Lok U-Wai, Huang Chengwu, Trzasko Joshua D, Kim Yohan, Lucien Fabrice, Tang Shanshan, Gong Ping, Song Pengfei, Chen Shigao
Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN.
Department of Urology, Mayo Clinic College of Medicine and Science, Rochester, MN.
J Med Biol Eng. 2022 Dec;42(6):767-779. doi: 10.1007/s40846-022-00755-y. Epub 2022 Oct 28.
Three-dimensional (3D) ultrasound localization microscopy (ULM) using a 2-D matrix probe and microbubbles (MBs) has been recently proposed to visualize microvasculature beyond the ultrasound diffraction limit in three spatial dimensions. However, 3D ULM suffers from several limitations: (1) high system complexity due to numerous channel counts, (2) complex MB flow dynamics in 3D, and (3) extremely long acquisition time. To reduce the system complexity while maintaining high image quality, we used a sub-aperture process to reduce received channel counts. To address the second issue, a 3D bipartite graph-based method with Kalman filtering-based tracking was used in this study for MB tracking. An MB separation approach was incorporated to separate high concentration MB data into multiple, sparser MB datasets, allowing better MB localization and tracking for a limited acquisition time. The proposed method was first validated in a flow channel phantom, showing improved spatial resolutions compared with the contrasted enhanced power Doppler image. Then the proposed method was evaluated with an chicken embryo brain dataset. Results showed that the reconstructed 3D super-resolution image achieved a spatial resolution of around 52 μm (smaller than the wavelength of around 200 μm). Microvessels that cannot be resolved clearly using localization only, can be well identified with the tailored 3D pairing and tracking algorithms. To sum up, the feasibility of the 3D ULM is shown, indicating the great possibility in clinical applications.
最近有人提出使用二维矩阵探头和微泡(MBs)的三维(3D)超声定位显微镜(ULM),以在三个空间维度上可视化超出超声衍射极限的微血管。然而,3D ULM存在几个局限性:(1)由于通道数量众多,系统复杂度高;(2)3D中复杂的MB流动动力学;(3)采集时间极长。为了在保持高图像质量的同时降低系统复杂度,我们使用子孔径处理来减少接收通道数量。为了解决第二个问题,本研究采用了基于三维二分图和基于卡尔曼滤波的跟踪方法来跟踪MB。采用MB分离方法将高浓度MB数据分离为多个更稀疏的MB数据集,以便在有限的采集时间内更好地进行MB定位和跟踪。所提出的方法首先在流动通道模型中得到验证,与对比增强功率多普勒图像相比,显示出更高的空间分辨率。然后,使用鸡胚脑数据集对所提出的方法进行评估。结果表明,重建的3D超分辨率图像实现了约52μm的空间分辨率(小于约200μm的波长)。仅使用定位无法清晰分辨的微血管,可以通过定制的3D配对和跟踪算法很好地识别。总之,展示了3D ULM的可行性,表明其在临床应用中具有很大的可能性。