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Robustness of diffuse reflectance spectra analysis by inverse adding doubling algorithm.

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Fixing optical parameters improves skin spectra analysis accuracy for simulated data but can harm in-vivo results. Pre-estimating parameters enhances fitting performance for real-world biological tissue analysis.

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Area of Science:

  • Biomedical Optics
  • Photonic Spectroscopy
  • Tissue Optics

Background:

  • Accurate biological tissue optical property extraction relies on light transport modeling within layered structures.
  • Skin tissue models commonly use fixed optical properties to simplify spectral analysis.
  • Inconsistent parameter fixing strategies exist across studies.

Purpose of the Study:

  • To evaluate the impact of fixing model parameters on the accuracy and robustness of a GPU-accelerated Inverse Adding-Doubling (IAD) algorithm for skin spectra fitting.
  • To compare the IAD algorithm's performance on simulated and in-vivo measured skin reflectance spectra under varying conditions.

Main Methods:

  • Utilized a GPU-accelerated two-layer Inverse Adding-Doubling (IAD) algorithm.
  • Tested the algorithm with noiseless simulated spectra, noisy simulated spectra, and in-vivo hyperspectral images of human hands during arterial occlusion.
  • Assessed the effect of fixing various optical and geometric model parameters.

Main Results:

  • Fixing multiple parameters improved IAD algorithm accuracy and robustness for simulated skin spectra.
  • Incorrectly fixed optical parameters significantly degraded performance for in-vivo measured spectra.
  • A pre-estimation method for model parameters was proposed to enhance fitting performance.

Conclusions:

  • Parameter fixing strategies significantly influence skin spectral analysis outcomes.
  • Pre-estimating model parameters is crucial for accurate in-vivo diffuse reflectance spectroscopy.
  • Findings offer guidance for future research in extracting skin optical properties.