# Super-resolution Ultrasound Localization Microscopy through Deep Learning

arXiv: https://arxiv.org/abs/1804.07661 · HTML: https://mlsanity.com/paper/1804.07661/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2018.

## Built on by (most cited first, 12 of 12)

- [Learned super resolution ultrasound for improved breast lesion characterization](https://mlsanity.com/paper/2107.05270/) (2021)
- [A Deep Learning Framework for Spatiotemporal Ultrasound Localization Microscopy](https://mlsanity.com/paper/2310.08143/) (2023)
- [Rethinking Degradation: Radiograph Super-Resolution via AID-SRGAN](https://mlsanity.com/paper/2208.03008/) (2022)
- [Geometric Ultrasound Localization Microscopy](https://mlsanity.com/paper/2306.15548/) (2023)
- [Ultrasound Signal Processing: From Models to Deep Learning](https://mlsanity.com/paper/2204.04466/) (2022)
- [RF-ULM: Ultrasound Localization Microscopy Learned from Radio-Frequency Wavefronts](https://mlsanity.com/paper/2310.01545/) (2023)
- [Deep Learning Empowered Super-Resolution: A Comprehensive Survey and Future Prospects](https://mlsanity.com/paper/2509.22692/) (2025)
- [StofNet: Super-resolution Time of Flight Network](https://mlsanity.com/paper/2308.12009/) (2023)
- [Learning Super-Resolution Ultrasound Localization Microscopy from Radio-Frequency Data](https://mlsanity.com/paper/2311.04081/) (2023)
- [Pruning Sparse Tensor Neural Networks Enables Deep Learning for 3D Ultrasound Localization Microscopy](https://mlsanity.com/paper/2402.09359/) (2024)
- [Vascular Segmentation of Functional Ultrasound Images using Deep Learning](https://mlsanity.com/paper/2410.22365/) (2024)
- [CycleULM: A unified label-free deep learning framework for ultrasound localisation microscopy](https://mlsanity.com/paper/2603.09840/) (2026)

## Abstract (arXiv)

Ultrasound localization microscopy has enabled super-resolution vascular imaging through precise localization of individual ultrasound contrast agents (microbubbles) across numerous imaging frames. However, analysis of high-density regions with significant overlaps among the microbubble point spread responses yields high localization errors, constraining the technique to low-concentration conditions. As such, long acquisition times are required to sufficiently cover the vascular bed. In this work, we present a fast and precise method for obtaining super-resolution vascular images from high-density contrast-enhanced ultrasound imaging data. This method, which we term Deep Ultrasound Localization Microscopy (Deep-ULM), exploits modern deep learning strategies and employs a convolutional neural network to perform localization microscopy in dense scenarios. This end-to-end fully convolutional neural network architecture is trained effectively using on-line synthesized data, enabling robust inference in-vivo under a wide variety of imaging conditions. We show that deep learning attains super-resolution with challenging contrast-agent densities, both in-silico as well as in-vivo. Deep-ULM is suitable for real-time applications, resolving about 70 high-resolution patches (128x128 pixels) per second on a standard PC. Exploiting GPU computation, this number increases to 1250 patches per second.