IEEE ICASSP 2022

Presented by: Alon Mamistvalov, The Weizmann Institute of Science Danah Yatim, The Weizmann Institute of Science Shlomi Savariego, The Weizmann Institute of Science Nimrod Glazer, The Weizmann Institute of Science Yonina C. Eldar, The Weizmann Institute of Science

Ultrasound Channel Data Transfer over WIFI Alon Mamistvalov, Danah Yatim, Shlomi Savariego, Nimrod Glazer, and Yonina C. Eldar

In this demo, we present a software prototype for transferring high quality ultrasound imaging over Wireless in real-time. The most widely used technique in US imaging is delay and sum (DAS) beamforming, where appropriate delays are applied to signals acquired by transducer elements. However, performing high-resolution digital beamforming requires sampling rates that are much higher than the signal Nyquist rate. Moreover, producing US images that exhibit good resolution and high image contrast typically requires many transducer elements. This leads to large amounts of data making it impractical to transmit US channel data over WIFI. We use a compressed frequency domain convolutional beamforming (CFCOBA) [1] scheme for US imaging which allows to recover high quality images from small size data sets. This method combines sparse Fourier domain beamforming [2], sparse convolutional beamforming (SCOBA) [3] and compressed sensing methods to enable the reconstruction of high resolution images from sub–Nyquist sampled measurements taken at a sparse subset of array elements. We demonstrate the above-mentioned beamforming technique through a software prototype of real-time Ultrasound imaging over WIFI. In our system we use a Verasonics US machine to transmit ultrasound signals to the Tx computer. On the Tx computer we sample the signal at sub -Nyquist rate using subset of the original element array. The data is transferred to the Rx computer over wireless (The protocol used for data transfer is TCP, enabling reliable data stream in our system). On the Rx computer we reconstruct the image using a compressed sensing algorithm. In our demo we enabled overall data reduction of 21~ times less data. Instead of transferring 20~ Mbyte over WIFI per each frame, we transfer only 0.95~ Mbyte per frame, with real-time rate: 2 Ultrasound frames per second. Hence, paving the way towards wireless ultrasound imaging.

References: [1] T. Chernyakova and Y. C. Eldar, “Fourier-domain beamforming: the path to compressed ultrasound imaging,” IEEE transactions on ultrasonics, ferroelectrics, and frequency control, vol. 61, no. 8, pp. 1252–1267, 2014. [2] A. Mamistvalov and Y. C. Eldar, “Compressed fourier-domain convolutional beamforming for sub-nyquist ultrasound imaging,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 69, no. 2, pp. 489–499, 2022. [3] R. Cohen and Y. C. Eldar, “Sparse convolutional beamforming for ultrasound imaging,” IEEE transactions on ultrasonics, ferroelectrics, and frequency control, vol. 65, no. 12, pp. 2390–2406, 2018.