https://github.com/hahnec/rf-ulm

RF-ULM: Ultrasound Localization Microscopy Learned from Radio-Frequency Wavefronts
https://github.com/hahnec/rf-ulm

ceus contrast-enhancement deep-learning imaging localization medical medical-imaging microbubble microscopy neural-network pytorch ulm ultrasound vascular vascular-flow

Last synced: about 1 month ago
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RF-ULM: Ultrasound Localization Microscopy Learned from Radio-Frequency Wavefronts

• Host: GitHub
• URL: https://github.com/hahnec/rf-ulm
• Owner: hahnec
• License: gpl-3.0
• Created: 2022-12-26T09:23:52.000Z (almost 2 years ago)
• Default Branch: master
• Last Pushed: 2024-08-16T18:08:20.000Z (4 months ago)
• Last Synced: 2024-08-16T19:30:28.682Z (4 months ago)
• Topics: ceus, contrast-enhancement, deep-learning, imaging, localization, medical, medical-imaging, microbubble, microscopy, neural-network, pytorch, ulm, ultrasound, vascular, vascular-flow
• Language: Python
• Homepage:
• Size: 122 MB
• Stars: 17
• Watchers: 3
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
## RF-ULM: Radio-Frequency Ultrasound Localization Microscopy

[![arXiv paper link](https://img.shields.io/badge/paper-arXiv:2306.08281-red)](https://arxiv.org/pdf/2310.01545.pdf)

### Overview







NMS: Non-Maximum-Suppression




Map: Geometric point transformation from RF to B-mode coordinate space







### SG-SPCN Architecture










### Demos

#### 1. ULM Animation Demo

    Link: https://github.com/hahnec/rf-ulm/assets/33809838/e37aee11-c07f-4d9b-8672-5a9b466edd26

Note: The video starts in slow motion and then exponentially increases the frame rate for better visualization.




#### 2. Prediction Frames Demo

    Link: https://github.com/hahnec/rf-ulm/assets/33809838/4f4002bb-01e1-405f-aa56-e3c6b7a3b654

Note: Colors represent localizations from each plane wave emission angle.

### Datasets

*In vivo* (inference): https://doi.org/10.5281/zenodo.7883227

*In silico* (training+inference): https://doi.org/10.5281/zenodo.4343435







### Short presentation at IUS 2023

[](https://www.youtube.com/embed/eJJXnXay-fU)

### Installation

It is recommended to use a UNIX-based system for development. For installation, run (or work along) the following bash script:

```

> bash install.sh

```

Note that the dataloader module is missing in this repository. My implementation is a hacky version of the work found at https://github.com/AChavignon/PALA, which was used as a reference in this project. When using data other than mentioned here, one would need to start writing this part from scratch. The simpletracker repository has not been used in the TMI publication and can be ignored.

### Citation

If you use this project for your work, please cite:

```

@article{hahne:2024:rfulm,

  author={Hahne, Christopher and Chabouh, Georges and Chavignon, Arthur and Couture, Olivier and Sznitman, Raphael},

  journal={IEEE Transactions on Medical Imaging}, 

  title={RF-ULM: Ultrasound Localization Microscopy Learned From Radio-Frequency Wavefronts}, 

  year={2024},

  volume={43},

  number={9},

  pages={3253-3262},

  keywords={Location awareness;Radio frequency;Array signal processing;Superresolution;Convolution;Ultrasonic imaging;Kernel;Super-resolution;ultrasound;localization;microscopy;deep learning;neural network;beamforming},

  doi={10.1109/TMI.2024.3391297}

}

```

### Acknowledgment

This research is funded by the Hasler Foundation under project number 22027.