SIRT ==== This is a CPU 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, matrix. Configuration options --------------------- =============================== ======== ================================================================================================================================================== name type description =============================== ======== ================================================================================================================================================== cfg.ProjectorId required The astra_mex_projector ID of the projector. 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. =============================== ======== ================================================================================================================================================== Example ------- .. tabs:: .. group-tab:: Python .. code-block:: python 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('linear', 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') 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) .. group-tab:: Matlab .. code-block:: matlab %% create phantom V_exact = phantom(256); %% create geometries and projector proj_geom = astra_create_proj_geom('parallel', 1.0, 256, linspace2(0,pi,180)); vol_geom = astra_create_vol_geom(256,256); proj_id = astra_create_projector('linear', proj_geom, vol_geom); %% create forward projection [sinogram_id, sinogram] = astra_create_sino(V_exact, proj_id); %% reconstruct recon_id = astra_mex_data2d('create', '-vol', vol_geom, 0); cfg = astra_struct('SIRT'); cfg.ProjectorId = proj_id; cfg.ProjectionDataId = sinogram_id; cfg.ReconstructionDataId = recon_id; cfg.option.MinConstraint = 0; cfg.option.MaxConstraint = 255; sirt_id = astra_mex_algorithm('create', cfg); astra_mex_algorithm('iterate', sirt_id, 100); V = astra_mex_data2d('get', recon_id); imshow(V, []); %% garbage disposal astra_mex_data2d('delete', sinogram_id, recon_id); astra_mex_projector('delete', proj_id); astra_mex_algorithm('delete', sirt_id);