# CycleULM: A unified label-free deep learning framework for ultrasound localisation microscopy

arXiv: https://arxiv.org/abs/2603.09840 · HTML: https://mlsanity.com/paper/2603.09840/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 52 references that are in the map.
- First posted on arXiv 10 Mar 2026.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Super-resolution Ultrasound Localization Microscopy through Deep Learning](https://mlsanity.com/paper/1804.07661/) (2018)
- [A Deep Learning Framework for Spatiotemporal Ultrasound Localization Microscopy](https://mlsanity.com/paper/2310.08143/) (2023)
- [Super-Resolution Ultrasound Localization Microscopy Based on a High Frame-rate Clinical Ultrasound Scanner: An In-human Feasibility Study](https://mlsanity.com/paper/2009.13477/) (2020)
- [BUbble Flow Field: a Simulation Framework for Evaluating Ultrasound Localization Microscopy Algorithms](https://mlsanity.com/paper/2211.00754/) (2022)
- [Transthoracic super-resolution ultrasound localisation microscopy of myocardial vasculature in patients](https://mlsanity.com/paper/2303.14003/) (2023)
- [Enhancing super-resolution ultrasound localisation through multi-frame deconvolution exploiting spatiotemporal coherence](https://mlsanity.com/paper/2407.06373/) (2024)

## Abstract (arXiv)

Super-resolution ultrasound via microbubble (MB) localisation and tracking, also known as ultrasound localisation microscopy (ULM), can resolve microvasculature beyond the acoustic diffraction limit. However, significant challenges remain in localisation performance and data acquisition and processing time. Deep learning methods for ULM have shown promise to address these challenges, however, they remain limited by in vivo label scarcity and the simulation-to-reality domain gap. We present CycleULM, the first unified label-free deep learning framework for ULM. CycleULM learns a physics-emulating translation between the real contrast-enhanced ultrasound (CEUS) data domain and a simplified MB-only domain, leveraging the power of CycleGAN without requiring paired ground truth data. With this translation, CycleULM removes dependence on high-fidelity simulators or labelled data, and makes MB localisation and tracking substantially easier. Deployed as modular plug-and-play components within existing pipelines or as an end-to-end processing framework, CycleULM delivers substantial performance gains across both in silico and in vivo datasets. Specifically, CycleULM improves image contrast (contrast-to-noise ratio) by up to 15.3 dB and sharpens CEUS resolution with a 2.5× reduction in the full width at half maximum of the point spread function. CycleULM also improves MB localisation performance, with up to +40% recall, +46% precision, and a -14.0 μm mean localisation error, yielding more faithful vascular reconstructions. Importantly, CycleULM achieves real-time processing throughput at 18.3 frames per second with order-of-magnitude speed-ups (up to ~14.5×). By combining label-free learning, performance enhancement, and computational efficiency, CycleULM provides a practical pathway toward robust, real-time ULM and accelerates its translation to clinical applications.