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RF-ULM: Ultrasound Localization Microscopy Learned from Radio-Frequency Wavefronts
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hahnec/rf-ulm
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Map: Geometric point transformation from RF to B-mode coordinate space
rfulm_anim.mp4
rfulm_rat18_short_clip.mp4
Note: Colors represent localizations from each plane wave emission angle.
In vivo (inference):https://doi.org/10.5281/zenodo.7883227
In silico (training+inference):https://doi.org/10.5281/zenodo.4343435
It is recommended to use a UNIX-based system for development. For installation, run (or work along) the following bash script:
> bash install.shNote that the dataloader module is missing in this repository. My implementation is a hacky version of the work found athttps://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.
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}}This research is funded by the Hasler Foundation under project number 22027.
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RF-ULM: Ultrasound Localization Microscopy Learned from Radio-Frequency Wavefronts