Phase aberration is one of the primary sources of image quality degradation in ultrasound, which is induced by spatial variations in sound speed across the heterogeneous medium. This effect disrupts transmitted waves and prevents coherent summation of echo signals, resulting in suboptimal image quality. In real experiments, obtaining non-aberrated ground truths can be extremely challenging, if not infeasible. It hinders the performance of deep learning-based phase aberration correction techniques due to sole reliance on simulated data and the presence of domain shift between simulated and experimental data. Here, for the first time, we propose a deep learning-based method that does not require reference data to compensate for the phase aberration effect. We train a network wherein both input and target output are randomly aberrated radio frequency (RF) data. Moreover, we demonstrate that a conventional loss function such as mean square error is inadequate for training the network to achieve optimal performance. Instead, we propose an adaptive mixed loss function that employs both B-mode and RF data, resulting in more efficient convergence and enhanced performance. Source code is available at \url{http://code.sonography.ai}.

翻译：相位畸变是导致超声图像质量下降的主要原因之一，其由异质介质中声速的空间变化引起。该效应会破坏传输波并阻止回波信号的相干叠加，导致图像质量欠佳。在实际实验中，获取无畸变的真实数据极具挑战性，甚至不可行。这阻碍了基于深度学习的相位畸变校正技术的性能，因为此类技术完全依赖模拟数据，且模拟数据与实验数据之间存在域偏移。本文首次提出一种无需参考数据即可补偿相位畸变效应的深度学习方法。我们训练了一个网络，其输入和目标输出均为随机畸变的射频数据。此外，我们证明了均方误差等传统损失函数不足以使网络达到最优性能。为此，我们提出一种自适应混合损失函数，同时利用B模式和射频数据，从而实现更高效的收敛和增强的性能。源代码可在 \url{http://code.sonography.ai} 获取。