Atherosclerotic plaque motion analysis from ultrasound videos

Abstract

In this paper, we describe a collection of algorithms that are used to provide motion trajectory estimates from atherosclerotic plaque ultrasound videos. Our approach is based on the use of four different optical flow methods to estimate motion vectors (Horn and Schunk, Lucas, Nagel and Uras). To estimate the optimal motion estimation parameters, we perform hundreds of experiments on a Linux cluster, and further validate the results using synthetic simulations. Following motion estimation, we compute pixel motion trajectories over the plaque regions and vessel walls. Pixel trajectories are then used to assess plaque deformation.