A Self-supervised Deep Learning Approach for High Frame Rate Plane Wave Beamforming with Two-way Dynamic Focusing

By using plane wave for high frame rate imaging, the image quality is relatively poor due to the lack of transmit focusing. This paper proposes a deep-learning based approach to achieve two-way dynamic focusing in plane wave imaging, which improves the image quality of plane wave, particularly in image contrast. This method takes advantage of the complete dataset of synthetic transmit aperture (STA) imaging, by building the linear relationship between the channel data of plane waves with apodizations and the complete dataset of STA. A customized network is proposed to train a set of optimized plane wave apodization coefficients, and to recover the complete dataset of STA from the channel data of apodized plane waves. Compared with a similar method using fixed apodizations and compressed-sensing (CS) based dataset recovery in in-vivo experiments, the proposed method significantly improves the contrast ratio (CR) by around 9 dB and contrast-to-noise ratio (CNR) by around 6 dB. Meanwhile, the computational time of dataset recovery is reduced from thousands of seconds to less than 2 seconds.