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

Abstract

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 study 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 study, 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-section profile with full width at half maximum of 57 μm could be imaged at approximately 2 cm depth (ultrasound transmit center frequency = 8 MHz, theoretical spatial resolution ~200 μm). Cortical microvessels that are 76 μm 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.

Ultrasound localization microscopy of renal tumor xenografts in chicken embryo is correlated to hypoxia

“…Microbubble tracks were then reconstructed via a bipartite graph-based pairing algorithm ( Fig. 2E) 34 and Kalman filtering 35 . The resulting microbubble tracks could then be accumulated to produce microvascular structure maps (Fig.…”

Functional ultrasound speckle decorrelation-based velocimetry of the brain

“…The ultrasound localization microscopy (ULM) images and the ULM-based velocity maps (vULM) were obtained based on a microbubble tracking and accumulation method described in 12,13 . Briefly, a frame-to-frame subtraction was applied to the IQ data to get the dynamic microbubble signal.…”

“…For the phantom validation experiment, a plastic micro tube (inner diameter 580 , Intramedic Inc.) was buried in a homemade agar phantom with an angle of ~ 30° (angled flow), and another plastic micro tube was aligned close to ~ 0° (transvers flow) in another homemade agar phantom. A blood solution was pumped through the tubes with a syringe pump (Harvard Apparatus) at speeds of 1,3,5,7,9,11,13,15,20,25, and 30 mm/s. SVD was performed to filter the background signal clutter by removing the first two highest singular value components.…”

“…On the other hand, ultrasound Color Doppler is able to measure the axial blood flow velocity but suffers from unstable 2/32 estimations of mean speed due to the presence of noise 3,10,11 and from incorrect estimation if opposite flows exist within the measurement voxel. The microbubble tracking-based ultrasound localization microscopy (ULM 12 ) is able to map with superresolution the whole mouse whole brain vasculature (coronal plane) and quantify the inplane blood flow velocity (vULM 12,13 ) with ~10 resolution. However, it suffers from a fundamental limitation of low temporal resolution as it requires extended data acquisition periods (~150 seconds for 75,000 images 12 ) to accumulate sufficient microbubble events to form a single vascular image and corresponding velocity map, hindering its potential for functional brain imaging studies.…”

“…We developed a comprehensive ultrasound-based 1 ( ) analysis theory and data processing methodology for cerebral blood flow velocity measurement. We validated vUS with numerical simulations, phantom experiments, and in vivo measurements compared with ULM velocimetry (vULM 12,13 ). We demonstrated the functional imaging ability of vUS by quantifying blood flow velocity changes during whisker stimulation in awake head restrained mice.…”