hahnec / rf-ulm

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

RF-ULM: Radio-Frequency Ultrasound Localization Microscopy

Overview

NMS: Non-Maximum-Suppression
Map: Geometric point transformation from RF to B-mode coordinate space

SG-SPCN Architecture

Demos

1. ULM Animation Demo

rfulm_anim.mp4

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

2. Prediction Frames Demo

rfulm_rat18_short_clip.mp4

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

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

Citation

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

@inproceedings{hahne:2023:learning,
    author = {Christopher Hahne and Georges Chabouh and Olivier Couture and Raphael Sznitman},
    title = {Learning Super-Resolution Ultrasound Localization Microscopy from Radio-Frequency Data},
    booktitle= {2023 IEEE International Ultrasonics Symposium (IUS)},
    address={},
    month={Sep},
    year={2023},
    pages={1-4},
}

Acknowledgment

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