Passive acoustic mapping enables the spatial mapping and temporal monitoring of cavitation activity, playing a crucial role in therapeutic ultrasound applications. Most conventional beamforming methods, whether implemented in the time or frequency domains, suffer from limited axial resolution due to the absence of a reference emission onset time. While frequency-domain methods, the most efficient of which are based on the cross-spectral matrix, require long signals for accurate estimation, time-domain methods typically achieve lower spatial resolution. To address these limitations, we propose a linear model-based beamforming framework fully formulated in the time domain. The linear forward model relates a discretized spatiotemporal distribution of cavitation activity to the temporal signals recorded by a probe, explicitly accounting for time-of-flight delays dictated by the acquisition geometry. This model is then inverted using regularization techniques that exploit prior knowledge of cavitation activity in both spatial and temporal domains. Experimental results show that the proposed framework achieves enhanced or competitive cavitation map quality while using only 20\% of the data typically required by frequency-domain methods. This highlights the substantial gain in data efficiency and the flexibility of our spatiotemporal regularization to adapt to diverse passive cavitation scenarios, outperforming state-of-the-art techniques.
翻译:被动声学映射技术能够实现空化活动的空间映射与时间监测,在治疗性超声应用中发挥着关键作用。大多数传统的波束形成方法(无论基于时域或频域实现)由于缺乏参考发射起始时间,均存在轴向分辨率受限的问题。频域方法(其中最有效的基于互谱矩阵)需要长信号以实现精确估计,而时域方法通常获得较低的空间分辨率。为克服这些局限,我们提出了一种完全在时域构建的基于线性模型的波束形成框架。该线性前向模型将空化活动的离散化时空分布与探头记录的时域信号相关联,显式地计入了由采集几何结构决定的飞行时间延迟。随后利用正则化技术对该模型进行反演,该技术融合了空化活动在空间域和时间域的先验知识。实验结果表明,所提框架仅需使用频域方法通常所需数据的20%,即可实现更优或具有竞争力的空化图质量。这凸显了本方法在数据效率上的显著提升,以及时空正则化对多样化被动空化场景的适应灵活性,其性能优于现有先进技术。