This repository contains the code for the study of: R. Zorgdrager, N. Blanken, J. M. Wolterink, M. Versluis and G. Lajoinie, "Waveform-Specific Performance of Deep Learning-Based Super-Resolution for Ultrasound Contrast Imaging", in prepraration for the spotlight issue Breaking the Resolution Barrier in Ultrasound of IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.
The code here is based on a clone of https://github.com/MIAGroupUT/SRML-1D.
Fig. 1 Methods overview. 1 A random distribution of microbubbles is stimulated with the selected pulse using a virtual P4-1 transducer. The simulator computes the local shapes of the pressure wave by accounting for nonlinear propagation in the medium and solves the RP-equation. The received signal by the transducer is used as RF lines for training, validation, and testing.
The code is organized into four folders:
- 📂 RF_simulator: Pulse definition, RF signal simulation, ground truth generation, and optional RF decoding. Section IIA, IIB and, IIC-1 in the article.
- 📂 Network_pulse_types: Neural network training and evaluation. Sections IIC-2, IID, IIE-1 in the article.
- 📂 DelayAndSum: Delay-and-sum image reconstruction with unprocessed and deconvolved RF signals. Section IIE-2 in the article.
- 📂 experimental_validation: Processing of experimental data. Section II-F in the article.
- RF_simulator: MATLAB with Signal Processing Toolbox.
- Network: Python with PyTorch, NumPy, Matplotlib, SciPy
- DelayAndSum: MATLAB with export_fig module.
- GPU with CUDA cores for network training
- Verasonics Vantage 256 for experimental validation
The code can be found by scanning:
