  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.
