Estimation of FFR in coronary arteries with deep learning

TL;DR

This paper introduces a deep learning method that estimates fractional flow reserve (FFR) in coronary arteries from CTA data, significantly reducing computational time while maintaining accuracy, thus offering a promising non-invasive diagnostic alternative.

Contribution

The study presents a novel hybrid neural network approach that uses point cloud representations of vessel geometry for efficient vFFR estimation, improving speed over traditional CFD methods.

Findings

Deep learning model reduces computation time significantly.

Method maintains high accuracy comparable to CFD-based approaches.

Potential to replace invasive FFR measurement in clinical practice.

Abstract

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.