3-D Passive Cavitation Imaging Using Adaptive Beamforming and Matrix Array Transducer With Random Apodization.

Area of Science:

• Acoustics
• Biomedical Engineering
• Medical Imaging

Background:

• Passive acoustic mapping (PAM) is increasingly vital for monitoring cavitation-based treatments.
• Existing 3D PAM often uses delay-and-sum (DAS) beamforming with commercial probes, leading to artifacts and poor resolution.
• Adaptive beamforming techniques show promise for improving 3D PAM performance.

Purpose of the Study:

• To implement and evaluate adaptive beamforming algorithms in 3D frequency domain (FD) for passive acoustic mapping.
• To assess the performance of DAS, robust capon beamformer (RCB), and MidWay (MW) beamformers using sparse apodization on commercial matrix probes.
• To demonstrate high-performance 3D PAM with manageable devices.

Main Methods:

• Developed 3D frequency domain adaptive beamforming algorithms: DAS, RCB, and MW.
• Utilized random sparse apodization on a commercial 1024-element matrix array, activating only 256 elements.
• Evaluated algorithms through simulations and experiments with harmonic sources and real cavitation signals.

Main Results:

• Adaptive beamformers significantly improved mapping performance compared to DAS for small-aperture commercial probes.
• The MidWay (MW) beamformer offered a good balance of performance, low computation time, and parameter-free operation.
• Sparse apodization combined with adaptive beamforming enables high-performance 3D PAM.

Conclusions:

• Adaptive beamforming, particularly the MW algorithm, substantially enhances 3D PAM resolution and reduces artifacts.
• Sparse apodization with adaptive beamforming provides a practical solution for high-performance 3D PAM using commercial probes.
• This approach facilitates better monitoring of cavitation-based therapies.