| # Deep learning for speed of sound inversion in ultrasound imaging | |
| This repository contains the code and models for the following papers: | |
| 1. Feigin M, Freedman D, Anthony B. W. A Deep Learning Framework for Single-Sided Sound Speed Inversion in Medical Ultrasound. IEEE Trans Biomed Eng. 2020;67(4):1142-1151. doi:10.1109/TBME.2019.2931195 | |
| 2. Feigin M, Zwecker M, Freedman D, Anthony BW. Detecting muscle activation using ultrasound speed of sound inversion with deep learning. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE; 2020:2092-2095. doi:10.1109/EMBC44109.2020.9175237 | |
| 3. Feigin M, Freedman D, Anthony BW. Computing Speed-of-Sound from ultrasound: user-agnostic recovery and a new benchmark. IEEE Trans Biomed Eng. 2023; doi:TBF | |
| This repository contain the network code and models for the algorithms and results contained in the paper. | |
| The code was tested under python 3.9. The anaconda environment is defined in environment.yml (setup environment with the `command conda env create -f environment.yml`) | |
| ## Data | |
| The dataset used is available on huggingface at https://huggingface.co./datasets/laughingrice/Ultrasound_planewave_sos_inversion | |
| Variables in the files are `[sample, layer, x/channel, y/sample]` order | |
| * `alpha_coeff` -- Alpha coefficient used for simulations, full resolution | |
| * `c0` -- Speed-of-sound used for simulations, full resolution | |
| * `data` -- Channel data (first 2048 samples, 64 active channels, first layer with flat plane wave, to | |
| match existing physical hardware were used for the results in the paper) | |
| * `dx` -- spatial dx value of `c0` and `alpha_coef` | |
| * `f` -- temporal sampling frequency of channel data (40MHz) | |
| ## Models | |
| Model files appearing under the `models` directory for results presented in the paper with teh matching | |
| execution parameters are as follows: | |
| * `tbme_sos.pt` -- network weights for the network presented in [1] | |
| * `python . --test_files data/supplamentary_sample.mat --test_fname tbme_sos.h5 --load_ver models/tbme_sos.pt --net_type tbme` | |
| * `embc_sos.pt` -- network weights for the network presented in [2] | |
| * `python . --test_files data/supplamentary_sample.mat --test_fname embc_sos.h5 --load_ver models/embc_sos.pt --net_type embc` | |
| * `tbme2_sos.pt` -- network weights for the network presented in [3] | |
| * `python . --test_files data/supplamentary_sample.mat --test_fname tbme2_sos.h5 --load_ver models/tbme2_sos.pt` | |
| * `tbme2_sos_rand_gain.pt` -- [3] trained to recover the speed-of-sound map with random gain profile and scaling | |
| * `python . --test_files data/supplamentary_sample.mat --test_fname tbme2_sos_gain.h5 --load_ver models/tbme2_sos_rand_gain.pt` | |
| * `tbme2_attn.pt` -- [3] trained to recover the attenuation coefficient | |
| * `python . --test_files data/supplamentary_sample.mat --test_fname tbme2_attn.h5 --load_ver models/tbme2_attn.pt --label_vars alpha_coeff` | |
| * `tbme2_sos_attn.pt` -- [3] trained to recover both the speed-of-sound map and attenuation coefficient | |
| * `python . --test_files data/supplamentary_sample.mat --test_fname tbme2_sos_attn.h5 --load_ver models/tbme2_sos_attn.pt --label_vars c0 alpha_coeff` | |
| * `tbme2_phase_sos.pt` -- [3] trained to recover the speed-of-sound map using the IQ phase component | |
| * `python . --test_files data/supplamentary_sample.mat --test_fname tbme2_phase_sos.h5 --load_ver models/tbme2_phase_sos.pt --phase_inv 1` | |