Fractional flow reserve and the diagnosis of coronary artery disease

Abstract

Coronary artery disease (CAD) is a widespread cardiovascular illness that causes high rates of mortality all around the world (World Health Organization 2016; Kaptoge et al. 2019). The diagnosis of coronary artery disease progressed over the years from the use of invasive coronary angiography (ICA) only procedure to the use of non-invasive coronary computed tomography angiography (CCTA) and invasive FFR for making treatment decisions. While ICA and CCTA provide an anatomical-visual assessment of the disease, the invasive FFR measures the quantitative functional effect of the disease by measuring fractional flow reserve (FFR), a pressure ratio index based on the correlation between blood flow and pressure drop in coronary arteries. To obtain both the anatomical and functional assessment of the disease using non-invasive measures, CT-FFR method was developed in last decade. The thesis presents the framework of CT-FFR methodology using open source tools. We provide a review of the clinical trials that led to the approval of CT-FFR process for clinical use. The CT-FFR method is based on the development of patient-specific coronary blood flow models created from Coronary Computed Tomography Angiography(CCTA) image datasets. To acquire the clinical data, the data-collection was undertaken and a data repository of CCTA images, ICA images and FFR data was created. The patient-specific three-dimensional geometries for two subjects from the study were constructed. The blood flow was simulated using these 3D models as the computational domain with lumped parameter models of the cardiovascular system as boundary conditions. The FFR values were computed using pressure distribution results from the simulation and compared against the invasive FFR values obtained from clinical measurement.