Zhu Yifei, Jiang Lingyin, Zhang Qi, Yin Jun, Du Bingze, Zhang Guofeng, Zhang Haijun, Ding Bo, Lin Han, Xue Honghui, Guo Xiasheng, Zhang Xiao-Yang, Zhu Jing-Ning, Zhang Dong, Tu Juan, Gu Ning
Key Laboratory of Modern Acoustics, Department of Physics, Nanjing University, Nanjing 210093, China.
School of Computer and Electronic Information, Nanjing Normal University, Nanjing 210023, China.
Research (Wash D C). 2025 Jun 4;8:0725. doi: 10.34133/research.0725. eCollection 2025.
Ultrasound localization microscopy (ULM) is a novel imaging technique that overcomes the diffraction limit to achieve super-resolution imaging at the 10-μm scale. Despite its remarkable progress, challenges persist in enhancing the precision of microbubble tracking and fulfilling the requirements for high frame rates in practical circumstances, especially in moving organs. To address these issues, an enhanced ULM approach (shorten as vc-Kalman) integrating rapid motion compensation was developed to achieve excellent image quality. Unlike traditional methods relying on observed bubble positions, the proposed algorithm combined statistical information derived from historical data with Kalman-filter-predicted positions to enable more accurate bubble localization. Meanwhile, microbubble brightness in adjacent frames was incorporated as multidimensional feature to further improve the matching efficacy. Furthermore, velocity constraint was applied to minimize possible erroneous traces and enhance the contrast-to-noise ratio of ULM images, while ensuring the continuity of vascular reconstruction and the accuracy of the blood flow analysis to generate a reduced normalized root mean square error in velocity estimation, even at a relatively low frame rate of 146 Hz. More important, to effectively suppress the impact of physiological movements in moving organs like kidneys, this algorithm fulfilled subpixel displacement vector identification through parabolic fitting and expedited motion compensation via dynamic programming-based cross-correlation search. The results indicated that this advanced vc-Kalman method substantially boosted both the robustness and accuracy of ULM imaging, thereby opening more opportunities for clinical applications of super-resolution ULM technology.
超声定位显微镜（ULM）是一种新型成像技术，它克服了衍射极限，可在10微米尺度上实现超分辨率成像。尽管取得了显著进展，但在提高微泡跟踪精度以及满足实际情况下（尤其是在运动器官中）对高帧率的要求方面，仍存在挑战。为了解决这些问题，开发了一种集成快速运动补偿的增强型ULM方法（简称为vc-Kalman），以实现出色的图像质量。与依赖观察到的气泡位置的传统方法不同，该算法将从历史数据中得出的统计信息与卡尔曼滤波器预测的位置相结合，以实现更精确的气泡定位。同时，将相邻帧中的微泡亮度作为多维特征纳入，以进一步提高匹配效果。此外，应用速度约束来最小化可能的错误轨迹并提高ULM图像的对比度噪声比，同时确保血管重建的连续性和血流分析的准确性，即使在相对较低的146Hz帧率下，也能在速度估计中产生降低的归一化均方根误差。更重要的是，为了有效抑制肾脏等运动器官中生理运动的影响，该算法通过抛物线拟合实现亚像素位移矢量识别，并通过基于动态规划的互相关搜索加快运动补偿。结果表明，这种先进的vc-Kalman方法大大提高了ULM成像的鲁棒性和准确性，从而为超分辨率ULM技术的临床应用开辟了更多机会。
