A Deep Learning Framework for Single-Sided Sound Speed Inversion in Medical Ultrasound

Abstract

Objective: Ultrasound elastography is gaining traction as an accessible and useful diagnostic tool for such things as cancer detection and differentiation and thyroid disease diagnostics. Unfortunately, state of the art shear wave imaging techniques, essential to promote this goal, are limited to high-end ultrasound hardware due to high power requirements; are extremely sensitive to patient and sonographer motion, and generally, suffer from low frame rates. Motivated by research and theory showing that pressure wave sound speed carries similar diagnostic abilities to shear wave imaging, we present an alternative approach using single sided pressure-wave sound speed measurements from a conventional ultrasound probe, enabling elasticity based diagnostics using portable and low-cost devices. Methods: In this paper, we present a single-sided sound speed inversion solution using a fully convolutional deep neural network. We use simulations for training, allowing the generation of large volumes of ground truth data. Results: We show that it is possible to invert for longitudinal sound speed in soft tissue at super real-time frame rates. Our method shows exceptional results on simulated data and highly encouraging initial results on real data. Conclusion: Sound speed inversion on channel data has significant potential, made possible in real time with deep learning technologies. Significance: High-end ultrasound devices remain inaccessible in many locations. Utilizing pressure sound speed and deep learning technologies brings the same quality diagnostic abilities to low power devices at real-time frame rates. High potential frame rates also enable dynamic functional imaging, impossible with shear wave imaging.