研究超分辨超声成像中假阳性与假阴性对图像质量的影响
Evaluating Detection Thresholds: The Impact of False Positives and Negatives on Super-Resolution Ultrasound Localization Microscopy
- 通过模拟数据系统性注入检测误差，评估阈值设置对微泡定位的影响
- 假阴性导致结构相似度下降45%，远高于假阳性7%的降幅
- 稀疏区域对检测误差更敏感，需更强鲁棒性算法支持
超分辨率超声成像中的超声定位显微镜（ULM）可实现微血管结构的高分辨率可视化。然而，其图像质量高度依赖于微泡（MB）检测的准确性。尽管定位算法至关重要，但关于检测阈值设置等实际问题的研究仍有限。本研究通过在模拟数据中系统引入可控的检测误差，分析假阳性（FP）和假阴性（FN）对ULM图像质量的影响。结果表明，尽管两者对峰值信噪比（PSNR）的影响相似，但假阳性率从0%升至20%时，结构相似性指数（SSIM）下降7%；而相同假阴性率则导致约45%的显著下降。此外，密集微泡区域对检测误差更具鲁棒性，而稀疏区域表现出高度敏感性，凸显了构建稳健微泡检测框架对提升超分辨率成像的重要性。
原文摘要 · Abstract (English)
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.
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