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

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.

Automated summary

The study aims to achieve quantitative measurement of laser speckle contrast imaging (LSCI) using artificial intelligence. A three-dimensional convolutional neural network (3D-CNN) was trained with experimental data from a rotating diffuser, and a LSCI velocities prediction model (CNN-LSCI) was established. The trained model showed a mean squared error (MSE) of 0.33 and a mean absolute percentage error (MAPE) of 0.34, and it was validated with ten phantom velocities covering the typical blood flow velocity range of the human body, achieving a correlation of 0.98. The proposed model outperformed traditional LSCI and multi-exposure laser speckle contrast imaging (MELSCI). This study demonstrates the potential of applying machine learning to achieve quantitative blood perfusion measurement with LSCI.