CycleULM: A unified label-free deep learning framework for ultrasound localisation microscopy
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| Autores principales: | , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866912959307972608 |
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| author | Yan, Su Gonzalez, Clara Rodrigo Leung, Vincent C. H. Verinaz-Jadan, Herman Chen, Jiakang Toulemonde, Matthieu Riemer, Kai Yan, Jipeng Vié, Clotilde Tan, Qingyuan Weinberg, Peter D. Dragotti, Pier Luigi Murphy, Kevin G. Tang, Meng-Xing |
| author_facet | Yan, Su Gonzalez, Clara Rodrigo Leung, Vincent C. H. Verinaz-Jadan, Herman Chen, Jiakang Toulemonde, Matthieu Riemer, Kai Yan, Jipeng Vié, Clotilde Tan, Qingyuan Weinberg, Peter D. Dragotti, Pier Luigi Murphy, Kevin G. Tang, Meng-Xing |
| contents | 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{\times} 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{\times}). 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_09840 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CycleULM: A unified label-free deep learning framework for ultrasound localisation microscopy Yan, Su Gonzalez, Clara Rodrigo Leung, Vincent C. H. Verinaz-Jadan, Herman Chen, Jiakang Toulemonde, Matthieu Riemer, Kai Yan, Jipeng Vié, Clotilde Tan, Qingyuan Weinberg, Peter D. Dragotti, Pier Luigi Murphy, Kevin G. Tang, Meng-Xing Image and Video Processing Computer Vision and Pattern Recognition 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{\times} 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{\times}). 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. |
| title | CycleULM: A unified label-free deep learning framework for ultrasound localisation microscopy |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.09840 |