Evaluating Detection Thresholds: The Impact of False Positives and Negatives on Super-Resolution Ultrasound Localization Microscopy

TL;DR

This paper investigates how false positives and negatives in microbubble detection affect super-resolution ultrasound images, revealing that false negatives have a more significant impact on image quality, especially in sparse regions.

Contribution

It systematically analyzes the effects of detection errors on ULM image quality, highlighting the importance of robust detection thresholds for improved super-resolution imaging.

Findings

False negatives cause greater image quality degradation than false positives.

Dense microbubble regions are more resilient to detection errors.

Increasing false positive rate from 0% to 20% reduces SSIM by 7%.

Abstract

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.