# RF-ULM: Ultrasound Localization Microscopy Learned from Radio-Frequency Wavefronts

arXiv: https://arxiv.org/abs/2310.01545 · HTML: https://mlsanity.com/paper/2310.01545/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 49 references that are in the map.
- First posted on arXiv 2 Oct 2023.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Super-resolution Ultrasound Localization Microscopy through Deep Learning](https://mlsanity.com/paper/1804.07661/) (2018)
- [Learned super resolution ultrasound for improved breast lesion characterization](https://mlsanity.com/paper/2107.05270/) (2021)
- [Super-Resolved Microbubble Localization in Single-Channel Ultrasound RF Signals Using Deep Learning](https://mlsanity.com/paper/2204.04537/) (2022)
- [Transformer-Based Microbubble Localization](https://mlsanity.com/paper/2209.11859/) (2022)
- [Geometric Ultrasound Localization Microscopy](https://mlsanity.com/paper/2306.15548/) (2023)

## Built on by (most cited first, 1 of 1)

- [Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging](https://mlsanity.com/paper/2607.25967/) (2026)

## Abstract (arXiv)

In Ultrasound Localization Microscopy (ULM), achieving high-resolution images relies on the precise localization of contrast agent particles across a series of beamformed frames. However, our study uncovers an enormous potential: The process of delay-and-sum beamforming leads to an irreversible reduction of Radio-Frequency (RF) channel data, while its implications for localization remain largely unexplored. The rich contextual information embedded within RF wavefronts, including their hyperbolic shape and phase, offers great promise for guiding Deep Neural Networks (DNNs) in challenging localization scenarios. To fully exploit this data, we propose to directly localize scatterers in RF channel data. Our approach involves a custom super-resolution DNN using learned feature channel shuffling, non-maximum suppression, and a semi-global convolutional block for reliable and accurate wavefront localization. Additionally, we introduce a geometric point transformation that facilitates seamless mapping to the B-mode coordinate space. To understand the impact of beamforming on ULM, we validate the effectiveness of our method by conducting an extensive comparison with State-Of-The-Art (SOTA) techniques. We present the inaugural in vivo results from a wavefront-localizing DNN, highlighting its real-world practicality. Our findings show that RF-ULM bridges the domain shift between synthetic and real datasets, offering a considerable advantage in terms of precision and complexity. To enable the broader research community to benefit from our findings, our code and the associated SOTA methods are made available at https://github.com/hahnec/rf-ulm.