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Real-time three dimensional (3D) ultrasound provides complete visualizationof inner body organs and blood vasculature, which is crucial for diagnosis andtreatment of diverse diseases. However, 3D systems require massive hardware dueto the huge number of transducer elements and consequent data size. Thisincreases cost significantly and limits both frame rate and image quality, thuspreventing 3D ultrasound from being common practice in clinics worldwide. Arecent study proposed a technique, called convolutional beamforming algorithm(COBA), which obtains improved image quality while allowing notable elementreduction. COBA was developed and tested for 2D focused imaging using full andsparse arrays. The later was referred to as sparse COBA (SCOBA). In this paper,we build upon previous work and introduce a nonlinear beamformer for 3Dimaging, called COBA-3D, consisting of 2D spatial convolution of the in-phaseand quadrature received signals. The proposed technique considersdiverging-wave transmission, thus, achieves improved image resolution andcontrast compared with standard delay-and-sum beamforming, while enabling highframe rate. Incorporating 2D sparse arrays into our method creates SCOBA-3D: asparse beamformer which offers significant element reduction and thus allows toperform 3D imaging with the resources typically available for 2D setups. Tocreate 2D thinned arrays, we present a scalable and systematic way to design 2Dfractal sparse arrays. The proposed framework paves the way for affordableultrafast ultrasound devices that perform high-quality 3D imaging, asdemonstrated using phantom and ex-vivo data.

Sparse Convolutional Beamforming for 3D Ultrafast Ultrasound Imaging