Super-resolution ultrasound imaging with ultrasound localization microscopy (ULM) offers a high-resolution view of microvascular structures. Yet, ULM image quality heavily relies on precise microbubble (MB) detection. Despite the crucial role of localization algorithms, there has been limited focus on the practical pitfalls in MB detection tasks such as setting the detection threshold. This study examines how False Positives (FPs) and False Negatives (FNs) affect ULM image quality by systematically adding controlled detection errors to simulated data. Results indicate that while both FP and FN rates impact Peak Signal-to-Noise Ratio (PSNR) similarly, increasing FP rates from 0\% to 20\% decreases Structural Similarity Index (SSIM) by 7\%, whereas same FN rates cause a greater drop of around 45\%. Moreover, dense MB regions are more resilient to detection errors, while sparse regions show high sensitivity, showcasing the need for robust MB detection frameworks to enhance super-resolution imaging.

翻译：基于超声定位显微成像（ULM）的超分辨率超声成像技术能够提供微血管结构的高分辨率视图。然而，ULM图像质量在很大程度上依赖于微泡（MB）的精确检测。尽管定位算法至关重要，但针对MB检测任务中实际存在的缺陷（例如检测阈值的设定）的关注仍然有限。本研究通过向模拟数据中系统性地添加受控检测误差，探讨了假阳性（FP）与假阴性（FN）如何影响ULM图像质量。结果表明，虽然FP率与FN率对峰值信噪比（PSNR）的影响相似，但将FP率从0%提升至20%会使结构相似性指数（SSIM）下降7%，而相同的FN率则会导致约45%的更大降幅。此外，密集MB区域对检测误差具有更强的鲁棒性，而稀疏区域则表现出较高的敏感性，这凸显了构建稳健的MB检测框架以提升超分辨率成像质量的必要性。