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. 1 Methods overview. 1 A 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. 2 The bubble coordinates are also used to compute the 1D ground truth distributions (arrival times of bubble echoes). 3 A 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}
}