Humans perceive physical properties such as motion and elastic force by observing objects in visual scenes. Recent research has proven that computers are capable of inferring physical properties from camera images like humans. However, few studies perceive the physical properties in more complex environment, i.e. humans have difficulty estimating physical quantities directly from the visual observation, or encounter difficulty visualizing the physical process in mind according to their daily experiences. As an appropriate example, fractional flow reserve (FFR), which measures the blood pressure difference across the vessel stenosis, becomes an important physical quantitative value determining the likelihood of myocardial ischemia in clinical coronary intervention procedure. In this study, we propose a novel deep neural network solution (TreeVes-Net) that allows machines to perceive FFR values directly from static coronary CT angiography images. Our framework fully utilizes a tree-structured recurrent neural network (RNN) with a coronary representation encoder. The encoder captures coronary geometric information providing the blood fluid-related representation. The tree-structured RNN builds a long-distance spatial dependency of blood flow information inside the coronary tree. The experiments performed on 13000 synthetic coronary trees and 180 real coronary trees from clinical patients show that the values of the area under ROC curve (AUC) are 0.92 and 0.93 under two clinical criterions. These results can demonstrate the effectiveness of our framework and its superiority to seven FFR computation methods based on machine learning.

中文翻译：

---

在复杂的视觉场景中学习物理特性：智能机器，可从静态CT血管造影成像中感知血流动态。

人们通过观察视觉场景中的物体来感知诸如运动和弹力之类的物理特性。最近的研究证明，计算机能够从像人一样的相机图像中推断出物理属性。然而，很少有研究能感知到更复杂环境中的物理性质，即人类难以直接通过视觉观察来估计物理量，或难以根据其日常经验可视化脑海中的物理过程。作为一个适当的例子，分数血流储备（FFR）可以测量整个血管狭窄处的血压差，它成为决定临床冠状动脉介入手术中心肌缺血可能性的重要物理定量值。在这项研究中，我们提出了一种新颖的深度神经网络解决方案（TreeVes-Net），该解决方案允许机器直接从静态冠状动脉CT血管造影图像中感知FFR值。我们的框架充分利用了带有冠状动脉代表编码器的树状结构递归神经网络（RNN）。编码器捕获冠状动脉几何信息，以提供与血液有关的信息。树状的RNN建立了冠状树内部血流信息的长距离空间依赖性。对来自临床患者的13000棵合成冠状树和180棵真实冠状树进行的实验表明，在两种临床标准下，ROC曲线下面积（AUC）的值分别为0.92和0.93。这些结果可以证明我们的框架的有效性及其相对于基于机器学习的7种FFR计算方法的优越性。