Abstract

Single-track location shear wave elastography (STL-SWEI) is robust against speckle-induced noise in shear wave speed (SWS) estimates; however, it is not immune to other incoherent sources of noise (such as electronic noise) that increases the variance in SWS estimates. Although estimation averaging enabled by parallel receive beamforming adequately suppresses these noise sources, these beamforming techniques often rely on broad transmit beams (plane or diverging). While broad beam approaches, such as plane-wave imaging, are becoming ubiquitous in research ultrasound systems, clinical systems usually employ focused transmit beams due to compatibility with hardware beamforming and deeper penetration. Consequently, improving the noise robustness of focused transmit-based STL-SWEI may enable easier translation to clinical scenarios. In this article, we experimentally evaluated the performance of parallel beamforming for STL-SWEI using fixed or multiple transmit focus. By imaging tissue-mimicking phantoms, we found that parallel beamforming improved the focal zone elastographic signal-to-noise ratio (SNRe) by 40.9%. For a receive line spacing equivalent to transducer pitch, averaging estimates from three parallel lines produced peak SNRe at the focal zone (25 mm), while, at the shallower regions (< 20 mm), a larger number of parallel lines (>7) were needed. Increasing the beamforming line density by a factor of 8 increased the focal zone SNRe only by 13.2%. When SWS quantification was desirable at a fixed depth (such as within the push focal depth), using a deeper tracking focal zone enabled higher parallel line count and improved the peak SNRe by 33%. The multifocusing strategy produced a lower SNRe than the single-focus configurations. For a fixed tracking focal zone, a depth-dependent averaging based on the simulated transmit intensity adequately accounted for the transmit beamwidth. The results in this work demonstrated that STL-SWEI can be implemented using focused transmit beams with robust noise-suppression capability.

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