Tsui Sheng-Yang, Wang Chiao-Yi, Huang Tsan-Hsueh, Sung Kung-Bin
Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan.
Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
Biomed Opt Express. 2018 Mar 7;9(4):1531-1544. doi: 10.1364/BOE.9.001531. eCollection 2018 Apr 1.
A robust modelling method was proposed to extract chromophore information in multi-layered skin tissue with spatially-resolved diffuse reflectance spectroscopy. Artificial neural network models trained with a pre-simulated database were first built to map geometric and optical parameters into diffuse reflectance spectra. Nine fitting parameters including chromophore concentrations and oxygen saturation were then determined by solving the inverse problem of fitting spectral measurements from three different parts of the skin. Compared to the Monte Carlo simulation accelerated by a graphics processing unit, the proposed modelling method not only reduced the computation time, but also achieved a better fitting performance.
提出了一种稳健的建模方法，用于通过空间分辨漫反射光谱法提取多层皮肤组织中的发色团信息。首先构建了用预模拟数据库训练的人工神经网络模型，以将几何和光学参数映射到漫反射光谱中。然后通过求解来自皮肤三个不同部位的光谱测量拟合的反问题，确定了包括发色团浓度和氧饱和度在内的九个拟合参数。与由图形处理单元加速的蒙特卡罗模拟相比，所提出的建模方法不仅减少了计算时间，而且获得了更好的拟合性能。
