Real-time tracking of radial artery vessel wall using a Kalman filter-based ultrasound single-plane wave RF signal time-frequency information fusion algorithm

Objective: The goal of our study was to achieve real-time dynamic tracking of the radial artery vessel wall in everyday life, which is crucial for blood pressure estimation and cardiovascular disease prediction. Methods: The algorithm integrates time-frequency information from ultrasound single-plane wave RF (radiofrequency) signals and consists of three main components: feature frame ROI (region of interest) selection, interframe cross-correlation and intra-frame autocorrelation, and feature frame Kalman filtering. Results: Experimental validation on a silicone gel ultrasound phantom with simulated blood vessels demonstrates an average diameter estimation error of less than 0.5% compared to ground truth values. Comparative experiments show similar relative errors (2.83%, 2.43%) to the optical flow method, with a computational speed 7.32 times faster. The algorithm accurately aligns extracted pulse waves with pressure pulse waves at six local feature points per cycle. Conclusion: The proposed method achieves a favorable balance between speed and accuracy in tracking the radial artery vessel wall. It holds significant potential for real-time monitoring of physiological health parameters, offering precise and dynamic tracking capabilities. Significance: This algorithm addresses the challenges of non-invasive real-time tracking, benefiting blood pressure estimation and cardiovascular disease prediction. Its integration of time-frequency information and efficient computational speed make it valuable for the scientific community and public in monitoring physiological health parameters.