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

arXiv: https://arxiv.org/abs/2411.07426 · HTML: https://mlsanity.com/paper/2411.07426/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 11 Nov 2024.

## Builds on (most cited first, 6 of 6)

- [Inverse Problem of Ultrasound Beamforming with Denoising-Based Regularized Solutions](https://mlsanity.com/paper/2206.07926/) (2022)
- [A Deep Learning Framework for Spatiotemporal Ultrasound Localization Microscopy](https://mlsanity.com/paper/2310.08143/) (2023)
- [A Unifying Approach to Inverse Problems of Ultrasound Beamforming and Deconvolution](https://mlsanity.com/paper/2112.14294/) (2021)
- [Transformer-Based Microbubble Localization](https://mlsanity.com/paper/2209.11859/) (2022)
- [Deformable-Detection Transformer for Microbubble Localization in Ultrasound Localization Microscopy](https://mlsanity.com/paper/2308.09845/) (2023)
- [Analytic Optimization-Based Microbubble Tracking in Ultrasound Super-Resolution Microscopy](https://mlsanity.com/paper/2209.10057/) (2022)

## Abstract (arXiv)

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.