Search

Fractional flow reserve (FFR) is the gold standard for diagnosing coronary artery disease (CAD). FFRCT uses computational fluid dynamics (CFD) to evaluate FFR non-invasively by simulating coronary flow in geometries reconstructed from computed tomography (CT). However, it faces challenges related to the cost of computing and uncertainties in defining patient-specific boundary conditions (BCs). We investigated using time-averaged steady BCs instead of pulsatile ones to reduce computational time and deployed a self-adjusting method for tuning BCs to patient clinical data. 133 coronary arteries were reconstructed from CT images of CAD patients. For each vessel, invasive FFR was measured. Steady BCs for CFD were defined in two steps: i) rest BCs were extrapolated from clinical and image-derived data; ii) hyperemic BCs were computed from resting conditions. Flow rate was iteratively adjusted during the simulation until patient aortic pressure was matched. Pulsatile BCs were defined using the convergence values of steady BCs. Lesion-specific hemodynamic indexes were computed and compared between groups of patients indicated for surgery and those not. The whole pipeline was implemented as a straightforward, fully automated process. Steady and pulsatile FFRCT showed a strong correlation (r=0.988) and correlated with invasive FFR (r=0.797). The per-point difference between the pressure and FFRCT field predicted by the two methods was below 0.01 and 0.02, respectively. Both approaches exhibited good diagnostic performance: accuracy was 0.860 and 0.864, with AUCs of 0.923 and 0.912, for steady and pulsatile cases, respectively. Computational time for steady BCs CFD was approximately 30-fold lower than for the pulsatile case. This work demonstrates the feasibility of using steady BCs CFD for computing FFRCT in coronary arteries and its performance in a fully automated pipeline.