Institute of Fundamental Technological Research

Ultrasound shear wave elastography (SWE) has emerged as a promising technique that enables the quantitative estimation of soft tissue stiffness. However, its practical implementation is complicated and presents a number of engineering challenges, including high-energy burst transmission, high-frame rate data acquisition and high computational requirements to process huge datasets. Therefore, to date, SWE has only been available for high-end commercial systems or bulk and expensive research platforms. In this work, we present a low-cost, portable and fully configurable 256-channel research system that is able to implement various SWE techniques. We evaluated its transmit capabilities using various push beam patterns and developed algorithms for the reconstruction of tissue stiffness maps. Three different push beam generation methods were evaluated in both homogeneous and heterogeneous experiments using an industry-standard elastography phantom. The results showed that it is possible to implement the SWE modality using a portable and cost-optimized system without significant image quality losses.

ultrasound imaging, shear wave elastography, high-frame rate imaging, medical system design

Portable and hand-held ultrasound imagers have the potential to revolutionize Point-of-Care medical diagnostics. There is great need for low-cost, portable scanners with extended battery life. In this paper, we focus on hardware-software partitioning in heterogeneous systems where both field-programmable gate array (FPGA) and graphics processing unit (GPU) resources are available. We present the architecture of a prototype test scanner for the evaluation of various hardware-software partitioning strategies. The system is equipped with the Intel Arria 10 FPGA and the Nvidia Tegra X2 mobile GPU. FPGA-based beamformers: Delay-and-Sum and Filtered Multiply-and-Sum, were implemented. These 32-channel beamformer blocks are integrated into a complete dataflow along with the data acquisition, RF filter, quadrature demodulator, and envelope detector. The designed dataflow allows one to allocate processing functions to either hardware (FPGA) or software (GPU) to explore various imaging scenarios and optimize power consumption. A dedicated measurement setup facilitates measuring power consumption of both FPGA and GPU. The developed setup will provide a reliable experimental system power characterization.

ultrasound imaging, ultrasound scanner, point-ofcare ultrasound, beamforming, low-power, FPGA, GPU processing

The paper describes two different techniques of ultrasound measurements performed in fibre cement boards, the material widely applied in building technology. An in-house device dedicated for fibre-cement board testing is also presented. To overcome the difficulty of determining the arrival time of the waveform of a longitudinal wave travelling across a thin and inhomogeneous body, a cross-correlation method of signal processing was proposed and applied

ultrasound, fibre-cement board, waveform cross correlation coefficient

In this work, we present a low-cost, portable, and fully configurable ultrasound system implementing 2-D real-time Shear Wave Elastography (SWE) imaging mode. To achieve that we have enhanced the transmit capabilities of the 256 TX/64 RX us4R-lite research system, developed by our team, to support push pulses generation. This system was combined with a signal processing pipeline reconstructing stiffness maps from raw RF data. Real-time imaging performance was provided by an efficient reconstruction algorithm execution that incorporated graphics processing unit (GPU). The overall system performance was assessed experimentally using an industry-standard elasticity Q/A phantom. Relevant reconstruction parameters were evaluated in terms of reconstruction time. The system achieved stiffness estimation with a bias <5% and SNR of 30 dB and was able to detect lesions of size >4 mm and various stiffness with CNR in the range of 13–17 dB. The system throughput of up to 5 fps has been achieved on a PC notebook equipped with NVIDIA RTX 3060 GPU.