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

2025-11-25

AI Summary

Conventional passive acoustic imaging suffers from low axial resolution, frequency-domain methods’ reliance on long-duration signals, and insufficient spatial resolution in time-domain approaches—hindering high-fidelity spatiotemporal localization of cavitation activity. To address these limitations, this work proposes a fully time-domain linear beamforming framework. It formulates an explicit discrete spatiotemporal forward model that rigorously incorporates acoustic propagation delays and employs spatiotemporal regularization priors to enable robust inverse reconstruction. Crucially, the method operates without requiring transmit-time references, markedly improving both axial and lateral resolution while achieving high data efficiency—delivering performance comparable to state-of-the-art techniques using only 20% of the sampled data. Experimental validation across diverse cavitation scenarios demonstrates superior imaging quality over existing methods, particularly for dynamic monitoring of transient, non-stationary cavitation events.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

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.

Problem

• Improving axial resolution in cavitation mapping without reference emission time
• Addressing low spatial resolution in time-domain acoustic beamforming methods
• Reducing data requirements compared to frequency-domain cavitation mapping techniques

Innovation

• Time-domain linear model with time-of-flight delays
• Spatiotemporal regularization using prior knowledge
• Enhanced resolution with 20% data requirement