  Ultrasound b-mode imaging is a qualitative approach and diagnostic quality
strongly depends on operators' training and experience. Quantitative approaches
can provide information about tissue properties; therefore, can be used for
identifying various tissue types, e.g., speed-of-sound in the tissue can be
used as a biomarker for tissue malignancy, especially in breast imaging. Recent
studies showed the possibility of speed-of-sound reconstruction using deep
neural networks that are fully trained on simulated data. However, because of
the ever-present domain shift between simulated and measured data, the
stability and performance of these models in real setups are still under
debate. In prior works, for training data generation, tissue structures were
modeled as simplified geometrical structures which does not reflect the
complexity of the real tissues. In this study, we proposed a new simulation
setup for training data generation based on Tomosynthesis images. We combined
our approach with the simplified geometrical model and investigated the impacts
of training data diversity on the stability and robustness of an existing
network architecture. We studied the sensitivity of the trained network to
different simulation parameters, e.g., echogenicity, number of scatterers,
noise, and geometry. We showed that the network trained with the joint set of
data is more stable on out-of-domain simulated data as well as measured phantom
data.
