Published Online:https://doi.org/10.1148/radiol.14140992

On-site, anatomy-based computation of the fractional flow reserve (FFR) can be performed by clinicians using a computationally less demanding coronary CT angiography–derived FFR algorithm; this algorithm improves the diagnostic performance of coronary CT angiography in the identification of functionally important coronary artery disease within a population with a high number of borderline and significantly obstructed coronary arteries.

Purpose

To validate an on-site algorithm for computation of fractional flow reserve (FFR) from coronary computed tomographic (CT) angiography data against invasively measured FFR and to test its diagnostic performance as compared with that of coronary CT angiography.

Materials and Methods

The institutional review board provided a waiver for this retrospective study. From coronary CT angiography data in 106 patients, FFR was computed at a local workstation by using a computational fluid dynamics algorithm. Invasive FFR measurement was performed in 189 vessels (80 of which had an FFR ≤ 0.80); these measurements were regarded as the reference standard. The diagnostic characteristics of coronary CT angiography–derived computational FFR, coronary CT angiography, and quantitative coronary angiography were evaluated against those of invasively measured FFR by using C statistics. Sensitivity and specificity were compared by using a two-sided McNemar test.

Results

For computational FFR, sensitivity was 87.5% (95% confidence interval [CI]: 78.2%, 93.8%), specificity was 65.1% (95% CI: 55.4%, 74.0%), and accuracy was 74.6% (95% CI: 68.4%, 80.8%), as compared with the finding of lumen stenosis of 50% or greater at coronary CT angiography, for which sensitivity was 81.3% (95% CI: 71.0%, 89.1%), specificity was 37.6% (95% CI: 28.5%, 47.4%), and accuracy was 56.1% (95% CI: 49.0%, 63.2%). C statistics revealed a larger area under the receiver operating characteristic curve (AUC) for computational FFR (AUC, 0.83) than for coronary CT angiography (AUC, 0.64). For vessels with intermediate (25%–69%) stenosis, the sensitivity of computational FFR was 87.3% (95% CI: 76.5%, 94.3%) and the specificity was 59.3% (95% CI: 47.8%, 70.1%).

Conclusion

With use of a reduced-order algorithm, computation of the FFR from coronary CT angiography data can be performed locally, at a regular workstation. The diagnostic accuracy of coronary CT angiography–derived computational FFR for the detection of functionally important coronary artery disease (CAD) was good and was incremental to that of coronary CT angiography within a population with a high prevalence of CAD.

© RSNA, 2014

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Article History

Received April 28, 2014; revision requested June 11; revision received July 15; accepted July 30; final version accepted August 13.
Published online: Oct 13 2014
Published in print: Mar 2015