IEEE Trans Ultrason Ferroelectr Freq Control. 2017 Oct;64(10):1637-1639. doi: 10.1109/TUFFC.2017.2736890. Epub 2017 Aug 7.
Single plane wave (PW) imaging produces ultrasound images of poor quality at high frame rates (ultrafast). High-quality PW imaging usually relies on the coherent compounding of several successive steered emissions (typically more than ten), which in turn results in a decreased frame rate. We propose a new strategy to reduce the number of emitted PWs by learning a compounding operation from data, i.e., by training a convolutional neural network to reconstruct high-quality images using a small number of transmissions. We present experimental evidence that this approach is promising, as we were able to produce high-quality images from only three PWs, competing in terms of contrast ratio and lateral resolution with the standard compounding of 31 PWs ( 10× speedup factor).
单平面波（PW）成像是在高帧率（超快）下产生超声图像质量较差的一种技术。高质量的 PW 成像通常依赖于几个连续导向发射的相干复合（通常超过十个），这反过来又导致帧率降低。我们提出了一种新策略，通过从数据中学习复合操作来减少发射 PW 的数量，即通过训练卷积神经网络使用少量传输来重建高质量图像。我们提供了实验证据表明，这种方法是有前途的，因为我们能够仅使用三个 PW 生成高质量的图像，在对比度和横向分辨率方面与标准的 31 个 PW 复合（10 倍加速因子）相竞争。
