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.

翻译：超声B模式成像是一种定性方法，其诊断质量高度依赖于操作者的训练与经验。定量方法可提供组织特性的信息，因此可用于识别不同类型的组织，例如组织中的声速可作为组织恶性程度的生物标志物，尤其在乳腺成像中。近期研究表明，通过完全基于模拟数据进行训练的深度神经网络实现声速重建具有可行性。然而，由于模拟数据与测量数据之间始终存在领域偏移，这些模型在实际场景中的稳定性和性能仍存在争议。此前的研究中，训练数据生成时将组织结构建模为简化的几何结构，这无法反映真实组织的复杂性。本研究基于断层合成图像提出了一种新的训练数据生成模拟方案。我们将该方法与简化几何模型相结合，探讨了训练数据多样性对现有网络架构稳定性和鲁棒性的影响。我们分析了训练网络对不同模拟参数的敏感性，包括回声强度、散射体数量、噪声及几何形状。结果表明，采用联合数据集训练的网络在域外模拟数据及测量体模数据上均表现出更优的稳定性。