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Multispectral Optoacoustic Tomography for Functional Imaging in Vascular Research
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A Bayesian Approach to Eigenspectra Optoacoustic Tomography.

Ivan Olefir, Stratis Tzoumas, Hong Yang

    IEEE Transactions on Medical Imaging
    |July 12, 2018
    PubMed
    Summary

    A new Bayesian method enhances multispectral optoacoustic tomography (MSOT) for accurate hemoglobin oxygen saturation (sO2) measurement in deep tissues, even with significant noise.

    Area of Science:

    • Biomedical Optics
    • Medical Imaging
    • Photoacoustics

    Background:

    • Quantifying hemoglobin oxygen saturation (sO2) using multispectral optoacoustic tomography (MSOT) is challenging due to spectral variations caused by tissue depth and optical fluence.
    • The eigenspectra MSOT (eMSOT) method was developed to improve sO2 quantification in deep tissues by accounting for spectral dependencies.
    • However, eMSOT's performance can be degraded by noise and image reconstruction artifacts, limiting its accuracy in real-world scenarios.

    Purpose of the Study:

    • To develop a novel Bayesian method to enhance the performance of eMSOT in noisy environments for improved sO2 quantification.
    • To introduce a spectral reliability map for estimating noise levels in recorded optoacoustic spectra.
    • To formulate eMSOT as a Bayesian inverse problem utilizing probabilistic graphical models for robust inversion.

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    Main Methods:

    • A spectral reliability map was developed to estimate noise superimposed on optoacoustic spectra.
    • The eigenspectra MSOT (eMSOT) method was reformulated as a Bayesian inverse problem.
    • Probabilistic graphical models were employed to define inversion constraints based on noise estimates.

    Main Results:

    • Numerical simulations demonstrated that the proposed Bayesian method significantly improves the accuracy of sO2 quantification.
    • The method exhibits enhanced robustness in the presence of high noise levels.
    • The adaptive nature of the Bayesian approach allows for better performance compared to conventional methods under noisy conditions.

    Conclusions:

    • The novel Bayesian method effectively addresses noise limitations in eMSOT for accurate deep tissue sO2 quantification.
    • The spectral reliability map and Bayesian framework provide a more robust approach to spectral unmixing in optoacoustic imaging.
    • This technique holds promise for improving diagnostic capabilities in applications requiring precise blood oxygenation measurements.