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    This study presents an optimized coded aperture design for compressive X-ray tomosynthesis, reducing radiation dose. Optimized apertures improve image quality and sensing uniformity compared to random designs.

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    Area of Science:

    • Medical Imaging
    • Computational Imaging
    • X-ray Imaging

    Background:

    • Radiation dose is a significant concern in X-ray tomographic imaging.
    • Conventional tomosynthesis requires sequential projections, increasing scan time and radiation exposure.
    • Coded aperture compressive X-ray tomosynthesis offers a potential solution for dose reduction.

    Purpose of the Study:

    • To present an optimization approach for designing coded apertures in multiple-source compressive X-ray tomosynthesis.
    • To ensure uniform energy distribution across object voxels and detector elements for improved sensing.
    • To evaluate the performance of optimized coded apertures against random designs.

    Main Methods:

    • Utilizing a multiple-source system with different coded apertures to acquire multiplexed projections.
    • Employing compressed sensing (CS) reconstruction algorithms to recover 3D data.
    • Implementing a uniform energy criterion for optimizing coded aperture structure.

    Main Results:

    • Simulations and experimental results demonstrate the effectiveness of optimized coded apertures.
    • Optimized apertures achieve better performance compared to random coded apertures.
    • Uniform energy criteria lead to uniformly sensed objects and detector elements.

    Conclusions:

    • Optimized coded apertures are crucial for enhancing coded aperture compressive X-ray tomosynthesis.
    • This approach effectively reduces radiation dose while maintaining or improving image quality.
    • The presented optimization strategy provides a pathway for advanced X-ray imaging systems.