Image Quality Enhancement Using a Deep Neural Network for Plane Wave Medical Ultrasound Imaging


Plane wave imaging (PWI), a typical ultrafast medical ultrasound imaging mode, adopts single plane wave emission without focusing to achieve a high frame rate. However, the imaging quality is severely degraded in comparison with the commonly used focused line scan mode. Conventional adaptive beamformers can improve imaging quality at the cost of additional computation. In this article, we propose to use a deep neural network (DNN) to enhance the performance of PWI while maintaining a high frame rate. In particular, the PWI response from a single point target is used as the network input, while the focused scan response from the same point serves as the desired output, which is the main contribution of this method. To evaluate the performance of the proposed method, simulations, phantom experiments and in vivo studies are conducted. The delay-and-sum (DAS), the coherence factor (CF), a previously proposed deep learning-based method and the DAS with focused scan are used for comparison. Numerical metrics, including the contrast ratio (CR), the contrast-to-noise ratio (CNR), and the speckle signal-to-noise ratio (sSNR), are used to quantify the performance. The results indicate that the proposed method can achieve superior resolution and contrast performance. Specifically, the proposed method performs better than the DAS in all metrics. Although the CF provides a higher CR, its CNR and sSNR are much lower than those of the proposed method. The overall performance is also better than that of the previous deep learning method and at the same level with focused scan performance. Additionally, in comparison with the DAS, the proposed method requires little additional computation, which ensures high temporal resolution. These results validate that the proposed method can achieve a high imaging quality while maintaining the high frame rate associated with PWI.



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