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    Area of Science:

    • Optics and Photonics
    • Biophysics
    • Computer Graphics

    Background:

    • Volume scattering models are vital across disciplines like biology and lighting.
    • Parameter estimation typically uses inverse methods fitting simulations to experimental data.
    • Scattered intensity distributions are commonly used but can yield poor estimates for samples with long optical path lengths.

    Purpose of the Study:

    • To introduce and evaluate an inverse procedure for estimating volume scattering parameters using scattered radiance distributions.
    • To compare the efficacy of radiance versus intensity distributions for parameter estimation across varying optical path lengths.
    • To leverage graphics processing units (GPUs) for efficient simulation and analysis.

    Main Methods:

    • Developed an inverse procedure fitting simulations to scattered radiance distributions.
    • Utilized modern graphics processing units (GPUs) for computationally efficient simulations.
    • Conducted an in-depth simulation study comparing radiance and intensity distribution fitting.

    Main Results:

    • For moderate optical path lengths, intensity distributions provide sufficient information for accurate parameter estimation.
    • For longer optical path lengths, intensity distributions become insufficient, leading to poor parameter estimates.
    • The proposed radiance distribution-based inverse method demonstrates superior accuracy for samples with longer optical path lengths.

    Conclusions:

    • Radiance distribution-based inverse methods are better suited for accurate volume scattering parameter estimation in samples with longer optical path lengths.
    • The choice of data distribution (intensity vs. radiance) is critical for accurate parameter estimation, especially with increasing optical path length.
    • Efficient GPU implementation enables detailed studies on the impact of data distribution on scattering parameter estimation.