Fudan University, Department of Electronic Engineering, Shanghai 200433, China.
Fudan University, Department of Electronic Engineering, Shanghai 200433, China.
Med Image Anal. 2021 Jul;71:102086. doi: 10.1016/j.media.2021.102086. Epub 2021 Apr 28.
Ultrasound beamforming is a principal factor in high-quality ultrasound imaging. The conventional delay-and-sum (DAS) beamformer generates images with high computational speed but low spatial resolution; thus, many adaptive beamforming methods have been introduced to improve image qualities. However, these adaptive beamforming methods suffer from high computational complexity, which limits their practical applications. Hence, an advanced beamformer that can overcome spatiotemporal resolution bottlenecks is eagerly awaited. In this paper, we propose a novel deep-learning-based algorithm, called the multiconstrained hybrid generative adversarial network (MC-HGAN) beamformer that rapidly achieves high-quality ultrasound imaging. The MC-HGAN beamformer directly establishes a one-shot mapping between the radio frequency signals and the reconstructed ultrasound images through a hybrid generative adversarial network (GAN) model. Through two specific branches, the hybrid GAN model extracts both radio frequency-based and image-based features and integrates them through a fusion module. We also introduce a multiconstrained training strategy to provide comprehensive guidance for the network by invoking intermediates to co-constrain the training process. Moreover, our beamformer is designed to adapt to various ultrasonic emission modes, which improves its generalizability for clinical applications. We conducted experiments on a variety of datasets scanned by line-scan and plane wave emission modes and evaluated the results with both similarity-based and ultrasound-specific metrics. The comparisons demonstrate that the MC-HGAN beamformer generates ultrasound images whose quality is higher than that of images generated by other deep learning-based methods and shows very high robustness in different clinical datasets. This technology also shows great potential in real-time imaging.
超声束形成是高质量超声成像的主要因素。传统的延时求和（DAS）波束形成器具有较高的计算速度，但空间分辨率较低；因此，引入了许多自适应波束形成方法来提高图像质量。然而，这些自适应波束形成方法存在计算复杂度高的问题，限制了它们的实际应用。因此，迫切需要一种能够克服时空分辨率瓶颈的先进的波束形成器。在本文中，我们提出了一种新的基于深度学习的算法，称为多约束混合生成对抗网络（MC-HGAN）波束形成器，它可以快速实现高质量的超声成像。MC-HGAN 波束形成器通过混合生成对抗网络（GAN）模型直接在射频信号和重建的超声图像之间建立一次性映射。通过两个特定的分支，混合 GAN 模型提取基于射频和基于图像的特征，并通过融合模块对它们进行集成。我们还引入了多约束训练策略，通过调用中间值来共同约束训练过程，为网络提供全面的指导。此外，我们的波束形成器设计为适应各种超声发射模式，从而提高其在临床应用中的通用性。我们在各种由线扫描和平面波发射模式扫描的数据集上进行了实验，并使用基于相似性和超声特定的指标来评估结果。比较表明，MC-HGAN 波束形成器生成的超声图像质量高于其他基于深度学习的方法生成的图像，并且在不同的临床数据集上具有很高的鲁棒性。该技术在实时成像中也显示出巨大的潜力。
