Examples 2: CT Scanner

In CT scanner example, a water box is simulated associated to a CT curved detector. One projection is computed simulating 1e9 particles.

$ python ct_scanner.py [-h] [-d DEVICE] [-b BALANCE] [-n N_PARTICLES] [-s SEED] [-v VERBOSE]
-h/--help           Printing help into the screen
-d/--device         OpenCL device (all, cpu, gpu, gpu_nvidia, gpu_intel, gpu_amd, "X;Y;Z"...)
                    using all gpu: -d gpu
                    using device index 0 and 2: -d "0;2"
-b/--balance        Balance computation for device if many devices are selected "X;Y;Z"
                    60% computation on device 0 and 40% computatio on device 2: -b "0.6;0.4"
-n/--nparticles     Number of particles (default: 1000000)
-s/--seed           Seed of pseudo generator number (default: 777)
-v/--verbose        Setting level of verbosity

Load a water box phantom:

phantom = GGEMSVoxelizedPhantom('phantom')
phantom.set_phantom('data/phantom.mhd', 'data/range_phantom.txt')
phantom.set_rotation(0.0, 0.0, 0.0, 'deg')
phantom.set_position(0.0, 0.0, 0.0, 'mm')

Create a CT curved detector:

ct_detector = GGEMSCTSystem('Stellar')
ct_detector.set_ct_type('curved')
ct_detector.set_number_of_modules(1, 46)
ct_detector.set_number_of_detection_elements(64, 16, 1)
ct_detector.set_size_of_detection_elements(0.6, 0.6, 0.6, 'mm')
ct_detector.set_material('GOS')
ct_detector.set_source_detector_distance(1085.6, 'mm')
ct_detector.set_source_isocenter_distance(595.0, 'mm')
ct_detector.set_rotation(0.0, 0.0, 0.0, 'deg')
ct_detector.set_threshold(10.0, 'keV')
ct_detector.save('data/projection')
ct_detector.store_scatter(True)

Create a cone-beam X-ray source:

point_source = GGEMSXRaySource('point_source')
point_source.set_source_particle_type('gamma')
point_source.set_number_of_particles(1000000000)
point_source.set_position(-595.0, 0.0, 0.0, 'mm')
point_source.set_rotation(0.0, 0.0, 0.0, 'deg')
point_source.set_beam_aperture(12.5, 'deg')
point_source.set_focal_spot_size(0.0, 0.0, 0.0, 'mm')
point_source.set_polyenergy('data/spectrum_120kVp_2mmAl.dat')

Performance on Windows 11 system and Visual C++ 2022:

Device	Computation Time [s]
GeForce GTX 1050 Ti	112
Quadro P400	385
Xeon X-2245 8 cores / 16 threads	421
GeForce GTX 1050 Ti (80%) Quadro P400 (20%)	91

Performance on Ubuntu 20.04 and GNU GCC 9.3:

Device	Computation Time [s]
GeForce GTX 1050 Ti	90
Quadro P400	360
Xeon X-2245 8 cores / 16 threads	395
GeForce GTX 1050 Ti (80%) Quadro P400 (20%)	70