Ultrasound is an adjunct tool to mammography that can quickly and safely aid physicians with diagnosing breast abnormalities. Clinical ultrasound often assumes a constant sound speed to form B-mode images for diagnosis. However, the various types of breast tissue, such as glandular, fat, and lesions, differ in sound speed. These differences can degrade the image reconstruction process. Alternatively, sound speed can be a powerful tool for identifying disease. To this end, we propose a deep-learning approach for sound speed estimation from in-phase and quadrature ultrasound signals. First, we develop a large-scale simulated ultrasound dataset that generates quasi-realistic breast tissue by modeling breast gland, skin, and lesions with varying echogenicity and sound speed. We developed a fully convolutional neural network architecture trained on a simulated dataset to produce an estimated sound speed map from inputting three complex-value in-phase and quadrature ultrasound images formed from plane-wave transmissions at separate angles. Furthermore, thermal noise augmentation is used during model optimization to enhance generalizability to real ultrasound data. We evaluate the model on simulated, phantom, and in-vivo breast ultrasound data, demonstrating its ability to accurately estimate sound speeds consistent with previously reported values in the literature. Our simulated dataset and model will be publicly available to provide a step towards accurate and generalizable sound speed estimation for pulse-echo ultrasound imaging.

翻译：超声是乳腺X线摄影的辅助工具，可快速安全地帮助医生诊断乳腺异常。临床超声通常假设恒定声速形成B模式图像用于诊断。然而，不同类型的乳腺组织（如腺体、脂肪和病变）的声速存在差异，这些差异会降低图像重建质量。反之，声速也可成为识别疾病的有力工具。为此，我们提出一种基于深度学习的方法，从同相正交超声信号中估计声速。首先，我们构建大规模模拟超声数据集，通过建模具有不同回波性和声速的乳腺腺体、皮肤和病变，生成准真实乳腺组织。我们开发了全卷积神经网络架构，基于模拟数据集进行训练，通过输入由不同角度平面波发射形成的三个复值同相正交超声图像，生成声速估计图。此外，模型优化过程中采用热噪声增强技术以提升对真实超声数据的泛化能力。我们在模拟数据、仿体数据和活体乳腺超声数据上评估模型，证明其能准确估计与文献报道值一致的声速。我们将公开模拟数据集和模型，为脉冲回波超声成像中准确且可泛化的声速估计提供基础。