A quantitative laser speckle-based velocity prediction approach using machine learning

被引：8

作者：

Hao, Xiaoqi ^{[1
]}

Wu, Shuicai ^{[1
]}

Lin, Lan ^{[1
]}

Chen, Yixiong ^{[2
]}

Morgan, Stephen P. ^{[3
]}

Sun, Shen ^{[1
]}

机构：

[1] Beijing Univ Technol, Fac Environm & Life, 100 Pingleyuan, Beijing 10012, Peoples R China

[2] Beijing Sci & Technol Project Manager Management C, Beijing, Peoples R China

[3] Univ Nottingham, Opt & Photon Res Grp, Nottingham, Nottinghamshire, England

来源：

OPTICS AND LASERS IN ENGINEERING | 2023年 / 166卷

关键词：

Laser speckle contrast imaging (LSCI); Blood flow imaging; Machine learning; Convolutional neural network (CNN); Microcirculation;

D O I：

10.1016/j.optlaseng.2023.107587

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

Laser speckle contrast imaging (LSCI) can be applied to non-invasive blood perfusion measurement with high resolution and fast speed. However, it is lack of measurement accuracy. The aim of this study is to enable quanti-tative measurement of LSCI by using Artificial Intelligence (AI), and this is achieved by using a set of experimental data obtained from a rotating diffuser (a tissue phantom mimicking blood flow under skins) within simulated flow velocity of 0.08-10.74 mm/s. These data were used to train a three-dimensional convolutional neural net-work (3D-CNN) to establish a LSCI velocities prediction model (CNN-LSCI) with behavioral feature learning. The trained model has 0.33 MSE (mean squared error) and 0.34 MAPE (mean absolute percentage error) and is verified by ten phantom velocities (0.2-4 mm/s, step is 0.445 mm/s) covering the typical blood flow velocity range of human body (0-2 mm/s) with the correlation of 0.98. The better performance of the proposed model is demonstrated by the results compared to traditional LSCI and multi-exposure laser speckle contrast imaging (MELSCI). This study shows the potential of LSCI to achieve quantitative blood perfusion measurement using machine learning.

引用

页数：10