Passive Acoustic Dynamic Differentiation and Mapping: A Time-Domain Passive Cavitation Localization and Classification Approach
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 2026.
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. 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. PADAM offers improved lateral and axial resolutions, artifact suppression, and a physically meaningful input parameter that is intuitive and requires minimal tuning. Additionally, PADAM is computationally more efficient than RCB, as it avoids matrix inversions for every pixel. PADAM’s ability to dynamically distinguish cavitation mechanisms has significant implications in image-guided FUS therapy, particularly for applications requiring selective monitoring of stable versus inertial cavitation.