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Updated: Aug 17, 2025

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Multi-material spectral photon-counting micro-CT with minimum residual decomposition and self-supervised deep

V Di Trapani, L Brombal, F Brun

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    This study introduces a novel spectral micro-CT system that overcomes limitations in energy resolution and noise. The enhanced system accurately differentiates materials with closely spaced K-edges, improving spectral imaging capabilities.

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    Area of Science:

    • Medical Imaging
    • Materials Science
    • Physics

    Background:

    • Spectral micro-CT imaging faces challenges with energy resolution and noise amplification.
    • Small pixel size detectors (< 100x100 µm²) exacerbate charge sharing and noise issues.

    Purpose of the Study:

    • To develop a cone-beam micro-CT setup addressing spectral imaging limitations.
    • To enhance material discrimination and image quality in spectral micro-CT.

    Main Methods:

    • Utilized a CdTe photon counting detector with hardware charge summing.
    • Implemented an image processing pipeline with spectral response modeling and minimum-residual basis material decomposition (MR-BMD).
    • Applied self-supervised deep convolutional denoising to acquired projections (45x45 µm² pixel size).

    Main Results:

    • Successfully discriminated between materials with K-edges separated by only a few keV (e.g., Iodine and Barium).
    • Demonstrated sharp discrimination capabilities using combined hardware and software solutions.
    • Evaluated quantitative performance of reconstructed decomposed images (water, bone, I, Ba, Gd).

    Conclusions:

    • The developed spectral micro-CT system effectively overcomes hardware and software limitations.
    • The innovative approach significantly improves material differentiation and image quality in spectral micro-CT.
    • This work provides a robust platform for quantitative spectral micro-CT analysis.