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In the medical ultrasound field, ultrafast imaging has recently become a hot topic. However, the diagnostic reliability of ultrafast high-frame rate plane-wave (PW) imaging is reduced by its low-quality images. The medical ultrasound equipment on the market usually adopts the line-scanning mode, which can obtain high-quality images at a very low frame rate. In addition, many proven data-driven ultrasound image processing methods are trained by line-scan images. Since the gray-level distributions of line-scan images and PW images are very different, these gray-level distribution-sensitive methods cannot be generalized to ultrafast ultrasound imaging, which limits further applications. Hence, we propose an ultrasound-transfer generative adversarial network to improve the quality of PW images and extend the existing image processing methods to ultrafast ultrasound imaging by reconstructing PW images into line-scan images. This network adopts a residual dense generator with a self-attention system that fully uses the hierarchical features and generates details from all the relevant physiological information. A projection discriminator and spectral normalization are introduced to increase the discernibility and to maintain a balance between the generator and the discriminator. Moreover, we reorganize the transmit sequence of the transducer array to eliminate the negative influence of human movements and facilitate the convergence of the proposed model. The experimental results are evaluated with five metrics, which confirm the feasibility of the proposed method to obtain a line-scan-quality image with a very high frame rate. This technology could significantly popularize ultrafast medical ultrasound imaging.