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    Area of Science:

    • Medical Imaging
    • X-ray Tomography
    • Computational Imaging

    Background:

    • Dynamic coded x-ray tomosynthesis (CXT) enables object reconstruction from encoded measurements using linear array detectors.
    • Balancing reconstruction quality and radiation dose is crucial for effective dynamic CXT systems.

    Purpose of the Study:

    • To propose a multi-objective optimization (MO) method for optimizing structured illuminations in dynamic CXT.
    • To balance reconstruction quality and radiation dose in dynamic CXT systems.

    Main Methods:

    • Developed an MO framework utilizing a dynamic sensing geometry with binary coding masks.
    • Employed the Strength Pareto Evolutionary Algorithm 2 to optimize coding masks, source locations, and exposure timings.
    • Conducted computational experiments to evaluate the proposed MO method.

    Main Results:

    • The MO method generated a set of Pareto optimal solutions, demonstrating trade-offs between radiation dose and reconstruction quality.
    • Achieved better reconstruction quality compared to the initial system settings.
    • Successfully optimized parameters for dynamic CXT imaging.

    Conclusions:

    • The proposed multi-objective optimization strategy effectively balances reconstruction quality and radiation dose in dynamic CXT.
    • This method provides a valuable tool for enhancing the performance and safety of dynamic CXT systems.
    • Further research can explore real-world implementation and validation of the optimized parameters.