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基于全变差正则化的反卷积改进超声微血管成像。

Improved Ultrasound Microvessel Imaging Using Deconvolution with Total Variation Regularization.

机构信息

Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, Minnesota, USA.

Department of Urology, Mayo Clinic College of Medicine and Science, Mayo Clinic, Rochester, Minnesota, USA.

出版信息

Ultrasound Med Biol. 2021 Apr;47(4):1089-1098. doi: 10.1016/j.ultrasmedbio.2020.12.025. Epub 2021 Jan 16.

Abstract

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.

摘要

基于奇异值分解的杂波滤波器可以稳健地抑制组织杂波,从而检测到微血管中的缓慢血流。然而,为了识别微血管,必须使用高超声频率来提高空间分辨率,这会牺牲穿透深度。反卷积使用 Tikhonov 正则化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不存在的微血管等图像伪影,从而降低图像质量。因此,本研究提出了一种使用全变差的反卷积方法来提高空间分辨率并减轻振铃伪影。使用鸡胚脑、鸡胚绒毛尿囊膜外和肿瘤数据评估了所提出方法的性能。结果表明,与原始 PD 图像相比,所提出方法重建的功率多普勒(PD)图像在空间分辨率上有了显著提高:微血管的横截面轮廓的全宽半最大值(FWHM)从 132 µm 提高到 83 µm。使用所提出的方法可以更好地分离两个相距 154 µm 的相邻微血管,而使用常规 PD 成像则无法分离。此外,使用 223 个血管的横截面轮廓测量了 223 个 FWHM,以从统计学上确定所提出的方法对 FWHM 的改进。无和有提出方法的 FWHM 的平均值±标准偏差分别为 233.19±85.08 µm 和 172.31±75.11 µm;无和有提出方法的最大 FWHM 分别为 693.01 µm 和 668.69 µm;无和有提出方法的最小 FWHM 分别为 73.92 µm 和 45.74 µm。根据秩和检验,无和有提出方法的 FWHM 之间存在统计学显著差异,p<0.0001。使用所提出的方法后,对比噪声比从 1.06 dB 提高到 4.03 dB。我们还使用鸡胚绒毛尿囊膜外数据比较了所提出的方法和 Tikhonov 正则化。我们发现,与 Tikhonov 正则化相比,所提出的方法性能更好,因为 Tikhonov 正则化会出现虚假微血管,而所提出的方法则不会。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噪声比的更稳健的 PD 图像。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6be7/7908678/7aa9caa2b0ff/nihms-1664071-f0001.jpg

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Debiasing-Based Noise Suppression for Ultrafast Ultrasound Microvessel Imaging.基于去偏的超快超声微血管成像噪声抑制。
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