Study on Internal Information of the Two-Layered Tissue by Optimizing the Detection Position

Area of Science:

• Biomedical Optics
• Medical Imaging
• Tissue Optics

Background:

• Detecting internal information in inhomogeneous tissues is challenging due to complex structures and individual variations.
• Existing methods often suffer from ambiguous detection positions and measurement errors from multiple backscattered photons.

Purpose of the Study:

• To determine the optimal source-detector distance (SDSbest) for accurate detection of internal information in fat-muscle tissue.
• To develop a robust method minimizing measurement errors caused by individual discrepancies and scattered light.

Main Methods:

• Utilized spatially resolved diffuse reflectance spectra and a transmission model for light in biological tissue.
• Introduced a constraint of two ideal "banana shape" photon paths to define an effective photon ratio (signal-to-noise ratio, SNR).
• Employed Monte Carlo simulations, statistical analysis, and linear regression modeling with fat thickness (hf) as the independent variable.

Main Results:

• The optimal source-detector distance (SDSbest) was found to be independent of fat (μaf) and muscle (μam) absorption coefficients for fat thickness (hf) between 0 and 0.6 cm.
• A linear regression model predicting SDSbest achieved a high correlation coefficient of 0.9918.
• Prediction errors for SDSbest were within 5% for randomly selected fat thicknesses (hf=0.12 cm and hf=0.22 cm).

Conclusions:

• The developed method provides an easier and faster way to select the optimal source-detector distance (SDSbest) for turbid tissue analysis.
• This approach effectively reduces interference from non-target tissue layers and multiple backscattered photons, improving detection accuracy.
• The findings offer a significant advancement for non-invasive optical methods in biomedical diagnostics.