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 super-resolution (SR) microvessel imaging technologies are rapidly emerging and evolving. The unprecedented combination of imaging resolution and penetration promises a wide range of preclinical and clinical applications. This study concerns spatial quantization error in SR imaging, a common issue that involves a majority of current SR imaging methods. While quantization error can be alleviated by the microbubble localization process (e.g., via upsampling or parametric fitting), it is unclear to what extent the localization process can suppress the spatial quantization error induced by discrete sampling. It is also unclear when low spatial sampling frequency will result in irreversible quantization errors that cannot be suppressed by the localization process. This study had two goals: 1) to systematically investigate the effect of quantization in SR imaging and establish principles of adequate SR imaging spatial sampling that yield minimal quantization error with proper localization methods; 2) to compare the performance of various localization methods and study the level of tolerance of each method to quantization. We conducted experiments on a small wire target and on a microbubble flow phantom. We found that Fourier analysis of an oversampled spatial profile of the microbubble signal could provide reliable guidance for selecting beamforming spatial sampling frequency. Among various localization methods, parametric Gaussian fitting and centroid-based localization on upsampled data had better microbubble localization performance and were less susceptible to quantization error than peak intensity-based localization methods. When spatial sampling resolution was low, parametric Gaussian fitting-based localization had the best performance in suppressing quantization error, and could produce acceptable SR microvessel imaging with no significant quantization artifacts. The findings from this paper can be used in practice to help intelligently determine the minimum requirement of spatial sampling for robust microbubble localization to avoid adding or even reduce the burden of computational cost and data storage that are commonly associated with SR imaging.

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Contrast-enhanced ultrasound (CEUS) imaging has great potential for use in new ultrasound clinical applications such as myocardial perfusion imaging and abdominal lesion characterization. In CEUS imaging, contrast agents (i.e., microbubbles) are used to improve contrast between blood and tissue because of their high nonlinearity under low ultrasound pressure. However, the quality of CEUS imaging sometimes suffers from a low signal-to-noise ratio (SNR) in deeper imaging regions when a low mechanical index (MI) is used to avoid microbubble disruption, especially for imaging at off-resonance transmit frequencies. In this paper, we propose a new strategy of combining CEUS sequences with the recently proposed multiplane wave (MW) compounding method to improve the SNR of CEUS in deeper imaging regions without increasing MI or sacrificing frame rate. The MW-CEUS method emits multiple Hadamard-coded CEUS pulses in each transmission event (i.e., pulse-echo event). The received echo signals first undergo fundamental bandpass filtering (i.e., the filter is centered on the transmit frequency) to eliminate the microbubble’s second harmonic signals because they cannot be encoded by pulse inversion. The filtered signals are then Hadamard decoded and re-aligned in fast time to recover the signals as they would have been obtained using classic CEUS pulses, followed by designed recombination to cancel the linear tissue responses. The MW-CEUS method significantly improved contrast-to-tissue ratio (CTR) and signal-to-noise ratio (SNR) of CEUS imaging by transmitting longer coded pulses. The image resolution was also preserved. The microbubble disruption ratio and motion artifacts in MW-CEUS were similar to those of classic CEUS imaging. In addition, the MW-CEUS sequence can be adapted to other transmission coding formats. These properties of MW-CEUS can potentially facilitate CEUS imaging for many clinical applications, especially assessing deep abdominal organs or the heart.