Coronary artery disease (CAD) is one of the most common causes of death in
the European Union and the USA. The crucial biomarker in its diagnosis is
called Fractional Flow Reserve (FFR) and its in-vivo measurement is obtained
via an invasive diagnostic technique in the form of coronagraphy. In order to
address the invasive drawbacks associated with a procedure, a new approach
virtual FFR (vFFR) measurement has emerged in recent years. This technique
involves using computed tomography angiography (CTA) to obtain virtual
measurements of FFR. By utilizing Computational Fluid Dynamics (CFD), vFFR
estimates can be derived from CTA data, providing a promising in-silico
alternative to traditional methods. However, the widespread adoption of vFFR
from CTA as a diagnostic technique is hindered by two main challenges: time and
computational requirements. In this work, we explore the usage of deep learning
techniques as surrogate CFD engine models in the task of vFFR estimation in
coronary arteries to drastically limit the required time and computational
costs without a major drop in quality. We propose a novel approach to vFFR
estimation by representing the input vessel geometry as a point cloud and
utilizing the hybrid neural network that learns geometry representation based
on both explicitly and implicitly given features. We evaluate the method from
the clinical point of view and showcase that it can serve as a compelling
replacement for commonly utilized CFD-based approaches.

冠状动脉疾病是欧盟和美国最常见的死因之一，其中关键的生物标志物是分数流量减少 (FFR)，通过侵入性诊断技术冠状动脉造影获得其体内测量。为了解决侵入性的缺点，近年来出现了一种名为虚拟 FFR (vFFR) 测量的新方法。本文中，我们探讨了使用深度学习技术作为替代 CFD 引擎模型在冠状动脉 vFFR 估计中的任务中，以极大地减少所需时间和计算成本而不会显著降低质量的方法。我们提出了一种新颖的 vFFR 估计方法，通过将输入血管几何表示为点云，并利用学习几何表示的混合神经网络，基于明确和隐式给定的特征。我们从临床角度评估了该方法，并展示它可以作为常用的基于 CFD 的方法的有力替代。