Super-Resolved Microbubble Localization in Single-Channel Ultrasound RF Signals Using Deep Learning

This repository contains all the code necessary to reproduce the results presented in the article: N. Blanken, J. M. Wolterink, H. Delingette, C. Brune, M. Versluis and G. Lajoinie, "Super-Resolved Microbubble Localization in Single-Channel Ultrasound RF Signals Using Deep Learning," in IEEE Transactions on Medical Imaging, doi: 10.1109/TMI.2022.3166443.

Link to the article: https://ieeexplore.ieee.org/document/9755198

Fig. 1Methods overview.1A randomly distributed microbubble cloud (random number of bubbles and random coordinates) serves as input to a simulator that generates one-dimensional (1D) RF signals. The individual bubble radii are drawn from a narrow (monodisperse) distribution. The acoustic pressure amplitude is also randomly selected.2The bubble coordinates are also used to compute the 1D ground truth distributions (arrival times of bubble echoes).3A 1D dilated convolutional neural network (CNN) is trained with a dual-loss function to detect and localize microbubbles in an RF signal.

The code is organized into three folders:

• 📂 RF_simulator: RF signal simulation and ground truth generation (Fig. 1, step 1 and 2). Section II.A in the article.
• 📂 Network: Neural network training and evaluation (Fig. 1, step 3). Sections II.B, II.C, II.D.1 in the article.
• 📂 DelayAndSum: Delay-and-sum image reconstruction with unprocessed and deconvolved RF signals. Section II.D.2 in the article.

Required software

• RF_simulator: MATLAB with Signal Processing Toolbox
• Network: Python with PyTorch, NumPy, Matplotlib, SciPy
• DelayAndSum: MATLAB

License and citation

This code is available under an MIT licencse. If you use (parts of) the code, please cite our IEEE TMI article:

N. Blanken, J. M. Wolterink, H. Delingette, C. Brune, M. Versluis and G. Lajoinie, "Super-Resolved Microbubble Localization in Single-Channel Ultrasound RF Signals Using Deep Learning," in IEEE Transactions on Medical Imaging, vol. 41, no. 9, pp. 2532-2542, Sept. 2022, doi: 10.1109/TMI.2022.3166443.

@article{9755198,
author={Blanken, Nathan and Wolterink, Jelmer M. and Delingette, Hervé and Brune, Christoph and Versluis, Michel and Lajoinie, Guillaume},
journal={IEEE Transactions on Medical Imaging}, title={Super-Resolved Microbubble Localization in Single-Channel Ultrasound RF Signals Using Deep Learning}, year={2022},
volume={41},
number={9},
pages={2532-2542},
doi={10.1109/TMI.2022.3166443}
}