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首页> 外文期刊>IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control >Adaptive and Compressive Beamforming Using Deep Learning for Medical Ultrasound
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Adaptive and Compressive Beamforming Using Deep Learning for Medical Ultrasound

机译：使用深度学习进行医学超声的自适应和压缩波束形成

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摘要

In ultrasound (US) imaging, various types of adaptive beamforming techniques have been investigated to improve the resolution and the contrast-to-noise ratio of the delay and sum (DAS) beamformers. Unfortunately, the performance of these adaptive beamforming approaches degrades when the underlying model is not sufficiently accurate and the number of channels decreases. To address this problem, here, we propose a deep-learning-based beamformer to generate significantly improved images over widely varying measurement conditions and channel subsampling patterns. In particular, our deep neural network is designed to directly process full or subsampled radio frequency (RF) data acquired at various subsampling rates and detector configurations so that it can generate high-quality US images using a single beamformer. The origin of such input-dependent adaptivity is also theoretically analyzed. Experimental results using the B-mode focused US confirm the efficacy of the proposed methods.
机译：在超声（US）成像中，已经研究了各种类型的自适应波束形成技术，以改善延迟和和（DAS）波束形成器的分辨率和对比度。遗憾的是，当底层模型没有足够准确并且通道数减小时，这些自适应波束形成方法的性能降低。为了解决这个问题，在这里，我们提出了一种基于深度学习的波束形成器，以在广泛变化的测量条件和信道附带模式上产生显着改善的图像。特别地，我们的深度神经网络被设计为直接处理以各种数据采样率和检测器配置获取的完整或限定射频（RF）数据，以便它可以使用单个波束形成器产生高质量的美国图像。理论上还分析了这种输入依赖性适应性的起源。使用B模式的实验结果聚焦美国证实了所提出的方法的功效。

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