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_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	type	description
cfg.ProjectionDataId	required	The astra_mex_data3d ID of the projection data
cfg.ReconstructionDataId	required	The astra_mex_data3d ID of the reconstruction data. The content of this when starting SIRT3D_CUDA is used as the initial reconstruction.
cfg.option.ReconstructionMaskId	optional	If specified, the astra_mex_data3d ID of a volume-data-sized volume to be used as a mask. It should only have values 0.0 and 1.0. See the section on [Masks] for details.
cfg.option.GPUindex	optional	The index (zero-based) of the GPU to use. (default: 0)
cfg.option.DetectorSuperSampling	optional	For the forward projection, DetectorSuperSampling^2 rays will be used. This should only be used if your detector pixels are larger than the voxels in the reconstruction volume. (default: 1)
cfg.option.VoxelSuperSampling	optional	For the backward projection, VoxelSuperSampling^3 rays will be used. This should only be used if your voxels in the reconstruction volume are larger than the detector pixels. (default: 1)

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.)