Passive Acoustic Dynamic Differentiation and Mapping (PADAM): A Time-Domain Passive Cavitation Localization and Classification Approach.

Objective: Passive cavitation imaging has explored various beamforming algorithms to optimize spatial resolution, suppress imaging artifacts, and maintain computational efficiency. These factors are crucial for the clinical translation of Focused Ultrasound (FUS) therapies, where precise cavitation localization and dose control are required to minimize off-target effects. Commonly used methods such as Delay-Sum-Integrate (DSI) and Robust Capon Beamforming (RCB) have shown utility, but are limited by either significant artifacts or the need for a nonphysical input parameter. To address these challenges, we aimed to develop a method that enhances resolution and introduces a physically grounded parameter for signal characterization, without compromising computational speed and robustness.

Methods: This work introduces Passive Acoustic Dynamic Differentiation and Mapping (PADAM), which adapts the Multiple Signal Classification algorithm to the time domain to improve cavitation localization and classification. PADAM incorporates a physically meaningful input parameter that dynamically reflects the frequency richness of the received signal.

Results: PADAM achieves up to a 6-fold improvement in lateral beamwidth compared to RCB, and a 4-fold reduction in mean-square artifact intensity reduction. Its input parameter provides a novel physical insight, enabling differentiation between stable and inertial cavitation based on spectral content. This reduces reliance on empirically tuned or arbitrary thresholds and simplifies integration into therapy workflows.

Conclusion: With its ability to improve resolution, reduce artifacts, and provide computational efficiency, PADAM represents a promising advancement for precise cavitation localization and therapy monitoring.

Significance: This work introduces PADAM, a time-domain passive cavitation imaging method that offers superior resolution and artifact reduction compared to DSI and RCB. Its physically intuitive input parameter enables dynamic differentiation between stable and inertial cavitation, enhancing precision in the monitoring and control of FUS therapy.