CGLS3D_CUDA

This is a GPU implementation of the CGLS algorithm for 3D data sets. It takes projection data and an initial reconstruction as input, and returns the reconstruction after a specified number of CGLS iterations.

The internal state of the CGLS algorithm is reset every time astra.algorithm.run/astra_mex_algorithm('iterate') is called. This implies that running CGLS for N iterations and then running it for another N iterations may yield different results from running it 2N iterations at once.

Supported geometries: all 3D geometries.

Configuration options

Name	Description
ProjectionDataId	Projection data object ID.
ReconstructionDataId	ID of data object to store the result. The content of this data is used as the initial reconstruction.
option.ReconstructionMaskId	If specified, data object ID of a volume-data-sized volume to be used as a mask.
option.DetectorSuperSampling	During forward projection, each detector pixel will be subdivided by this factor along each dimension. This should only be used if detector pixels are larger than the voxels in the volume (default: 1).
option.VoxelSuperSampling	During backprojection, 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)

# Reconstruct
recon_id = astra.data3d.create('-vol', vol_geom)
cfg = astra.astra_dict('CGLS3D_CUDA')
cfg['ProjectionDataId'] = sinogram_id
cfg['ReconstructionDataId'] = recon_id
algorithm_id = astra.algorithm.create(cfg)

astra.algorithm.run(algorithm_id, iterations=100)

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

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

%% Create phantom
N = 256;
phantom_ = repmat(phantom(N), [1, 1, N]);

%% Create geometries
det_spacing = 1.0;
N_ANGLES = 180;
angles = linspace(0, 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);

%% Create forward projection
[sinogram_id, sinogram] = astra_create_sino3d_cuda(phantom_, proj_geom, vol_geom);

%% Reconstruct
recon_id = astra_mex_data3d('create', '-vol', vol_geom);
cfg = astra_struct('CGLS3D_CUDA');
cfg.ProjectionDataId = sinogram_id;
cfg.ReconstructionDataId = recon_id;
algorithm_id = astra_mex_algorithm('create', cfg);

astra_mex_algorithm('iterate', algorithm_id, 100);

reconstruction = astra_mex_data3d('get', recon_id);
imshow(reconstruction(:, :, N/2), []);

%% Clean up
astra_mex_data3d('delete', sinogram_id, recon_id);
astra_mex_algorithm('delete', algorithm_id);

Extra features

CGLS3D_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.)