SART_CUDA

This is a GPU implementation of the Simultaneous Algebraic Reconstruction Technique (SART) for 2D data sets. It takes projection data and an initial reconstruction as input, and returns the reconstruction after a specified number of SART iterations. Each iteration of SART consists of an FP and BP of one single projection direction. The order of the projections can be specified.

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 SART is used as the initial reconstruction.
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.ProjectionOrder	optional	This specifies the order in which the projections are used. Possible values are: ‘random’ (default), ‘sequential’, and ‘custom’. If ‘custom’ is specified, the option.ProjectionOrderList is required.
cfg.option.ProjectionOrderList	optional	Required if option.ProjectionOrder = ‘custom’, ignored otherwise. A matlab vector containing the custom order in which the projections are used.
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

PythonMatlab

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('SART_CUDA')
cfg['ProjectorId'] = proj_id
cfg['ProjectionDataId'] = sinogram_id
cfg['ReconstructionDataId'] = recon_id
cfg['option'] = { }
cfg['option']['ProjectionOrder'] =  'custom'
# set projection order to 0, 5, 10, ..., 175, 1, 6, 11, ...., 176, 2, 7, .....
cfg['option']['ProjectionOrderList'] = numpy.array(range(180)).reshape(-1,5).T.reshape(-1)

sart_id = astra.algorithm.create(cfg)
astra.algorithm.run(sart_id, 10*180)
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(sart_id)

Extra features

SART_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.)