可切换深度波束形成器
Switchable Deep Beamformer
摘要：
最近提出的使用深度神经网络的深度波束形成器作为自适应和压缩波束形成器的计算高效替代方案已经引起了极大的关注。此外，深波束形成器是通用的，因为图像后处理算法可以与波束形成相结合。不幸的是，在当前技术中，应该针对每个应用训练和存储单独的波束形成器，这需要大量的扫描器资源。为了解决这个问题，这里我们提出了一种{ \em可切换}深度波束形成器，其可以产生各种类型的输出，例如DAS、散斑去除、去卷积等，使用具有简单交换机的单个网络。特别是，切换是通过自适应实例规范化（AdaIN）层实现的，因此只需更改AdaIN代码就可以生成各种输出。使用B型聚焦超声的实验结果证实了所提出的方法在各种应用中的灵活性和有效性。
英文摘要：
Recent proposals of deep beamformers using deep neural networks have attracted significant attention as computational efficient alternatives to adaptive and compressive beamformers. Moreover, deep beamformers are versatile in that image post-processing algorithms can be combined with the beamforming. Unfortunately, in the current technology, a separate beamformer should be trained and stored for each application, demanding significant scanner resources. To address this problem, here we propose a {\em switchable} deep beamformer that can produce various types of output such as DAS, speckle removal, deconvolution, etc., using a single network with a simple switch. In particular, the switch is implemented through Adaptive Instance Normalization (AdaIN) layers, so that various output can be generated by merely changing the AdaIN code. Experimental results using B-mode focused ultrasound confirm the flexibility and efficacy of the proposed methods for various applications.
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