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
---------------------

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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)
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Extra features
--------------

CGLS3D_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.)