Time-Domain Linear Model-based Framework for Passive Acoustic Mapping of Cavitation Activity

TL;DR

This paper introduces a time-domain linear model-based framework for passive acoustic mapping of cavitation activity, significantly improving spatial resolution and data efficiency over traditional methods in ultrasound applications.

Contribution

The proposed framework is a novel time-domain linear model that enhances cavitation mapping accuracy and reduces data requirements compared to existing frequency-domain and time-domain techniques.

Findings

Achieves high-quality cavitation maps with only 20% of the data needed by frequency-domain methods.

Outperforms state-of-the-art techniques in spatial resolution and data efficiency.

Flexible regularization allows adaptation to various cavitation scenarios.

Abstract

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.