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    Real-time three dimensional (3D) ultrasound provides complete visualization of inner body organs and blood vasculature, which is crucial for diagnosis and treatment of diverse diseases. However, 3D systems require massive hardware due to the huge number of transducer elements and consequent data size. This increases cost significantly and limits both frame rate and image quality, thus preventing 3D ultrasound from being common practice in clinics worldwide. A recent study presented a technique called sparse convolutional beamforming algorithm (S/COBA), which obtains improved image quality while allowing notable element reduction in the context of 2D focused imaging. In this paper, we build upon previous work and introduce a nonlinear beamformer for 3D imaging, called COBA-3D, consisting of 2D spatial convolution of the in-phase and quadrature received signals. The proposed technique considers diverging-wave transmission and achieves improved image resolution and contrast, compared with standard delay-and-sum beamforming, while enabling high frame rate. Incorporating 2D sparse arrays into our method creates SCOBA-3D: a sparse beamformer which offers significant element reduction and thus allows to perform 3D imaging with the resources typically available for 2D setups. To create 2D thinned arrays, we present a scalable and systematic way to design 2D fractal sparse arrays. The proposed framework paves the way for affordable ultrafast ultrasound devices that perform high-quality 3D imaging, as demonstrated using phantom and ex-vivo data.


    Regev Cohen, Nitai Fingerhut, Francois Varray, Herve Liebgott, Yonina C Eldar. Sparse Convolutional Beamforming for 3D Ultrafast Ultrasound Imaging. IEEE transactions on ultrasonics, ferroelectrics, and frequency control. 2021 Mar 23;PP

    PMID: 33755562

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