High-Frequency Ultrasound Imaging With Sub-Nyquist Sampling

Abstract

Implementation of a high-frequency ultrasound (HFUS) beamformer is computationally challenging because of its high sampling rate. This article introduces an efficient beamformer with sub-Nyquist sampling (or bandpass sampling) that is suitable for HFUS imaging. Our approach used channel radio frequency data sampled at bandpass sampling rate (i.e., 4/ 3f_c ) and postfiltering-based interpolation to reduce the computational complexity. A polyphase structure for interpolation was used to further reduce the computational burden while maintaining an adequate delay resolution ( δ ). The performance of the proposed beamformer (i.e., 4/ 3f_c sampling with sixfold interpolation, δ = 8f_c ) was compared with that of the conventional method (i.e., 4f_c sampling with fourfold interpolation, δ = 16f_c ). Ultrafast coherent compounding imaging was used in simulation, in vitro and in vivo imaging experiments. Axial/lateral resolution and contrast-to-noise ratio (CNR) values were measured for quantitative evaluation. The number of transmit pulse cycles was varied from 1 to 3 using two transducers with different fractional bandwidths (67% and 98%). In the simulation, the proposed and conventional methods showed the similar -6-dB axial beam widths (63.5 and 61.5 μm , respectively) from the two-cycle transmit pulse using the transducer with a bandwidth of 67%. In vitro and in vivo imaging experiments were performed using a Verasonics ultrasound research platform equipped with a high-frequency array transducer (20-46 MHz). The in vitro imaging results using a wire target showed consistent results with the simulation study (i.e., disparity at -6-dB axial resolution). The in vivo feasibility study with a murine mouse model with breast cancer was also performed, and the proposed method yielded a similar image quality compared with the conventional method. From these studies, it was demonstrated that the proposed HFUS beamformer based on the bandpass sampling can substantially reduce the computational complexity while minimizing the loss of spatial resolution for HFUS imaging.