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Neural network-based optimization of sub-diffuse reflectance spectroscopy for improved parameter prediction and

Jingyi An¹, Qi Zhang¹, Limin Zhang^1,2

¹College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin, China.

Journal of Biophotonics

|February 6, 2023

Summary

This summary is machine-generated.

This study introduces an efficient method using neural networks and sub-diffuse reflectance spectroscopy to accurately measure optical properties. This technique aids in diagnosing mucosal diseases using a novel measurement system.

Keywords:

neural network optical properties reflectance spectroscopy source-detector separation sub-diffuse

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Area of Science:

Biomedical Optics
Medical Physics
Spectroscopy

Background:

Sub-diffuse reflectance spectroscopy (SRS) is crucial for non-invasive tissue analysis.
Accurate determination of optical properties (absorption coefficient, reduced scattering coefficient, sub-diffusive quantifier) is essential for SRS applications.
Computational efficiency and accuracy challenges exist in traditional SRS data analysis.

Purpose of the Study:

To develop a general and systematic method for sub-diffuse reflectance spectroscopy.
To enhance computational efficiency and accuracy in predicting key optical properties.
To validate a novel measurement system for mucosal disease diagnosis.

Main Methods:

Utilized a Gegenbauer-kernel phase function-based Monte Carlo simulation for photon transport.
Employed back propagation neural network (BPNN) and radial basis function neural network (RBFNN) for property prediction.
Designed a four-wavelength (520, 650, 785, 830 nm) measurement system with five source-detector separations (SDSs) using phase-lock-in technique.

Main Results:

Neural networks accurately predicted absorption coefficient, reduced scattering coefficient, and sub-diffusive quantifier with five or more SDSs.
The designed measurement system demonstrated feasibility and effectiveness.
Extracted optical properties from phantom and in vivo data validated the proposed methods.

Conclusions:

The combined approach of advanced Monte Carlo simulation, neural networks, and a multi-wavelength SRS system provides accurate optical property extraction.
The developed system and methods are effective for mucosal disease diagnosis.
This work advances non-invasive diagnostic capabilities in biomedical optics.