Neural network‐based optimization of sub‐diffuse reflectance spectroscopy for improved parameter prediction and efficient data collection

Author:

An Jingyi1ORCID,Zhang Qi1,Zhang Limin12,Liu Chenlu34,Liu Dongyuan12ORCID,Jia Mengyu12,Gao Feng12

Affiliation:

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

2. Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments Tianjin China

3. Department of Oral Medicine, Tianjin Stomatological Hospital, School of Medicine Nankai University Tianjin China

4. Tianjin Key Laboratory of Oral and Maxillofacial Function Reconstruction Tianjin China

Abstract

AbstractIn this study, a general and systematical investigation of sub‐diffuse reflectance spectroscopy is implemented. A Gegenbauer‐kernel phase function‐based Monte Carlo is adopted to describe photon transport more efficiently. To improve the computational efficiency and accuracy, two neural network algorithms, namely, back propagation neural network and radial basis function neural network are utilized to predict the absorption coefficient , reduced scattering coefficient and sub‐diffusive quantifier , simultaneously, at multiple source‐detector separations (SDS). The predicted results show that the three parameters can be predicated accurately by selecting five SDSs or above. Based on the simulation results, a four wavelength (520, 650, 785 and 830 nm) measurement system using five SDSs is designed by adopting phase‐lock‐in technique. Furtherly, the trained neural‐network models are utilized to extract optical properties from the phantom and in vivo experimental data. The results verify the feasibility and effectiveness of our proposed system and methods in mucosal disease diagnosis.

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

General Physics and Astronomy,General Engineering,General Biochemistry, Genetics and Molecular Biology,General Materials Science,General Chemistry

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