Ultrasonic Carotid Blood Flow Velocimetry Based on Deep Complex Neural Network

Precise measurement of carotid artery blood flow is of vital importance for studying thrombosis and early carotid atherosclerotic plaque. However, the traditional non-parametric methods are limited by the weak detection ability to low-velocity blood flow, and show problems including the large measurement deviation and long algorithm running time. Motivated by the above status quo, a novel method based on deep complex convolutional neural network (DCCNN) is proposed for carotid blood flow velocimetry. Based on supervised learning, DCCNN feeds the echo signals into complex convolutional layers for the purpose of rejecting clutter signals. Then, the outputs of complex convolutional layers are processed by the complex fully connected layers to estimate the blood flow velocity. The effectiveness of the proposed method is verified by simulation as well as in vivo data of healthy volunteers. Compared with typical velocimetry methods such as the high-pass filter and singular value decomposition, the normalized root mean square error (NRMSE) of the velocimetry result obtained from the proposed method is reduced by 47.20%) and 45.45%, and the goodness-of-fit is improved by 5.64%, 3.36%, respectively. In addition, the running time of DCCNN is reduced by 82.10% and 21.11%, respectively. Such results show that the proposed method is a promising tool for blood flow velocity measurement due to its higher velocity measurement accuracy and good real-time performance.