Artificial neural network-based determination of denoised optical properties in double integrating spheres measurement

Abstract

Accurate determination of the optical properties of biological tissues enables quantitative understanding of light propagation in these tissues for optical diagnosis and treatment applications. The absorption ([Formula: see text]) and scattering ([Formula: see text]) coefficients of biological tissues are inversely analyzed from their diffuse reflectance (R) and total transmittance (T), which are measured using a double integrating spheres (DIS) system. The inversion algorithms, for example, inverse adding doubling method and inverse Monte Carlo method, are sensitive to noise signals during the DIS measurements, resulting in reduced accuracy during determination. In this study, we propose an artificial neural network (ANN) to estimate [Formula: see text] and [Formula: see text] at a target wavelength from the R and T spectra measured via the DIS to reduce noise in the optical properties. Approximate models of the optical properties and Monte Carlo calculations that simulated the DIS measurements were used to generate spectral datasets comprising [Formula: see text], [Formula: see text], R and T. Measurement noise signals were added to R and T, and the ANN model was then trained using the noise-added datasets. Numerical results showed that the trained ANN model reduced the effects of noise in [Formula: see text] and [Formula: see text] estimation. Experimental verification indicated noise-reduced estimation from the R and T values measured by the DIS with a small number of scans on average, resulting in measurement time reduction. The results demonstrated the noise robustness of the proposed ANN-based method for optical properties determination and will contribute to shorter DIS measurement times, thus reducing changes in the optical properties due to desiccation of the samples.

Keywords

• Absorption coefficient
• scattering coefficient
• bio-tissue
• tissue spectroscopy
• noise reduction