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Compositional prior information in computed infrared spectroscopic imaging.

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    This study introduces a new method combining optical coherence tomography (OCT) and imaging spectroscopy using compositional prior information. This approach accurately determines sample structure and spectral composition from limited measurements.

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    Area of Science:

    • Biomedical Optics
    • Spectroscopy
    • Image Analysis

    Background:

    • Optical coherence tomography (OCT) excels at structural imaging but ignores spectral data.
    • Imaging spectroscopy captures spectral information but lacks structural detail.
    • A theoretical gap exists between these complementary imaging modalities.

    Purpose of the Study:

    • To bridge the gap between OCT and imaging spectroscopy.
    • To develop a method for determining both structural and spectral composition of a sample.
    • To enable accurate sample analysis using limited spectral measurements.

    Main Methods:

    • Utilized compositional prior information assuming samples comprise N distinct materials with known spectra.
    • Developed a forward model for samples with heterogeneities along the optical axis.
    • Employed N-species constraint for unambiguous inversion of Fourier transform interferometric data.

    Main Results:

    • Demonstrated through simulation that the N-species constraint allows accurate reconstruction of sample composition.
    • Showcased unambiguous inversion of interferometric data within the optical system's passband.
    • Extended the model to address general 3D heterogeneous samples.

    Conclusions:

    • Compositional prior information effectively integrates structural and spectral data from OCT and imaging spectroscopy.
    • The proposed N-species constraint provides a robust framework for quantitative sample analysis.
    • This method offers a pathway to enhanced understanding of complex biological and material samples.