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Neighborhood Singular Value Decomposition Filter and Application in Adaptive Beamforming for Coherent Plane-Wave Compounding
Applied Sciences ( IF 2.838 ) Pub Date : 2020-08-12 , DOI: 10.3390/app10165595
Shuai Feng , Yadan Wang , Chichao Zheng , Zhihui Han , Hu Peng

Coherent plane-wave compounding (CPWC) is widely used in medical ultrasound imaging, in which plane-waves tilted at multiple angles are used to reconstruct ultrasound images. CPWC helps to achieve a balance between frame rate and image quality. However, the image quality of CPWC is limited due to sidelobes and noise interferences. Filtering techniques and adaptive beamforming methods are commonly used to suppress noise and sidelobes. Here, we propose a neighborhood singular value decomposition (NSVD) filter to obtain high-quality images in CPWC. The NSVD filter is applied to adaptive beamforming by combining with adaptive weighting factors. The NSVD filter is advantageous because of its singular value decomposition (SVD) and smoothing filters, performing the SVD processing in neighboring regions while using a sliding rectangular window to filter the entire imaging region. We also tested the application of NSVD in adaptive beamforming. The NSVD filter was combined with short-lag spatial coherence (SLSC), coherence factor (CF), and generalized coherence factor (GCF) to enhance performances of adaptive beamforming methods. The proposed methods were evaluated using simulated and experimental datasets. We found that NSVD can suppress noise and achieve improved contrast (contrast ratio (CR), contrast-to-noise ratio (CNR) and generalized CNR (gCNR)) compared to CPWC. When the NSVD filter is used, adaptive weighting methods provide higher CR, CNR, gCNR and speckle signal-to-noise ratio (sSNR), indicating that NSVD is able to improve the imaging performance of adaptive beamforming in noise suppression and speckle pattern preservation.

中文翻译:

邻域奇异值分解滤波器及其在相干波面自适应波束形成中的应用

相干平面波复合(CPWC)广泛用于医学超声成像,其中以多个角度倾斜的平面波用于重建超声图像。CPWC有助于在帧频和图像质量之间取得平衡。但是,由于旁瓣和噪声干扰,CPWC的图像质量受到限制。滤波技术和自适应波束形成方法通常用于抑制噪声和旁瓣。在此,我们提出一种邻域奇异值分解(NSVD)滤波器,以在CPWC中获得高质量的图像。通过结合自适应加权因子,将NSVD滤波器应用于自适应波束成形。NSVD滤波器的优势在于其奇异值分解(SVD)和平滑滤波器,在使用滑动矩形窗口对整个成像区域进行滤波的同时,在相邻区域中执行SVD处理。我们还测试了NSVD在自适应波束形成中的应用。NSVD滤波器与短时滞空间相干性(SLSC),相干因子(CF)和广义相干因子(GCF)相结合,以增强自适应波束形成方法的性能。使用模拟和实验数据集对提出的方法进行了评估。我们发现,与CPWC相比,NSVD可以抑制噪声并获得更高的对比度(对比度(CR),对比度-噪声比(CNR)和广义CNR(gCNR))。使用NSVD滤波器时,自适应加权方法可提供更高的CR,CNR,gCNR和散斑信噪比(sSNR),
更新日期:2020-08-12
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