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Wireless ultrasound (US) systems that produce high-quality images can improvecurrent clinical diagnosis capabilities by making the imaging process much moreefficient, affordable, and accessible to users. The most common technique forgenerating B-mode US images is delay and sum (DAS) beamforming, where anappropriate delay is introduced to signals sampled and processed at eachtransducer element. However, sampling rates that are much higher than theNyquist rate of the signal are required for high resolution DAS beamforming,leading to large amounts of data, making transmission of channel data over WIFIimpractical. Moreover, the production of US images that exhibit high resolutionand good image contrast requires a large set of transducers which furtherincreases the data size. Previous works suggest methods for reduction insampling rate and in array size. In this work, we introduce compressed Fourierdomain convolutional beamforming, combining Fourier domain beamforming, sparseconvolutional beamforming, and compressed sensing methods. This allows reducingboth the number of array elements and the sampling rate in each element, whileachieving high resolution images. Using in vivo data we demonstrate that theproposed method can generate B-mode images using 142 times less data than DAS.Our results pave the way towards wireless US and demonstrate that highresolution US images can be produced using sub-Nyquist sampling and a smallnumber of receiving channels.

Compressed Fourier-Domain Convolutional Beamforming for Wireless Ultrasound imaging