A new frequency domain passive acoustic mapping method using passive Hilbert beamforming to reduce the computational complexity of fast Fourier transform

Abstract

Passive acoustic mapping (PAM) is the current state-of-the-art imaging tool for monitoring cavitation activity during focused ultrasound therapy such as blood-brain barrier opening. However, PAM incurs huge computational complexity. To address this issue, frequency-domain PAM (FD-PAM) was proposed. Nevertheless, FD-PAM still requires a large number of fast Fourier transforms (FFTs) to produce the frequency components utilized for cavitation monitoring with PAM. Hence, in this paper, we proposes a frequency domain PAM method using passive Hilbert beamforming (PHB-PAM), which can significantly reduce the number of input samples for FFT by down-sampling the analytic signal of the received RF samples at each channel at a rate equal to the bandwidth of the frequency components of interest. The experimental results show that the proposed PHB-PAM provides comparable image quality to that of FD-PAM (correlation coefficient > 0.98). Additionally, the study experimentally verifies that the pre-processing block for generating the decimated analytic signal and FFT in PHB-PAM can be realized using lesser logic resources than FFT in FD-PAM when implemented in an FPGA. Especially, with 128-fold decimation, PHB-PAM reduces the amount of LUTs and DSP slices to implement the pre-processing block by 72.16% and 53.4%, respectively, compared to those of FD-PAM, which allows the 64-channel implementation of the pre-processing block in a low-cost single FPGA. Finally, a hardware-efficient architecture for the pre-processing block of PHB-PAM is described, which can be implemented by replacing the two lowpass filters of an off-the-shelf analog front-end component for ultrasound imaging with a pair of band-pass filters. If PHB-PAM is realized using such a component, it can truly minimize the computational complexity of FD-PAM.