Multiple signal classification in the time domain for passive cavitation imaging

The field of passive acoustic imaging for cavitation control has investigated adaptive and non-adaptive beamforming algorithms to optimize the trade-offs among imaging resolution, artifact reduction, and computational speed. These parameters are critical for clinical translation, where precise control of cavitation location and dosage is essential to ensure the safety and efficacy of focused ultrasound (FUS) therapies while minimizing off-target effects. Conventional beamforming methods, such as Delay-Sum-Integrate (DSI) and Robust Capon Beamformers (RCB), are fast and effective but have limitations including significant artifacts and the need for extensive parameter tuning to achieve optimal performance. This work introduces an adaptation of a Multiple Signal Classification (MUSIC) algorithm to the time domain and evaluates its performance in artifact reduction, resolution enhancement, and practical implementation compared to DSI and RCB. Our MUSIC algorithm achieves a 3.5× improvement in lateral resolution and a 1.3× improvement in axial resolution compared to RCB, while introducing a novel parameter that quantifies the richness of frequency content in the received signal. This parameter enables differentiation between cavitation modalities based on their spectral characteristics. MUSIC is a strong candidate for precise cavitation localization, with scalable artifact reduction, simplicity, and computational speed. Its physically meaningful input parameter further enhances its utility in clinical and research applications.