Open AccessMulti-objective optimization for structured illumination in dynamic x-ray tomosynthesis
Author(s) -
Xu Ma,
Huilong Xu,
Carlos M. Restrepo,
Gonzalo R. Arce
Publication year - 2021
Publication title -
applied optics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.668
H-Index - 197
eISSN - 2155-3165
pISSN - 1559-128X
DOI - 10.1364/ao.428871
Subject(s) - tomosynthesis , computer science , structured light , coding (social sciences) , detector , optics , attenuation , image quality , algorithm , computer vision , physics , mathematics , image (mathematics) , telecommunications , medicine , statistics , cancer , breast cancer , mammography
Dynamic coded x-ray tomosynthesis (CXT) uses a set of encoded x-ray sources to interrogate objects lying on a moving conveyor mechanism. The object is reconstructed from the encoded measurements received by the uniform linear array detectors. We propose a multi-objective optimization (MO) method for structured illuminations to balance the reconstruction quality and radiation dose in a dynamic CXT system. The MO framework is established based on a dynamic sensing geometry with binary coding masks. The Strength Pareto Evolutionary Algorithm 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 method. They show that the proposed strategy can obtain a set of Pareto optimal solutions with different levels of radiation dose and better reconstruction quality than the initial setting.