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 \(\mu _\mathrm{a}\) and scattering \(\mu _\mathrm{s}\) 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 \(\mu _\mathrm{a}\) and \(\mu _\mathrm{s}\). Due to the simple and fast calculation, the ANN-based method can calculate the \(\mu _\mathrm{a}\) and \(\mu _\mathrm{s}\) 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 \(\mu _\mathrm{a}\) and \(\mu _\mathrm{s}\), respectively. Each optical property spectra (total 662 points) was obtained in 1.1 ms. The proposed method can determine \(\mu _\mathrm{a}\) and \(\mu _\mathrm{s}\) in the DIS measurement assuming wavelength dependent g and n.

双积分球（DIS）测量中的精确反演技术对于确定生物组织的光学特性至关重要。尽管已经建立了几种技术，但是光谱分析的计算时间和复杂性需要各向异性因子g和折射率n的一些近似值。我们旨在演示一种基于人工神经网络（ANN）的方法，用于确定生物组织从扩散物中的吸收系数（\ mu _ \ mathrm {a} \）和散射\（\ mu _ \ mathrm {s} \）反射率R，总透射率T，g和n。使用通过使用Monte Carlo方法在DIS设置中计算光传输的数据集来训练ANN。输入测得的R和T光谱以及与波长有关的g和n来计算\（\ mu _ \ mathrm {a} \）和\（\ mu _ \ mathrm {s} \）。由于计算简单，快速，因此基于g和n的波长依赖性，基于ANN的方法可以计算\（\ mu _ \ mathrm {a} \）和\（\ mu _ \ mathrm {s} \}光谱。由受过训练的网络重建的相对误差分别为1.1％和0.95％\（\亩_ \ mathrm {A} \）和\（\ mu _ \ mathrm {s} \）。在1.1毫秒内获得每个光学特性光谱（共662个点）。假设波长与g和n有关，建议的方法可以在DIS测量中确定\（\ mu _ \ mathrm {a} \）和\（\ mu _ \ mathrm {s} \}。