Tabu-DART: a dynamic update strategy for efficient discrete algebraic reconstruction

Abstract In X-ray computed tomography, discrete tomography (DT) algorithms have been successful at reconstructing objects composed of only a few distinct materials. Many DT-based methods rely on divide-and-conquer procedure to reconstruct the volume in parts, which improves their run-time and reconstruction quality. However, this is based static rules, introduces redundant computation diminishes efficiency. work, we introduce an update strategy framework that allows for dynamic rules increases control DT. We illustrate by introducing Tabu-DART, combines our proposed with Discrete Algebraic Reconstruction Technique (DART). Through simulated real data experiments, show approach yields similar or improved quality compared DART, substantially lower computational complexity.