Computationally simulated fractional flow reserve from coronary computed tomography angiography based on fractional myocardial mass.

Computed tomography angiography (CCTA)-based calculations of fractional flow reserve (FFR) can improve the diagnostic performance of CCTA for physiologically significant stenosis but the computational resource requirements are high. This study aimed at establishing a simple and efficient algorithm for computing simulated FFR (S-FFR). A total of 107 patients who underwent CCTA and invasive FFR measurements were enrolled in the study. S-FFR was calculated using 145 evaluable coronary arteries with off-the-shelf softwares. FFR ≤ 0.80 was a reference threshold for diagnostic performance of diameter stenosis (DS) ≥ 50%, DS ≥ 70%, or S-FFR ≤ 0.80. FFR ≤ 0.80 was identified in 78 vessels (54%). In per-vessel analysis, S-FFR showed good correlation (r = 0.83) and agreement (mean difference = 0.02 ± 0.08) with FFR. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of S-FFR ≤ 0.80 for FFR ≤ 0.80 were 84%, 92%, 92%, 83%, and 88%, respectively. S-FFR ≤ 0.80 showed much higher predictive performance for FFR ≤ 0.80 compared with DS ≥ 50% or DS ≥ 70% (c-statistics = 0.92 vs. 0.58 or 0.65, p < 0.001, all). The classification agreement between FFR and S-FFR was > 80% when the average of FFR and S-FFR was < 0.76 or > 0.86. Per-patient analysis showed consistent results. In this study, a simple and computationally efficient simulated FFR (S-FFR) algorithm is designed and tested using non-proprietary off-the-shelf software. This algorithm may expand the accessibility of clinical applications for non-invasive coronary physiology study.

Non-invasive coronary physiology based on computational analysis of intracoronary transluminal attenuation gradient.

Invasive procedure is a prerequisite for studying coronary physiology. We established the measurement of non-invasive physiological parameters including coronary blood flow (CBF), flow velocity, and microvascular resistance using coronary computed tomography angiography (CCTA). Vessel-specific CBF was derived from transluminal attenuation flow encoding (TAFE) and then tested using three separate datasets consisted of computational simulation, human perfusion CT, and human CCTA. TAFE-derived CBF correlated well with measured vessel-specific myocardial blood flow and CBF. TAFE-derived CBF per myocardial mass consistently decreased with the progressive severity of stenosis, and it was found to better to detect significant stenosis than transluminal attenuation gradient (TAG). With the addition of vessel anatomy, TAFE-derived CBF could calculate flow velocity and microvascular resistance. The results of non-invasively acquired parameters according to the severity of stenosis were similar to those obtained through invasive physiology studies. Our study demonstrated that non-invasive comprehensive coronary physiology parameters can be derived from CCTA without any pre-specified condition or performing complex heavy computational processes. Our findings are expected to expand the clinical coverage of CCTA and coronary physiology.