SIRT_CUDA

This is a GPU implementation of the Simultaneous Iterative Reconstruction Technique (SIRT) for 2D data sets. It takes projection data and an initial reconstruction as input, and returns the reconstruction after a specified number of SIRT iterations.

Supported geometries: parallel, parallel_vec, fanflat, fanflat_vec.

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 when starting SIRT is used as the initial reconstruction.
cfg.option.SinogramMaskId	optional	If specified, the astra_mex_data2d ID of a projection-data-sized volume to be used as a mask.
cfg.option.ReconstructionMaskId	optional	If specified, the astra_mex_data2d ID of a volume-data-sized volume to be used as a mask.
cfg.option.MinConstraint	optional	If specified, all values below MinConstraint will be set to MinConstraint. This can, for example, be used to enforce non-negative reconstructions.
cfg.option.MaxConstraint	optional	If specified, all values above MaxConstraint will be set to MaxConstraint.
cfg.option.GPUindex	optional	Specifies which GPU to use. Default = 0.
cfg.option.DetectorSuperSampling	optional	Specifies the amount of detector supersampling, i.e. how many rays are cast per detector.
cfg.option.PixelSuperSampling	optional	Specifies the amount of pixel supersampling, i.e. how many (one dimension) subpixels are generated from a single parent pixel.

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('SIRT_CUDA')
cfg['ProjectorId'] = proj_id
cfg['ProjectionDataId'] = sinogram_id
cfg['ReconstructionDataId'] = recon_id
cfg['option'] = { 'MinConstraint': 0, 'MaxConstraint': 1 }
sirt_id = astra.algorithm.create(cfg)
astra.algorithm.run(sirt_id, 100)
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(sirt_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('SIRT_CUDA');
cfg.ProjectionDataId = sinogram_id;
cfg.ReconstructionDataId = recon_id;
cfg.option.MinConstraint = 0;
cfg.option.MaxConstraint = 255;
sirt_id = astra_mex_algorithm('create', cfg);
astra_mex_algorithm('iterate', sirt_id, 100);
V = astra_mex_data2d('get', recon_id);
imshow(V, []);

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

Extra features

SIRT_CUDA supports astra.algorithm.get_res_norm() / astra_mex_algorithm(‘get_res_norm’) to get the 2-norm of the difference between the projection data and the projection of the reconstruction. (The square root of the sum of squares of differences.)