Application of Nature-Inspired Algorithms to Computed Tomography with Incomplete Data

This paper discusses and compares several computed tomography (CT) algorithms capable of dealing with incomplete data. type problem has been proposed for a symmetrical grid symmetrically distributed transmitters receivers. The use symmetry significantly speeds up the process constructing system equations that is foundation all CT algebraic algorithms. Classic approaches are effective in data scenarios, but suffer from low convergence speed. For this reason, we propose nature-inspired which proven to be many practical optimization problems various domains. efficacy strongly depends on number parameters they maintain reproduce, usually substantial case applications. However, taking into account specificity reconstructed object allows reduce effectively heuristic field CT. suitability three algorithms: Artificial BeeColony (ABC), Ant Colony Optimization (ACO), Clonal Selection Algorithm (CSA) context, showing their advantages weaknesses. best algorithm identified some ideas how remaining methods could improved so as better solve tasks presented.