Particle Tracking with Neighbourhood Similarities: A New Method for Super Resolution Ultrasound Imaging

Single particle tracking (SPT) is a method for the observation of the motion of individual particles within a medium. It is broadly used to quantify the dynamics of particle flow, such as molecules/proteins in life sciences. In this paper, we will improve the performance of SPT by considering the local neighbourhood dynamical and structural information of a particle when it is tracked in a medium through consecutive frames, referred to as particle tracking with neighbourhood similarities (PTNS). This method is applied to track microbubbles in contrast enhanced ultrasound. We will test the method on synthetic data for method validation before applying to animal and human prostate data. We show that PTNS can make a significant improvement in the tracking performance in synthetic data, and in animal data it was able to accurately produce complex structures. In human prostate data, we find that by varying the control parameters we can inspect different behaviours of the tracks and from t hat understand the characteristics of the blood vessels they travel along.