## 5 Discussion
However, experimental images obtained by adaptive weighting methods with NSVD (\(\beta=2\)) show severe dark artifacts alongside point targets and in the deep region. Figures 18 and 19 presents the effect of the \(\beta\) value on the image quality. It can be seen that all values of these metrics increase as \(\beta\) decreases. Nevertheless, artifacts alongside point targets become more visible with a small \(\beta\). Generally, these artifacts can be reduced by increasing \(\beta\). Therefore, \(\beta=3\) is a suitable choice for the application of NSVD. Figure 17: Effect of the width of the sliding window on **(a)** CR, **(b)** CNR, **(c)** gCNR, and **(d)** sSNR in simulated cyst target images. To evaluate the impact of the number of plane-waves on the quality of compounded images obtained by the proposed methods, we performed several tests using simulated cyst data and calculated the CR, CNR, and sSNR. SLSC (\(\beta=3\)) is used as a representation of adaptive weighting methods, and \(\beta=3\) is used for SVD and NSVD. Figure 20 shows a comparison of different methods. CR and CNR increase as the number of compounding angles increases, while sSNR is relatively stable. When decreasing the number of compounded plane-waves, CPWC+NSVD can still provide higher CR and CNR than CPWC and CPWC+SVD. It is also observed that the CPWC+NSVD with 25 plane-waves can achieve comparable performance to CPWC with 75 plane-waves in CR, CNR, and sSNR. The SLSC (\(\beta=3\)) has a consistent variation, as shown in Figure 18. For both the near and deep regions, SLSC+NSVD outperforms SLSC in CR, CNR, and sSNR. Since the simulated and experimental phantoms usually have omnidirectional scatters and uniform propagation velocity, the influence of angle dependency on simulated and experimental images would not be apparent. For human tissue imaging, the echo signals include coherent backscattered signals from the focal plane and incoherent reverberation clutter signals [43]. Coherent signals show high coherence between different transmit angles, while reverberation clutter signals show high angle-dependency. This indicates that the influence of transmitting angles may be more apparent in the in vivo image compared to simulated and experimental images. Therefore, we also present the effect of plane-wave numbers on the image quality metrics of in vivo images in Figure 21. Similar to the simulation case, 25 plane-waves are required for CPWC+NSVD to provide equivalent CR, CNR, and sSNR compared to CPWC, with 75 plane-waves. With an equal number of plane-waves, SLSC+NSVD shows improved image quality compared to SLSC in terms of CR, CNR, and sSNR. Figure 19: Experimental images with point targets formed by SLSC+NSVD with \(\beta\) from 1 (**a**) to 6 (**f**). Figure 20: Effect of the number of compounding angles on (**a**) CR, (**b**) CNR, and (**c**) sSNR in simulated cysts target images. The SVD filter was performed for the entire image, while the NSVD is a pixel-based filter. For each imaging point in an image, NSVD must perform an SVD filter for a neighbor region centered at the imaging point. Therefore, the computational complexity of NSVD is higher compared to SVD. We recorded the runtime of SVD (\(\beta=3\)) and NSVD (\(\beta=3\)) using simulated datasets on a PC with an Intel(R) Core(TM) i7-7700 HQ CPU at 2.8 GHz and RAM of 8 GB. The number of plane-waves was 25, and each image contained \(3328\times 387\) pixels. SVD and NSVD required a runtime of 0.32 s and 149.62 s, respectively.