上海科技大学知识管理系统

Passive acoustic mapping (PAM) is a promising tool to monitor acoustic cavitation activities for focused ultrasound (FUS) therapies. While 2D matrix arrays allow three-dimensional (3D) 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 potentials in addressing the difficulties in real-time 3D ultrasound imaging. However, currently, there is no applicable method for 3D PAM with RCA arrays. In this work, we propose a deep beamformer for real-time 3D PAM with RCA arrays. The deep beamformer leverages a deep neural network to map radio-frequency microbubble (MB) cavitation signals acquired with the RCA array to 3D PAM images, achieving similar image quality to the reconstructions performed using the fully populated 2D matrix array with the angular spectrum 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 and image signal-to-noise ratio, compared to 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 128×128×128. Experimental data further validated the capabilities of the deep beamformer to accurately localize MB cavitation activities in 3D space. These results clearly demonstrated the feasibility of real-time and 3D monitoring of MB cavitation activities with RCA arrays and neural network based beamformers.