  Quantitative ultrasound, e.g., speed-of-sound (SoS) in tissues, provides
information about tissue properties that have diagnostic value. Recent studies
showed the possibility of extracting SoS information from pulse-echo ultrasound
raw data (a.k.a. RF data) using deep neural networks that are fully trained on
simulated data. These methods take sensor domain data, i.e., RF data, as input
and train a network in an end-to-end fashion to learn the implicit mapping
between the RF data domain and SoS domain. However, such networks are prone to
overfitting to simulated data which results in poor performance and instability
when tested on measured data. We propose a novel method for SoS mapping
employing learned representations from two linked autoencoders. We test our
approach on simulated and measured data acquired from human breast mimicking
phantoms. We show that SoS mapping is possible using linked autoencoders. The
proposed method has a Mean Absolute Percentage Error (MAPE) of 2.39% on the
simulated data. On the measured data, the predictions of the proposed method
are close to the expected values with MAPE of 1.1%. Compared to an end-to-end
trained network, the proposed method shows higher stability and
reproducibility.
