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 this study, we investigated the impacts of training data diversity
on the robustness of these networks by using multiple kinds of geometrical and
natural simulated phantom structures. On the simulated data, we investigated
the performance of the networks on out-of-domain echogenicity, geometries, and
in the presence of noise. We further inspected the stability of employing such
tissue modeling in a real data acquisition setup. We demonstrated that training
the network with a joint set of datasets including both geometrical and natural
tissue models improves the stability of the predicted speed-of-sound values
both on simulated and measured data.

本文研究了使用多种几何和自然的模拟幻影结构对深度神经网络训练数据多样性的影响，并证明在使用同时包含几何和自然组织模型的数据集来训练网络时，可以提高模型在模拟和实测数据中预测声速的稳定性。