Ultrafast ultrasound (US) imaging is a pioneering imaging modality that achieves higher frame rates than traditional US imaging, enabling the visualization and analysis of fast dynamics in tissues and flows. Nevertheless, images resulting from this technique suffer from a low-quality level. Recently, convolutional neural networks (CNN) have demonstrated great potential for reducing image artifacts and recovering speckle patterns without compromising the frame rate. As yet, CNNs have been mostly trained on large datasets of simulated or in vitro  phantom images, but their performances on in vivo images remains suboptimal. In the current study, we present a method to enhance the image quality of single unfocused acquisitions by relying on a CNN. We introduce a training loss function that accounts for the high dynamic range of the radio frequency data and uses the Kullback–Leibler (KL) divergence to preserve the probability distributions of the echogenicity values. We conduct an extensive performance analysis of our approach using a new large in vivo dataset of 20,000 images. The predicted images are compared qualitatively to the target images obtained from the coherent compounding of 87 plane waves (PW). The structural similarity index measure, peak signal-to-noise ratio and KL divergence are used to quantitatively analyze the performance of our method. Our results demonstrate significant improvements in image quality of single PW acquisitions, highly reducing artifacts.

Ultrafast ultrasound has recently emerged as an alternative to traditional focused ultrasound. By virtue of the low number of insonifications it requires, ultrafast ultrasound enables the imaging of the human body at potentially very high frame rates. However, unaccounted for speed-of-sound variations in the insonified medium often result in phase aberrations in the reconstructed images. The diagnosis capability of ultrafast ultrasound is thus ultimately impeded. Therefore, there is a strong need for adaptive beamforming methods that are resilient to speed-of-sound aberrations. Several of such techniques have been proposed recently but they often lack parallelizability or the ability to directly correct both transmit and receive phase aberrations. In this article, we introduce an adaptive beamforming method designed to address these shortcomings. To do so, we compute the windowed Radon transform of several complex radio-frequency images reconstructed using delay-and-sum. Then, we apply to the obtained local sinograms weighted tensor rank-1 decompositions and their results are eventually used to reconstruct a corrected image. We demonstrate using simulated data that our method is able to successfully recover aberration-free images and that it outperforms both coherent compounding and the recently introduced SVD beamformer. Finally, we validate the proposed beamforming technique on in-vivo data, resulting in a significant improvement of image quality compared to the two reference methods.