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首页> 外文期刊>IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control >Beamforming and Speckle Reduction Using Neural Networks
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Beamforming and Speckle Reduction Using Neural Networks

机译：使用神经网络的波束成形和斑点减少

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With traditional beamforming methods, ultrasound B-mode images contain speckle noise caused by the random interference of subresolution scatterers. In this paper, we present a framework for using neural networks to beamform ultrasound channel signals into speckle-reduced B-mode images. We introduce log-domain normalization-independent loss functions that are appropriate for ultrasound imaging. A fully convolutional neural network was trained with the simulated channel signals that were coregistered spatially to ground-truth maps of echogenicity. Networks were designed to accept 16 beamformed subaperture radio frequency (RF) signals. Training performance was compared as a function of training objective, network depth, and network width. The networks were then evaluated on the simulation, phantom, and in vivo data and compared against the existing speckle reduction techniques. The most effective configuration was found to be the deepest (16 layer) and widest (32 filter) networks, trained to minimize a normalization-independent mixture of the l(1) and multiscale structural similarity (MS-SSIM) losses. The neural network significantly outperformed delay-and-sum (DAS) and receive-only spatial compounding in speckle reduction while preserving resolution and exhibited improved detail preservation over a nonlocal means method. This work demonstrates that ultrasound B-mode image reconstruction using machine-learned neural networks is feasible and establishes that networks trained solely in silico can be generalized to real-world imaging in vivo to produce images with significantly reduced speckle.
机译：使用传统的波束成形方法，超声B模式图像包含由亚分辨率散射体的随机干扰引起的斑点噪声。在本文中，我们提供了一个使用神经网络将超声通道信号波束形成斑点减少B型图像的框架。我们介绍适用于超声成像的对数域归一化无关的损失函数。利用模拟通道信号训练了一个全卷积神经网络，该通道信号在空间上与回声的地面真相图共配准。网络被设计为接受16个波束形成的子孔径射频（RF）信号。根据培训目标，网络深度和网络宽度对培训绩效进行了比较。然后根据仿真，体模和体内数据对网络进行评估，并与现有的斑点减少技术进行比较。发现最有效的配置是最深的网络（16层）和最宽的网络（32个过滤器），它们经过训练以最小化l（1）和多尺度结构相似性（MS-SSIM）损失的不依赖于规范化的混合。在保留分辨率的同时，神经网络在减少斑点方面显着胜过延迟总和（DAS）和仅接收的空间复合，并且与非局部均值方法相比，保留的细节得以改善。这项工作表明，使用机器学习的神经网络进行超声B型图像重建是可行的，并建立了可以将仅经过计算机训练的网络推广到体内的真实世界成像，以产生斑点明显减少的图像。

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