Rapid coronary computed tomography angiography‐derived fractional flow reserve: A feasibility study for on‐site assessment

Background Fractional flow reserve (FFR) is the reference standard for physiological assessment of coronary artery stenosis severity, but invasive measurement requires catheterization and hyperemic agents. Coronary computed tomography angiography (CCTA)‐derived FFR offers a non‐invasive alternative, though existing approaches based on computational fluid dynamics (CFD) are computationally intensive and impractical for on‐site clinical use. Purpose To evaluate the feasibility of rapidly estimating FFR from CCTA using three mathematical, centerline‐based computational methodologies. This study investigates whether pressure‐drop formulations relying solely on coronary geometric information can provide rapid FFR estimates suitable for potential on‐site clinical application, without the need for computationally intensive fluid dynamics simulations. Methods Three CCTA‐derived FFR methodologies were evaluated: fixed Fractional Flow Reserve (fFFR3D), mathematical virtual Functional Assessment Index (mvFAI3D), and tomographic Fractional Flow Reserve (tomoFFR). Each method uses the patient mean arterial pressure (MAP) along with the coronary centerline geometry to characterize vessel dimensions and estimate pressure loss under hyperemic conditions. fFFR3D assumes a fixed hyperaemic blood velocity of 0.35 m/s; mvFAI3D extends the virtual Functional Assessment Index (vFAI) using a mathematical pressure‐drop formulation; and tomoFFR estimates hyperaemic flow and pressure loss using a baseline fixed‐velocity approach. Fourteen coronary vessels from a publicly available dataset with reference invasive FFR measurements were analyzed. Predicted FFR values were compared with clinical measurements (wire measured FFR), with emphasis on classification relative to the clinical threshold of and on computational time. Results Relative to the FFR = 0.80 threshold, fFFR3D and tomoFFR correctly classified 12 of the 14 vessels (85.7%) and mvFAI3D correctly classified 11 of the 14 vessels (78.6%). Given the limited sample size, no definitive conclusions regarding diagnostic accuracy or generalizability can be drawn. Nevertheless, the results demonstrate the feasibility of applying mathematical pressure‐drop models directly to CCTA‐derived coronary centerlines. Once centerlines were available, all three approaches required less than 1 s to calculate FFR, enabling substantially faster estimation than conventional CFD‐based approaches and supporting potential integration into on‐site CCTA analysis workflows. Conclusions These findings demonstrate the feasibility of rapid, centerline‐based CCTA‐derived FFR estimation using mathematical models of coronary pressure loss. Sub‐second computational time, combined with the availability of automated AI‐based coronary centerline extraction and physician correction in contemporary CCTA workstations, provides a potential pathway toward on‐site FFR assessment during routine CT interpretation. Larger prospective, multicenter studies with invasive FFR reference measurements are required to establish diagnostic accuracy, reproducibility, and clinical robustness prior to clinical implementation.