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DeNet: Optimizing of transmit delays for high frame rate synthetic transmit aperture imaging

Synthetic transmit aperture (STA) imaging can achieve the highest spatial resolution, owing to its two-way dynamic focusing. However, STA acquires the complete dataset for beamforming at a relatively low frame rate and transmit power. In this study, we propose a neural network (DeNet) to achieve high frame rate STA imaging by recovering the complete STA dataset from a smaller number of transmissions with optimized transmit delay. DeNet consists of an encoder and a joint decoder. The weight of the encoder is trained to be used as transmit delay. The decoder recovers the complete STA dataset from the acquired channel data to achieve high frame rate and high signal-to-noise ratio at the same time. The results show that when the frame rate is four times higher than that of STA, DeNet achieves comparable lateral resolution and contrast as STA in both simulations and in-vivo experiments. When the frame rate is twice higher than that of STA, the above two indexes of DeNet are better than those of STA. The optimization of delay by DeN et may play a guiding role in the design of transmit delay in the future.