rf-ulm
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hahnec
RF-ULM: Ultrasound Localization Microscopy Learned from Radio-Frequency Wavefronts
RF-ULM: Radio-Frequency Ultrasound Localization Microscopy
Overview
Map: Geometric point transformation from RF to B-mode coordinate space
SG-SPCN Architecture
Demos
1. ULM Animation Demo
Note: The video starts in slow motion and then exponentially increases the frame rate for better visualization.2. Prediction Frames Demo
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
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:
@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.
hahnec