Demonstration of Vector Flow Imaging using Convolutional Neural Networks
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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 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.
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