Multi-objective optimization for structured illumination in dynamic x-ray tomosynthesis

Xu Ma; Hao Xu; Carlos M. Restrepo; Gonzalo R. Arce

doi:10.1364/AO.428871

Multi-objective optimization for structured illumination in dynamic x-ray tomosynthesis

Xu Ma^*
, Hao Xu
, Carlos M. Restrepo
, Gonzalo R. Arce

^*Corresponding author for this work

Beijing Institute of Technology
University of Delaware

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

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 fromthe 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 dynamicCXTsystem. TheMOframework 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.

Original language	English
Pages (from-to)	6177-6188
Number of pages	12
Journal	Applied Optics
Volume	60
Issue number	21
DOIs	https://doi.org/10.1364/AO.428871
Publication status	Published - 20 Jul 2021
Externally published	Yes

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title = "Multi-objective optimization for structured illumination in dynamic x-ray tomosynthesis",

abstract = "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 fromthe 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 dynamicCXTsystem. TheMOframework 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.",

author = "Xu Ma and Hao Xu and Restrepo, \{Carlos M.\} and Arce, \{Gonzalo R.\}",

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AU - Ma, Xu

AU - Xu, Hao

AU - Restrepo, Carlos M.

AU - Arce, Gonzalo R.

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Y1 - 2021/7/20

N2 - 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 fromthe 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 dynamicCXTsystem. TheMOframework 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.

AB - 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 fromthe 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 dynamicCXTsystem. TheMOframework 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.

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SP - 6177

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JO - Applied Optics

JF - Applied Optics

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Multi-objective optimization for structured illumination in dynamic x-ray tomosynthesis

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