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    Compressive spectral X-ray imaging (CSXI) uses K-edge coded apertures for lower-dose material characterization. This novel approach enables high-quality tomographic reconstruction from fewer measurements using advanced algorithms.

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    Area of Science:

    • Medical Imaging
    • Materials Science
    • Computational Imaging

    Background:

    • Spectral computed tomography (CT) uses X-ray attenuation's spectral dependence for material characterization.
    • Current methods require multiple energy bins for accurate material identification.
    • Applications span national security and medical diagnostics.

    Purpose of the Study:

    • Introduce a novel compressive spectral X-ray imaging (CSXI) method.
    • Develop a new approach for tomographic material characterization with reduced X-ray dose.
    • Explore innovative spatial and spectral coding strategies.

    Main Methods:

    • Utilized K-edge coded apertures for spatially and spectrally coded X-ray bundles.
    • Employed low-cost integrating detectors to acquire compressive measurements.
    • Investigated various spectral and spatial subsampling strategies (view angle, block-unblock apertures).
    • Developed a multi-stage algorithm and ADMM for reconstructing energy-binned images from limited data.

    Main Results:

    • Demonstrated CSXI's ability to reconstruct energy-binned images from fewer measurements.
    • Showcased the critical role of coded aperture design and subsampling strategies in reconstruction quality.
    • Successfully addressed the non-linear, ill-posed forward imaging model of CSXI.
    • Reconstructed spectral X-ray data cubes by solving a joint sparse and low-rank optimization problem.

    Conclusions:

    • CSXI offers a radical departure from conventional spectral imaging, enabling lower-dose material characterization.
    • The developed reconstruction algorithms effectively handle the complexities of compressive spectral data.
    • This technique holds significant potential for advancing medical imaging and national security applications.