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)