A Deep learning framework for Single sided sound speed inversion in medical ultrasound
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Ultrasound elastography is gaining traction as an accessible and useful diagnostic tool for such things as cancer detection and differentiation as well as liver and thyroid disease diagnostics. Unfortunately, state of the art acoustic radiation force techniques 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. Researchers have shown that pressure wave velocity possesses similar diagnostic abilities to shear wave velocity. Using pressure waves removes the need for generating shear waves, in turn, enabling elasticity based diagnostic techniques on portable and low cost devices. However, current travel time tomography and full waveform inversion techniques for recovering pressure wave velocities, require a full circumferential field of view. Focus based techniques on the other hand provide only localized measurements, and are sensitive to the intermediate medium. In this paper, we present a single sided sound speed inversion solution using a fully convolutional deep neural network. We show that it is possible to invert for longitudinal sound speed in soft tissue at real time frame rates. For the computation, analysis is performed on channel data information from three diagonal plane waves. This is the first step towards a full waveform solver using a deep learning framework for the elastic and viscoelastic inverse problem.
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