Efficient Convolutional Forward Model for Passive Acoustic Mapping and Temporal Monitoring

2026-01-12

AI Summary

This study addresses the limitations of conventional passive acoustic imaging in capturing time-varying cavitation dynamics, which suffers from high computational complexity and poor temporal resolution. The authors propose a beamforming framework based on a time-domain convolutional forward model, formulating the imaging process as an inverse problem that incorporates geometric propagation delays. A regularized inversion algorithm is introduced, leveraging prior knowledge of cavitation characteristics to enhance reconstruction fidelity. Notably, this approach constructs the forward operator in the time domain using a convolutional representation for the first time, achieving high temporal resolution while substantially reducing computational cost. Compared to classical beamforming methods, the proposed framework demonstrates marked improvements in reconstruction quality, temporal resolution, and computational efficiency.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Learning & Optimization for CV
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

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.

Problem

• Passive Acoustic Mapping
• cavitation monitoring
• computational efficiency
• temporal resolution
• therapeutic ultrasound

Innovation

• convolutional forward model
• passive acoustic mapping
• time-domain beamforming
• regularized inversion
• cavitation monitoring