Comparison of genetic algorithm and algebraic reconstruction for X-ray tomography in bubbling fluidized beds

Abstract

The performance of two tomographic reconstruction algorithms, Simultaneous Algebraic Reconstruction Technique (SART) and Adaptive Genetic Algorithm (AGA), is evaluated based on synthetic data mimicking X-ray computed tomography of a bubbling fluidized bed. The simulations are based on a high speed X-ray tomography system, consisting of 3 X-ray sources and 32 detectors for each source. The comparison between SART and AGA is made for image resolutions ranging from 20×20 to 50×50pixels, for the cases of 2 phantoms (artificial voids) and 3 phantoms in a 23cm diameter column. The influence of noise on the reconstructions for both algorithms is also considered. It is found that AGA provides better reconstructions than SART at low resolutions. At high resolutions, the reconstruction quality is comparable, but the calculation times for AGA are much longer. AGA is better at finding the phantom position as it is less sensitive to measurement noise.