Self-Supervised Transmission Waveform Learning for Ultrafast Pulse-Echo Ultrasound Imaging with Sparse Reconstruction

Ultrafast pulse-echo ultrasound imaging is performed by recording only one measurement cycle, which insonifies the entire region of interest. It has been shown that mathematical model of the received signal is suitable for image reconstruction based on sparsity-promoting algorithms if material parameters of measurement area can be sparsely represented with a suitable basis. However, reconstruction performance of these algorithms is affected by correlation of measured samples, which depends on the scatterer locations in the measurement area and transmitted signal parameters. In this paper, we create a model-based and self-supervised neural network, which learns the transmitted waveforms and delays of each transducer element with the task of minimizing the reconstruction error. It employs forward model as encoder and the Fast Iterative Shrinkage Thresholding Algorithm as decoder. The outcomes are evaluated against two existing scenarios: one involving randomly delayed and weighted transmission signals, and the other based on conventional plane wave imaging. Utilizing learned transmission waveform and time delays outperformed other cases in terms of contrast level and reconstruction accuracy.