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Hyperspectral image reconstruction for x-ray fluorescence tomography.

Doǧa Gürsoy, Tekin Biçer, Antonio Lanzirotti

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    This study introduces a new hyperspectral X-ray fluorescence tomography method for faster, higher-quality elemental mapping. The penalized maximum-likelihood approach improves image reconstruction, reducing data acquisition times and enhancing scientific discovery.

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    Area of Science:

    • Materials Science
    • Biophysics
    • Photon Science

    Background:

    • X-ray fluorescence tomography (XFT) is crucial for elemental mapping.
    • Current XFT reconstruction methods face limitations in resolution and data processing.
    • Hyperspectral imaging offers rich spectral information but poses reconstruction challenges.

    Purpose of the Study:

    • To develop a novel penalized maximum-likelihood estimation for hyperspectral XFT image reconstruction.
    • To improve the quality and efficiency of elemental distribution mapping.
    • To enable the reconstruction of full energy-dispersive spectra without artifacts.

    Main Methods:

    • A penalized maximum-likelihood estimation approach was employed.
    • The method minimizes a Poisson-based negative log-likelihood function.
    • A penalty term was introduced to ensure local continuity in spatio-spectral dimensions.

    Main Results:

    • The proposed method demonstrated superior reconstruction quality compared to conventional analytical inversion techniques.
    • Experimental data from Arabidopsis thaliana seeds showed enhanced element distribution estimates.
    • A high data compression factor was achieved, significantly reducing data acquisition times.

    Conclusions:

    • The penalized maximum-likelihood method offers a significant advancement in hyperspectral XFT.
    • This technique enables artifact-free tomographic reconstruction of full energy-dispersive spectra.
    • The approach has the potential to revolutionize elemental analysis in biological and material sciences.