Département Image et Traitement de l'Information, Institue Mines-Télécom (IMT) Atlantique, 29200 Brest, France.
Sensors (Basel). 2024 Aug 7;24(16):5111. doi: 10.3390/s24165111.
Plane wave imaging persists as a focal point of research due to its high frame rate and low complexity. However, in spite of these advantages, its performance can be compromised by several factors such as noise, speckle, and artifacts that affect the image quality and resolution. In this paper, we propose an attention-based complex convolutional residual U-Net to reconstruct improved in-phase/quadrature complex data from a single insonification acquisition that matches diverging wave imaging. Our approach introduces an attention mechanism to the complex domain in conjunction with complex convolution to incorporate phase information and improve the image quality matching images obtained using coherent compounding imaging. To validate the effectiveness of this method, we trained our network on a simulated phased array dataset and evaluated it using in vitro and in vivo data. The experimental results show that our approach improved the ultrasound image quality by focusing the network's attention on critical aspects of the complex data to identify and separate different regions of interest from background noise.
平面波成像是研究的焦点，因为它具有高帧率和低复杂性的优点。然而，尽管有这些优点，但它的性能可能会受到一些因素的影响，如噪声、斑点和伪影，这些因素会影响图像的质量和分辨率。在本文中，我们提出了一种基于注意力的复卷积残差 U-Net，从单次激发采集重建改进的同相信号/正交信号复数据，与发散波成像匹配。我们的方法在复域中引入注意力机制与复卷积相结合，以结合相位信息并提高图像质量，与使用相干复合成像获得的图像匹配。为了验证该方法的有效性，我们在模拟相控阵数据集上训练了我们的网络，并使用离体和体内数据进行了评估。实验结果表明，我们的方法通过将网络的注意力集中在复数据的关键方面，来识别和分离感兴趣区域与背景噪声，从而提高了超声图像的质量。
