Detection and Tracking of Multiple Microbubbles in Ultrasound B-Mode Images.

Abstract

The imaging of microvessels and the quantification of their blood flow is of particular interest in the characterization of tumor vasculature. The imaging resolution (50-200 μm) of high-frequency ultrasound (US) (20-50 MHz) is not sufficient to image microvessels (~10 μm) and Doppler sensitivity is not high enough to measure capillary blood flow (~1 mm/s). For imaging of blood flow in microvessels, our approach is to detect single microbubbles (MBs), track them over several frames, and to estimate their velocity. First, positions of MBs will be detected by separating B-mode frames in a moving foreground and a static background. For the crucial task of association of these positions to tracks, we implemented a modified Markov chain Monte Carlo data association (MCMCDA) algorithm, which can handle a high number of MBs. False alarms, the detection, initiation, and termination of MBs tracks are incorporated in the underlying model. To test the performance of algorithms, a US imaging simulation of a vessel tree with flowing MBs was set up (resolution 148 μm). The trajectories and flow velocity in the vessels with a lateral distance of 100 μm were reconstructed with super-resolution. In a phantom experiment, a suspension of MBs was pumped through a tube (diameter 0.4 mm) at speeds of 2.2, 4.2, 6.3, and 10.5 mm/s and was imaged with a Vevo2100 system (Visualsonics). Estimated mean speeds of the MBs were 2.1, 4.7, 7, and 10.5 mm/s. To demonstrate the applicability for in vivo measurements, a tumor xenograft-bearing mouse was imaged by this approach. The tumor vasculature was visualized with higher resolution than in a maximum intensity persistence image and the velocity values were in the expected range 0-1 mm/s.