Abstract
Synthetic Aperture Vector Flow Imaging (SA-VFI) can visualize complex cardiac and vascular blood flow patterns at high temporal resolution with a large field of view. Convolutional neural networks (CNNs) are commonly used in image and video recognition and classification. However, most recently presented CNNs also allow for making per-pixel predictions as needed in optical flow velocimetry. To our knowledge we demonstrate here for the first time a CNN architecture to produce 2D full flow field predictions from high frame rate SA ultrasound images using supervised learning. The CNN was initially trained using CFD-generated and augmented noiseless SA ultrasound data of a realistic vessel geometry. Subsequently, a mix of noisy simulated and real \textit{in vivo} acquisitions were added to increase the network's robustness. The resulting flow field of the CNN resembled the ground truth accurately with an endpoint-error percentage between 6.5\% to 14.5\%. Furthermore, when confronted with an unknown geometry of an arterial bifurcation, the CNN was able to predict an accurate flow field indicating its ability for generalization. Remarkably, the CNN also performed well for rotational flows, which usually requires advanced, computationally intensive VFI methods. We have demonstrated that convolutional neural networks can be used to estimate complex multidirectional flow.
Abstract (translated)
合成孔径矢量流成像(SA-VFI)可以在大视场下以高时间分辨率显示复杂的心脏和血管血流模式。卷积神经网络(CNN)是图像和视频识别与分类中常用的网络。然而,最近提出的CNN也允许在光流测速中根据需要进行每像素预测。据我们所知,我们在这里首次展示了CNN的架构,利用监督学习从高帧率SA超声图像生成二维全流场预测。CNN最初使用CFD生成的增强无噪音SA超声数据进行训练,该数据是真实的血管几何结构。随后,加入了噪声模拟和真实的活体内采集的组合,以增强网络的鲁棒性。CNN得到的流场与地面真实情况非常相似,端点误差百分比在6.5%到14.5%之间。此外,当面对一个未知几何的动脉分叉时,CNN能够预测一个准确的流场,表明它的泛化能力。值得注意的是,CNN在旋转流方面也表现良好,这通常需要先进的、计算密集的VFI方法。我们已经证明卷积神经网络可以用来估计复杂的多向流动。
URL
https://arxiv.org/abs/1903.06254