IEEE Trans Med Imaging. 2018 Dec;37(12):2582-2592. doi: 10.1109/TMI.2018.2843291. Epub 2018 Jul 2.
Sources of nonlinear acoustic emissions, particularly those associated with cavitation activity, play a key role in the safety and efficacy of current and emerging therapeutic ultrasound applications, such as oncological drug delivery, blood-brain barrier opening, and histotripsy. Passive acoustic mapping (PAM) is the first technique to enable real-time and non-invasive imaging of cavitation activity during therapeutic ultrasound exposure, through the recording and passive beamforming of broadband acoustic emissions using an array of ultrasound detectors. Initial limitations in PAM spatial resolution led to the adoption of optimal data-adaptive beamforming algorithms, such as the robust capon beamformer (RCB), that provide improved interference suppression and calibration error mitigation compared to non-adaptive beamformers. However, such approaches are restricted by the assumption that the recorded signals have a Gaussian distribution. To overcome this limitation and further improve the source resolvability of PAM, we propose a new beamforming approach termed robust beamforming by linear programming (RLPB). Along with the variance, this optimization-based method uses higher-order-statistics of the recorded signals, making no prior assumption on the statistical distribution of the acoustic signals. The RLPB is found via numerical simulations to improve resolvability over time exposure acoustics and RCB. In vitro experimentation yielded improved resolvability with respect to the source-to-array distance on the order of 22% axially and 13% transversely relative to RCB, whilst successfully accounting for array calibration errors. The improved resolution and decreased dependence on accurate calibration of RLPB is expected to facilitate the clinical translation of PAM for diagnostic, including super-resolution, and therapeutic ultrasound applications.
非线性声发射源，特别是与空化活动相关的声发射源，在当前和新兴的治疗性超声应用（如肿瘤药物输送、血脑屏障开放和组织破碎）的安全性和有效性方面起着关键作用。被动声映射（PAM）是第一种能够在治疗性超声照射过程中实时、非侵入性地对空化活动进行成像的技术，它通过使用超声探测器阵列记录和被动波束形成宽带声发射来实现。PAM 空间分辨率的初始限制导致采用了最优的数据自适应波束形成算法，如稳健 Capon 波束形成器（RCB），与非自适应波束形成器相比，它提供了更好的干扰抑制和校准误差缓解。然而，这些方法受到记录信号具有高斯分布的假设的限制。为了克服这一限制，并进一步提高 PAM 的源可分辨性，我们提出了一种新的波束形成方法，称为基于线性规划的稳健波束形成（RLPB）。除了方差外，这种基于优化的方法还使用记录信号的高阶统计量，不对声信号的统计分布做出先验假设。数值模拟表明，RLPB 能够提高时间暴露声学和 RCB 的可分辨性。体外实验表明，与 RCB 相比，RLPB 相对于源到阵列的距离在轴向方向上提高了约 22%，在横向方向上提高了 13%的可分辨性，同时成功地考虑了阵列校准误差。RLPB 的分辨率提高和对准确校准的依赖性降低，有望促进 PAM 在诊断、包括超分辨率和治疗性超声应用中的临床转化。
