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)

Extra features

SIRT_CUDA supports 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.)