使用互相关角谱法的高质量被动声成像
High-Quality Passive Acoustic Mapping with the Cross-Correlated Angular Spectrum Method
AI总结：
针对PAM实时性与图像质量难以兼顾的问题，提出AS-TAX方法，通过子孔径AS波场互相关抑制伪影，以低两个数量级的计算量实现高质量毫秒级空化成像。
摘要：
尽管被动声成像（PAM）已在监测聚焦超声（FUS）治疗中的声空化活动方面取得进展，但同时实现实时和高质量成像能力仍具挑战性。角谱（AS）方法是PAM中最高效的算法，但由于成像阵列的衍射图样，它存在伪影和分辨率低的问题。数据自适应波束形成器能很好地抑制伪影，但其计算复杂度过高——比经典的时间曝光声学（TEA）方法高出两个数量级以上——阻碍了其在实时场景中的应用。在本工作中，我们引入互相关AS方法来应对这一挑战。该方法基于在频域中对换能器阵列不同切趾子孔径所测得的AS反向传播波场进行互相关，以提供用于伪影抑制的归一化相关系数（NCC）矩阵。我们观察到NCC矩阵的空间模式是可变的，这可被采用AS方案的三重切趾互相关（TAX）方法（即AS-TAX方法）利用，以获得最佳伪影抑制效果。体模和小鼠肿瘤实验均表明：1）AS-TAX方法具有与数据自适应波束形成器相当的图像质量，与TEA相比，其能量扩散面积减少34.8–66.6%，图像信噪比提高10.7–14.5 dB；2）与TEA相比，它将计算复杂度降低了两个数量级，通过并行实现可达到毫秒级图像重建速度；3）它能很好地绘制不同状态（稳定或惯性）的微泡空化活动。AS-TAX方法代表了一种以高图像质量监测基于空化的FUS治疗的实时方法。
英文摘要：
While passive acoustic mapping (PAM) has been advanced for monitoring acoustic cavitation activity in focused ultrasound (FUS) therapy, achieving both real-time and high-quality imaging capabilities is still challenging. The angular spectrum (AS) method presents the most efficient algorithm for PAM, but it suffers from artifacts and low resolution due to the diffraction pattern of the imaging array. Data-adaptive beamformers suppress artifacts well, but their overwhelming computational complexity, more than two orders of magnitude higher than the classical time exposure acoustic (TEA) method, hinders their application in real-time. In this work, we introduce the cross-correlated AS method to address the challenge. This method is based on cross-correlating the AS back-propagated wave fields, in the frequency domain, measured by different apodized sub-apertures of the transducer array to provide the normalized correlation coefficient (NCC) matrix for artifacts suppression. We observed that the spatial pattern of NCC matrix is variable which can be utilized by the triple apodization with cross-correlation (TAX) with AS scheme, namely the AS-TAX method, for optimal artifacts suppression outcomes. Both the phantom and mouse tumor experiments showed that: 1) the AS-TAX method has comparable image quality as the data-adaptive beamformers, reducing the energy spread area by 34.8-66.6% and improving image signal-to-noise ratio by 10.7-14.5 dB compared to TEA; 2) it reduces the computational complexity by two orders of magnitude compared to TEA allowing millisecond-level image reconstruction speed with a parallel implementation; 3) it can well map microbubble cavitation activity of different status (stable or inertial). The AS-TAX method represents a real-time approach to monitor cavitation-based FUS therapy with high image quality.
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