# ----------------------------------------------------------------------- # Copyright: 2010-2016, iMinds-Vision Lab, University of Antwerp # 2013-2016, CWI, Amsterdam # # Contact: astra@astra-toolbox.com # Website: http://www.astra-toolbox.com/ # # This file is part of the ASTRA Toolbox. # # # The ASTRA Toolbox is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # The ASTRA Toolbox is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with the ASTRA Toolbox. If not, see . # # ----------------------------------------------------------------------- import astra import numpy as np vol_geom = astra.create_vol_geom(256, 256) proj_geom = astra.create_proj_geom('parallel', 3.0, 128, np.linspace(0,np.pi,180,False)) import scipy.io P = scipy.io.loadmat('phantom.mat')['phantom256'] # Because the astra.create_sino method does not have support for # all possible algorithm options, we manually create a sinogram phantom_id = astra.data2d.create('-vol', vol_geom, P) sinogram_id = astra.data2d.create('-sino', proj_geom) cfg = astra.astra_dict('FP_CUDA') cfg['VolumeDataId'] = phantom_id cfg['ProjectionDataId'] = sinogram_id # Set up 3 rays per detector element cfg['option'] = {} cfg['option']['DetectorSuperSampling'] = 3 alg_id = astra.algorithm.create(cfg) astra.algorithm.run(alg_id) astra.algorithm.delete(alg_id) astra.data2d.delete(phantom_id) sinogram3 = astra.data2d.get(sinogram_id) import pylab pylab.gray() pylab.figure(1) pylab.imshow(P) pylab.figure(2) pylab.imshow(sinogram3) # Create a reconstruction, also using supersampling rec_id = astra.data2d.create('-vol', vol_geom) cfg = astra.astra_dict('SIRT_CUDA') cfg['ReconstructionDataId'] = rec_id cfg['ProjectionDataId'] = sinogram_id # Set up 3 rays per detector element cfg['option'] = {} cfg['option']['DetectorSuperSampling'] = 3 # There is also an option for supersampling during the backprojection step. # This should be used if your detector pixels are smaller than the voxels. # Set up 2 rays per image pixel dimension, for 4 rays total per image pixel. # cfg['option']['PixelSuperSampling'] = 2 alg_id = astra.algorithm.create(cfg) astra.algorithm.run(alg_id, 150) astra.algorithm.delete(alg_id) rec = astra.data2d.get(rec_id) pylab.figure(3) pylab.imshow(rec) pylab.show()