Abstract
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.
Highlights
Because of its safety and non-invasiveness, ultrasound imaging is commonly used in medical applications
Images obtained using adaptive weighting factors (CF, generalized coherence factor (GCF), and short-lag spatial coherence (SLSC)) to weight the output of Coherent plane-wave compounding (CPWC) were presented as a comparison of the adaptive weighting methods based on neighborhood singular value decomposition (NSVD)
Hereinafter, CPWC weighted by coherence factor (CF), GCF, and SLSC are referred to as CF, GCF, and SLSC, respectively
Summary
Because of its safety and non-invasiveness, ultrasound imaging is commonly used in medical applications. The plane-wave method is a conventional way to achieve ultrafast ultrasound imaging, and has been applied in various new ultrasound technologies with high frame rate requirements [1,2,3]. A high frame rate imaging method based on the Fourier spectrum of the object function using the limited diffraction beams was developed by Lu et al to accelerate image formation [4,5]. Compressed sensing has been successfully applied for fast image acquisition in pulse-echo ultrasound [6,7]. The convolutional neural network (CNN) was successfully implemented to learn a compounding operation from data, and reconstruct high-quality images using a small number of transmissions [8]. Montaldo et al [9] coherently compounded plane-waves
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