Recent proposals of deep learning-based beamformers for ultrasound imaging (US) 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 readily combined. Unfortunately, with the existing technology, a large number of beamformers need to be trained and stored for different probes, organs, depth ranges, operating frequency, and desired target ‘styles’, demanding significant resources such as training data, etc. To address this problem, here we propose a switchable and tunable deep beamformer that can switch between various types of outputs such as DAS, MVBF, DMAS, GCF, etc., and also adjust noise removal levels at the inference phase, by using a simple switch or tunable nozzle. This novel mechanism is implemented through Adaptive Instance Normalization (AdaIN) layers, so that distinct outputs can be generated using a single generator by merely changing the AdaIN codes. Experimental results using B-mode focused ultrasound confirm the flexibility and efficacy of the proposed method for various applications.

中文翻译：

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用于医学超声的使用自适应实例归一化的可切换和可调谐深度波束形成器

最近提出的用于超声成像 (US) 的基于深度学习的波束形成器作为自适应和压缩波束形成器的计算有效替代方案引起了极大的关注。此外，深波束形成器是通用的，因为可以很容易地组合图像后处理算法。不幸的是，使用现有技术，需要针对不同的探头、器官、深度范围、工作频率和所需目标“风格”对大量波束形成器进行训练和存储，需要大量资源，例如训练数据等。为了解决这个问题问题，这里我们提出一个可切换和可调谐的深度波束形成器，可以在 DAS、MVBF、DMAS、GCF 等各种类型的输出之间切换，还可以通过使用简单的开关或可调喷嘴在推理阶段调整噪声去除水平。这种新颖的机制是通过自适应实例规范化 (AdaIN) 层实现的，因此可以使用单个生成器仅通过更改 AdaIN 代码来生成不同的输出。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。