Method for measuring blood flow and depth of blood vessel based on laser speckle contrast imaging using 3D convolutional neural network: A preliminary study

Abstract

Laser speckle contrast imaging (LSCI) is a technology that can acquire real-time two-dimensional perfusion images non-invasively. However, measuring perfusion velocity under a tissue is difficult due to its optical properties. Dynamic scattered light occurs by perfusion is diffused by the surrounding tissue and it depends on the depth of tissue. We aimed to develop a three-dimensional convolutional neural network (3D-CNN) model based on laser speckle contrast images to measure the flow velocity and the depth of tissue using a flow phantom. The training data was selected as a region containing both static speckles and dynamic speckles, and the model was trained on the laser speckle contrast image data. The trained model showed a measurement accuracy of more than 90 % for velocity and 99 % for depth measurement. This study has the potential of a deep-learning model based on LSCI to analyze the blood flow and depth of blood vessels in bio-applications.