Efficient Convolutional Forward Model for Passive Acoustic Mapping and Temporal Monitoring

• URL: http://arxiv.org/abs/2601.07356v1
• Date: Mon, 12 Jan 2026 09:32:26 GMT
• Title: Efficient Convolutional Forward Model for Passive Acoustic Mapping and Temporal Monitoring
• Authors: Tatiana Gelvez-Barrera, Barbara Nicolas, Bruno Gilles, Adrian Basarab, Denis Kouamé,
• Abstract summary: We introduce a PAM beamforming framework based on a convolutional formulation in the time domain.<n>In this framework, PAM is formulated as an inverse problem in which the forward operator maps cavitation activity to recorded radio-frequency signals.<n>We then formulate a regularized inversion that incorporates prior knowledge on cavitation activity.
• Score: 4.219150964619931
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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