Fourier Domain Beamforming: The Path to Compressed Ultrasound Imaging

TL;DR

This paper introduces a frequency domain beamforming approach combined with compressed sensing to significantly reduce sampling rates in ultrasound imaging, maintaining image quality while decreasing data and hardware requirements.

Contribution

It extends previous compressed beamforming methods to frequency domain, enabling sub-Nyquist sampling and further data reduction in ultrasound imaging.

Findings

Achieves up to 1/28 reduction in sampling rate compared to standard methods.

Demonstrates feasibility of sub-Nyquist processing on an ultrasound machine.

Maintains image quality with fewer samples, reducing hardware size and power consumption.

Abstract

Sonography techniques use multiple transducer elements for tissue visualization. Signals detected at each element are sampled prior to digital beamforming. The sampling rates required to perform high resolution digital beamforming are significantly higher than the Nyquist rate of the signal and result in considerable amount of data, that needs to be stored and processed. A recently developed technique, compressed beamforming, based on the finite rate of innovation model, compressed sensing (CS) and Xampling ideas, allows to reduce the number of samples needed to reconstruct an image comprised of strong reflectors. A drawback of this method is its inability to treat speckle, which is of significant importance in medical imaging. Here we build on previous work and extend it to a general concept of beamforming in frequency. This allows to exploit the low bandwidth of the ultrasound signal and bypass the oversampling dictated by digital implementation of beamforming in time. Using beamforming in frequency, the same image quality is obtained from far fewer samples. We next present a CS-technique that allows for further rate reduction, using only a portion of the beamformed signal's bandwidth. We demonstrate our methods on in vivo cardiac data and show that reductions up to 1/28 over standard beamforming rates are possible. Finally, we present an implementation on an ultrasound machine using sub-Nyquist sampling and processing. Our results prove that the concept of sub-Nyquist processing is feasible for medical ultrasound, leading to the potential of considerable reduction in future ultrasound machines size, power consumption and cost.