Phase Aberration Correction: A Convolutional Neural Network Approach

One of the main sources of image degradation in ultrasound imaging is the phase aberration effect, which imposes limitations to both data acquisition and reconstruction. Phase aberration is induced by spatial changes in sound velocity compared to the default values and degrades the quality of beam focusing. In addition, it prevents received channel signals to be summed coherently. In this paper, for the first time, we propose a method to estimate the aberrator profile from an ultrasound B-mode image using a deep convolutional neural network (CNN) in order to compensate for the phase aberration effect. In contrast to traditional methods, which mostly apply time-consuming processing techniques on channel RF signals and need several iterations for reasonable accuracy, the proposed approach is computationally efficient and utilizes only the B-mode image to estimate the aberrator profile in one shot with a high accuracy. We experimentally investigate the main characteristics of the proposed approach and present a quantitative evaluation of the estimated aberrator profile. The proposed method is compared with the conventional delay-and-sum (DAS) method and a method based on nearest-neighbor cross-correlation (NNCC). Results demonstrate that the proposed CNN method substantially outperforms other methods.