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

Objective: Ultrasound elastography is gaining traction as an accessible and useful diagnostic tool for things such 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, and are extremely sensitive to patient and sonographer motion, and generally suffer from low frame rates. Motivated by research and theory showing that longitudinal 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 channel data. 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 limitless ground truth data. Results: We show that it is possible to invert for longitudinal sound speed in soft tissue at high frame rates. We validate the method on simulated data. We present highly encouraging results on limited real data. Conclusion: Sound speed inversion on channel data has made significant potential possible in real time with deep learning technologies. Significance: Specialized shear wave ultrasound systems remain inaccessible in many locations. Longitudinal sound speed and deep learning technologies enable an alternative approach to diagnosis based on tissue elasticity. High frame rates are also possible.