A Portable Ultrasound Imaging System Utilizing Deep Generative Learning-Based Compressive Sensing on Pre-Beamformed RF Signals

Gupta, SK and Kumar, K and Seelamantula, CS and Thakur, CS (2019) A Portable Ultrasound Imaging System Utilizing Deep Generative Learning-Based Compressive Sensing on Pre-Beamformed RF Signals. In: 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 23-27 July 2019, Berlin, Germany, Germany, pp. 2740-2743.

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Abstract

Recent advances in the unsupervised and generative models of deep learning have shown promise for application in biomedical signal processing. In this work, we present a portable resource-constrained ultrasound (US) system trained using Variational Autoencoder (VAE) network which performs compressive-sensing on pre-beamformed RF signals. The encoder network compresses the RF data, which is further transmitted to the cloud. At the cloud, the decoder reconstructs back the ultrasound image, which can be used for inferencing. The compression is done with an undersampling ratio of 1/2, 1/3, 1/5 and 1/10 without significant loss of the resolution. We also compared the model by state-of-the-art compressive-sensing reconstruction algorithm and it shows significant improvement in terms of PSNR and MSE. The innovation in this approach resides in training with binary weights at the encoder, shows its feasibility for the hardware implementation at the edge. In the future, we plan to include our field-programmable gate array (FPGA) based design directly interfaced with sensors for real-time analysis of Ultrasound images during medical procedures.

Item Type:	Conference Paper
Publication:	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Publisher:	IEEE
Additional Information:	Copyright of this article belongs to IEEE
Department/Centre:	Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology) Division of Electrical Sciences > Electrical Engineering
Date Deposited:	11 Feb 2020 07:47
Last Modified:	11 Feb 2020 07:47
URI:	http://eprints.iisc.ac.in/id/eprint/64455

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