Reverberation Noise Suppression in Ultrasound Channel Signals Using a 3D Fully Convolutional Neural Network.
Reverberation Noise Suppression in Ultrasound Channel Signals Using a 3D Fully Convolutional Neural Network.
复制标题
利用三维全卷积神经网络抑制超声通道信号中的混响噪声。
DOI：
10.1109/tmi.2021.3049307
复制
发表时间：
2021-04
影响因子：
10.6
通讯作者：
Dahl JJ
中科院分区：
文献类型：
--
作者：
Brickson LL;Hyun D;Jakovljevic M;Dahl JJ
漫反射是由发射脉冲返回换能器之前的多次反射引起的超声图像噪声，它会降低图像质量并阻碍弹性成像和多普勒成像等技术中位移或流动的估计。扩散混响在通道信号中表现为空间不相干噪声，它还会降低自适应波束形成方法、声速估计以及需要从通道信号进行测量的方法的性能。在本文中，我们提出了一种定制的 3D 全卷积神经网络 (3DCNN) 来减少通道信号中的扩散混响噪声。 3DCNN 使用来自随机目标模拟的通道信号进行训练，其中包括混响和热噪声模型。然后根据模型和体内实验数据对其进行评估。 3DCNN 在体模和体内实验中显示了图像质量指标的改进，例如广义对比度噪声比 (GCNR)、滞后一相干性 (LOC) 对比度噪声比 (CNR) 以及消声区域对比度。从视觉上看，消声区域的对比度得到了极大的改善。在某些情况下，CNR 得到了改善，但 3DCNN 似乎强烈去除了不相关和低幅度信号。在体内颈动脉和甲状腺的图像中，将 3DCNN 与短滞后空间相干 (SLSC) 成像和空间预测滤波 (FXPF) 进行比较，结果显示对比度、GCNR 和 LOC 有所改善，而 FXPF 仅改善了对比度，SLSC 仅改善了 CNR。
Diffuse reverberation is ultrasound image noise caused by multiple reflections of the transmitted pulse before returning to the transducer, which degrades image quality and impedes the estimation of displacement or flow in techniques such as elastography and Doppler imaging. Diffuse reverberation appears as spatially incoherent noise in the channel signals, where it also degrades the performance of adaptive beamforming methods, sound speed estimation, and methods that require measurements from channel signals. In this paper, we propose a custom 3D fully convolutional neural network (3DCNN) to reduce diffuse reverberation noise in the channel signals. The 3DCNN was trained with channel signals from simulations of random targets that include models of reverberation and thermal noise. It was then evaluated both on phantom and in-vivo experimental data. The 3DCNN showed improvements in image quality metrics such as generalized contrast to noise ratio (GCNR), lag one coherence (LOC) contrast-to-noise ratio (CNR) and contrast for anechoic regions in both phantom and in-vivo experiments. Visually, the contrast of anechoic regions was greatly improved. The CNR was improved in some cases, however the 3DCNN appears to strongly remove uncorrelated and low amplitude signal. In images of in-vivo carotid artery and thyroid, the 3DCNN was compared to short-lag spatial coherence (SLSC) imaging and spatial prediction filtering (FXPF) and demonstrated improved contrast, GCNR, and LOC, while FXPF only improved contrast and SLSC only improved CNR.