Speed-of-sound (SoS) has been shown as a potential biomarker for breast cancer imaging, successfully differentiating malignant tumors from benign ones. SoS images can be reconstructed from time-of-flight measurements from ultrasound images acquired using conventional handheld ultrasound transducers. Variational networks (VNs) have recently been shown to be a potential learning-based approach for optimizing inverse problems in image reconstruction. Despite earlier promising results, these methods, however, do not generalize well from simulated to acquired data, due to the domain shift. In this work, we present for the first time a VN solution for a pulse-echo SoS image reconstruction problem using diverging waves with conventional transducers and single-sided tissue access. This is made possible by incorporating simulations with varying complexity into training. We use loop unrolling of gradient descent with momentum, with an exponentially weighted loss of outputs at each unrolled iteration in order to regularize the training. We learn norms as activation functions regularized to have smooth forms for robustness to input distribution variations. We evaluate reconstruction quality on the ray-based and full-wave simulations as well as on the tissue-mimicking phantom data, in comparison with a classical iterative [limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS)] optimization of this image reconstruction problem. We show that the proposed regularization techniques combined with multisource domain training yield substantial improvements in the domain adaptation capabilities of VN, reducing the median root mean squared error (RMSE) by 54% on a wave-based simulation data set compared to the baseline VN. We also show that on data acquired from a tissue-mimicking breast phantom, the proposed VN provides improved reconstruction in 12 ms.

中文翻译：

---

使用多域仿真训练变分网络：声速图像重建

声速（SoS）已被证明是乳腺癌成像的潜在生物标志物，成功地将恶性肿瘤与良性肿瘤区分开来。可以从飞行时间的测量结果重建SoS图像，这些测量结果来自使用常规手持式超声换能器采集的超声图像。变分网络（VN）最近已被证明是一种潜在的基于学习的方法，用于优化图像重建中的逆问题。尽管早先取得了令人鼓舞的结果，但是由于域移位，这些方法不能很好地推广从模拟到采集的数据。在这项工作中，我们首次提出了使用常规换能器和单面组织通路使用发散波的脉冲回波SoS图像重建问题的VN解决方案。通过将具有不同复杂性的模拟合并到训练中，可以实现这一点。我们使用具有动量的梯度下降的循环展开，以及在每次展开的迭代中输出的指数加权损失，以规范化训练。我们将激活函数正规化为具有平滑形式的激活函数，以提高输入分布变化的鲁棒性。与经典的迭代[有限内存Broyden–Fletcher–Goldfarb–Shanno（L-BFGS）]优化相比，我们在基于射线和全波模拟以及组织模拟体模数据上评估重建质量。这个图像重建问题。我们表明，提出的正则化技术与多源域训练相结合，在VN的域适应能力方面产生了实质性的改进，与基于基线的VN相比，基于波形的模拟数据集的均方根均方根误差（RMSE）降低了54％。我们还显示，在从模仿组织的乳房幻像获取的数据上，提出的VN在12毫秒内提供了改进的重建。