Singular value decomposition-based clutter filters can robustly reject tissue clutter, allowing for detection of slow blood flow in imaging microvasculature. However, to identify microvessels, high ultrasound frequency must be used to increase the spatial resolution at the expense of shorter depth of penetration. Deconvolution using Tikhonov regularization is an imaging processing method widely used to improve spatial resolution. The ringing artifact of Tikhonov regularization, though, can produce image artifacts such as non-existent microvessels, which degrade image quality. Therefore, a deconvolution method using total variation is proposed in this study to improve spatial resolution and mitigate the ringing artifact. Performance of the proposed method was evaluated using chicken embryo brain, ex ovo chicken embryo chorioallantoic membrane and tumor data. Results revealed that the reconstructed power Doppler (PD) images are substantially improved in spatial resolution compared with original PD images: the full width half-maximum (FWHM) of the cross-sectional profile of a microvessel was improved from 132 to 83 µm. Two neighboring microvessels that were 154 µm apart were better separated using the proposed method than conventional PD imaging. Additionally, 223 FWHMs measured from the cross-sectional profiles of 223 vessels were used to determine the improvement in FWHM with the proposed method statistically. The mean ± standard deviation of the FWHM without and with the proposed method was 233.19 ± 85.08 and 172.31 ± 75.11 μm, respectively; the maximum FWHM without and with the proposed method was 693.01 and 668.69 μm; and the minimum FWHM without and with the proposed method was 73.92 and 45.74 μm. There were statistically significant differences between FWHMs with and without the proposed method according to the rank-sum test, p < 0.0001. The contrast-to-noise ratio improved from 1.06 to 4.03 dB with use of the proposed method. We also compared the proposed method with Tikhonov regularization using ex ovo chicken embryo chorioallantoic membrane data. We found that the proposed method outperformed Tikhonov regularization as false microvessels appeared using the Tikhonov regularization but not with the proposed method. These results indicate that the proposed method is capable of providing more robust PD images with higher spatial resolution and higher contrast-to-noise ratio.

基于奇异值分解的杂波滤波器可以稳健地拒绝组织杂波，从而可以检测成像微脉管系统中的缓慢血流。然而，为了识别微血管，必须使用高超声频率来增加空间分辨率，但代价是穿透深度更短。使用 Tikhonov 正则化的反卷积是一种广泛用于提高空间分辨率的成像处理方法。然而，Tikhonov 正则化的振铃伪影会产生图像伪影，例如不存在的微血管，从而降低图像质量。因此，本研究提出了一种使用总变差的反卷积方法，以提高空间分辨率并减轻振铃伪影。使用鸡胚脑，ex ovo评估了所提出方法的性能鸡胚绒毛尿囊膜和肿瘤数据。结果表明，与原始 PD 图像相比，重建的功率多普勒 (PD) 图像的空间分辨率显着提高：微血管横截面轮廓的半峰全宽 (FWHM) 从 132 µm 提高到 83 µm。与传统的 PD 成像相比，使用所提出的方法可以更好地分离两个相隔 154 μm 的相邻微血管。此外，从 223 个血管的横截面轮廓测量的 223 个 FWHM 被用来确定 FWHM 在统计学上的改进。没有和使用所提出方法的 FWHM 的平均值 ± 标准偏差分别为 233.19 ± 85.08 和 172.31 ± 75.11 μm；没有和使用所提出方法的最大 FWHM 分别为 693.01 和 668.69 μm；并且没有和使用所提出的方法的最小 FWHM 分别为 73.92 和 45.74 μm。根据秩和检验，使用和不使用所提出方法的 FWHM 之间存在统计学显着差异，p < 0.0001。使用所提出的方法，对比度噪声比从 1.06 提高到 4.03 dB。我们还将提出的方法与 Tikhonov 正则化进行了比较，使用ex ovo鸡胚绒毛尿囊膜数据。我们发现所提出的方法优于 Tikhonov 正则化，因为使用 Tikhonov 正则化而不是所提出的方法出现了假微血管。这些结果表明，所提出的方法能够提供具有更高空间分辨率和更高对比度噪声比的更稳健的 PD 图像。