A Programmable Platform for Accelerating the Development of Smart Ultrasound Transducer Probe

TinyProbe: A Wearable 32-Channel Multimodal Wireless Ultrasound Probe

The need for continuous monitoring of cardiorespiratory activity, blood pressure, bladder, muscle motion analysis, and more is pushing for research and development of wearable ultrasound (US) devices. In this context, there is a critical need for highly configurable, energy-efficient wearable US systems with wireless access to raw data and long battery life. Previous exploratory works have primarily relied on bulky commercial research systems or custom-built prototypes with limited and narrowly focused field applicability. This article presents TinyProbe, a novel multimodal wearable US platform. TinyProbe integrates a 32-channel US receive (RX)/transmit (TX) front-end, including TX beamforming ( excitations, 16 delay profiles) and analog-to-digital conversion (up to 30 Ms/s, 10 bit), with a Wi-Fi link (21.6 Mb/s, UDP), for wireless raw data access, all in a compact ( mm) and lightweight (35 g) design. Using advanced power-saving techniques and optimized electronics design, TinyProbe achieves a power consumption of <1 W for imaging modes (32 ch., 33 Hz) and <1.3 W for high-PRF Doppler mode (2 ch., 1400 Hz). This results in a state-of-the-art power efficiency of 44.9 mW/Mb/s for wireless US systems, ensuring multihour operation with a compact 500 mAh Li-Po battery. We validate TinyProbe as a versatile, general-purpose wearable platform in multiple in vivo imaging scenarios, including muscle and bladder imaging, and blood flow velocity measurements.

Deconvolution based on sparsity and continuity improves the quality of ultrasound image

The application of ultrasound (US) image has been limited by its limited resolution, inherent speckle noise, and the impact of clutter and artifacts, especially in the miniaturized devices with restricted hardware conditions. In order to solve these problems, many researchers have explored a number of hardware modifications as well as algorithmic improvements, but further improvements in resolution, signal-to-noise ratio (SNR) and contrast are still needed. In this paper, a deconvolution algorithm based on sparsity and continuity (DBSC) is proposed to obtain the higher resolution, SNR, and, contrast. The algorithm begins with a relatively bold Wiener filtering for initial enhancement of image resolution in preprocessing, but it also introduces ringing noise and compromises the SNR. In further processing, the noise is suppressed based on the characteristic that the adjacent pixels of the US image are continuous as long as Nyquist sampling criterion is met, and the extraction of high-frequency information is balanced by using relatively sparse. Subsequently, the theory and experiments demonstrate that relative sparsity and continuity are general properties of US images. DBSC is compared with other deconvolution strategies through simulations and experiments, and US imaging under different transmission channels is also investigated. The final results show that the proposed method can greatly improve the resolution, as well as provide significant advantages in terms of contrast and SNR, and is also feasible in applications to devices with limited hardware.