Liu Jiacheng, Liang Meiling, Ma Jinxuan, Jiang Liyuan, Chu Hanbing, Guo Chao, Yu Jianjun, Zong Yujin, Wan Mingxi
The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of life Science and Technology, Xi'an Jiaotong University, Xi'an, PR China.
The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of life Science and Technology, Xi'an Jiaotong University, Xi'an, PR China.
Ultrasonics. 2025 Jan;145:107455. doi: 10.1016/j.ultras.2024.107455. Epub 2024 Sep 14.
Super-resolution ultrasound (SRUS) can image the vasculature at microscopic resolution according to microbubble (MB) localization, with velocity vector maps obtained based on MB tracking information. High MB concentrations can reduce the acquisition time of SRUS imaging, however adjacent and intersecting vessels are difficult to distinguish, thus decreasing resolution. Low acquisition frame rates affect the precision of flow velocity estimation. This study proposes a partial smoothing-based adaptive generalized labeled multi-Bernoulli filter (SAGLMB) to precisely track the MB motion at different flow velocities. SAGLMB employs a generalized labelled multi-Bernoulli filter (GLMB) for MB trajectory allocation to separate adjacent and intersecting vessels. Furthermore, the nonlinear motion of MB was predicted by an unscented Kalman filter, and a cardinalized probability hypothesis density filter was applied to suppress clutter interference. Finally, the trajectories were smoothed by unscented Rauch-Tung-Striebel to improve the resolution of the SRUS image. The simulation results demonstrate that SAGLMB outperforms the conventional bipartite graph-based tracking at high MB concentrations, achieving at least an 8.55 % improvement in the correctly paired precision, with 3 times increase in the structural similarity index measure. Moreover, SAGLMB can obtain more precise flow velocity estimations with a 4 times improvement than the conventional method. The SRUS results of rabbit kidney show that the proposed method significantly improves resolution of adjacent and intersecting vessels at higher MB concentrations and maintains this performance as the acquisition frame rate decreases. Furthermore, the rat brain microvascular network was reconstructed with 9.21 μm (λ/11.1) resolution. Therefore, SAGLMB can achieve robust SRUS imaging at high concentrations and low acquisition frame rates.
超分辨率超声（SRUS）可根据微泡（MB）定位以微观分辨率对脉管系统进行成像，并基于MB跟踪信息获得速度矢量图。高MB浓度可缩短SRUS成像的采集时间，然而相邻和交叉血管难以区分，从而降低分辨率。低采集帧率会影响流速估计的精度。本研究提出一种基于局部平滑的自适应广义标记多伯努利滤波器（SAGLMB），以精确跟踪不同流速下的MB运动。SAGLMB采用广义标记多伯努利滤波器（GLMB）进行MB轨迹分配，以分离相邻和交叉血管。此外，通过无迹卡尔曼滤波器预测MB的非线性运动，并应用基数概率假设密度滤波器抑制杂波干扰。最后，通过无迹Rauch-Tung-Striebel算法对轨迹进行平滑处理，以提高SRUS图像的分辨率。仿真结果表明，在高MB浓度下，SAGLMB的性能优于传统的基于二分图的跟踪方法，正确配对精度至少提高8.55%，结构相似性指数测量值提高3倍。此外，SAGLMB能够获得比传统方法更精确的流速估计，精度提高4倍。兔肾的SRUS结果表明，该方法在较高MB浓度下显著提高了相邻和交叉血管的分辨率，并且在采集帧率降低时仍能保持这一性能。此外，以9.21μm（λ/11.1）的分辨率重建了大鼠脑微血管网络。因此，SAGLMB能够在高浓度和低采集帧率下实现稳健的SRUS成像。
