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.

Microbubble Axial Localization Errors in Ultrasound Super-Resolution Imaging

“…This was performed at a high frequency of 40 MHz; at this frequency, a wavelength ~ 40 µm is already able to provide high resolution; in addition, penetration depth is limited by the use of high imaging frequencies. This group and others have also successfully demonstrated in vivo results using various localization-based methods in a stationary rat brain at high clinical frequency (15 MHz) using high frame rates (HFR) [23], in tumorbearing mice at 30 MHz [24] and at 4.5 MHz with HFR [25].…”

“…To the best of our knowledge, in all previous publications regarding US superresolution, the microbubble position is assumed to coincide with the maximum amplitude, center of mass, or a related measure, of the backscatter signal. Using these techniques, each microbubble from the insonated bubble population is expected to behave in the same way, hence generating a signal close to the system PSF and as such the determination of each microbubble position is typically done by calculating the centroid [20]- [22], finding a local axial maximum from the travelling hyperboloid in RF data lines and fitting a function, such as a parabola in the case of Desailly (2013) [26], cross-correlation of signals with an expected response [24], or fitting a 2-D Gaussian function either to the original beamformed backscatter signal [27], or after deconvolving with a predicted Gaussian PSF [23].…”

Robust microbubble tracking for super resolution imaging in ultrasound

“…This has been achieved with various MB tracking algorithms [1], [2], [8], [9]. An individual MB appears in the image as the PSF determined by the imaging system.…”

3-D In Vitro Acoustic Super-Resolution and Super-Resolved Velocity Mapping Using Microbubbles

“…Super-resolution US imaging has also been demonstrated by using these high frame-rates to identify bubbles from background in RF data based on their motion and/or disruption over time [21], [22]. A bubble-motion based super-resolution imaging approach has also been demonstrated using a clinical US scanner [23]. A third approach for identifying single bubbles in ex vivo samples is to acquire an US image of the sample with no microbubbles present and to then subtract this static background from frames acquired with microbubbles present [24].…”

“…Much of this previous work has been based around the use of 1-D linear array transducers that has restricted the superresolved image information to 2D [15], [16], [23]. Here, as well as in the case of 3-D volume acquisition using mechanical scanning in the third dimension [24], the elevational resolution remains at the conventional diffraction limit.…”