论文标题
可开关的深横峰形式
Switchable Deep Beamformer
论文作者
论文摘要
使用深度神经网络的深界面的最新建议吸引了对自适应和压缩束缚器的计算有效替代方案的大幅关注。此外，深边式形式具有通用性，因为该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，应为每个应用程序培训和存储一个单独的波束形式，要求大量的扫描仪资源。为了解决这个问题，我们在这里提出了一个{\ em Switchable}深边尺度，可以使用带有简单开关的单个网络来产生各种类型的输出，例如DAS，Speckle Removal，Deonvolution等。特别是，通过自适应实例归一化（ADAIN）层实现了开关，因此只需更改ADAIN代码就可以生成各种输出。使用B模式集中超声的实验结果证实了所提出的各种应用方法的灵活性和功效。
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.
