An Adaptive Estimation of Ultrasound Transit Time-based Local PWV in Carotid Artery using Particle Swarm Optimization Algorithm

Pulse wave velocity (PWV) is currently the gold standard for quantitatively measuring arterial stiffness, which is of great significance for the early diagnosis and effective prevention of cardio-cerebral vascular diseases. With the development of the ultrafast ultrasound imaging based on the plane wave technique, the ultrasonic transit time-based method with straightforward principle is preferable for the detection of the local PWV of carotid artery. However, the filtering parameter settings in this method are difficult to be flexibly controlled, and the results of pulse wave transit time estimations are not stable. Aiming at these problems, an adaptive local PWV estimation based on the particle swarm optimization algorithm is proposed. First, An improved particle swarm optimization (PSO) with a fitness function evaluated in terms of the Savitzky-Golay (SG) filter length, SG polynomial degree, tracking position and tracking points parameters in regional upstroke tracking (RUT) approach is used to find the most optimum parameter settings for PWV estimation. To quantitatively evaluate the proposed adaptive local PWV estimation method (PSO_RUT), an ultrasound simulation model for three age groups is used. Results show that the mean PWV deviations from theoretical values estimated by the proposed PSO_RUT method are just within 3.8%, and lower than that estimated by RUT method. Therefore, the PSO_RUT approach proposed in this study can provide effective means for the improvement on the accuracy of ultrasound-based carotid PWV estimation.