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Modelling spatially-resolved diffuse reflectance spectra of a multi-layered skin model by artificial neural networks trained with Monte Carlo simulations

机译：通过蒙特卡洛模拟训练的人工神经网络对多层皮肤模型的空间分辨漫反射光谱建模

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摘要

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.

机译：提出了一种鲁棒的建模方法，利用空间分辨漫反射光谱技术提取多层皮肤组织中的发色团信息。首先建立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，通过解决皮肤三个不同部位的光谱拟合反问题，确定了包括发色团浓度和氧饱和度在内的九个拟合参数。与图形处理单元加速的蒙特卡洛仿真相比，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。

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期刊名称 Biomedical Optics Express
作者
Sheng-Yang Tsui; Chiao-Yi Wang; Tsan-Hsueh Huang; Kung-Bin Sung;
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作者单位

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年(卷),期 2018(9),4
年度 2018
页码 1531–1544
总页数 14
原文格式 PDF
正文语种
中图分类放射医学;影像诊断学;生化遗传学;生化药理学;
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1. Modelling personal exposure to particulate air pollution: An assessment of time-integrated activity modelling, Monte Carlo simulation & artificial neural network approaches [J] . A. McCreddin, M.S. Alam, A. McNabola International Journal of Hygiene and Environmental Health . 2015,第1期

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2. Response to comment of Taher Rajaee and Salar Khani on "Artificial neural network modelling of biological oxygen demand in rivers at the national level with input selection based on Monte Carlo simulations" [Siljic et al., Environ Sci Pollut Res (2015) 22: 4230-4241] [J] . Tomic Aleksandra Siljic, Antanasijevic Davor, Peric-Grujic Aleksandra, Environmental Science and Pollution Research . 2016,第4期

机译：对Taher Rajaee和Salar Khani关于“基于蒙特卡洛模拟的输入选择对国家水平河流生物需氧量进行人工神经网络建模”的评论的回应[Siljic等人，Environ Sci Pollut Res（2015）22：4230 -4241]
3. Comment on "Artificial neural network modelling of biological oxygen demand in rivers at the national level with input selection based on Monte Carlo simulations A. Siljic & D. Antanasijevic & A. Peric-Grujic & M. Ristic & V. Pocajt. Environ Sci Pollut Res (2014) 22: 4230-4241" [J] . Rajaee Taher, Khani Salar Environmental Science and Pollution Research . 2015,第23期

机译：评论“基于蒙特卡洛模拟A. Siljic＆D. Antanasijevic＆A. Peric-Grujic＆M. Ristic＆V. Pocajt。Environ Sci Pollut的输入选择，在国家一级对河流生物需氧量进行人工神经网络建模Res（2014）22：4230-4241“
4. Spectral reflectance fitting based on Monte Carlo simulation using a multi-layered skin tissue model [C] . Yoshihisa Aizu, Takaaki Maeda, Tomohiro Kuwahara, Diffuse optical imaging III . 2011

机译：基于多层皮肤组织模型的基于蒙特卡洛模拟的光谱反射拟合
5. Markov Chain Monte Carlo Bayesian predictive framework for artificial Neural Network Committee modeling and simulation. [D] . Goodrich, Michael S. 2014

机译：马尔可夫链蒙特卡洛贝叶斯预测框架用于人工神经网络委员会建模和仿真。
6. Fast and accurate Monte Carlo simulations of subdiffusive spatially resolved reflectance for a realistic optical fiber probe tip model aided by a deep neural network [O] . Yevhen Zelinskyi, Peter Naglič, Franjo Pernuš, 2020

机译：快速准确的蒙特卡罗模拟用于深度神经网络的逼真光纤探头尖端模型的沉淀物空间上解决的反射率
7. Sequential Monte Carlo Methods to Train Neural Network Models [O] . De Freitas, Nando, M.A. Niranjan, 2000

机译：序列蒙特卡洛方法训练神经网络模型

Modelling spatially-resolved diffuse reflectance spectra of a multi-layered skin model by artificial neural networks trained with Monte Carlo simulations

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