Comparison of synthetic aperture architectures for miniaturised ultrasound imaging front-ends

Abstract

BackgroundPoint of care ultrasonography has been the focus of extensive research over the past few decades. Miniaturised, wireless systems have been envisaged for new application areas, such as capsule endoscopy, implantable ultrasound and wearable ultrasound. The hardware constraints of such small-scale systems are severe, and tradeoffs between power consumption, size, data bandwidth and cost must be carefully balanced.MethodsIn this work, two receiver architectures are proposed and compared to address these challenges. Both architectures uniquely combine low-rate sampling with synthetic aperture beamforming to reduce the data bandwidth and system complexity. The first architecture involves the use of quadrature sampling to minimise the signal bandwidth and computational load. Synthetic aperture beamforming (SAB) is carried out using a single-channel, pipelined protocol suitable for implementation on an FPGA/ASIC. The second architecture employs compressive sensing within the finite rate of innovation framework to further reduce the bandwidth. Low-rate signals are transmitted to a computational back-end (computer), which sequentially reconstructs each signal and carries out beamforming.ResultsBoth architectures were tested using a custom hardware front-end and synthetic aperture database to yield B-mode images. The normalised root-mean-squared-error between the quadrature SAB image and the RF reference image was while the compressive SAB error was for the same degree of spatial compounding. The sampling rate is reduced by a factor of 2 (quadrature SAB) and 4.7 (compressive SAB), compared to the RF sampling rate. The quadrature method is implemented on FPGA, with a total power consumption of mW, which is comparable to state-of-the-art hardware topologies, but with significantly reduced circuit area.ConclusionsThrough a novel combination of SAB and low-rate sampling techniques, the proposed architectures achieve a significant reduction in data transmission rate, system complexity and digital/analogue circuit area. This allows for aggressive miniaturisation of the imaging front-end in portable imaging applications.

A Review of Optical Ultrasound Imaging Modalities for Intravascular Imaging

“…Recent advances in medical imaging research have shown machine learning and artificial intelligence being used for image reconstruction. One such method is using a deep learning algorithm as a reconstruction method, as proposed by Kim et al (2020) and Peyton et al (2018). Kim et al (2020) worked on deep convolutional neural networks to overcome the issue of limited bandwidth and detection views, leading to severe structural loos and low image contrast.…”