CGLS_CUDA
This is a GPU implementation of the Conjugate Gradient Least Squares (CGLS) algorithm for 2D data sets. It takes projection data and an initial reconstruction as input, and returns the reconstruction after a specified number of iterations.
The internal state of the CGLS algorithm is reset every time 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: 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 CGLS 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.GPUindex |
optional |
Specifies which GPU to use. Default = 0. |
cfg.option.DetectorSuperSampling |
optional |
For the forward projection, DetectorSuperSampling rays will be used. This should only be used if your detector pixels are larger than the voxels in the reconstruction volume. Defaults to 1. |
cfg.option.PixelSuperSampling |
optional |
For the backward projection, PixelSuperSampling^2 rays will be used. This should only be used if your voxels in the reconstruction volume are larger than the detector pixels. Defaults to 1. |
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('CGLS_CUDA')
cfg['ProjectorId'] = proj_id
cfg['ProjectionDataId'] = sinogram_id
cfg['ReconstructionDataId'] = recon_id
cgls_id = astra.algorithm.create(cfg)
astra.algorithm.run(cgls_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(cgls_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('CGLS_CUDA');
cfg.ProjectionDataId = sinogram_id;
cfg.ReconstructionDataId = recon_id;
cgls_id = astra_mex_algorithm('create', cfg);
astra_mex_algorithm('iterate', cgls_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', cgls_id);