Vector-Quantization Variational Autoencoder Based Data Rate Reduction for Wireless Ultrasound Imaging Systems

This is the authors' abstract. We don't add key points for this paper.

Ultrasound imaging has become a preferred medical diagnostics tool for many applications due to its cost-effectiveness, non-ionizing nature, and real-time capabilities. There has been a significant progress in the development of new ultrasound probes and systems, particularly portable and wearable devices, incorporating new transducer technologies, sophisticated electronics integration, artificial intelligence and advanced beamforming strategies. Wearable ultrasound systems, equipped with wireless data transfer interfaces, offer unique advantages for continuous signal monitoring of the patients for their critical conditions both in and out-of-hospital settings. Many challenges specifically in data rate reduction for wireless real-time systems needs to be explored. To address this issue, in this paper, we present a vector quantized variational autoencoder model to effectively compress ultrasound RF signals without compromising image quality. We tested and evaluated the performance of the model on real ultrasound datasets. The experimental results demonstrate 92% of data reduction enabling achievable real-time imaging speeds over wireless channels.