Deep Beamforming for Real-Time 3-D Passive Acoustic Mapping With Row-Column-Addressed Arrays

Abstract

Passive acoustic mapping (PAM) is a promising tool to monitor acoustic cavitation activities for focused ultrasound (FUS) therapies. While 2-D matrix arrays allow 3-D PAM, the high channel count requirement and the complexity of the receiving electronics limit their practical value in real-time imaging applications. In this regard, row-column-addressed (RCA) arrays have shown great potential in addressing the difficulties in real-time 3-D ultrasound imaging. However, currently, there is no applicable method for 3-D PAM with RCA arrays. In this work, we propose a deep beamformer for real-time 3-D PAM with RCA arrays. The deep beamformer leverages a deep neural network (DNN) to map radio frequency (RF) microbubble (MB) cavitation signals acquired with the RCA array to 3-D PAM images, achieving similar image quality to the reconstructions performed using the fully populated 2-D matrix array with the angular spectrum (AS) method. In the simulation, the images reconstructed by the deep beamformer showed less than 13.2% and 1.8% differences in the energy spread volume (ESV) and image signal-to-noise ratio (ISNR), compared with those reconstructed using the matrix array. However, the image reconstruction time was reduced by 11 and 30 times on the CPU and GPU, respectively, achieving 42.4 volumes per second image reconstruction speed on a GPU for a volume sized $128\times 128\times 128$ . Experimental data further validated the capabilities of the deep beamformer to accurately localize MB cavitation activities in 3-D space. These results clearly demonstrated the feasibility of real-time and 3-D monitoring of MB cavitation activities with RCA arrays and neural network-based beamformers.