论文标题
深度学习应用于合成孔径超声
Deep Learning Applied to Beamforming in Synthetic Aperture Ultrasound
论文作者
论文摘要
深度学习方法可以在许多医学成像应用中找到。最近，这些方法直接应用于RF超声多通道数据，以增强重建图像的质量。在本文中，我们将深层神经网络应用于光束成型阶段的医学超声成像。具体来说，我们使用来自两个不同尺寸的模拟多渠道数据训练网络，使用各种到达方向（DOA）角度训练网络，并在实际心脏数据上测试其概括性能。我们证明，在分辨率和对比度方面，我们的方法可用于改善图像质量而不是标准方法。另外，它可用于减少数组中的元素数量，同时保持图像质量。我们的方法的实用性在模拟和真实数据上得到了证明。
Deep learning methods can be found in many medical imaging applications. Recently, those methods were applied directly to the RF ultrasound multi-channel data to enhance the quality of the reconstructed images. In this paper, we apply a deep neural network to medical ultrasound imaging in the beamforming stage. Specifically, we train the network using simulated multi-channel data from two arrays with different sizes, using a variety of direction of arrival (DOA) angles, and test its generalization performance on real cardiac data. We demonstrate that our method can be used to improve image quality over standard methods, both in terms of resolution and contrast. Alternatively, it can be used to reduce the number of elements in the array, while maintaining the image quality. The utility of our method is demonstrated on both simulated and real data.
