Conventional delay-and-sum (DAS) beamforming is highly efficient but also suffers from various sources of image degradation. Several adaptive beamformers have been proposed to address this problem, including more recently proposed deep learning methods. With deep learning, adaptive beamforming is typically framed as a regression problem, where clean ground-truth physical information is used for training. Because it is difficult to know ground truth information in vivo, training data are usually simulated. However, deep networks trained on simulations can produce suboptimal in vivo image quality because of a domain shift between simulated and in vivo data. In this work, we propose a novel domain adaptation (DA) scheme to correct for domain shift by incorporating unlabeled in vivo data during training. Unlike classification tasks for which both input domains map to the same target domain, a challenge in our regression-based beamforming scenario is that domain shift exists in both the input and target data. To solve this problem, we leverage cycle-consistent generative adversarial networks to map between simulated and in vivo data in both the input and ground truth target domains. Additionally, to account for separate as well as shared features between simulations and in vivo data, we use augmented feature mapping to train domain-specific beamformers. Using various types of training data, we explore the limitations and underlying functionality of the proposed DA approach. Additionally, we compare our proposed approach to several other adaptive beamformers. Using the DA DNN beamformer, consistent in vivo image quality improvements are achieved compared to established techniques.

中文翻译：

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使用未标记的体内数据训练深度网络超声波束形成器


传统的延迟求和 (DAS) 波束形成非常高效，但也受到各种图像质量下降的影响。已经提出了几种自适应波束形成器来解决这个问题，包括最近提出的深度学习方法。通过深度学习，自适应波束成形通常被视为回归问题，其中干净的地面实况物理信息用于训练。由于很难知道体内的真实信息，因此通常对训练数据进行模拟。然而，由于模拟数据和体内数据之间的域转移，经过模拟训练的深度网络可能会产生次优的体内图像质量。在这项工作中，我们提出了一种新颖的域适应（DA）方案，通过在训练期间合并未标记的体内数据来纠正域移位。与两个输入域映射到同一目标域的分类任务不同，基于回归的波束成形场景中的一个挑战是输入和目标数据中都存在域移位。为了解决这个问题，我们利用循环一致的生成对抗网络来映射输入和地面真实目标域中的模拟数据和体内数据。此外，为了考虑模拟和体内数据之间的单独和共享特征，我们使用增强特征映射来训练特定领域的波束形成器。使用各种类型的训练数据，我们探索了所提出的 DA 方法的局限性和底层功能。此外，我们将我们提出的方法与其他几种自适应波束形成器进行比较。与现有技术相比，使用 DA DNN 波束形成器可实现一致的体内图像质量改进。