Dual apodization with cross-correlation combined with robust Capon beamformer applied to ultrasound passive cavitation mapping
Dual apodization with cross-correlation combined with robust Capon beamformer applied to ultrasound passive cavitation mapping
复制标题
具有互相关性的双变迹结合稳健的 Capon 波束形成器应用于超声被动空化测绘
DOI：
10.1002/mp.14093
复制
发表时间：
2020
期刊：
影响因子：
3.8
通讯作者：
Mingxi Wan
中科院分区：
文献类型：
--
作者：
Shukuan Lu;Renyan Li;Yan Zhao;Xianbo Yu;Diya Wang;Mingxi Wan
目的：被动声标测技术近年来受到越来越多的关注，在超声治疗的实时监测中具有极其广泛的应用前景。当使用诊断超声换能器时，例如线性阵列换能器，最初使用的时间暴露声学（TEA）算法将产生高水平伪影。为了解决这个问题，我们最近提出了一种增强算法的线阵PAM引入双切趾与互相关（DAX）方法到TEA。但是由于用于产生RX 1和RX 2的延迟和求和波束形成器是非自适应的，剩余的X型伪影不能被完全抑制，从而产生不令人满意的图像质量。本研究的目的是提出一种改进的版本，结合DAX和鲁棒Capon波束形成器（DAX-RCB）。研究方法：与DAX-TEA算法中的延迟和波束形成器不同，该算法通过RCB方法对来自一对互补接收变迹的两组通道信号进行波束形成，从而使被动空化图像对X型伪影的敏感性大大降低。通过仿真和体外实验验证了DAX-RCB算法的性能，并与最初使用的TEA算法和以前的DAX-TEA和RCB算法进行了比较。采用被动能量束（PEB）尺寸、图像信号背景比（ISBR）、能量估计比（EER）和计算时间四个指标对算法性能进行评价。结果如下：考虑一个8-8交替模式的例子实验结果表明，在A（-6dB）区域，当与TEA和DAX-TEA相比时，所提出的DAX-RCB的（2D PEB尺寸）显著减小11.0和6.8 mm（2），并且当与RCB相比时没有显著减小，与TEA、DAX-TEA和RCB相比，ISBR分别提高了19.6、10.8和5.6dB，DAX-RCB的EER超过90%。仿真实验表明，DAX-RCB算法同样适用于双源场景和高噪声场景下的图像增强，但存在能量估计过低的风险。算法性能的提高伴随着计算时间的增加。所提出的DAX-RCB比TEA、DAX-TEA和RCB多消耗113.3%、29.5%和17.8%的时间。结论：所提出的DAX-RCB可以被认为是一种有效的重建算法的被动空化映射，并提供了一个适当的监测手段，超声治疗，特别是空化介导的应用。(C)2020年美国医学物理学家协会
Purpose: Passive acoustic mapping (PAM) has received increasing attention in recent years and has an extremely widespread application prospect in real-time monitoring of ultrasound treatment. When using a diagnostic ultrasound transducer, such as a linear-array transducer, the initially used time exposure acoustics (TEA) algorithm will produce high-level artifacts. To address this problem, we recently proposed an enhanced algorithm for linear-array PAM by introducing dual apodization with the cross-correlation (DAX) method into TEA. But due to that the delay and sum beamformer used to create RX1 and RX2 is non-adaptive, the remaining X-type artifacts cannot be completely suppressed, yielding unsatisfactory image quality. This study aims to propose an improved version by combining DAX and robust Capon beamformer (DAX-RCB). Methods: Different from the delay and sum beamformer in the DAX-TEA algorithm, in the proposed version, the two sets of channel signals from a pair of complementary receive apodizations are beamformed by the RCB method, which may make passive cavitation images much less sensitive to X-type artifacts. The performance of the DAX-RCB algorithm is validated by simulations and in vitro experiments and compared with the initially used TEA algorithm and the previous DAX-TEA and RCB algorithms. Four indexes, including passive energy beam (PEB) size, image signal-to-background ratio (ISBR), energy estimation ratio (EER), and computing time, are used to evaluate the algorithm performance. Results: Consider an example of the 8-8 alternating pattern (a pair of complementary apodizations are obtained by extracting eight elements every eight elements), the experimental results show that the A(-6dB) area (2D PEB size) of the proposed DAX-RCB is significantly reduced by 11.0 and 6.8 mm(2) when compared with TEA and DAX-TEA and is not significantly reduced when compared with RCB, the ISBR is significantly improved by 19.6, 10.8, and 5.6 dB compared with TEA, DAX-TEA, and RCB, and the EER of DAX-RCB is over 90%. The simulation tests indicate that the DAX-RCB algorithm is also applicable to the image enhancement in the double-source scenario and the high-level noise scenario but at a risk of low energy estimation. The improvement of algorithm performance is accompanied by an increase in the computing time. The proposed DAX-RCB consumes 113.3%, 29.5%, and 17.8% more time than TEA, DAX-TEA, and RCB. Conclusions: The proposed DAX-RCB can be considered as an effective reconstruction algorithm for passive cavitation mapping and provide an appropriate monitoring means for ultrasound therapy, especially for cavitation-mediated applications. (C) 2020 American Association of Physicists in Medicine