Efficient Convolutional Forward Model for Passive Acoustic Mapping and Temporal Monitoring

TL;DR

This paper presents a novel convolutional time-domain framework for passive acoustic mapping that enhances temporal resolution and computational efficiency in cavitation monitoring for therapeutic ultrasound.

Contribution

It introduces a new convolutional formulation for PAM that improves temporal resolution and reduces computational load compared to existing methods.

Findings

Outperforms classical beamforming in reconstruction quality

Provides higher temporal resolution than frequency-domain methods

Reduces computational burden significantly

Abstract

Passive acoustic mapping (PAM) is a key imaging technique for characterizing cavitation activity in therapeutic ultrasound applications. Recent model-based beamforming algorithms offer high reconstruction quality and strong physical interpretability. However, their computational burden and limited temporal resolution restrict their use in applications with time-evolving cavitation. To address these challenges, we introduce a PAM beamforming framework based on a novel convolutional formulation in the time domain, which enables efficient computation. In this framework, PAM is formulated as an inverse problem in which the forward operator maps spatiotemporal cavitation activity to recorded radio-frequency signals accounting for time-of-flight delays defined by the acquisition geometry. We then formulate a regularized inversion algorithm that incorporates prior knowledge on cavitation activity. Experimental results demonstrate that our framework outperforms classical beamforming methods, providing higher temporal resolution than frequency-domain techniques while substantially reducing computational burden compared with iterative time-domain formulations.