SweiNet is an uncertainty-quantifying shear wave speed (SWS) estimator for ultrasound shear wave elasticity (SWE) imaging.

SweiNet takes as input a 2D space-by-time array of tracked particle motion. It outputs the estimated SWS and estimated uncertainty, both in units of meters per second.

SweiNet was originally trained on a large dataset of in vivo cervix SWE acquisitions, and the predicted uncertainty is well-calibrated to these data. With a few pre-processing steps, SweiNet can be applied to other datasets.

Please see the notebook Example.ipynb to get started with these two examples:

If you find SweiNet useful, please consider citing the following manuscript:

@misc{jin2022sweinet,
      title={SweiNet: Deep Learning Based Uncertainty Quantification for Ultrasound Shear Wave Elasticity Imaging}, 
      author={Felix Q. Jin and Lindsey C. Carlson and Helen Feltovich and Timothy J. Hall and Mark L. Palmeri},
      year={2022},
      eprint={2203.10678},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}

This work was supported by NIH grants T32GM007171, R01HD072077, R01HD096361.

Software in this repository is licensed under the Apache License, Version 2.0, as detailed in the LICENSE file.

The trained SweiNet_weights.pt is licensed under a Creative Commons Attribution 4.0 International License.

Copyright 2022 Felix Q. Jin