionDataId = sinogram_id;
cfg.ReconstructionDataId = recon_id;
fbp_id = astra_mex_algorithm('create', cfg);
astra_mex_algorithm('run', fbp_id);
V = astra_mex_data2d('get', recon_id);
imshow(V, []);

%% garbage disposal
astra_mex_data2d('delete', sinogram_id, recon_id);
astra_mex_algorithm('delete', fbp_id);