Determination of optical properties in double integrating sphere measurement by artificial neural network based method

An accurate inversion technique in double integrating sphere (DIS) measurement is essential for determining the optical properties of biological tissue. Although there are several established techniques, the computational time and complexity for spectral analysis require some approximations of the anisotropy factor g and refractive index n. We aim to demonstrate an artificial neural network (ANN) based method to determine the absorption mu(a) and scattering mu(s) coefficients of biological tissue from the diffuse reflectance R, total transmittance T, g, and n. ANNs were trained using dataset generated by calculating light transport in the DIS setup with a Monte Carlo method. The measured R and T spectra and the wavelength-dependent g and n were inputted to calculate mu(a) and mu(s). Due to the simple and fast calculation, the ANN-based method can calculate the mu(a) and mu(s) spectra assuming the wavelength dependence of g and n. The relative errors of reconstruction by the trained networks were 1.1% and 0.95% for mu(a) and mu(s), respectively. Each optical property spectra (total 662 points) was obtained in 1.1 ms. The proposed method can determine mu(a) and mu(s) in the DIS measurement assuming wavelength dependent g and n.