.png){kind=icon}
返回
Phase function estimation from a diffuse optical image via deep learning
.png){kind=icon}
OA
AI
DOI：10.1088/1361-6560/ac5b21.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Objective. The phase function is a key element of a light propagation model for Monte Carlo (MC) simulation, which is usually fitted with an analytic function with associated parameters. In recent years, machine learning methods were reported to estimate the parameters of the phase function of a particular form such as the Henyey-Greenstein phase function but, to our knowledge, no studies have been performed to determine the form of the phase function. Approach. Here we design a convolutional neural network (CNN) to estimate the phase function from a diffuse optical image without any explicit assumption on the form of the phase function. Specifically, we use a Gaussian mixture model (GMM) as an example to represent the phase function generally and learn the model parameters accurately. The GMM is selected because it provides the analytic expression of phase function to facilitate deflection angle sampling in MC simulation, and does not significantly increase the number of free parameters. Main Results. Our proposed method is validated on MC-simulated reflectance images of typical biological tissues using the Henyey-Greenstein phase function with different anisotropy factors. The mean squared error of the phase function is 0.01 and the relative error of the anisotropy factor is 3.28%. Significance. We propose the first data-driven CNN-based inverse MC model to estimate the form of scattering phase function. The effects of field of view and spatial resolution are analyzed and the findings provide guidelines for optimizing the experimental protocol in practical applications.
Keyword:
light propagation
phase function
Henyey-Greenstein phase function
Gaussian mixture model
Monte Carlo simulation
convolutional neural network
期刊
IF:
3.4
论文数:
1.4W
被引数:
3.1W
机构
引用论文
Detecting structural information of scatterers using spatial frequency domain imaging基于空间频域成像的散射体结构信息检测
OAAI
OAAI分享收藏分享收藏分享收藏Efficient estimation of subdiffusive optical parameters in real time from spatially resolved reflectance by artificial neural networks.png)
OPTICS LETTERS
OPTICS LETTERSIF3.3
PREAI
分享收藏
查看更多内容