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基于 Curvelet 变换的稀疏促进算法在快速超声定位显微镜中的应用。

Curvelet Transform-Based Sparsity Promoting Algorithm for Fast Ultrasound Localization Microscopy.

出版信息

IEEE Trans Med Imaging. 2022 Sep;41(9):2385-2398. doi: 10.1109/TMI.2022.3162839. Epub 2022 Aug 31.

Abstract

Ultrasound localization microscopy (ULM) based on microbubble (MB) localization was recently introduced to overcome the resolution limit of conventional ultrasound. However, ULM is currently challenged by the requirement for long data acquisition times to accumulate adequate MB events to fully reconstruct vasculature. In this study, we present a curvelet transform-based sparsity promoting (CTSP) algorithm that improves ULM imaging speed by recovering missing MB localization signal from data with very short acquisition times. CTSP was first validated in a simulated microvessel model, followed by the chicken embryo chorioallantoic membrane (CAM), and finally, in the mouse brain. In the simulated microvessel study, CTSP robustly recovered the vessel model to achieve an 86.94% vessel filling percentage from a corrupted image with only 4.78% of the true vessel pixels. In the chicken embryo CAM study, CTSP effectively recovered the missing MB signal within the vasculature, leading to marked improvement in ULM imaging quality with a very short data acquisition. Taking the optical image as reference, the vessel filling percentage increased from 2.7% to 42.2% using 50ms of data acquisition after applying CTSP. CTSP used 80% less time to achieve the same 90% maximum saturation level as compared with conventional MB localization. We also applied CTSP on the microvessel flow speed maps and found that CTSP was able to use only 1.6s of microbubble data to recover flow speed images that have similar qualities as those constructed using 33.6s of data. In the mouse brain study, CTSP was able to reconstruct the majority of the cerebral vasculature using 1-2s of data acquisition. Additionally, CTSP only needed 3.2s of microbubble data to generate flow velocity maps that are comparable to those using 129.6s of data. These results suggest that CTSP can facilitate fast and robust ULM imaging especially under the circumstances of inadequate microbubble localizations.

摘要

超声微泡定位显微镜(ULM)基于微泡(MB)定位,最近被引入以克服传统超声的分辨率限制。然而,ULM 目前面临着需要长时间采集数据以积累足够的 MB 事件来完全重建脉管系统的挑战。在这项研究中,我们提出了一种基于曲线波变换的稀疏促进(CTSP)算法,该算法通过从极短采集时间的数据中恢复缺失的 MB 定位信号来提高 ULM 成像速度。CTSP 首先在模拟微血管模型中进行了验证,然后在鸡胚绒毛尿囊膜(CAM)中进行了验证,最后在小鼠脑中进行了验证。在模拟微血管研究中,CTSP 稳健地恢复了血管模型,从仅包含 4.78%真实血管像素的损坏图像中实现了 86.94%的血管填充百分比。在鸡胚 CAM 研究中,CTSP 有效地恢复了脉管系统中的缺失 MB 信号,从而在极短的数据采集时间内显著提高了 ULM 成像质量。以光学图像为参考,在应用 CTSP 后,仅用 50ms 的数据采集,血管填充百分比从 2.7%增加到 42.2%。与传统 MB 定位相比,CTSP 节省了 80%的时间即可达到相同的 90%最大饱和度水平。我们还将 CTSP 应用于微血管血流速度图,发现 CTSP 仅用 1.6s 的微泡数据就可以恢复与使用 33.6s 数据构建的速度图像质量相当的速度图像。在小鼠脑研究中,CTSP 仅用 1-2s 的数据采集就可以重建大部分脑脉管系统。此外,CTSP 仅需 3.2s 的微泡数据即可生成与使用 129.6s 数据相当的流速图。这些结果表明,CTSP 可以促进快速稳健的 ULM 成像,特别是在微泡定位不足的情况下。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/255e/9496596/38ee5e1c9ea6/nihms-1834065-f0001.jpg

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