Improved Super-Resolution Ultrasound Microvessel Imaging With Spatiotemporal Nonlocal Means Filtering and Bipartite Graph-Based Microbubble Tracking.

Super-resolution ultrasound microvessel imaging with contrast microbubbles has recently been proposed by multiple studies, demonstrating outstanding resolution with high potential for clinical applications. This paper aims at addressing the potential noise issue in in vivo human super-resolution imaging with ultrafast plane-wave imaging. The rich spatiotemporal information provided by ultrafast imaging presents features that allow microbubble signals to be separated from background noise. In addition, the high-frame-rate recording of microbubble data enables the implementation of robust tracking algorithms commonly used in particle tracking velocimetry. In this paper, we applied the nonlocal means (NLM) denoising filter on the spatiotemporal domain of the microbubble data to preserve the microbubble tracks caused by microbubble movement and suppress random background noise. We then implemented a bipartite graph-based pairing method with the use of persistence control to further improve the microbubble signal quality and microbubble tracking fidelity. In an in vivo rabbit kidney perfusion study, the NLM filter showed effective noise rejection and substantially improved microbubble localization. The bipartite graph pairing and persistence control demonstrated further noise reduction, improved microvessel delineation, and a more consistent microvessel blood flow speed measurement. With the proposed methods and freehand scanning on a free-breathing rabbit, a single microvessel cross-sectional profile with full-width at half-maximum of could be imaged at approximately 2-cm depth (ultrasound transmit center frequency = 8 MHz, theoretical spatial resolution ). Cortical microvessels that are apart can also be clearly separated. These results suggest that the proposed methods have good potential in facilitating robust in vivo clinical super-resolution microvessel imaging.

Duke Scholars

Author Pengfei Song Biomedical Engineering