Abstract
Obtaining ultrafast images using steered plane wave (PW) imaging remains a challenge due to the trade-off between image quality and frame rate. PW imaging indeed relies on compounding in order to preserve a good image quality, usually using multiple successive emissions, which in turn yields a decrease of the frame rate. As opposed to this classical approach, we propose a new strategy to reduce the number of emitted PWs. This is done using a deep learning technique, i.e. by training a convolutional neural network (CNN) to reconstruct high quality images using a small number of PW emissions (typically two).
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