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<p>Single-fiber reflectance (SFR) spectroscopy enables quantitative retrieval of tissue optical properties from highly localized measurements through a single multimode fiber, but its diagnostic yield can be limited by small sampling volumes and probe-pressure artifacts. Imaging SFR (iSFR) mitigates these issues by enabling contact-free, point-scanning spectral imaging using optics to project the fiber’s illumination–collection cone onto the tissue surface. We present a quantitative model tailored to iSFR that supports inverse retrieval of tissue absorption and scattering properties from reflectance spectra. The model is based on large-scale Monte Carlo simulations using a generalizable reflectance computation that replaces explicit simulation of arbitrary finite source–detector configurations with a distance-based probability weighting, substantially reducing computational cost. Across broad optical and geometric ranges, the iSFR model predicts reflectance with a median error of 6.2% (SFR: 4.2%). In inverse mode, using wavelength parameterizations for optical properties, the approach recovered absorption and scattering coefficients to within approximately 10% over a wide range of added noise for simulated spectra from two simplified tissue models, serving as a proof-of-concept. These results establish a quantitative model and an efficient computational pipeline for (i)SFR spectroscopy.</p>

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Quantitative model for imaging single fiber reflectance spectroscopy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月14日

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  • Robin van Zutphen,
  • Ton G. van Leeuwen,
  • Xavier Attendu

摘要

Single-fiber reflectance (SFR) spectroscopy enables quantitative retrieval of tissue optical properties from highly localized measurements through a single multimode fiber, but its diagnostic yield can be limited by small sampling volumes and probe-pressure artifacts. Imaging SFR (iSFR) mitigates these issues by enabling contact-free, point-scanning spectral imaging using optics to project the fiber’s illumination–collection cone onto the tissue surface. We present a quantitative model tailored to iSFR that supports inverse retrieval of tissue absorption and scattering properties from reflectance spectra. The model is based on large-scale Monte Carlo simulations using a generalizable reflectance computation that replaces explicit simulation of arbitrary finite source–detector configurations with a distance-based probability weighting, substantially reducing computational cost. Across broad optical and geometric ranges, the iSFR model predicts reflectance with a median error of 6.2% (SFR: 4.2%). In inverse mode, using wavelength parameterizations for optical properties, the approach recovered absorption and scattering coefficients to within approximately 10% over a wide range of added noise for simulated spectra from two simplified tissue models, serving as a proof-of-concept. These results establish a quantitative model and an efficient computational pipeline for (i)SFR spectroscopy.