Restrepo, Carlos2024-01-242024-01-242023https://udspace.udel.edu/handle/19716/33913Dynamic coded X-ray tomosynthesis (CXT) uses a set of encoded X-ray sources to interrogate objects lying on a moving conveyor mechanism. The sample is reconstructed from encoded measurements received by uniform linear array detectors. This work introduces a multi-objective optimization (MO) method for structured illuminations, balancing reconstruction quality and radiation dose in dynamic CXT systems. The MO framework is established based on a dynamic sensing geometry with binary coding masks. The Strength Pareto Evolutionary Algorithm 2 (SPEA-2) is used to solve the MO problem by jointly optimizing the coding masks, locations of X-ray sources, and exposure moments. Computational experiments are implemented to assess the proposed MO strategy. Additionally, an analysis based on singular value decomposition was carried out to examine the condition number of the resulting sampling matrices. To ensure that the reconstruction framework does not have dependencies on a particular sample movement, two different movement sequences were employed. The results show that the proposed strategy can obtain a set of Pareto optimal solutions with different levels of radiation dose and better reconstruction quality than benchmark settings under diverse sampling conditions.Coded X-ray tomosynthesisMulti-objective optimizationX-ray sourcesPareto optimal solutionsComputational experimentsThesis1439062625https://doi.org/10.58088/e9k8-h6402024-01-22en